Recognize some more cards (5070, Granite Ridge) via a torchruntime upgrade

Tjcomserv patch 1 pydantic

.github/FUNDING.yml

@@ -1,3 +1,3 @@
 # These are supported funding model platforms
 
-ko_fi: cmdr2_stablediffusion_ui
+ko_fi: easydiffusion

.gitignore

@@ -3,4 +3,6 @@ installer
 installer.tar
 dist
 .idea/*
-node_modules/*
+node_modules/*
+.tmp1
+.tmp2

3rd-PARTY-LICENSES

@@ -712,3 +712,411 @@ FileSaver.js is licensed under the MIT license:
     SOFTWARE.
     
       [1]: http://eligrey.com
+
+croppr.js
+=========
+https://github.com/jamesssooi/Croppr.js
+
+croppr.js is licensed under the MIT license:
+
+    MIT License
+
+    Copyright (c) 2017 James Ooi
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in all
+    copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+    SOFTWARE.
+
+ExifReader
+==========
+https://github.com/mattiasw/ExifReader
+
+ExifReader is licensed under the Mozilla Public License:
+
+    Mozilla Public License Version 2.0
+    ==================================
+
+    1. Definitions
+    --------------
+
+    1.1. "Contributor"
+        means each individual or legal entity that creates, contributes to
+        the creation of, or owns Covered Software.
+
+    1.2. "Contributor Version"
+        means the combination of the Contributions of others (if any) used
+        by a Contributor and that particular Contributor's Contribution.
+
+    1.3. "Contribution"
+        means Covered Software of a particular Contributor.
+
+    1.4. "Covered Software"
+        means Source Code Form to which the initial Contributor has attached
+        the notice in Exhibit A, the Executable Form of such Source Code
+        Form, and Modifications of such Source Code Form, in each case
+        including portions thereof.
+
+    1.5. "Incompatible With Secondary Licenses"
+        means
+
+        (a) that the initial Contributor has attached the notice described
+            in Exhibit B to the Covered Software; or
+
+        (b) that the Covered Software was made available under the terms of
+            version 1.1 or earlier of the License, but not also under the
+            terms of a Secondary License.
+
+    1.6. "Executable Form"
+        means any form of the work other than Source Code Form.
+
+    1.7. "Larger Work"
+        means a work that combines Covered Software with other material, in
+        a separate file or files, that is not Covered Software.
+
+    1.8. "License"
+        means this document.
+
+    1.9. "Licensable"
+        means having the right to grant, to the maximum extent possible,
+        whether at the time of the initial grant or subsequently, any and
+        all of the rights conveyed by this License.
+
+    1.10. "Modifications"
+        means any of the following:
+
+        (a) any file in Source Code Form that results from an addition to,
+            deletion from, or modification of the contents of Covered
+            Software; or
+
+        (b) any new file in Source Code Form that contains any Covered
+            Software.
+
+    1.11. "Patent Claims" of a Contributor
+        means any patent claim(s), including without limitation, method,
+        process, and apparatus claims, in any patent Licensable by such
+        Contributor that would be infringed, but for the grant of the
+        License, by the making, using, selling, offering for sale, having
+        made, import, or transfer of either its Contributions or its
+        Contributor Version.
+
+    1.12. "Secondary License"
+        means either the GNU General Public License, Version 2.0, the GNU
+        Lesser General Public License, Version 2.1, the GNU Affero General
+        Public License, Version 3.0, or any later versions of those
+        licenses.
+
+    1.13. "Source Code Form"
+        means the form of the work preferred for making modifications.
+
+    1.14. "You" (or "Your")
+        means an individual or a legal entity exercising rights under this
+        License. For legal entities, "You" includes any entity that
+        controls, is controlled by, or is under common control with You. For
+        purposes of this definition, "control" means (a) the power, direct
+        or indirect, to cause the direction or management of such entity,
+        whether by contract or otherwise, or (b) ownership of more than
+        fifty percent (50%) of the outstanding shares or beneficial
+        ownership of such entity.
+
+    2. License Grants and Conditions
+    --------------------------------
+
+    2.1. Grants
+
+    Each Contributor hereby grants You a world-wide, royalty-free,
+    non-exclusive license:
+
+    (a) under intellectual property rights (other than patent or trademark)
+        Licensable by such Contributor to use, reproduce, make available,
+        modify, display, perform, distribute, and otherwise exploit its
+        Contributions, either on an unmodified basis, with Modifications, or
+        as part of a Larger Work; and
+
+    (b) under Patent Claims of such Contributor to make, use, sell, offer
+        for sale, have made, import, and otherwise transfer either its
+        Contributions or its Contributor Version.
+
+    2.2. Effective Date
+
+    The licenses granted in Section 2.1 with respect to any Contribution
+    become effective for each Contribution on the date the Contributor first
+    distributes such Contribution.
+
+    2.3. Limitations on Grant Scope
+
+    The licenses granted in this Section 2 are the only rights granted under
+    this License. No additional rights or licenses will be implied from the
+    distribution or licensing of Covered Software under this License.
+    Notwithstanding Section 2.1(b) above, no patent license is granted by a
+    Contributor:
+
+    (a) for any code that a Contributor has removed from Covered Software;
+        or
+
+    (b) for infringements caused by: (i) Your and any other third party's
+        modifications of Covered Software, or (ii) the combination of its
+        Contributions with other software (except as part of its Contributor
+        Version); or
+
+    (c) under Patent Claims infringed by Covered Software in the absence of
+        its Contributions.
+
+    This License does not grant any rights in the trademarks, service marks,
+    or logos of any Contributor (except as may be necessary to comply with
+    the notice requirements in Section 3.4).
+
+    2.4. Subsequent Licenses
+
+    No Contributor makes additional grants as a result of Your choice to
+    distribute the Covered Software under a subsequent version of this
+    License (see Section 10.2) or under the terms of a Secondary License (if
+    permitted under the terms of Section 3.3).
+
+    2.5. Representation
+
+    Each Contributor represents that the Contributor believes its
+    Contributions are its original creation(s) or it has sufficient rights
+    to grant the rights to its Contributions conveyed by this License.
+
+    2.6. Fair Use
+
+    This License is not intended to limit any rights You have under
+    applicable copyright doctrines of fair use, fair dealing, or other
+    equivalents.
+
+    2.7. Conditions
+
+    Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted
+    in Section 2.1.
+
+    3. Responsibilities
+    -------------------
+
+    3.1. Distribution of Source Form
+
+    All distribution of Covered Software in Source Code Form, including any
+    Modifications that You create or to which You contribute, must be under
+    the terms of this License. You must inform recipients that the Source
+    Code Form of the Covered Software is governed by the terms of this
+    License, and how they can obtain a copy of this License. You may not
+    attempt to alter or restrict the recipients' rights in the Source Code
+    Form.
+
+    3.2. Distribution of Executable Form
+
+    If You distribute Covered Software in Executable Form then:
+
+    (a) such Covered Software must also be made available in Source Code
+        Form, as described in Section 3.1, and You must inform recipients of
+        the Executable Form how they can obtain a copy of such Source Code
+        Form by reasonable means in a timely manner, at a charge no more
+        than the cost of distribution to the recipient; and
+
+    (b) You may distribute such Executable Form under the terms of this
+        License, or sublicense it under different terms, provided that the
+        license for the Executable Form does not attempt to limit or alter
+        the recipients' rights in the Source Code Form under this License.
+
+    3.3. Distribution of a Larger Work
+
+    You may create and distribute a Larger Work under terms of Your choice,
+    provided that You also comply with the requirements of this License for
+    the Covered Software. If the Larger Work is a combination of Covered
+    Software with a work governed by one or more Secondary Licenses, and the
+    Covered Software is not Incompatible With Secondary Licenses, this
+    License permits You to additionally distribute such Covered Software
+    under the terms of such Secondary License(s), so that the recipient of
+    the Larger Work may, at their option, further distribute the Covered
+    Software under the terms of either this License or such Secondary
+    License(s).
+
+    3.4. Notices
+
+    You may not remove or alter the substance of any license notices
+    (including copyright notices, patent notices, disclaimers of warranty,
+    or limitations of liability) contained within the Source Code Form of
+    the Covered Software, except that You may alter any license notices to
+    the extent required to remedy known factual inaccuracies.
+
+    3.5. Application of Additional Terms
+
+    You may choose to offer, and to charge a fee for, warranty, support,
+    indemnity or liability obligations to one or more recipients of Covered
+    Software. However, You may do so only on Your own behalf, and not on
+    behalf of any Contributor. You must make it absolutely clear that any
+    such warranty, support, indemnity, or liability obligation is offered by
+    You alone, and You hereby agree to indemnify every Contributor for any
+    liability incurred by such Contributor as a result of warranty, support,
+    indemnity or liability terms You offer. You may include additional
+    disclaimers of warranty and limitations of liability specific to any
+    jurisdiction.
+
+    4. Inability to Comply Due to Statute or Regulation
+    ---------------------------------------------------
+
+    If it is impossible for You to comply with any of the terms of this
+    License with respect to some or all of the Covered Software due to
+    statute, judicial order, or regulation then You must: (a) comply with
+    the terms of this License to the maximum extent possible; and (b)
+    describe the limitations and the code they affect. Such description must
+    be placed in a text file included with all distributions of the Covered
+    Software under this License. Except to the extent prohibited by statute
+    or regulation, such description must be sufficiently detailed for a
+    recipient of ordinary skill to be able to understand it.
+
+    5. Termination
+    --------------
+
+    5.1. The rights granted under this License will terminate automatically
+    if You fail to comply with any of its terms. However, if You become
+    compliant, then the rights granted under this License from a particular
+    Contributor are reinstated (a) provisionally, unless and until such
+    Contributor explicitly and finally terminates Your grants, and (b) on an
+    ongoing basis, if such Contributor fails to notify You of the
+    non-compliance by some reasonable means prior to 60 days after You have
+    come back into compliance. Moreover, Your grants from a particular
+    Contributor are reinstated on an ongoing basis if such Contributor
+    notifies You of the non-compliance by some reasonable means, this is the
+    first time You have received notice of non-compliance with this License
+    from such Contributor, and You become compliant prior to 30 days after
+    Your receipt of the notice.
+
+    5.2. If You initiate litigation against any entity by asserting a patent
+    infringement claim (excluding declaratory judgment actions,
+    counter-claims, and cross-claims) alleging that a Contributor Version
+    directly or indirectly infringes any patent, then the rights granted to
+    You by any and all Contributors for the Covered Software under Section
+    2.1 of this License shall terminate.
+
+    5.3. In the event of termination under Sections 5.1 or 5.2 above, all
+    end user license agreements (excluding distributors and resellers) which
+    have been validly granted by You or Your distributors under this License
+    prior to termination shall survive termination.
+
+    ************************************************************************
+    *                                                                      *
+    *  6. Disclaimer of Warranty                                           *
+    *  -------------------------                                           *
+    *                                                                      *
+    *  Covered Software is provided under this License on an "as is"       *
+    *  basis, without warranty of any kind, either expressed, implied, or  *
+    *  statutory, including, without limitation, warranties that the       *
+    *  Covered Software is free of defects, merchantable, fit for a        *
+    *  particular purpose or non-infringing. The entire risk as to the     *
+    *  quality and performance of the Covered Software is with You.        *
+    *  Should any Covered Software prove defective in any respect, You     *
+    *  (not any Contributor) assume the cost of any necessary servicing,   *
+    *  repair, or correction. This disclaimer of warranty constitutes an   *
+    *  essential part of this License. No use of any Covered Software is   *
+    *  authorized under this License except under this disclaimer.         *
+    *                                                                      *
+    ************************************************************************
+
+    ************************************************************************
+    *                                                                      *
+    *  7. Limitation of Liability                                          *
+    *  --------------------------                                          *
+    *                                                                      *
+    *  Under no circumstances and under no legal theory, whether tort      *
+    *  (including negligence), contract, or otherwise, shall any           *
+    *  Contributor, or anyone who distributes Covered Software as          *
+    *  permitted above, be liable to You for any direct, indirect,         *
+    *  special, incidental, or consequential damages of any character      *
+    *  including, without limitation, damages for lost profits, loss of    *
+    *  goodwill, work stoppage, computer failure or malfunction, or any    *
+    *  and all other commercial damages or losses, even if such party      *
+    *  shall have been informed of the possibility of such damages. This   *
+    *  limitation of liability shall not apply to liability for death or   *
+    *  personal injury resulting from such party's negligence to the       *
+    *  extent applicable law prohibits such limitation. Some               *
+    *  jurisdictions do not allow the exclusion or limitation of           *
+    *  incidental or consequential damages, so this exclusion and          *
+    *  limitation may not apply to You.                                    *
+    *                                                                      *
+    ************************************************************************
+
+    8. Litigation
+    -------------
+
+    Any litigation relating to this License may be brought only in the
+    courts of a jurisdiction where the defendant maintains its principal
+    place of business and such litigation shall be governed by laws of that
+    jurisdiction, without reference to its conflict-of-law provisions.
+    Nothing in this Section shall prevent a party's ability to bring
+    cross-claims or counter-claims.
+
+    9. Miscellaneous
+    ----------------
+
+    This License represents the complete agreement concerning the subject
+    matter hereof. If any provision of this License is held to be
+    unenforceable, such provision shall be reformed only to the extent
+    necessary to make it enforceable. Any law or regulation which provides
+    that the language of a contract shall be construed against the drafter
+    shall not be used to construe this License against a Contributor.
+
+    10. Versions of the License
+    ---------------------------
+
+    10.1. New Versions
+
+    Mozilla Foundation is the license steward. Except as provided in Section
+    10.3, no one other than the license steward has the right to modify or
+    publish new versions of this License. Each version will be given a
+    distinguishing version number.
+
+    10.2. Effect of New Versions
+
+    You may distribute the Covered Software under the terms of the version
+    of the License under which You originally received the Covered Software,
+    or under the terms of any subsequent version published by the license
+    steward.
+
+    10.3. Modified Versions
+
+    If you create software not governed by this License, and you want to
+    create a new license for such software, you may create and use a
+    modified version of this License if you rename the license and remove
+    any references to the name of the license steward (except to note that
+    such modified license differs from this License).
+
+    10.4. Distributing Source Code Form that is Incompatible With Secondary
+    Licenses
+
+    If You choose to distribute Source Code Form that is Incompatible With
+    Secondary Licenses under the terms of this version of the License, the
+    notice described in Exhibit B of this License must be attached.
+
+    Exhibit A - Source Code Form License Notice
+    -------------------------------------------
+
+      This Source Code Form is subject to the terms of the Mozilla Public
+      License, v. 2.0. If a copy of the MPL was not distributed with this
+      file, You can obtain one at https://mozilla.org/MPL/2.0/.
+
+    If it is not possible or desirable to put the notice in a particular
+    file, then You may include the notice in a location (such as a LICENSE
+    file in a relevant directory) where a recipient would be likely to look
+    for such a notice.
+
+    You may add additional accurate notices of copyright ownership.
+
+    Exhibit B - "Incompatible With Secondary Licenses" Notice
+    ---------------------------------------------------------
+
+      This Source Code Form is "Incompatible With Secondary Licenses", as
+      defined by the Mozilla Public License, v. 2.0.

CHANGES.md

@@ -1,5 +1,61 @@
 # What's new?
 
+## v3.0
+### Major Changes
+- **ControlNet** - Full support for ControlNet, with native integration of the common ControlNet models. Just select a control image, then choose the ControlNet filter/model and run. No additional configuration or download necessary. Supports custom ControlNets as well.
+- **SDXL** - Full support for SDXL. No configuration necessary, just put the SDXL model in the `models/stable-diffusion` folder.
+- **Multiple LoRAs** - Use multiple LoRAs, including SDXL and SD2-compatible LoRAs. Put them in the `models/lora` folder.
+- **Embeddings** - Use textual inversion embeddings easily, by putting them in the `models/embeddings` folder and using their names in the prompt (or by clicking the `+ Embeddings` button to select embeddings visually). Thanks @JeLuf.
+- **Seamless Tiling** - Generate repeating textures that can be useful for games and other art projects. Works best in 512x512 resolution. Thanks @JeLuf.
+- **Inpainting Models** - Full support for inpainting models, including custom inpainting models. No configuration (or yaml files) necessary.
+- **Faster than v2.5** - Nearly 40% faster than Easy Diffusion v2.5, and can be even faster if you enable xFormers.
+- **Even less VRAM usage** - Less than 2 GB for 512x512 images on 'low' VRAM usage setting (SD 1.5). Can generate large images with SDXL.
+- **WebP images** - Supports saving images in the lossless webp format.
+- **Undo/Redo in the UI** - Remove tasks or images from the queue easily, and undo the action if you removed anything accidentally. Thanks @JeLuf.
+- **Three new samplers, and latent upscaler** - Added `DEIS`, `DDPM` and `DPM++ 2m SDE` as additional samplers. Thanks @ogmaresca and @rbertus2000.
+- **Significantly faster 'Upscale' and 'Fix Faces' buttons on the images**
+- **Major rewrite of the code** - We've switched to using diffusers under-the-hood, which allows us to release new features faster, and focus on making the UI and installer even easier to use.
+
+### Detailed changelog
+* 3.0.9c - 6 Feb 2025 - (Internal code change) Remove hardcoded references to `torch.cuda`, and replace with torchruntime's device utilities.
+* 3.0.9b - 28 Jan 2025 - Fix a bug affecting older versions of Easy Diffusion, which tried to upgrade to an incompatible version of PyTorch.
+* 3.0.9b - 4 Jan 2025 - Replace the use of WMIC (deprecated) with a powershell call.
+* 3.0.9 - 28 May 2024 - Slider for controlling the strength of controlnets.
+* 3.0.8 - 27 May 2024 - SDXL ControlNets for Img2Img and Inpainting.
+* 3.0.7 - 11 Dec 2023 - Setting to enable/disable VAE tiling (in the Image Settings panel). Sometimes VAE tiling reduces the quality of the image, so this setting will help control that.
+* 3.0.6 - 18 Sep 2023 - Add thumbnails to embeddings from the UI, using the new `Upload Thumbnail` button in the Embeddings popup. Thanks @JeLuf.
+* 3.0.6 - 15 Sep 2023 - Fix broken embeddings dialog when LoRA information couldn't be fetched.
+* 3.0.6 - 14 Sep 2023 - UI for adding notes to LoRA files (to help you remember which prompts to use). Also added a button to automatically fetch prompts from Civitai for a LoRA file, using the `Import from Civitai` button. Thanks @JeLuf.
+* 3.0.5 - 2 Sep 2023 - Support SDXL ControlNets.
+* 3.0.4 - 1 Sep 2023 - Fix incorrect metadata generated for embeddings, when the exact word doesn't match the case, or is part of a larger word.
+* 3.0.4 - 1 Sep 2023 - Simplify the installation for AMD users on Linux. Thanks @JeLuf.
+* 3.0.4 - 1 Sep 2023 - Allow using a different folder for models. This is useful if you want to share a models folder across different software, or on a different drive. You can change this path in the Settings tab.
+* 3.0.3 - 31 Aug 2023 - Auto-save images to disk (if enabled by the user) when upscaling/fixing using the buttons on the image.
+* 3.0.3 - 30 Aug 2023 - Allow loading NovelAI-based custom models.
+* 3.0.3 - 30 Aug 2023 - Fix broken VAE tiling. This allows you to create larger images with lesser VRAM usage.
+* 3.0.3 - 30 Aug 2023 - Allow blocking NSFW images using a server-side config. This prevents the browser from generating NSFW images or changing the config. Open `config.yaml` in a text editor (e.g. Notepad), and add `block_nsfw: true` at the end, and save the file.
+* 3.0.2 - 29 Aug 2023 - Fixed incorrect matching of embeddings from prompts.
+* 3.0.2 - 24 Aug 2023 - Fix broken seamless tiling.
+* 3.0.2 - 23 Aug 2023 - Fix styling on mobile devices.
+* 3.0.2 - 22 Aug 2023 - Full support for inpainting models, including custom models. Support SD 1.x and SD 2.x inpainting models. Does not require you to specify a yaml config file.
+* 3.0.2 - 22 Aug 2023 - Reduce VRAM consumption of controlnet in 'low' VRAM mode, and allow accelerating controlnets using xformers.
+* 3.0.2 - 22 Aug 2023 - Improve auto-detection of SD 2.0 and 2.1 models, removing the need for custom yaml files for SD 2.x models. Improve the model load time by speeding-up the black image test.
+* 3.0.1 - 18 Aug 2023 - Rotate an image if EXIF rotation is present. For e.g. this is common in images taken with a smartphone.
+* 3.0.1 - 18 Aug 2023 - Resize control images to the task dimensions, to avoid memory errors with high-res control images.
+* 3.0.1 - 18 Aug 2023 - Show controlnet filter preview in the task entry.
+* 3.0.1 - 18 Aug 2023 - Fix drag-and-drop and 'Use these Settings' for LoRA and ControlNet.
+* 3.0.1 - 18 Aug 2023 - Auto-save LoRA models and strengths.
+* 3.0.1 - 17 Aug 2023 - Automatically use the correct yaml config file for custom SDXL models, even if a yaml file isn't present in the folder.
+* 3.0.1 - 17 Aug 2023 - Fix broken embeddings with SDXL.
+* 3.0.1 - 16 Aug 2023 - Fix broken LoRA with SDXL.
+* 3.0.1 - 15 Aug 2023 - Fix broken seamless tiling.
+* 3.0.1 - 15 Aug 2023 - Fix textual inversion embeddings not working in `low` VRAM usage mode.
+* 3.0.1 - 15 Aug 2023 - Fix for custom VAEs not working in `low` VRAM usage mode.
+* 3.0.1 - 14 Aug 2023 - Slider to change the image dimensions proportionally (in Image Settings). Thanks @JeLuf.
+* 3.0.1 - 14 Aug 2023 - Show an error to the user if an embedding isn't compatible with the model, instead of failing silently without informing the user. Thanks @JeLuf.
+* 3.0.1 - 14 Aug 2023 - Disable watermarking for SDXL img2img. Thanks @AvidGameFan.
+* 3.0.0 - 3 Aug 2023 - Enabled diffusers for everyone by default. The old v2 engine can be used by disabling the "Use v3 engine" option in the Settings tab.
+
 ## v2.5
 ### Major Changes
 - **Nearly twice as fast** - significantly faster speed of image generation. Code contributions are welcome to make our project even faster: https://github.com/easydiffusion/sdkit/#is-it-fast
@@ -22,6 +78,12 @@
 Our focus continues to remain on an easy installation experience, and an easy user-interface. While still remaining pretty powerful, in terms of features and speed.
 
 ### Detailed changelog
+* 2.5.48 - 1 Aug 2023 - (beta-only) Full support for ControlNets. You can select a control image to guide the AI. You can pick a filter to pre-process the image, and one of the known (or custom) controlnet models. Supports `OpenPose`, `Canny`, `Straight Lines`, `Depth`, `Line Art`, `Scribble`, `Soft Edge`, `Shuffle` and `Segment`.
+* 2.5.47 - 30 Jul 2023 - An option to use `Strict Mask Border` while inpainting, to avoid touching areas outside the mask. But this might show a slight outline of the mask, which you will have to touch up separately.
+* 2.5.47 - 29 Jul 2023 - (beta-only) Fix long prompts with SDXL.
+* 2.5.47 - 29 Jul 2023 - (beta-only) Fix red dots in some SDXL images.
+* 2.5.47 - 29 Jul 2023 - Significantly faster `Fix Faces` and `Upscale` buttons (on the image). They no longer need to generate the image from scratch, instead they just upscale/fix the generated image in-place.
+* 2.5.47 - 28 Jul 2023 - Lots of internal code reorganization, in preparation for supporting Controlnets. No user-facing changes.
 * 2.5.46 - 27 Jul 2023 - (beta-only) Full support for SD-XL models (base and refiner)!
 * 2.5.45 - 24 Jul 2023 - (beta-only) Hide the samplers that won't be supported in the new diffusers version.
 * 2.5.45 - 22 Jul 2023 - (beta-only) Fix the recently-broken inpainting models.

CONTRIBUTING.md

@@ -47,3 +47,5 @@ Build the Windows installer using Windows, and the Linux installer using Linux.
 
 1. Run `build.bat` or `./build.sh` depending on whether you're in Windows or Linux.
 2. Make a new GitHub release and upload the Windows and Linux installer builds created inside the `dist` folder.
+
+For NSIS (on Windows), you need to have these plugins in the `nsis/Plugins` folder: `amd64-unicode`, `x86-ansi`, `x86-unicode`

How to install and run.txt

@@ -1,18 +1,18 @@
-Congrats on downloading Stable Diffusion UI, version 2!
+Congrats on downloading Easy Diffusion, version 3!
 
-If you haven't downloaded Stable Diffusion UI yet, please download from https://github.com/easydiffusion/easydiffusion#installation
+If you haven't downloaded Easy Diffusion yet, please download from https://github.com/easydiffusion/easydiffusion#installation
 
 After downloading, to install please follow these instructions:
 
 For Windows:
-- Please double-click the "Start Stable Diffusion UI.cmd" file inside the "stable-diffusion-ui" folder.
+- Please double-click the "Easy-Diffusion-Windows.exe" file and follow the instructions.
 
-For Linux:
-- Please open a terminal, and go to the "stable-diffusion-ui" directory. Then run ./start.sh
+For Linux and Mac:
+- Please open a terminal, and go to the "easy-diffusion" directory. Then run ./start.sh
 
-That file will automatically install everything. After that it will start the Stable Diffusion interface in a web browser.
+That file will automatically install everything. After that it will start the Easy Diffusion interface in a web browser.
 
-To start the UI in the future, please run the same command mentioned above.
+To start Easy Diffusion in the future, please run the same command mentioned above.
 
 
 If you have any problems, please:
@@ -21,4 +21,4 @@ If you have any problems, please:
 3. Or, file an issue at https://github.com/easydiffusion/easydiffusion/issues
 
 Thanks
-cmdr2 (and contributors to the project)
+cmdr2 (and contributors to the project)

NSIS/nsisconf.nsh

@@ -1 +0,0 @@
-!define EXISTING_INSTALLATION_DIR "D:\path\to\installed\easy-diffusion"

NSIS/sdui.nsi

@@ -7,9 +7,9 @@ RequestExecutionLevel user
 !AddPluginDir /amd64-unicode "."
 ; HM NIS Edit Wizard helper defines
 !define PRODUCT_NAME "Easy Diffusion"
-!define PRODUCT_VERSION "2.5"
+!define PRODUCT_VERSION "3.0"
 !define PRODUCT_PUBLISHER "cmdr2 and contributors"
-!define PRODUCT_WEB_SITE "https://stable-diffusion-ui.github.io"
+!define PRODUCT_WEB_SITE "https://easydiffusion.github.io"
 !define PRODUCT_DIR_REGKEY "Software\Microsoft\Easy Diffusion\App Paths\installer.exe"
 
 ; MUI 1.67 compatible ------
@@ -165,9 +165,9 @@ FunctionEnd
 ; MUI Settings
 ;---------------------------------------------------------------------------------------------------------
 !define MUI_ABORTWARNING
-!define MUI_ICON "cyborg_flower_girl.ico"
+!define MUI_ICON "${EXISTING_INSTALLATION_DIR}\installer_files\cyborg_flower_girl.ico"
 
-!define MUI_WELCOMEFINISHPAGE_BITMAP "cyborg_flower_girl.bmp"
+!define MUI_WELCOMEFINISHPAGE_BITMAP "${EXISTING_INSTALLATION_DIR}\installer_files\cyborg_flower_girl.bmp"
 
 ; Welcome page
 !define MUI_WELCOMEPAGE_TEXT "This installer will guide you through the installation of Easy Diffusion.$\n$\n\
@@ -176,8 +176,8 @@ Click Next to continue."
 Page custom MediaPackDialog
 
 ; License page
-!insertmacro MUI_PAGE_LICENSE "..\LICENSE"
-!insertmacro MUI_PAGE_LICENSE "..\CreativeML Open RAIL-M License"
+!insertmacro MUI_PAGE_LICENSE "${EXISTING_INSTALLATION_DIR}\LICENSE"
+!insertmacro MUI_PAGE_LICENSE "${EXISTING_INSTALLATION_DIR}\CreativeML Open RAIL-M License"
 ; Directory page
 !define MUI_PAGE_CUSTOMFUNCTION_LEAVE "DirectoryLeave"
 !insertmacro MUI_PAGE_DIRECTORY
@@ -210,29 +210,33 @@ ShowInstDetails show
 ; List of files to be installed
 Section "MainSection" SEC01
   SetOutPath "$INSTDIR"
-  File "..\CreativeML Open RAIL-M License"
-  File "..\How to install and run.txt"
-  File "..\LICENSE"
-  File "..\scripts\Start Stable Diffusion UI.cmd"
+  File "${EXISTING_INSTALLATION_DIR}\CreativeML Open RAIL-M License"
+  File "${EXISTING_INSTALLATION_DIR}\How to install and run.txt"
+  File "${EXISTING_INSTALLATION_DIR}\LICENSE"
+  File "${EXISTING_INSTALLATION_DIR}\Start Stable Diffusion UI.cmd"
   File /r "${EXISTING_INSTALLATION_DIR}\installer_files"
-  File /r "${EXISTING_INSTALLATION_DIR}\profile"
   File /r "${EXISTING_INSTALLATION_DIR}\sd-ui-files"
-  SetOutPath "$INSTDIR\installer_files"
-  File "cyborg_flower_girl.ico"
+
   SetOutPath "$INSTDIR\scripts"
   File "${EXISTING_INSTALLATION_DIR}\scripts\install_status.txt"
-  File "..\scripts\on_env_start.bat"
+  File "${EXISTING_INSTALLATION_DIR}\scripts\on_env_start.bat"
   File "C:\windows\system32\curl.exe"
-  CreateDirectory "$INSTDIR\models"
+  File "${EXISTING_INSTALLATION_DIR}\scripts\config.yaml.sample"
+
   CreateDirectory "$INSTDIR\models\stable-diffusion"
   CreateDirectory "$INSTDIR\models\gfpgan"
   CreateDirectory "$INSTDIR\models\realesrgan"
   CreateDirectory "$INSTDIR\models\vae"
+
+  CreateDirectory "$INSTDIR\profile\.cache\huggingface\hub"
+  SetOutPath "$INSTDIR\profile\.cache\huggingface\hub"
+  File /r /x pytorch_model.bin "${EXISTING_INSTALLATION_DIR}\profile\.cache\huggingface\hub\models--openai--clip-vit-large-patch14"
+
   CreateDirectory "$SMPROGRAMS\Easy Diffusion"
   CreateShortCut "$SMPROGRAMS\Easy Diffusion\Easy Diffusion.lnk" "$INSTDIR\Start Stable Diffusion UI.cmd" "" "$INSTDIR\installer_files\cyborg_flower_girl.ico"
 
-  DetailPrint 'Downloading the Stable Diffusion 1.4 model...'
-  NScurl::http get "https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt" "$INSTDIR\models\stable-diffusion\sd-v1-4.ckpt" /CANCEL /INSIST /END
+  DetailPrint 'Downloading the Stable Diffusion 1.5 model...'
+  NScurl::http get "https://github.com/easydiffusion/sdkit-test-data/releases/download/assets/sd-v1-5.safetensors" "$INSTDIR\models\stable-diffusion\sd-v1-5.safetensors" /CANCEL /INSIST /END
 
   DetailPrint 'Downloading the GFPGAN model...'
   NScurl::http get "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth" "$INSTDIR\models\gfpgan\GFPGANv1.4.pth" /CANCEL /INSIST /END

README.md

@@ -1,28 +1,36 @@
-# Easy Diffusion 2.5
+# Easy Diffusion 3.0
 ### The easiest way to install and use [Stable Diffusion](https://github.com/CompVis/stable-diffusion) on your computer.
 
 Does not require technical knowledge, does not require pre-installed software. 1-click install, powerful features, friendly community.
 
-[Installation guide](#installation) | [Troubleshooting guide](https://github.com/easydiffusion/easydiffusion/wiki/Troubleshooting) | <sub>[![Discord Server](https://img.shields.io/discord/1014774730907209781?label=Discord)](https://discord.com/invite/u9yhsFmEkB)</sub> <sup>(for support queries, and development discussions)</sup>
+️‍🔥🎉 **New!** Support for Flux has been added in the beta branch (v3.5 engine)!
+
+[Installation guide](#installation) | [Troubleshooting guide](https://github.com/easydiffusion/easydiffusion/wiki/Troubleshooting) | [User guide](https://github.com/easydiffusion/easydiffusion/wiki) | <sub>[![Discord Server](https://img.shields.io/discord/1014774730907209781?label=Discord)](https://discord.com/invite/u9yhsFmEkB)</sub> <sup>(for support queries, and development discussions)</sup>
+
+---
+![262597678-11089485-2514-4a11-88fb-c3acc81fc9ec](https://github.com/easydiffusion/easydiffusion/assets/844287/050b5e15-e909-45bf-8162-a38234830e38)
 
-![t2i](https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/assets/stable-samples/txt2img/768/merged-0006.png)
 
 # Installation
 Click the download button for your operating system:
 
 <p float="left">
-  <a href="https://github.com/easydiffusion/easydiffusion/releases/download/v2.5.24/Easy-Diffusion-Windows.exe"><img src="https://github.com/easydiffusion/easydiffusion/raw/main/media/download-win.png" width="200" /></a>
-  <a href="https://github.com/easydiffusion/easydiffusion/releases/download/v2.5.24/Easy-Diffusion-Linux.zip"><img src="https://github.com/easydiffusion/easydiffusion/raw/main/media/download-linux.png" width="200" /></a>
-  <a href="https://github.com/easydiffusion/easydiffusion/releases/download/v2.5.24/Easy-Diffusion-Mac.zip"><img src="https://github.com/easydiffusion/easydiffusion/raw/main/media/download-mac.png" width="200" /></a>
+  <a href="https://github.com/cmdr2/stable-diffusion-ui/releases/latest/download/Easy-Diffusion-Linux.zip"><img src="https://github.com/cmdr2/stable-diffusion-ui/raw/main/media/download-linux.png" width="200" /></a>
+  <a href="https://github.com/cmdr2/stable-diffusion-ui/releases/latest/download/Easy-Diffusion-Mac.zip"><img src="https://github.com/cmdr2/stable-diffusion-ui/raw/main/media/download-mac.png" width="200" /></a>
+  <a href="https://github.com/cmdr2/stable-diffusion-ui/releases/latest/download/Easy-Diffusion-Windows.exe"><img src="https://github.com/cmdr2/stable-diffusion-ui/raw/main/media/download-win.png" width="200" /></a>
 </p>
 
 **Hardware requirements:**
-- **Windows:** NVIDIA graphics card (minimum 2 GB RAM), or run on your CPU.
-- **Linux:** NVIDIA or AMD graphics card (minimum 2 GB RAM), or run on your CPU.
-- **Mac:** M1 or M2, or run on your CPU.
+- **Windows:** NVIDIA¹ or AMD graphics card (minimum 2 GB RAM), or run on your CPU.
+- **Linux:** NVIDIA¹ or AMD² graphics card (minimum 2 GB RAM), or run on your CPU.
+- **Mac:** M1/M2/M3/M4 or AMD graphics card (Intel Mac), or run on your CPU.
 - Minimum 8 GB of system RAM.
 - Atleast 25 GB of space on the hard disk.
 
+¹) [CUDA Compute capability](https://en.wikipedia.org/wiki/CUDA#GPUs_supported) level of 3.7 or higher required.
+
+²) ROCm 5.2 (or newer) support required.
+
 The installer will take care of whatever is needed. If you face any problems, you can join the friendly [Discord community](https://discord.com/invite/u9yhsFmEkB) and ask for assistance.
 
 ## On Windows:
@@ -58,17 +66,19 @@ Just delete the `EasyDiffusion` folder to uninstall all the downloaded packages.
 - **UI Themes**: Customize the program to your liking.
 - **Searchable models dropdown**: organize your models into sub-folders, and search through them in the UI.
 
-### Image generation
-- **Supports**: "*Text to Image*" and "*Image to Image*".
-- **21 Samplers**: `ddim`, `plms`, `heun`, `euler`, `euler_a`, `dpm2`, `dpm2_a`, `lms`, `dpm_solver_stability`, `dpmpp_2s_a`, `dpmpp_2m`, `dpmpp_sde`, `dpm_fast`, `dpm_adaptive`, `ddpm`, `deis`, `unipc_snr`, `unipc_tu`, `unipc_tq`, `unipc_snr_2`, `unipc_tu_2`.
-- **In-Painting**: Specify areas of your image to paint into.
+### Powerful image generation
+- **Supports**: "*Text to Image*", "*Image to Image*" and "*InPainting*"
+- **ControlNet**: For advanced control over the image, e.g. by setting the pose or drawing the outline for the AI to fill in.
+- **16 Samplers**: `PLMS`, `DDIM`, `DEIS`, `Heun`, `Euler`, `Euler Ancestral`, `DPM2`, `DPM2 Ancestral`, `LMS`, `DPM Solver`, `DPM++ 2s Ancestral`, `DPM++ 2m`, `DPM++ 2m SDE`, `DPM++ SDE`, `DDPM`, `UniPC`.
+- **Stable Diffusion XL and 2.1**: Generate higher-quality images using the latest Stable Diffusion XL models.
+- **Textual Inversion Embeddings**: For guiding the AI strongly towards a particular concept.
 - **Simple Drawing Tool**: Draw basic images to guide the AI, without needing an external drawing program.
 - **Face Correction (GFPGAN)**
 - **Upscaling (RealESRGAN)**
-- **Loopback**: Use the output image as the input image for the next img2img task.
+- **Loopback**: Use the output image as the input image for the next image task.
 - **Negative Prompt**: Specify aspects of the image to *remove*.
-- **Attention/Emphasis**: () in the prompt increases the model's attention to enclosed words, and [] decreases it.
-- **Weighted Prompts**: Use weights for specific words in your prompt to change their importance, e.g. `red:2.4 dragon:1.2`.
+- **Attention/Emphasis**: `+` in the prompt increases the model's attention to enclosed words, and `-` decreases it. E.g. `apple++ falling from a tree`.
+- **Weighted Prompts**: Use weights for specific words in your prompt to change their importance, e.g. `(red)2.4 (dragon)1.2`.
 - **Prompt Matrix**: Quickly create multiple variations of your prompt, e.g. `a photograph of an astronaut riding a horse | illustration | cinematic lighting`.
 - **Prompt Set**: Quickly create multiple variations of your prompt, e.g. `a photograph of an astronaut on the {moon,earth}`
 - **1-click Upscale/Face Correction**: Upscale or correct an image after it has been generated.
@@ -78,10 +88,11 @@ Just delete the `EasyDiffusion` folder to uninstall all the downloaded packages.
 
 ### Advanced features
 - **Custom Models**: Use your own `.ckpt` or `.safetensors` file, by placing it inside the `models/stable-diffusion` folder!
-- **Stable Diffusion 2.1 support**
+- **Stable Diffusion XL and 2.1 support**
 - **Merge Models**
 - **Use custom VAE models**
-- **Use pre-trained Hypernetworks**
+- **Textual Inversion Embeddings**
+- **ControlNet**
 - **Use custom GFPGAN models**
 - **UI Plugins**: Choose from a growing list of [community-generated UI plugins](https://github.com/easydiffusion/easydiffusion/wiki/UI-Plugins), or write your own plugin to add features to the project!
 
@@ -93,24 +104,14 @@ Just delete the `EasyDiffusion` folder to uninstall all the downloaded packages.
 - **Auto scan for malicious models**: Uses picklescan to prevent malicious models.
 - **Safetensors support**: Support loading models in the safetensor format, for improved safety.
 - **Auto-updater**: Gets you the latest improvements and bug-fixes to a rapidly evolving project.
-- **Developer Console**: A developer-mode for those who want to modify their Stable Diffusion code, and edit the conda environment.
+- **Developer Console**: A developer-mode for those who want to modify their Stable Diffusion code, modify packages, and edit the conda environment.
 
 **(and a lot more)**
 
 ----
 
-## Easy for new users:
-![Screenshot of the initial UI](https://user-images.githubusercontent.com/844287/217043152-29454d15-0387-4228-b70d-9a4b84aeb8ba.png)
-
-
-## Powerful features for advanced users:
-![Screenshot of advanced settings](https://user-images.githubusercontent.com/844287/217042588-fc53c975-bacd-4a9c-af88-37408734ade3.png)
-
-
-## Live Preview
-Useful for judging (and stopping) an image quickly, without waiting for it to finish rendering.
-
-![live-512](https://user-images.githubusercontent.com/844287/192097249-729a0a1e-a677-485e-9ccc-16a9e848fabe.gif)
+## Easy for new users, powerful features for advanced users:
+![image](https://github.com/easydiffusion/easydiffusion/assets/844287/efbbac9f-42ce-4aef-8625-fd23c74a8241)
 
 ## Task Queue
 ![Screenshot of task queue](https://user-images.githubusercontent.com/844287/217043984-0b35f73b-1318-47cb-9eed-a2a91b430490.png)
@@ -124,14 +125,17 @@ Please refer to our [guide](https://github.com/easydiffusion/easydiffusion/wiki/
 # Bugs reports and code contributions welcome
 If there are any problems or suggestions, please feel free to ask on the [discord server](https://discord.com/invite/u9yhsFmEkB) or [file an issue](https://github.com/easydiffusion/easydiffusion/issues).
 
-We could really use help on these aspects (click to view tasks that need your help):
-* [User Interface](https://github.com/users/cmdr2/projects/1/views/1)
-* [Engine](https://github.com/users/cmdr2/projects/3/views/1)
-* [Installer](https://github.com/users/cmdr2/projects/4/views/1)
-* [Documentation](https://github.com/users/cmdr2/projects/5/views/1)
-
 If you have any code contributions in mind, please feel free to say Hi to us on the [discord server](https://discord.com/invite/u9yhsFmEkB). We use the Discord server for development-related discussions, and for helping users.
 
+# Credits
+* Stable Diffusion: https://github.com/Stability-AI/stablediffusion
+* CodeFormer: https://github.com/sczhou/CodeFormer (license: https://github.com/sczhou/CodeFormer/blob/master/LICENSE)
+* GFPGAN: https://github.com/TencentARC/GFPGAN
+* RealESRGAN: https://github.com/xinntao/Real-ESRGAN
+* k-diffusion: https://github.com/crowsonkb/k-diffusion
+* Code contributors and artists on the cmdr2 UI: https://github.com/cmdr2/stable-diffusion-ui and Discord (https://discord.com/invite/u9yhsFmEkB)
+* Lots of contributors on the internet
+
 # Disclaimer
 The authors of this project are not responsible for any content generated using this interface.
 

build.bat

@@ -1,48 +1,78 @@
 @echo off
+setlocal enabledelayedexpansion
 
 @echo "Hi there, what you are running is meant for the developers of this project, not for users." & echo.
-@echo "If you only want to use the Stable Diffusion UI, you've downloaded the wrong file."
+@echo "If you only want to use Easy Diffusion, you've downloaded the wrong file."
 @echo "Please download and follow the instructions at https://github.com/easydiffusion/easydiffusion#installation" & echo.
 @echo "If you are actually a developer of this project, please type Y and press enter" & echo.
 
 set /p answer=Are you a developer of this project (Y/N)?
 if /i "%answer:~,1%" NEQ "Y" exit /b
 
-mkdir dist\win\stable-diffusion-ui\scripts
-@REM mkdir dist\linux-mac\stable-diffusion-ui\scripts
+@rem verify dependencies
+call makensis /VERSION >.tmp1 2>.tmp2
+if "!ERRORLEVEL!" NEQ "0" (
+    echo makensis.exe not found! Download it from https://sourceforge.net/projects/nsisbi/files/ and set it on the PATH variable.
+    pause
+    exit
+)
 
-@rem copy the installer files for Windows
+set /p OUT_DIR=Output folder path (will create the installer files inside this, e.g. F:\EasyDiffusion): 
 
-copy scripts\on_env_start.bat dist\win\stable-diffusion-ui\scripts\
-copy scripts\bootstrap.bat dist\win\stable-diffusion-ui\scripts\
-copy scripts\config.yaml.sample dist\win\stable-diffusion-ui\scripts\config.yaml
-copy "scripts\Start Stable Diffusion UI.cmd" dist\win\stable-diffusion-ui\
-copy LICENSE dist\win\stable-diffusion-ui\
-copy "CreativeML Open RAIL-M License" dist\win\stable-diffusion-ui\
-copy "How to install and run.txt" dist\win\stable-diffusion-ui\
-echo. > dist\win\stable-diffusion-ui\scripts\install_status.txt
+mkdir "%OUT_DIR%\scripts"
+mkdir "%OUT_DIR%\installer_files"
 
-@rem copy the installer files for Linux and Mac
+set BASE_DIR=%cd%
 
-@REM copy scripts\on_env_start.sh dist\linux-mac\stable-diffusion-ui\scripts\
-@REM copy scripts\bootstrap.sh dist\linux-mac\stable-diffusion-ui\scripts\
-@REM copy scripts\start.sh dist\linux-mac\stable-diffusion-ui\
-@REM copy LICENSE dist\linux-mac\stable-diffusion-ui\
-@REM copy "CreativeML Open RAIL-M License" dist\linux-mac\stable-diffusion-ui\
-@REM copy "How to install and run.txt" dist\linux-mac\stable-diffusion-ui\
-@REM echo. > dist\linux-mac\stable-diffusion-ui\scripts\install_status.txt
+@rem STEP 1: copy the installer files for Windows
 
-@rem make the zip
-
-cd dist\win
-call powershell Compress-Archive -Path stable-diffusion-ui -DestinationPath ..\stable-diffusion-ui-windows.zip
-cd ..\..
-
-@REM cd dist\linux-mac
-@REM call powershell Compress-Archive -Path stable-diffusion-ui -DestinationPath ..\stable-diffusion-ui-linux.zip
-@REM call powershell Compress-Archive -Path stable-diffusion-ui -DestinationPath ..\stable-diffusion-ui-mac.zip
-@REM cd ..\..
-
-echo "Build ready. Upload the zip files inside the 'dist' folder."
+copy "%BASE_DIR%\scripts\on_env_start.bat" "%OUT_DIR%\scripts\"
+copy "%BASE_DIR%\scripts\config.yaml.sample" "%OUT_DIR%\scripts\config.yaml.sample"
+copy "%BASE_DIR%\scripts\Start Stable Diffusion UI.cmd" "%OUT_DIR%\"
+copy "%BASE_DIR%\LICENSE" "%OUT_DIR%\"
+copy "%BASE_DIR%\CreativeML Open RAIL-M License" "%OUT_DIR%\"
+copy "%BASE_DIR%\How to install and run.txt" "%OUT_DIR%\"
+copy "%BASE_DIR%\NSIS\cyborg_flower_girl.ico" "%OUT_DIR%\installer_files\"
+copy "%BASE_DIR%\NSIS\cyborg_flower_girl.bmp" "%OUT_DIR%\installer_files\"
+echo. > "%OUT_DIR%\scripts\install_status.txt"
 
+echo ----
+echo Basic files ready. Verify the files in %OUT_DIR%, then press Enter to initialize the environment, or close to quit.
+echo ----
 pause
+
+@rem STEP 2: Initialize the environment with git, python and conda
+
+cd /d "%OUT_DIR%\"
+call "%BASE_DIR%\scripts\bootstrap.bat"
+
+echo ----
+echo Environment ready. Verify the environment, then press Enter to download the necessary packages, or close to quit.
+echo ----
+pause
+
+@rem STEP 3: Download the packages and create a working installation
+
+cd /d "%OUT_DIR%\"
+start "Install Easy Diffusion" /D "%OUT_DIR%" "Start Stable Diffusion UI.cmd"
+
+echo ----
+echo Installation in progress (in a new window). Once complete, verify the installation, then press Enter to create an installer from these files, or close to quit.
+echo ----
+pause
+
+@rem STEP 4: Build the installer from a working installation
+
+cd /d "%OUT_DIR%\"
+
+echo ^^!define EXISTING_INSTALLATION_DIR "%OUT_DIR%" > nsisconf.nsh
+call makensis /NOCD /V4 "%BASE_DIR%\NSIS\sdui.nsi"
+
+echo ----
+if "!ERRORLEVEL!" EQU "0" (
+    echo Installer built successfully at %OUT_DIR%
+) else (
+    echo Installer failed to build at %OUT_DIR%
+)
+echo ----
+pause

build.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 
 printf "Hi there, what you are running is meant for the developers of this project, not for users.\n\n"
-printf "If you only want to use the Stable Diffusion UI, you've downloaded the wrong file.\n"
+printf "If you only want to use Easy Diffusion, you've downloaded the wrong file.\n"
 printf "Please download and follow the instructions at https://github.com/easydiffusion/easydiffusion#installation \n\n"
 printf "If you are actually a developer of this project, please type Y and press enter\n\n"
 
@@ -11,40 +11,30 @@ case $yn in
     * ) exit;;
 esac
 
-# mkdir -p dist/win/stable-diffusion-ui/scripts
-mkdir -p dist/linux-mac/stable-diffusion-ui/scripts
-
-# copy the installer files for Windows
-
-# cp scripts/on_env_start.bat dist/win/stable-diffusion-ui/scripts/
-# cp scripts/bootstrap.bat dist/win/stable-diffusion-ui/scripts/
-# cp "scripts/Start Stable Diffusion UI.cmd" dist/win/stable-diffusion-ui/
-# cp LICENSE dist/win/stable-diffusion-ui/
-# cp "CreativeML Open RAIL-M License" dist/win/stable-diffusion-ui/
-# cp "How to install and run.txt" dist/win/stable-diffusion-ui/
-# echo "" > dist/win/stable-diffusion-ui/scripts/install_status.txt
+mkdir -p dist/linux-mac/easy-diffusion/scripts
 
 # copy the installer files for Linux and Mac
 
-cp scripts/on_env_start.sh dist/linux-mac/stable-diffusion-ui/scripts/
-cp scripts/bootstrap.sh dist/linux-mac/stable-diffusion-ui/scripts/
-cp scripts/functions.sh dist/linux-mac/stable-diffusion-ui/scripts/
-cp scripts/config.yaml.sample dist/linux-mac/stable-diffusion-ui/scripts/config.yaml
-cp scripts/start.sh dist/linux-mac/stable-diffusion-ui/
-cp LICENSE dist/linux-mac/stable-diffusion-ui/
-cp "CreativeML Open RAIL-M License" dist/linux-mac/stable-diffusion-ui/
-cp "How to install and run.txt" dist/linux-mac/stable-diffusion-ui/
-echo "" > dist/linux-mac/stable-diffusion-ui/scripts/install_status.txt
+cp scripts/on_env_start.sh dist/linux-mac/easy-diffusion/scripts/
+cp scripts/bootstrap.sh dist/linux-mac/easy-diffusion/scripts/
+cp scripts/functions.sh dist/linux-mac/easy-diffusion/scripts/
+cp scripts/config.yaml.sample dist/linux-mac/easy-diffusion/scripts/config.yaml.sample
+cp scripts/start.sh dist/linux-mac/easy-diffusion/
+cp LICENSE dist/linux-mac/easy-diffusion/
+cp "CreativeML Open RAIL-M License" dist/linux-mac/easy-diffusion/
+cp "How to install and run.txt" dist/linux-mac/easy-diffusion/
+echo "" > dist/linux-mac/easy-diffusion/scripts/install_status.txt
+
+# set the permissions
+chmod u+x dist/linux-mac/easy-diffusion/scripts/on_env_start.sh
+chmod u+x dist/linux-mac/easy-diffusion/scripts/bootstrap.sh
+chmod u+x dist/linux-mac/easy-diffusion/start.sh
 
 # make the zip
 
-# cd dist/win
-# zip -r ../stable-diffusion-ui-windows.zip stable-diffusion-ui
-# cd ../..
-
 cd dist/linux-mac
-zip -r ../stable-diffusion-ui-linux.zip stable-diffusion-ui
-zip -r ../stable-diffusion-ui-mac.zip stable-diffusion-ui
+zip -r ../Easy-Diffusion-Linux.zip easy-diffusion
+zip -r ../Easy-Diffusion-Mac.zip easy-diffusion
 cd ../..
 
 echo "Build ready. Upload the zip files inside the 'dist' folder."

scripts/Developer Console.cmd

@@ -4,7 +4,7 @@ echo "Opening Stable Diffusion UI - Developer Console.." & echo.
 
 cd /d %~dp0
 
-set PATH=C:\Windows\System32;%PATH%
+set PATH=C:\Windows\System32;C:\Windows\System32\WindowsPowerShell\v1.0;%PATH%
 
 @rem set legacy and new installer's PATH, if they exist
 if exist "installer" set PATH=%cd%\installer;%cd%\installer\Library\bin;%cd%\installer\Scripts;%cd%\installer\Library\usr\bin;%PATH%
@@ -26,6 +26,7 @@ call conda --version
 echo.
 echo COMSPEC=%COMSPEC%
 echo.
+powershell -Command "(Get-WmiObject Win32_VideoController | Select-Object Name, AdapterRAM, DriverDate, DriverVersion)"
 
 @rem activate the legacy environment (if present) and set PYTHONPATH
 if exist "installer_files\env" (

scripts/Start Stable Diffusion UI.cmd

@@ -3,7 +3,8 @@
 cd /d %~dp0
 echo Install dir: %~dp0
 
-set PATH=C:\Windows\System32;%PATH%
+set PATH=C:\Windows\System32;C:\Windows\System32\WindowsPowerShell\v1.0;%PATH%
+set PYTHONHOME=
 
 if exist "on_sd_start.bat" (
     echo ================================================================================
@@ -14,7 +15,7 @@ if exist "on_sd_start.bat" (
     echo download. This will not work.
     echo.
     echo Recommended: Please close this window and download the installer from
-    echo https://stable-diffusion-ui.github.io/docs/installation/
+    echo https://easydiffusion.github.io/docs/installation/
     echo.
     echo ================================================================================
     echo.
@@ -38,6 +39,7 @@ call where conda
 call conda --version
 echo .
 echo COMSPEC=%COMSPEC%
+powershell -Command "(Get-WmiObject Win32_VideoController | Select-Object Name, AdapterRAM, DriverDate, DriverVersion)"
 
 @rem Download the rest of the installer and UI
 call scripts\on_env_start.bat

scripts/bootstrap.bat

@@ -14,6 +14,8 @@ set LEGACY_INSTALL_ENV_DIR=%cd%\installer
 set MICROMAMBA_DOWNLOAD_URL=https://github.com/easydiffusion/easydiffusion/releases/download/v1.1/micromamba.exe
 set umamba_exists=F
 
+set PYTHONHOME=
+
 set OLD_APPDATA=%APPDATA%
 set OLD_USERPROFILE=%USERPROFILE%
 set APPDATA=%cd%\installer_files\appdata
@@ -22,15 +24,12 @@ set USERPROFILE=%cd%\profile
 @rem figure out whether git and conda needs to be installed
 if exist "%INSTALL_ENV_DIR%" set PATH=%INSTALL_ENV_DIR%;%INSTALL_ENV_DIR%\Library\bin;%INSTALL_ENV_DIR%\Scripts;%INSTALL_ENV_DIR%\Library\usr\bin;%PATH%
 
-set PACKAGES_TO_INSTALL=
+set PACKAGES_TO_INSTALL=git python=3.9
 
 if not exist "%LEGACY_INSTALL_ENV_DIR%\etc\profile.d\conda.sh" (
-    if not exist "%INSTALL_ENV_DIR%\etc\profile.d\conda.sh" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% conda python=3.8.5
+    if not exist "%INSTALL_ENV_DIR%\etc\profile.d\conda.sh" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% conda
 )
 
-call git --version >.tmp1 2>.tmp2
-if "!ERRORLEVEL!" NEQ "0" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% git
-
 call "%MAMBA_ROOT_PREFIX%\micromamba.exe" --version >.tmp1 2>.tmp2
 if "!ERRORLEVEL!" EQU "0" set umamba_exists=T
 

scripts/bootstrap.sh

@@ -46,7 +46,7 @@ if [ -e "$INSTALL_ENV_DIR" ]; then export PATH="$INSTALL_ENV_DIR/bin:$PATH"; fi
 
 PACKAGES_TO_INSTALL=""
 
-if [ ! -e "$LEGACY_INSTALL_ENV_DIR/etc/profile.d/conda.sh" ] && [ ! -e "$INSTALL_ENV_DIR/etc/profile.d/conda.sh" ]; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL conda python=3.8.5"; fi
+if [ ! -e "$LEGACY_INSTALL_ENV_DIR/etc/profile.d/conda.sh" ] && [ ! -e "$INSTALL_ENV_DIR/etc/profile.d/conda.sh" ]; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL conda python=3.9"; fi
 if ! hash "git" &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL git"; fi
 
 if "$MAMBA_ROOT_PREFIX/micromamba" --version &>/dev/null; then umamba_exists="T"; fi

scripts/check_modules.py

@@ -8,26 +8,37 @@ a custom index URL depending on the platform.
 
 """
 
-import os
+import os, sys
 from importlib.metadata import version as pkg_version
 import platform
 import traceback
+import shutil
+from pathlib import Path
+from pprint import pprint
+import re
+import torchruntime
 
 os_name = platform.system()
 
 modules_to_check = {
-    "torch": ("1.11.0", "1.13.1", "2.0.0"),
-    "torchvision": ("0.12.0", "0.14.1", "0.15.1"),
-    "sdkit": "1.0.142",
-    "stable-diffusion-sdkit": "2.1.4",
+    "setuptools": "69.5.1",
+    # "sdkit": "2.0.15.6", # checked later
+    # "diffusers": "0.21.4", # checked later
+    "stable-diffusion-sdkit": "2.1.5",
     "rich": "12.6.0",
     "uvicorn": "0.19.0",
-    "fastapi": "0.85.1",
+    "fastapi": "0.115.6",
     "pycloudflared": "0.2.0",
     "ruamel.yaml": "0.17.21",
+    "sqlalchemy": "2.0.19",
+    "python-multipart": "0.0.6",
     # "xformers": "0.0.16",
+    "huggingface-hub": "0.21.4",
+    "wandb": "0.17.2",
+    "torchruntime": "1.14.1",
+    "torchsde": "0.2.6",
 }
-modules_to_log = ["torch", "torchvision", "sdkit", "stable-diffusion-sdkit"]
+modules_to_log = ["torchruntime", "torch", "torchvision", "sdkit", "stable-diffusion-sdkit", "diffusers"]
 
 
 def version(module_name: str) -> str:
@@ -37,26 +48,9 @@ def version(module_name: str) -> str:
         return None
 
 
-def install(module_name: str, module_version: str):
-    if module_name == "xformers" and (os_name == "Darwin" or is_amd_on_linux()):
-        return
-
-    index_url = None
-    if module_name in ("torch", "torchvision"):
-        module_version, index_url = apply_torch_install_overrides(module_version)
-
-    if is_amd_on_linux():  # hack until AMD works properly on torch 2.0 (avoids black images on some cards)
-        if module_name == "torch":
-            module_version = "1.13.1+rocm5.2"
-        elif module_name == "torchvision":
-            module_version = "0.14.1+rocm5.2"
-    elif os_name == "Darwin":
-        if module_name == "torch":
-            module_version = "1.13.1"
-        elif module_name == "torchvision":
-            module_version = "0.14.1"
-
+def install(module_name: str, module_version: str, index_url=None):
     install_cmd = f"python -m pip install --upgrade {module_name}=={module_version}"
+
     if index_url:
         install_cmd += f" --index-url {index_url}"
     if module_name == "sdkit" and version("sdkit") is not None:
@@ -66,24 +60,26 @@ def install(module_name: str, module_version: str):
     os.system(install_cmd)
 
 
-def init():
+def update_modules():
+    if version("torch") is None:
+        torchruntime.install(["torch", "torchvision"])
+
     for module_name, allowed_versions in modules_to_check.items():
-        if os.path.exists(f"../src/{module_name}"):
+        if os.path.exists(f"src/{module_name}"):
             print(f"Skipping {module_name} update, since it's in developer/editable mode")
             continue
 
         allowed_versions, latest_version = get_allowed_versions(module_name, allowed_versions)
 
-        requires_install = False
-        if module_name in ("torch", "torchvision"):
-            if version(module_name) is None:  # allow any torch version
-                requires_install = True
-            elif os_name == "Darwin" and (  # force mac to downgrade from torch 2.0
-                version("torch").startswith("2.") or version("torchvision").startswith("0.15.")
-            ):
-                requires_install = True
-        elif version(module_name) not in allowed_versions:
-            requires_install = True
+        if module_name == "setuptools":
+            if os_name == "Windows":
+                allowed_versions = ("59.8.0",)
+                latest_version = "59.8.0"
+            else:
+                allowed_versions = ("69.5.1",)
+                latest_version = "69.5.1"
+
+        requires_install = version(module_name) not in allowed_versions
 
         if requires_install:
             try:
@@ -91,60 +87,129 @@ def init():
             except:
                 traceback.print_exc()
                 fail(module_name)
+            else:
+                if version(module_name) != latest_version:
+                    print(
+                        f"WARNING! Tried to install {module_name}=={latest_version}, but the version is still {version(module_name)}!"
+                    )
 
-        if module_name in modules_to_log:
-            print(f"{module_name}: {version(module_name)}")
+    # different sdkit versions, with the corresponding diffusers
+    #  if sdkit is 2.0.15.x (or lower), then diffusers should be restricted to 0.21.4 (see below for the reason)
+    #  otherwise use the current sdkit version (with the corresponding diffusers version)
+
+    expected_sdkit_version_str = "2.0.22.7"
+    expected_diffusers_version_str = "0.28.2"
+
+    legacy_sdkit_version_str = "2.0.15.16"
+    legacy_diffusers_version_str = "0.21.4"
+
+    sdkit_version_str = version("sdkit")
+    if sdkit_version_str is None:  # first install
+        _install("sdkit", expected_sdkit_version_str)
+        _install("diffusers", expected_diffusers_version_str)
+    else:
+        sdkit_version = version_str_to_tuple(sdkit_version_str)
+        legacy_sdkit_version = version_str_to_tuple(legacy_sdkit_version_str)
+
+        if sdkit_version[:3] <= legacy_sdkit_version[:3]:
+            # stick to diffusers 0.21.4, since it preserves torch 0.11+ compatibility.
+            # upgrading beyond this will result in a 2+ GB download of torch on older installations
+            #  and a time-consuming chain of small package updates due to huggingface_hub upgrade.
+            # for now, the user will need to explicitly upgrade to a newer sdkit, to break this ceiling.
+
+            install_pkg_if_necessary("sdkit", legacy_sdkit_version_str)
+            install_pkg_if_necessary("diffusers", legacy_diffusers_version_str)
+        else:
+            torch_version = version_str_to_tuple(version("torch"))
+            if torch_version < (1, 13):
+                # install the gpu-compatible torch (if necessary), instead of the default CPU-only one
+                # from the diffusers dependency chain
+                torchruntime.install(["--upgrade", "torch", "torchvision"])
+
+            install_pkg_if_necessary("sdkit", expected_sdkit_version_str)
+            install_pkg_if_necessary("diffusers", expected_diffusers_version_str)
+
+    # hotfix accelerate
+    accelerate_version = version("accelerate")
+    if accelerate_version is None:
+        install("accelerate", "0.23.0")
+    else:
+        accelerate_version = accelerate_version.split(".")
+        accelerate_version = tuple(map(int, accelerate_version))
+        if accelerate_version < (0, 23):
+            install("accelerate", "0.23.0")
+
+    # hotfix - 29 May 2024. sdkit has stopped pulling its dependencies for some reason
+    # temporarily dumping sdkit's requirements here:
+    if os_name != "Windows":
+        sdkit_deps = [
+            "gfpgan",
+            "piexif",
+            "realesrgan",
+            "requests",
+            "picklescan",
+            "safetensors==0.3.3",
+            "k-diffusion==0.0.12",
+            "compel==2.0.1",
+            "controlnet-aux==0.0.6",
+            "invisible-watermark==0.2.0",  # required for SD XL
+        ]
+
+        for mod in sdkit_deps:
+            mod_name = mod
+            mod_force_version_str = None
+            if "==" in mod:
+                mod_name, mod_force_version_str = mod.split("==")
+
+            curr_mod_version_str = version(mod_name)
+            if curr_mod_version_str is None:
+                _install(mod_name, mod_force_version_str)
+            elif mod_force_version_str is not None:
+                curr_mod_version = version_str_to_tuple(curr_mod_version_str)
+                mod_force_version = version_str_to_tuple(mod_force_version_str)
+
+                if curr_mod_version != mod_force_version:
+                    _install(mod_name, mod_force_version_str)
+
+    for module_name in modules_to_log:
+        print(f"{module_name}: {version(module_name)}")
+
+
+def _install(module_name, module_version=None):
+    if module_version is None:
+        install_cmd = f"python -m pip install {module_name}"
+    else:
+        install_cmd = f"python -m pip install --upgrade {module_name}=={module_version}"
+
+    print(">", install_cmd)
+    os.system(install_cmd)
+
+
+def install_pkg_if_necessary(pkg_name, required_version):
+    if os.path.exists(f"src/{pkg_name}"):
+        print(f"Skipping {pkg_name} update, since it's in developer/editable mode")
+        return
+
+    pkg_version = version(pkg_name)
+    if pkg_version != required_version:
+        _install(pkg_name, required_version)
+
+
+def version_str_to_tuple(ver_str):
+    ver_str = ver_str.split("+")[0]
+    ver_str = re.sub("[^0-9.]", "", ver_str)
+    ver = ver_str.split(".")
+    return tuple(map(int, ver))
 
 
 ### utilities
-
-
 def get_allowed_versions(module_name: str, allowed_versions: tuple):
     allowed_versions = (allowed_versions,) if isinstance(allowed_versions, str) else allowed_versions
     latest_version = allowed_versions[-1]
 
-    if module_name in ("torch", "torchvision"):
-        allowed_versions = include_cuda_versions(allowed_versions)
-
     return allowed_versions, latest_version
 
 
-def apply_torch_install_overrides(module_version: str):
-    index_url = None
-    if os_name == "Windows":
-        module_version += "+cu117"
-        index_url = "https://download.pytorch.org/whl/cu117"
-    elif is_amd_on_linux():
-        index_url = "https://download.pytorch.org/whl/rocm5.2"
-
-    return module_version, index_url
-
-
-def include_cuda_versions(module_versions: tuple) -> tuple:
-    "Adds CUDA-specific versions to the list of allowed version numbers"
-
-    allowed_versions = tuple(module_versions)
-    allowed_versions += tuple(f"{v}+cu116" for v in module_versions)
-    allowed_versions += tuple(f"{v}+cu117" for v in module_versions)
-    allowed_versions += tuple(f"{v}+rocm5.2" for v in module_versions)
-    allowed_versions += tuple(f"{v}+rocm5.4.2" for v in module_versions)
-
-    return allowed_versions
-
-
-def is_amd_on_linux():
-    if os_name == "Linux":
-        try:
-            with open("/proc/bus/pci/devices", "r") as f:
-                device_info = f.read()
-                if "amdgpu" in device_info and "nvidia" not in device_info:
-                    return True
-        except:
-            return False
-
-    return False
-
-
 def fail(module_name):
     print(
         f"""Error installing {module_name}. Sorry about that, please try to:
@@ -157,6 +222,100 @@ Thanks!"""
     exit(1)
 
 
-### start
+### Launcher
 
-init()
+
+def get_config():
+    config_directory = os.path.dirname(__file__)  # this will be "scripts"
+    config_yaml = os.path.join(config_directory, "..", "config.yaml")
+    config_json = os.path.join(config_directory, "config.json")
+
+    config = None
+
+    # migrate the old config yaml location
+    config_legacy_yaml = os.path.join(config_directory, "config.yaml")
+    if os.path.isfile(config_legacy_yaml):
+        shutil.move(config_legacy_yaml, config_yaml)
+
+    if os.path.isfile(config_yaml):
+        from ruamel.yaml import YAML
+
+        yaml = YAML(typ="safe")
+        with open(config_yaml, "r") as configfile:
+            try:
+                config = yaml.load(configfile)
+            except Exception as e:
+                print(e, file=sys.stderr)
+    elif os.path.isfile(config_json):
+        import json
+
+        with open(config_json, "r") as configfile:
+            try:
+                config = json.load(configfile)
+            except Exception as e:
+                print(e, file=sys.stderr)
+
+    if config is None:
+        config = {}
+    return config
+
+
+def launch_uvicorn():
+    config = get_config()
+
+    pprint(config)
+
+    with open("scripts/install_status.txt", "a") as f:
+        f.write("sd_weights_downloaded\n")
+        f.write("sd_install_complete\n")
+
+    print("\n\nEasy Diffusion installation complete, starting the server!\n\n")
+
+    torchruntime.configure()
+    if hasattr(torchruntime, "info"):
+        torchruntime.info()
+
+    if os_name == "Windows":
+        os.environ["PYTHONPATH"] = str(Path(os.environ["INSTALL_ENV_DIR"], "lib", "site-packages"))
+    else:
+        os.environ["PYTHONPATH"] = str(Path(os.environ["INSTALL_ENV_DIR"], "lib", "python3.8", "site-packages"))
+    os.environ["SD_UI_PATH"] = str(Path(Path.cwd(), "ui"))
+
+    print(f"PYTHONPATH={os.environ['PYTHONPATH']}")
+    print(f"Python:  {shutil.which('python')}")
+    print(f"Version: {platform. python_version()}")
+
+    bind_ip = "127.0.0.1"
+    listen_port = 9000
+    if "net" in config:
+        print("Checking network settings")
+        if "listen_port" in config["net"]:
+            listen_port = config["net"]["listen_port"]
+            print("Set listen port to ", listen_port)
+        if "listen_to_network" in config["net"] and config["net"]["listen_to_network"] == True:
+            if "bind_ip" in config["net"]:
+                bind_ip = config["net"]["bind_ip"]
+            else:
+                bind_ip = "0.0.0.0"
+            print("Set bind_ip to ", bind_ip)
+
+    os.chdir("stable-diffusion")
+
+    print("\nLaunching uvicorn\n")
+
+    import uvicorn
+
+    uvicorn.run(
+        "main:server_api",
+        port=listen_port,
+        log_level="error",
+        app_dir=os.environ["SD_UI_PATH"],
+        host=bind_ip,
+        access_log=False,
+    )
+
+
+update_modules()
+
+if len(sys.argv) > 1 and sys.argv[1] == "--launch-uvicorn":
+    launch_uvicorn()

scripts/on_env_start.bat

@@ -1,6 +1,6 @@
 @echo off
 
-@echo. & echo "Easy Diffusion - v2" & echo.
+@echo. & echo "Easy Diffusion - v3" & echo.
 
 set PATH=C:\Windows\System32;%PATH%
 
@@ -46,6 +46,8 @@ if "%update_branch%"=="" (
 
     @cd sd-ui-files
 
+    @call git add -A .
+    @call git stash
     @call git reset --hard
     @call git -c advice.detachedHead=false checkout "%update_branch%"
     @call git pull

scripts/on_env_start.sh

@@ -2,7 +2,7 @@
 
 source ./scripts/functions.sh
 
-printf "\n\nEasy Diffusion\n\n"
+printf "\n\nEasy Diffusion - v3\n\n"
 
 export PYTHONNOUSERSITE=y
 
@@ -29,6 +29,8 @@ if [ -f "scripts/install_status.txt" ] && [ `grep -c sd_ui_git_cloned scripts/in
 
     cd sd-ui-files
 
+    git add -A .
+    git stash
     git reset --hard
     git -c advice.detachedHead=false checkout "$update_branch"
     git pull

scripts/on_sd_start.bat

@@ -34,6 +34,7 @@ call conda activate
 
 @REM remove the old version of the dev console script, if it's still present
 if exist "Open Developer Console.cmd" del "Open Developer Console.cmd"
+if exist "ui\plugins\ui\merge.plugin.js" del "ui\plugins\ui\merge.plugin.js"
 
 @rem create the stable-diffusion folder, to work with legacy installations
 if not exist "stable-diffusion" mkdir stable-diffusion
@@ -52,73 +53,26 @@ if exist ldm rename ldm ldm-old
 if not exist "%INSTALL_ENV_DIR%\DLLs\libssl-1_1-x64.dll"    copy "%INSTALL_ENV_DIR%\Library\bin\libssl-1_1-x64.dll"    "%INSTALL_ENV_DIR%\DLLs\"
 if not exist "%INSTALL_ENV_DIR%\DLLs\libcrypto-1_1-x64.dll" copy "%INSTALL_ENV_DIR%\Library\bin\libcrypto-1_1-x64.dll" "%INSTALL_ENV_DIR%\DLLs\"
 
+cd ..
+
+@rem set any overrides
+set HF_HUB_DISABLE_SYMLINKS_WARNING=true
+
 @rem install or upgrade the required modules
 set PATH=C:\Windows\System32;%PATH%
 
 @REM prevent from using packages from the user's home directory, to avoid conflicts
 set PYTHONNOUSERSITE=1
-set PYTHONPATH=%INSTALL_ENV_DIR%\lib\site-packages
-
-@rem Download the required packages
-call python ..\scripts\check_modules.py
-if "%ERRORLEVEL%" NEQ "0" (
-    pause
-    exit /b
-)
-
-call WHERE uvicorn > .tmp
-@>nul findstr /m "uvicorn" .tmp
-@if "%ERRORLEVEL%" NEQ "0" (
-    @echo. & echo "UI packages not found! Sorry about that, please try to:" & echo "  1. Run this installer again." & echo "  2. If that doesn't fix it, please try the common troubleshooting steps at https://github.com/easydiffusion/easydiffusion/wiki/Troubleshooting" & echo "  3. If those steps don't help, please copy *all* the error messages in this window, and ask the community at https://discord.com/invite/u9yhsFmEkB" & echo "  4. If that doesn't solve the problem, please file an issue at https://github.com/easydiffusion/easydiffusion/issues" & echo "Thanks!" & echo.
-    pause
-    exit /b
-)
-
-@>nul findstr /m "conda_sd_ui_deps_installed" ..\scripts\install_status.txt
-@if "%ERRORLEVEL%" NEQ "0" (
-    @echo conda_sd_ui_deps_installed >> ..\scripts\install_status.txt
-)
-
-@>nul findstr /m "sd_install_complete" ..\scripts\install_status.txt
-@if "%ERRORLEVEL%" NEQ "0" (
-    @echo sd_weights_downloaded >> ..\scripts\install_status.txt
-    @echo sd_install_complete >> ..\scripts\install_status.txt
-)
-
-@echo. & echo "Easy Diffusion installation complete! Starting the server!" & echo.
-
-@set SD_DIR=%cd%
-
 set PYTHONPATH=%INSTALL_ENV_DIR%\lib\site-packages
 echo PYTHONPATH=%PYTHONPATH%
 
+@rem Download the required packages
 call where python
 call python --version
 
-@cd ..
-@set SD_UI_PATH=%cd%\ui
+call python -m pip install -q torchruntime
 
-@FOR /F "tokens=* USEBACKQ" %%F IN (`python scripts\get_config.py --default=9000 net listen_port`) DO (
-    @SET ED_BIND_PORT=%%F
-)
+call python scripts\check_modules.py --launch-uvicorn
+pause
+exit /b
 
-@FOR /F "tokens=* USEBACKQ" %%F IN (`python scripts\get_config.py --default=False net listen_to_network`) DO (
-    if "%%F" EQU "True" (
-        @FOR /F "tokens=* USEBACKQ" %%G IN (`python scripts\get_config.py --default=0.0.0.0 net bind_ip`) DO (
-            @SET ED_BIND_IP=%%G
-        )
-    ) else (
-        @SET ED_BIND_IP=127.0.0.1
-    )
-)
-
-
-@cd stable-diffusion
-
-@rem set any overrides
-set HF_HUB_DISABLE_SYMLINKS_WARNING=true
-
-@python -m uvicorn main:server_api --app-dir "%SD_UI_PATH%" --port %ED_BIND_PORT% --host %ED_BIND_IP% --log-level error
-
-
-@pause

scripts/on_sd_start.sh

@@ -6,6 +6,7 @@ cp sd-ui-files/scripts/bootstrap.sh scripts/
 cp sd-ui-files/scripts/check_modules.py scripts/
 cp sd-ui-files/scripts/get_config.py scripts/
 cp sd-ui-files/scripts/config.yaml.sample scripts/
+     
 
 source ./scripts/functions.sh
 
@@ -20,6 +21,10 @@ if [ -e "open_dev_console.sh" ]; then
     rm "open_dev_console.sh"
 fi
 
+if [ -e "ui/plugins/ui/merge.plugin.js" ]; then
+    rm "ui/plugins/ui/merge.plugin.js"
+fi
+
 # set the correct installer path (current vs legacy)
 if [ -e "installer_files/env" ]; then
     export INSTALL_ENV_DIR="$(pwd)/installer_files/env"
@@ -41,45 +46,10 @@ fi
 if [ -e "src" ]; then mv src src-old; fi
 if [ -e "ldm" ]; then mv ldm ldm-old; fi
 
-# Download the required packages
-if ! python ../scripts/check_modules.py; then
-    read -p "Press any key to continue"
-    exit 1
-fi
-
-if ! command -v uvicorn &> /dev/null; then
-    fail "UI packages not found!"
-fi
-
-if [ `grep -c sd_install_complete ../scripts/install_status.txt` -gt "0" ]; then
-    echo sd_weights_downloaded >> ../scripts/install_status.txt
-    echo sd_install_complete >> ../scripts/install_status.txt
-fi
-
-printf "\n\nEasy Diffusion installation complete, starting the server!\n\n"
-
-SD_PATH=`pwd`
-
-export PYTORCH_ENABLE_MPS_FALLBACK=1
-export PYTHONPATH="$INSTALL_ENV_DIR/lib/python3.8/site-packages"
-echo "PYTHONPATH=$PYTHONPATH"
-
-which python
-python --version
+python -m pip install -q torchruntime
 
 cd ..
-export SD_UI_PATH=`pwd`/ui
-export ED_BIND_PORT="$( python scripts/get_config.py --default=9000 net listen_port )"
-case "$( python scripts/get_config.py --default=False net listen_to_network )" in
-    "True")
-        export ED_BIND_IP=$( python scripts/get_config.py --default=0.0.0.0 net bind_ip)
-        ;;
-    "False")
-        export ED_BIND_IP=127.0.0.1
-        ;;
-esac
-cd stable-diffusion
-
-uvicorn main:server_api --app-dir "$SD_UI_PATH" --port "$ED_BIND_PORT" --host "$ED_BIND_IP" --log-level error
+# Download the required packages
+python scripts/check_modules.py --launch-uvicorn
 
 read -p "Press any key to continue"

scripts/start.sh

@@ -11,7 +11,7 @@ if [ -f "on_sd_start.bat" ]; then
     echo download. This will not work.
     echo
     echo Recommended: Please close this window and download the installer from
-    echo https://stable-diffusion-ui.github.io/docs/installation/
+    echo https://easydiffusion.github.io/docs/installation/
     echo
     echo ================================================================================
     echo
@@ -19,6 +19,7 @@ if [ -f "on_sd_start.bat" ]; then
     exit 1
 fi
 
+unset PYTHONHOME
 
 # set legacy installer's PATH, if it exists
 if [ -e "installer" ]; then export PATH="$(pwd)/installer/bin:$PATH"; fi

ui/easydiffusion/app.py

@@ -32,10 +32,12 @@ logging.basicConfig(
 
 SD_DIR = os.getcwd()
 
+ROOT_DIR = os.path.abspath(os.path.join(SD_DIR, ".."))
+
 SD_UI_DIR = os.getenv("SD_UI_PATH", None)
 
 CONFIG_DIR = os.path.abspath(os.path.join(SD_UI_DIR, "..", "scripts"))
-MODELS_DIR = os.path.abspath(os.path.join(SD_DIR, "..", "models"))
+BUCKET_DIR = os.path.abspath(os.path.join(SD_DIR, "..", "bucket"))
 
 USER_PLUGINS_DIR = os.path.abspath(os.path.join(SD_DIR, "..", "plugins"))
 CORE_PLUGINS_DIR = os.path.abspath(os.path.join(SD_UI_DIR, "plugins"))
@@ -52,12 +54,12 @@ OUTPUT_DIRNAME = "Stable Diffusion UI"  # in the user's home folder
 PRESERVE_CONFIG_VARS = ["FORCE_FULL_PRECISION"]
 TASK_TTL = 15 * 60  # Discard last session's task timeout
 APP_CONFIG_DEFAULTS = {
-    # auto: selects the cuda device with the most free memory, cuda: use the currently active cuda device.
-    "render_devices": "auto",  # valid entries: 'auto', 'cpu' or 'cuda:N' (where N is a GPU index)
+    "render_devices": "auto",
     "update_branch": "main",
     "ui": {
         "open_browser_on_start": True,
     },
+    "use_v3_engine": True,
 }
 
 IMAGE_EXTENSIONS = [
@@ -88,14 +90,23 @@ CUSTOM_MODIFIERS_LANDSCAPE_EXTENSIONS = [
     "-landscape",
 ]
 
+MODELS_DIR = os.path.abspath(os.path.join(SD_DIR, "..", "models"))
+
 
 def init():
+    global MODELS_DIR
+
     os.makedirs(USER_UI_PLUGINS_DIR, exist_ok=True)
     os.makedirs(USER_SERVER_PLUGINS_DIR, exist_ok=True)
 
     # https://pytorch.org/docs/stable/storage.html
     warnings.filterwarnings("ignore", category=UserWarning, message="TypedStorage is deprecated")
 
+    config = getConfig()
+    config_models_dir = config.get("models_dir", None)
+    if (config_models_dir is not None and config_models_dir != ""):
+        MODELS_DIR = config_models_dir
+
 
 def init_render_threads():
     load_server_plugins()
@@ -112,9 +123,9 @@ def getConfig(default_val=APP_CONFIG_DEFAULTS):
         shutil.move(config_legacy_yaml, config_yaml_path)
 
     def set_config_on_startup(config: dict):
-        if getConfig.__test_diffusers_on_startup is None:
-            getConfig.__test_diffusers_on_startup = config.get("test_diffusers", False)
-        config["config_on_startup"] = {"test_diffusers": getConfig.__test_diffusers_on_startup}
+        if getConfig.__use_v3_engine_on_startup is None:
+            getConfig.__use_v3_engine_on_startup = config.get("use_v3_engine", True)
+        config["config_on_startup"] = {"use_v3_engine": getConfig.__use_v3_engine_on_startup}
 
     if os.path.isfile(config_yaml_path):
         try:
@@ -162,12 +173,15 @@ def getConfig(default_val=APP_CONFIG_DEFAULTS):
             return default_val
 
 
-getConfig.__test_diffusers_on_startup = None
+getConfig.__use_v3_engine_on_startup = None
 
 
 def setConfig(config):
+    global MODELS_DIR
+
     try:  # config.yaml
         config_yaml_path = os.path.join(CONFIG_DIR, "..", "config.yaml")
+        config_yaml_path = os.path.abspath(config_yaml_path)
         yaml = YAML()
 
         if not hasattr(config, "_yaml_comment"):
@@ -201,6 +215,9 @@ def setConfig(config):
     except:
         log.error(traceback.format_exc())
 
+    if config.get("models_dir"):
+        MODELS_DIR = config["models_dir"]
+
 
 def save_to_config(ckpt_model_name, vae_model_name, hypernetwork_model_name, vram_usage_level):
     config = getConfig()

ui/easydiffusion/bucket_manager.py

@@ -0,0 +1,107 @@
+from typing import List
+
+from fastapi import Depends, FastAPI, HTTPException, Response, File
+from sqlalchemy.orm import Session
+
+from easydiffusion.easydb import crud, models, schemas
+from easydiffusion.easydb.database import SessionLocal, engine
+
+from requests.compat import urlparse
+
+import base64, json
+
+MIME_TYPES = {
+    "jpg":  "image/jpeg",
+    "jpeg": "image/jpeg",
+    "gif":  "image/gif",
+    "png":  "image/png",
+    "webp": "image/webp",
+    "js":   "text/javascript",
+    "htm":  "text/html",
+    "html": "text/html",
+    "css":  "text/css",
+    "json": "application/json",
+    "mjs":  "application/json",
+    "yaml": "application/yaml",
+    "svg":  "image/svg+xml",
+    "txt":  "text/plain",
+}
+
+def init():
+    from easydiffusion.server import server_api
+
+    models.BucketBase.metadata.create_all(bind=engine)
+
+
+    # Dependency
+    def get_db():
+        db = SessionLocal()
+        try:
+            yield db
+        finally:
+            db.close()
+
+    @server_api.get("/bucket/{obj_path:path}")
+    def bucket_get_object(obj_path: str, db: Session = Depends(get_db)):
+        filename = get_filename_from_url(obj_path)
+        path = get_path_from_url(obj_path)
+
+        if filename==None:
+            bucket = crud.get_bucket_by_path(db, path=path)
+            if bucket == None:
+                raise HTTPException(status_code=404, detail="Bucket not found")
+            bucketfiles = db.query(models.BucketFile).with_entities(models.BucketFile.filename).filter(models.BucketFile.bucket_id == bucket.id).all()
+            bucketfiles = [ x.filename for x in bucketfiles ]
+            return bucketfiles
+
+        else:
+            bucket = crud.get_bucket_by_path(db, path)
+            if bucket == None:
+                raise HTTPException(status_code=404, detail="Bucket not found")
+            bucket_id = bucket.id
+            bucketfile = db.query(models.BucketFile).filter(models.BucketFile.bucket_id == bucket_id, models.BucketFile.filename == filename).first()
+            if bucketfile == None:
+                raise HTTPException(status_code=404, detail="File not found")
+
+            suffix = get_suffix_from_filename(filename)
+
+            return Response(content=bucketfile.data, media_type=MIME_TYPES.get(suffix, "application/octet-stream")) 
+
+    @server_api.post("/bucket/{obj_path:path}")
+    def bucket_post_object(obj_path: str, file: bytes = File(), db: Session = Depends(get_db)):
+        filename = get_filename_from_url(obj_path)
+        path = get_path_from_url(obj_path)
+        bucket = crud.get_bucket_by_path(db, path)
+
+        if bucket == None:
+            bucket = crud.create_bucket(db=db, bucket=schemas.BucketCreate(path=path))
+        bucket_id = bucket.id
+
+        bucketfile = schemas.BucketFileCreate(filename=filename, data=file)
+        result = crud.create_bucketfile(db=db, bucketfile=bucketfile, bucket_id=bucket_id)
+        result.data = base64.encodestring(result.data)
+        return result
+
+
+    @server_api.post("/buckets/{bucket_id}/items/", response_model=schemas.BucketFile)
+    def create_bucketfile_in_bucket(
+        bucket_id: int, bucketfile: schemas.BucketFileCreate, db: Session = Depends(get_db)
+    ):
+        bucketfile.data = base64.decodestring(bucketfile.data)
+        result =  crud.create_bucketfile(db=db, bucketfile=bucketfile, bucket_id=bucket_id)
+        result.data = base64.encodestring(result.data)
+        return result
+
+
+def get_filename_from_url(url):
+    path = urlparse(url).path
+    name = path[path.rfind('/')+1:]
+    return name or None 
+
+def get_path_from_url(url):
+    path = urlparse(url).path
+    path = path[0:path.rfind('/')]
+    return path or None 
+
+def get_suffix_from_filename(filename):
+    return filename[filename.rfind('.')+1:]

ui/easydiffusion/device_manager.py

@@ -6,6 +6,15 @@ import traceback
 import torch
 from easydiffusion.utils import log
 
+from torchruntime.utils import (
+    get_installed_torch_platform,
+    get_device,
+    get_device_count,
+    get_device_name,
+    SUPPORTED_BACKENDS,
+)
+from sdkit.utils import mem_get_info, is_cpu_device, has_half_precision_bug
+
 """
 Set `FORCE_FULL_PRECISION` in the environment variables, or in `config.bat`/`config.sh` to set full precision (i.e. float32).
 Otherwise the models will load at half-precision (i.e. float16).
@@ -22,33 +31,15 @@ mem_free_threshold = 0
 
 def get_device_delta(render_devices, active_devices):
     """
-    render_devices: 'cpu', or 'auto', or 'mps' or ['cuda:N'...]
-    active_devices: ['cpu', 'mps', 'cuda:N'...]
+    render_devices: 'auto' or backends listed in `torchruntime.utils.SUPPORTED_BACKENDS`
+    active_devices: [backends listed in `torchruntime.utils.SUPPORTED_BACKENDS`]
     """
 
-    if render_devices in ("cpu", "auto", "mps"):
-        render_devices = [render_devices]
-    elif render_devices is not None:
-        if isinstance(render_devices, str):
-            render_devices = [render_devices]
-        if isinstance(render_devices, list) and len(render_devices) > 0:
-            render_devices = list(filter(lambda x: x.startswith("cuda:") or x == "mps", render_devices))
-            if len(render_devices) == 0:
-                raise Exception(
-                    'Invalid render_devices value in config.json. Valid: {"render_devices": ["cuda:0", "cuda:1"...]}, or {"render_devices": "cpu"} or {"render_devices": "mps"} or {"render_devices": "auto"}'
-                )
+    render_devices = render_devices or "auto"
+    render_devices = [render_devices] if isinstance(render_devices, str) else render_devices
 
-            render_devices = list(filter(lambda x: is_device_compatible(x), render_devices))
-            if len(render_devices) == 0:
-                raise Exception(
-                    "Sorry, none of the render_devices configured in config.json are compatible with Stable Diffusion"
-                )
-        else:
-            raise Exception(
-                'Invalid render_devices value in config.json. Valid: {"render_devices": ["cuda:0", "cuda:1"...]}, or {"render_devices": "cpu"} or {"render_devices": "auto"}'
-            )
-    else:
-        render_devices = ["auto"]
+    # check for backend support
+    validate_render_devices(render_devices)
 
     if "auto" in render_devices:
         render_devices = auto_pick_devices(active_devices)
@@ -64,47 +55,39 @@ def get_device_delta(render_devices, active_devices):
     return devices_to_start, devices_to_stop
 
 
-def is_mps_available():
-    return (
-        platform.system() == "Darwin"
-        and hasattr(torch.backends, "mps")
-        and torch.backends.mps.is_available()
-        and torch.backends.mps.is_built()
-    )
+def validate_render_devices(render_devices):
+    supported_backends = ("auto",) + SUPPORTED_BACKENDS
+    unsupported_render_devices = [d for d in render_devices if not d.lower().startswith(supported_backends)]
 
-
-def is_cuda_available():
-    return torch.cuda.is_available()
+    if unsupported_render_devices:
+        raise ValueError(
+            f"Invalid render devices in config: {unsupported_render_devices}. Valid render devices: {supported_backends}"
+        )
 
 
 def auto_pick_devices(currently_active_devices):
     global mem_free_threshold
 
-    if is_mps_available():
-        return ["mps"]
+    torch_platform_name = get_installed_torch_platform()[0]
 
-    if not is_cuda_available():
-        return ["cpu"]
-
-    device_count = torch.cuda.device_count()
-    if device_count == 1:
-        return ["cuda:0"] if is_device_compatible("cuda:0") else ["cpu"]
+    if is_cpu_device(torch_platform_name):
+        return [torch_platform_name]
 
+    device_count = get_device_count()
     log.debug("Autoselecting GPU. Using most free memory.")
     devices = []
-    for device in range(device_count):
-        device = f"cuda:{device}"
-        if not is_device_compatible(device):
-            continue
+    for device_id in range(device_count):
+        device_id = f"{torch_platform_name}:{device_id}" if device_count > 1 else torch_platform_name
+        device = get_device(device_id)
 
-        mem_free, mem_total = torch.cuda.mem_get_info(device)
+        mem_free, mem_total = mem_get_info(device)
         mem_free /= float(10**9)
         mem_total /= float(10**9)
-        device_name = torch.cuda.get_device_name(device)
+        device_name = get_device_name(device)
         log.debug(
-            f"{device} detected: {device_name} - Memory (free/total): {round(mem_free, 2)}Gb / {round(mem_total, 2)}Gb"
+            f"{device_id} detected: {device_name} - Memory (free/total): {round(mem_free, 2)}Gb / {round(mem_total, 2)}Gb"
         )
-        devices.append({"device": device, "device_name": device_name, "mem_free": mem_free})
+        devices.append({"device": device_id, "device_name": device_name, "mem_free": mem_free})
 
     devices.sort(key=lambda x: x["mem_free"], reverse=True)
     max_mem_free = devices[0]["mem_free"]
@@ -117,69 +100,45 @@ def auto_pick_devices(currently_active_devices):
     #    always be very low (since their VRAM contains the model).
     #    These already-running devices probably aren't terrible, since they were picked in the past.
     #    Worst case, the user can restart the program and that'll get rid of them.
-    devices = list(
-        filter(
-            (lambda x: x["mem_free"] > mem_free_threshold or x["device"] in currently_active_devices),
-            devices,
-        )
-    )
-    devices = list(map(lambda x: x["device"], devices))
+    devices = [
+        x["device"] for x in devices if x["mem_free"] >= mem_free_threshold or x["device"] in currently_active_devices
+    ]
     return devices
 
 
-def device_init(context, device):
-    """
-    This function assumes the 'device' has already been verified to be compatible.
-    `get_device_delta()` has already filtered out incompatible devices.
-    """
+def device_init(context, device_id):
+    context.device = device_id
 
-    validate_device_id(device, log_prefix="device_init")
-
-    if "cuda" not in device:
-        context.device = device
+    if is_cpu_device(context.torch_device):
         context.device_name = get_processor_name()
         context.half_precision = False
-        log.debug(f"Render device available as {context.device_name}")
-        return
+    else:
+        context.device_name = get_device_name(context.torch_device)
 
-    context.device_name = torch.cuda.get_device_name(device)
-    context.device = device
+        # Some graphics cards have bugs in their firmware that prevent image generation at half precision
+        if needs_to_force_full_precision(context.device_name):
+            log.warn(f"forcing full precision on this GPU, to avoid corrupted images. GPU: {context.device_name}")
+            context.half_precision = False
 
-    # Force full precision on 1660 and 1650 NVIDIA cards to avoid creating green images
-    if needs_to_force_full_precision(context):
-        log.warn(f"forcing full precision on this GPU, to avoid green images. GPU detected: {context.device_name}")
-        # Apply force_full_precision now before models are loaded.
-        context.half_precision = False
-
-    log.info(f'Setting {device} as active, with precision: {"half" if context.half_precision else "full"}')
-    torch.cuda.device(device)
+    log.info(f'Setting {device_id} as active, with precision: {"half" if context.half_precision else "full"}')
 
 
-def needs_to_force_full_precision(context):
+def needs_to_force_full_precision(device_name):
     if "FORCE_FULL_PRECISION" in os.environ:
         return True
 
-    device_name = context.device_name.lower()
-    return (
-        ("nvidia" in device_name or "geforce" in device_name or "quadro" in device_name)
-        and (
-            " 1660" in device_name
-            or " 1650" in device_name
-            or " 1630" in device_name
-            or " t400" in device_name
-            or " t550" in device_name
-            or " t600" in device_name
-            or " t1000" in device_name
-            or " t1200" in device_name
-            or " t2000" in device_name
-        )
-    ) or ("tesla k40m" in device_name)
+    return has_half_precision_bug(device_name.lower())
 
 
 def get_max_vram_usage_level(device):
-    if "cuda" in device:
-        _, mem_total = torch.cuda.mem_get_info(device)
-    else:
+    "Expects a torch.device as the argument"
+
+    if is_cpu_device(device):
+        return "high"
+
+    _, mem_total = mem_get_info(device)
+
+    if mem_total < 0.001:  # probably a torch platform without a mem_get_info() implementation
         return "high"
 
     mem_total /= float(10**9)
@@ -191,51 +150,6 @@ def get_max_vram_usage_level(device):
     return "high"
 
 
-def validate_device_id(device, log_prefix=""):
-    def is_valid():
-        if not isinstance(device, str):
-            return False
-        if device == "cpu" or device == "mps":
-            return True
-        if not device.startswith("cuda:") or not device[5:].isnumeric():
-            return False
-        return True
-
-    if not is_valid():
-        raise EnvironmentError(
-            f"{log_prefix}: device id should be 'cpu', 'mps', or 'cuda:N' (where N is an integer index for the GPU). Got: {device}"
-        )
-
-
-def is_device_compatible(device):
-    """
-    Returns True/False, and prints any compatibility errors
-    """
-    # static variable "history".
-    is_device_compatible.history = getattr(is_device_compatible, "history", {})
-    try:
-        validate_device_id(device, log_prefix="is_device_compatible")
-    except:
-        log.error(str(e))
-        return False
-
-    if device in ("cpu", "mps"):
-        return True
-    # Memory check
-    try:
-        _, mem_total = torch.cuda.mem_get_info(device)
-        mem_total /= float(10**9)
-        if mem_total < 1.9:
-            if is_device_compatible.history.get(device) == None:
-                log.warn(f"GPU {device} with less than 2 GB of VRAM is not compatible with Stable Diffusion")
-                is_device_compatible.history[device] = 1
-            return False
-    except RuntimeError as e:
-        log.error(str(e))
-        return False
-    return True
-
-
 def get_processor_name():
     try:
         import subprocess
@@ -243,7 +157,8 @@ def get_processor_name():
         if platform.system() == "Windows":
             return platform.processor()
         elif platform.system() == "Darwin":
-            os.environ["PATH"] = os.environ["PATH"] + os.pathsep + "/usr/sbin"
+            if "/usr/sbin" not in os.environ["PATH"].split(os.pathsep):
+                os.environ["PATH"] = os.environ["PATH"] + os.pathsep + "/usr/sbin"
             command = "sysctl -n machdep.cpu.brand_string"
             return subprocess.check_output(command, shell=True).decode().strip()
         elif platform.system() == "Linux":

ui/easydiffusion/easydb/crud.py

@@ -0,0 +1,24 @@
+from sqlalchemy.orm import Session
+
+from easydiffusion.easydb import models, schemas
+
+
+def get_bucket_by_path(db: Session, path: str):
+    return db.query(models.Bucket).filter(models.Bucket.path == path).first()
+
+
+def create_bucket(db: Session, bucket: schemas.BucketCreate):
+    db_bucket = models.Bucket(path=bucket.path)
+    db.add(db_bucket)
+    db.commit()
+    db.refresh(db_bucket)
+    return db_bucket
+
+
+def create_bucketfile(db: Session, bucketfile: schemas.BucketFileCreate, bucket_id: int):
+    db_bucketfile = models.BucketFile(**bucketfile.dict(), bucket_id=bucket_id)
+    db.merge(db_bucketfile)
+    db.commit()
+    db_bucketfile = db.query(models.BucketFile).filter(models.BucketFile.bucket_id==bucket_id, models.BucketFile.filename==bucketfile.filename).first()
+    return db_bucketfile
+

ui/easydiffusion/easydb/database.py

@@ -0,0 +1,14 @@
+import os
+from easydiffusion import app
+
+from sqlalchemy import create_engine
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker
+
+os.makedirs(app.BUCKET_DIR, exist_ok=True)
+SQLALCHEMY_DATABASE_URL = "sqlite:///"+os.path.join(app.BUCKET_DIR, "bucket.db")
+
+engine = create_engine(SQLALCHEMY_DATABASE_URL, connect_args={"check_same_thread": False})
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+BucketBase = declarative_base()

ui/easydiffusion/easydb/models.py

@@ -0,0 +1,25 @@
+from sqlalchemy import Boolean, Column, ForeignKey, Integer, String, BLOB
+from sqlalchemy.orm import relationship
+
+from easydiffusion.easydb.database import BucketBase
+
+
+class Bucket(BucketBase):
+    __tablename__ = "bucket"
+
+    id = Column(Integer, primary_key=True, index=True)
+    path = Column(String, unique=True, index=True)
+
+    bucketfiles = relationship("BucketFile", back_populates="bucket")
+
+
+class BucketFile(BucketBase):
+    __tablename__ = "bucketfile"
+
+    filename = Column(String, index=True, primary_key=True)
+    bucket_id = Column(Integer, ForeignKey("bucket.id"), primary_key=True)
+
+    data = Column(BLOB, index=False)
+
+    bucket = relationship("Bucket", back_populates="bucketfiles")
+

ui/easydiffusion/easydb/schemas.py

@@ -0,0 +1,36 @@
+from typing import List, Union
+
+from pydantic import BaseModel
+
+
+class BucketFileBase(BaseModel):
+    filename: str
+    data: bytes
+
+
+class BucketFileCreate(BucketFileBase):
+    pass
+
+
+class BucketFile(BucketFileBase):
+    bucket_id: int
+
+    class Config:
+        orm_mode = True
+
+
+class BucketBase(BaseModel):
+    path: str
+
+
+class BucketCreate(BucketBase):
+    pass
+
+
+class Bucket(BucketBase):
+    id: int
+    bucketfiles: List[BucketFile] = []
+
+    class Config:
+        orm_mode = True
+

ui/easydiffusion/model_manager.py

@@ -5,11 +5,13 @@ import traceback
 from typing import Union
 
 from easydiffusion import app
-from easydiffusion.types import TaskData
+from easydiffusion.types import ModelsData
 from easydiffusion.utils import log
 from sdkit import Context
 from sdkit.models import load_model, scan_model, unload_model, download_model, get_model_info_from_db
+from sdkit.models.model_loader.controlnet_filters import filters as cn_filters
 from sdkit.utils import hash_file_quick
+from sdkit.models.model_loader.embeddings import get_embedding_token
 
 KNOWN_MODEL_TYPES = [
     "stable-diffusion",
@@ -19,6 +21,8 @@ KNOWN_MODEL_TYPES = [
     "realesrgan",
     "lora",
     "codeformer",
+    "embeddings",
+    "controlnet",
 ]
 MODEL_EXTENSIONS = {
     "stable-diffusion": [".ckpt", ".safetensors"],
@@ -26,9 +30,10 @@ MODEL_EXTENSIONS = {
     "hypernetwork": [".pt", ".safetensors"],
     "gfpgan": [".pth"],
     "realesrgan": [".pth"],
-    "lora": [".ckpt", ".safetensors"],
+    "lora": [".ckpt", ".safetensors", ".pt"],
     "codeformer": [".pth"],
     "embeddings": [".pt", ".bin", ".safetensors"],
+    "controlnet": [".pth", ".safetensors"],
 }
 DEFAULT_MODELS = {
     "stable-diffusion": [
@@ -57,10 +62,9 @@ def init():
 
 
 def load_default_models(context: Context):
-    set_vram_optimizations(context)
+    from easydiffusion import runtime
 
-    config = app.getConfig()
-    context.embeddings_path = os.path.join(app.MODELS_DIR, "embeddings")
+    runtime.set_vram_optimizations(context)
 
     # init default model paths
     for model_type in MODELS_TO_LOAD_ON_START:
@@ -72,7 +76,7 @@ def load_default_models(context: Context):
                 scan_model=context.model_paths[model_type] != None
                 and not context.model_paths[model_type].endswith(".safetensors"),
             )
-            if model_type in context.model_load_errors:
+            if hasattr(context, "model_load_errors") and model_type in context.model_load_errors:
                 del context.model_load_errors[model_type]
         except Exception as e:
             log.error(f"[red]Error while loading {model_type} model: {context.model_paths[model_type]}[/red]")
@@ -84,6 +88,8 @@ def load_default_models(context: Context):
                 log.exception(e)
             del context.model_paths[model_type]
 
+            if not hasattr(context, "model_load_errors"):
+                context.model_load_errors = {}
             context.model_load_errors[model_type] = str(e)  # storing the entire Exception can lead to memory leaks
 
 
@@ -96,7 +102,16 @@ def unload_all(context: Context):
 
 def resolve_model_to_use(model_name: Union[str, list] = None, model_type: str = None, fail_if_not_found: bool = True):
     model_names = model_name if isinstance(model_name, list) else [model_name]
-    model_paths = [resolve_model_to_use_single(m, model_type, fail_if_not_found) for m in model_names]
+    model_paths = []
+    for m in model_names:
+        if model_type == "embeddings":
+            try:
+                resolve_model_to_use_single(m, model_type)
+            except FileNotFoundError:  # try with spaces
+                m = m.replace("_", " ")
+
+        path = resolve_model_to_use_single(m, model_type, fail_if_not_found)
+        model_paths.append(path)
 
     return model_paths[0] if len(model_paths) == 1 else model_paths
 
@@ -135,77 +150,68 @@ def resolve_model_to_use_single(model_name: str = None, model_type: str = None,
                 return default_model_path
 
     if model_name and fail_if_not_found:
-        raise Exception(f"Could not find the desired model {model_name}! Is it present in the {model_dir} folder?")
+        raise FileNotFoundError(
+            f"Could not find the desired model {model_name}! Is it present in the {model_dir} folder?"
+        )
 
 
-def reload_models_if_necessary(context: Context, task_data: TaskData):
-    face_fix_lower = task_data.use_face_correction.lower() if task_data.use_face_correction else ""
-    upscale_lower = task_data.use_upscale.lower() if task_data.use_upscale else ""
-
-    model_paths_in_req = {
-        "stable-diffusion": task_data.use_stable_diffusion_model,
-        "vae": task_data.use_vae_model,
-        "hypernetwork": task_data.use_hypernetwork_model,
-        "codeformer": task_data.use_face_correction if "codeformer" in face_fix_lower else None,
-        "gfpgan": task_data.use_face_correction if "gfpgan" in face_fix_lower else None,
-        "realesrgan": task_data.use_upscale if "realesrgan" in upscale_lower else None,
-        "latent_upscaler": True if "latent_upscaler" in upscale_lower else None,
-        "nsfw_checker": True if task_data.block_nsfw else None,
-        "lora": task_data.use_lora_model,
-    }
+def reload_models_if_necessary(context: Context, models_data: ModelsData, models_to_force_reload: list = []):
     models_to_reload = {
         model_type: path
-        for model_type, path in model_paths_in_req.items()
-        if context.model_paths.get(model_type) != path
+        for model_type, path in models_data.model_paths.items()
+        if context.model_paths.get(model_type) != path or (path is not None and context.models.get(model_type) is None)
     }
 
-    if task_data.codeformer_upscale_faces:
+    if models_data.model_paths.get("codeformer"):
         if "realesrgan" not in models_to_reload and "realesrgan" not in context.models:
             default_realesrgan = DEFAULT_MODELS["realesrgan"][0]["file_name"]
             models_to_reload["realesrgan"] = resolve_model_to_use(default_realesrgan, "realesrgan")
         elif "realesrgan" in models_to_reload and models_to_reload["realesrgan"] is None:
             del models_to_reload["realesrgan"]  # don't unload realesrgan
 
-    if set_vram_optimizations(context) or set_clip_skip(context, task_data):  # reload SD
-        models_to_reload["stable-diffusion"] = model_paths_in_req["stable-diffusion"]
+    for model_type in models_to_force_reload:
+        if model_type not in models_data.model_paths:
+            continue
+        models_to_reload[model_type] = models_data.model_paths[model_type]
 
     for model_type, model_path_in_req in models_to_reload.items():
         context.model_paths[model_type] = model_path_in_req
 
         action_fn = unload_model if context.model_paths[model_type] is None else load_model
+        extra_params = models_data.model_params.get(model_type, {})
         try:
-            action_fn(context, model_type, scan_model=False)  # we've scanned them already
-            if model_type in context.model_load_errors:
+            action_fn(context, model_type, scan_model=False, **extra_params)  # we've scanned them already
+            if hasattr(context, "model_load_errors") and model_type in context.model_load_errors:
                 del context.model_load_errors[model_type]
         except Exception as e:
             log.exception(e)
             if action_fn == load_model:
+                if not hasattr(context, "model_load_errors"):
+                    context.model_load_errors = {}
                 context.model_load_errors[model_type] = str(e)  # storing the entire Exception can lead to memory leaks
 
 
-def resolve_model_paths(task_data: TaskData):
-    task_data.use_stable_diffusion_model = resolve_model_to_use(
-        task_data.use_stable_diffusion_model, model_type="stable-diffusion"
-    )
-    task_data.use_vae_model = resolve_model_to_use(task_data.use_vae_model, model_type="vae")
-    task_data.use_hypernetwork_model = resolve_model_to_use(task_data.use_hypernetwork_model, model_type="hypernetwork")
-    task_data.use_lora_model = resolve_model_to_use(task_data.use_lora_model, model_type="lora")
-
-    if task_data.use_face_correction:
-        if "gfpgan" in task_data.use_face_correction.lower():
-            model_type = "gfpgan"
-        elif "codeformer" in task_data.use_face_correction.lower():
-            model_type = "codeformer"
+def resolve_model_paths(models_data: ModelsData):
+    model_paths = models_data.model_paths
+    for model_type in model_paths:
+        skip_models = cn_filters + ["latent_upscaler", "nsfw_checker"]
+        if model_type in skip_models:  # doesn't use model paths
+            continue
+        if model_type == "codeformer" and model_paths[model_type]:
             download_if_necessary("codeformer", "codeformer.pth", "codeformer-0.1.0")
+        elif model_type == "controlnet" and model_paths[model_type]:
+            model_id = model_paths[model_type]
+            model_info = get_model_info_from_db(model_type=model_type, model_id=model_id)
+            if model_info:
+                filename = model_info.get("url", "").split("/")[-1]
+                download_if_necessary("controlnet", filename, model_id, skip_if_others_exist=False)
 
-        task_data.use_face_correction = resolve_model_to_use(task_data.use_face_correction, model_type)
-    if task_data.use_upscale and "realesrgan" in task_data.use_upscale.lower():
-        task_data.use_upscale = resolve_model_to_use(task_data.use_upscale, "realesrgan")
+        model_paths[model_type] = resolve_model_to_use(model_paths[model_type], model_type=model_type)
 
 
 def fail_if_models_did_not_load(context: Context):
     for model_type in KNOWN_MODEL_TYPES:
-        if model_type in context.model_load_errors:
+        if hasattr(context, "model_load_errors") and model_type in context.model_load_errors:
             e = context.model_load_errors[model_type]
             raise Exception(f"Could not load the {model_type} model! Reason: " + e)
 
@@ -222,28 +228,17 @@ def download_default_models_if_necessary():
         print(model_type, "model(s) found.")
 
 
-def download_if_necessary(model_type: str, file_name: str, model_id: str):
+def download_if_necessary(model_type: str, file_name: str, model_id: str, skip_if_others_exist=True):
     model_path = os.path.join(app.MODELS_DIR, model_type, file_name)
     expected_hash = get_model_info_from_db(model_type=model_type, model_id=model_id)["quick_hash"]
 
-    other_models_exist = any_model_exists(model_type)
+    other_models_exist = any_model_exists(model_type) and skip_if_others_exist
     known_model_exists = os.path.exists(model_path)
     known_model_is_corrupt = known_model_exists and hash_file_quick(model_path) != expected_hash
 
     if known_model_is_corrupt or (not other_models_exist and not known_model_exists):
         print("> download", model_type, model_id)
-        download_model(model_type, model_id, download_base_dir=app.MODELS_DIR)
-
-
-def set_vram_optimizations(context: Context):
-    config = app.getConfig()
-    vram_usage_level = config.get("vram_usage_level", "balanced")
-
-    if vram_usage_level != context.vram_usage_level:
-        context.vram_usage_level = vram_usage_level
-        return True
-
-    return False
+        download_model(model_type, model_id, download_base_dir=app.MODELS_DIR, download_config_if_available=False)
 
 
 def migrate_legacy_model_location():
@@ -266,21 +261,28 @@ def any_model_exists(model_type: str) -> bool:
     return False
 
 
-def set_clip_skip(context: Context, task_data: TaskData):
-    clip_skip = task_data.clip_skip
-
-    if clip_skip != context.clip_skip:
-        context.clip_skip = clip_skip
-        return True
-
-    return False
-
-
 def make_model_folders():
     for model_type in KNOWN_MODEL_TYPES:
         model_dir_path = os.path.join(app.MODELS_DIR, model_type)
 
-        os.makedirs(model_dir_path, exist_ok=True)
+        try:
+            os.makedirs(model_dir_path, exist_ok=True)
+        except Exception as e:
+            from rich.console import Console
+            from rich.panel import Panel
+
+            Console().print(
+                Panel(
+                    "\n"
+                    + f"Error while creating the models directory: '{model_dir_path}'\n"
+                    + f"Error: {e}\n\n"
+                    + f"[white]Check the 'models_dir:' line in the file '{os.path.join(app.ROOT_DIR, 'config.yaml')}'.[/white]\n",
+                    title="Fatal Error starting Easy Diffusion",
+                    style="bold yellow on red",
+                )
+            )
+            input("Press Enter to terminate...")
+            exit(1)
 
         help_file_name = f"Place your {model_type} model files here.txt"
         help_file_contents = f'Supported extensions: {" or ".join(MODEL_EXTENSIONS.get(model_type))}'
@@ -324,12 +326,27 @@ def is_malicious_model(file_path):
 def getModels(scan_for_malicious: bool = True):
     models = {
         "options": {
-            "stable-diffusion": ["sd-v1-4"],
+            "stable-diffusion": [],
             "vae": [],
             "hypernetwork": [],
             "lora": [],
-            "codeformer": ["codeformer"],
+            "codeformer": [{"codeformer": "CodeFormer"}],
             "embeddings": [],
+            "controlnet": [
+                {"control_v11p_sd15_canny": "Canny (*)"},
+                {"control_v11p_sd15_openpose": "OpenPose (*)"},
+                {"control_v11p_sd15_normalbae": "Normal BAE (*)"},
+                {"control_v11f1p_sd15_depth": "Depth (*)"},
+                {"control_v11p_sd15_scribble": "Scribble"},
+                {"control_v11p_sd15_softedge": "Soft Edge"},
+                {"control_v11p_sd15_inpaint": "Inpaint"},
+                {"control_v11p_sd15_lineart": "Line Art"},
+                {"control_v11p_sd15s2_lineart_anime": "Line Art Anime"},
+                {"control_v11p_sd15_mlsd": "Straight Lines"},
+                {"control_v11p_sd15_seg": "Segment"},
+                {"control_v11e_sd15_shuffle": "Shuffle"},
+                {"control_v11f1e_sd15_tile": "Tile"},
+            ],
         },
     }
 
@@ -338,9 +355,11 @@ def getModels(scan_for_malicious: bool = True):
     class MaliciousModelException(Exception):
         "Raised when picklescan reports a problem with a model"
 
-    def scan_directory(directory, suffixes, directoriesFirst: bool = True):
+    def scan_directory(directory, suffixes, directoriesFirst: bool = True, default_entries=[], nameFilter=None):
         nonlocal models_scanned
-        tree = []
+
+        tree = list(default_entries)
+
         for entry in sorted(
             os.scandir(directory),
             key=lambda entry: (entry.is_file() == directoriesFirst, entry.name.lower()),
@@ -359,15 +378,27 @@ def getModels(scan_for_malicious: bool = True):
                         raise MaliciousModelException(entry.path)
                 if scan_for_malicious:
                     known_models[entry.path] = mtime
-                tree.append(entry.name[: -len(matching_suffix)])
+
+                model_id = entry.name[: -len(matching_suffix)]
+                if callable(nameFilter):
+                    model_id = nameFilter(model_id)
+
+                model_exists = False
+                for m in tree:  # allows default "named" models, like CodeFormer and known ControlNet models
+                    if (isinstance(m, str) and model_id == m) or (isinstance(m, dict) and model_id in m):
+                        model_exists = True
+                        break
+                if not model_exists:
+                    tree.append(model_id)
+
             elif entry.is_dir():
-                scan = scan_directory(entry.path, suffixes, directoriesFirst=False)
+                scan = scan_directory(entry.path, suffixes, directoriesFirst=False, nameFilter=nameFilter)
 
                 if len(scan) != 0:
                     tree.append((entry.name, scan))
         return tree
 
-    def listModels(model_type):
+    def listModels(model_type, nameFilter=None):
         nonlocal models_scanned
 
         model_extensions = MODEL_EXTENSIONS.get(model_type, [])
@@ -376,7 +407,10 @@ def getModels(scan_for_malicious: bool = True):
             os.makedirs(models_dir)
 
         try:
-            models["options"][model_type] = scan_directory(models_dir, model_extensions)
+            default_tree = models["options"].get(model_type, [])
+            models["options"][model_type] = scan_directory(
+                models_dir, model_extensions, default_entries=default_tree, nameFilter=nameFilter
+            )
         except MaliciousModelException as e:
             models["scan-error"] = str(e)
 
@@ -388,7 +422,8 @@ def getModels(scan_for_malicious: bool = True):
     listModels(model_type="hypernetwork")
     listModels(model_type="gfpgan")
     listModels(model_type="lora")
-    listModels(model_type="embeddings")
+    listModels(model_type="embeddings", nameFilter=get_embedding_token)
+    listModels(model_type="controlnet")
 
     if scan_for_malicious and models_scanned > 0:
         log.info(f"[green]Scanned {models_scanned} models. Nothing infected[/]")

ui/easydiffusion/package_manager.py

@@ -0,0 +1,102 @@
+import sys
+import os
+import platform
+from importlib.metadata import version as pkg_version
+
+from sdkit.utils import log
+
+from easydiffusion import app
+
+# future home of scripts/check_modules.py
+
+manifest = {
+    "tensorrt": {
+        "install": [
+            "wheel",
+            "nvidia-cudnn-cu11==8.9.4.25",
+            "tensorrt==9.0.0.post11.dev1 --pre --extra-index-url=https://pypi.nvidia.com --trusted-host pypi.nvidia.com",
+        ],
+        "uninstall": ["tensorrt"],
+        # TODO also uninstall tensorrt-libs and nvidia-cudnn, but do it upon restarting (avoid 'file in use' error)
+    }
+}
+installing = []
+
+# remove this once TRT releases on pypi
+if platform.system() == "Windows":
+    trt_dir = os.path.join(app.ROOT_DIR, "tensorrt")
+    if os.path.exists(trt_dir) and os.path.isdir(trt_dir) and len(os.listdir(trt_dir)) > 0:
+        files = os.listdir(trt_dir)
+
+        packages = manifest["tensorrt"]["install"]
+        packages = tuple(p.replace("-", "_") for p in packages)
+
+        wheels = []
+        for p in packages:
+            p = p.split(" ")[0]
+            f = next((f for f in files if f.startswith(p) and f.endswith((".whl", ".tar.gz"))), None)
+            if f:
+                wheels.append(os.path.join(trt_dir, f))
+
+        manifest["tensorrt"]["install"] = wheels
+
+
+def get_installed_packages() -> list:
+    return {module_name: version(module_name) for module_name in manifest if is_installed(module_name)}
+
+
+def is_installed(module_name) -> bool:
+    return version(module_name) is not None
+
+
+def install(module_name):
+    if is_installed(module_name):
+        log.info(f"{module_name} has already been installed!")
+        return
+    if module_name in installing:
+        log.info(f"{module_name} is already installing!")
+        return
+
+    if module_name not in manifest:
+        raise RuntimeError(f"Can't install unknown package: {module_name}!")
+
+    commands = manifest[module_name]["install"]
+    if module_name == "tensorrt":
+        commands += [
+            "protobuf==3.20.3 polygraphy==0.47.1 onnx==1.14.0 --extra-index-url=https://pypi.ngc.nvidia.com --trusted-host pypi.ngc.nvidia.com"
+        ]
+    commands = [f"python -m pip install --upgrade {cmd}" for cmd in commands]
+
+    installing.append(module_name)
+
+    try:
+        for cmd in commands:
+            print(">", cmd)
+            if os.system(cmd) != 0:
+                raise RuntimeError(f"Error while running {cmd}. Please check the logs in the command-line.")
+    finally:
+        installing.remove(module_name)
+
+
+def uninstall(module_name):
+    if not is_installed(module_name):
+        log.info(f"{module_name} hasn't been installed!")
+        return
+
+    if module_name not in manifest:
+        raise RuntimeError(f"Can't uninstall unknown package: {module_name}!")
+
+    commands = manifest[module_name]["uninstall"]
+    commands = [f"python -m pip uninstall -y {cmd}" for cmd in commands]
+
+    for cmd in commands:
+        print(">", cmd)
+        if os.system(cmd) != 0:
+            raise RuntimeError(f"Error while running {cmd}. Please check the logs in the command-line.")
+
+
+def version(module_name: str) -> str:
+    try:
+        return pkg_version(module_name)
+    except:
+        return None

ui/easydiffusion/renderer.py

@@ -1,279 +0,0 @@
-import json
-import pprint
-import queue
-import time
-
-from easydiffusion import device_manager
-from easydiffusion.types import GenerateImageRequest
-from easydiffusion.types import Image as ResponseImage
-from easydiffusion.types import Response, TaskData, UserInitiatedStop
-from easydiffusion.model_manager import DEFAULT_MODELS, resolve_model_to_use
-from easydiffusion.utils import get_printable_request, log, save_images_to_disk
-from sdkit import Context
-from sdkit.filter import apply_filters
-from sdkit.generate import generate_images
-from sdkit.models import load_model
-from sdkit.utils import (
-    diffusers_latent_samples_to_images,
-    gc,
-    img_to_base64_str,
-    img_to_buffer,
-    latent_samples_to_images,
-    get_device_usage,
-)
-
-context = Context()  # thread-local
-"""
-runtime data (bound locally to this thread), for e.g. device, references to loaded models, optimization flags etc
-"""
-
-
-def init(device):
-    """
-    Initializes the fields that will be bound to this runtime's context, and sets the current torch device
-    """
-    context.stop_processing = False
-    context.temp_images = {}
-    context.partial_x_samples = None
-    context.model_load_errors = {}
-    context.enable_codeformer = True
-
-    from easydiffusion import app
-
-    app_config = app.getConfig()
-    context.test_diffusers = (
-        app_config.get("test_diffusers", False) and app_config.get("update_branch", "main") != "main"
-    )
-
-    log.info("Device usage during initialization:")
-    get_device_usage(device, log_info=True, process_usage_only=False)
-
-    device_manager.device_init(context, device)
-
-
-def make_images(
-    req: GenerateImageRequest,
-    task_data: TaskData,
-    data_queue: queue.Queue,
-    task_temp_images: list,
-    step_callback,
-):
-    context.stop_processing = False
-    print_task_info(req, task_data)
-
-    images, seeds = make_images_internal(req, task_data, data_queue, task_temp_images, step_callback)
-
-    res = Response(
-        req,
-        task_data,
-        images=construct_response(images, seeds, task_data, base_seed=req.seed),
-    )
-    res = res.json()
-    data_queue.put(json.dumps(res))
-    log.info("Task completed")
-
-    return res
-
-
-def print_task_info(req: GenerateImageRequest, task_data: TaskData):
-    req_str = pprint.pformat(get_printable_request(req, task_data)).replace("[", "\[")
-    task_str = pprint.pformat(task_data.dict()).replace("[", "\[")
-    log.info(f"request: {req_str}")
-    log.info(f"task data: {task_str}")
-
-
-def make_images_internal(
-    req: GenerateImageRequest,
-    task_data: TaskData,
-    data_queue: queue.Queue,
-    task_temp_images: list,
-    step_callback,
-):
-    images, user_stopped = generate_images_internal(
-        req,
-        task_data,
-        data_queue,
-        task_temp_images,
-        step_callback,
-        task_data.stream_image_progress,
-        task_data.stream_image_progress_interval,
-    )
-    gc(context)
-    filtered_images = filter_images(req, task_data, images, user_stopped)
-
-    if task_data.save_to_disk_path is not None:
-        save_images_to_disk(images, filtered_images, req, task_data)
-
-    seeds = [*range(req.seed, req.seed + len(images))]
-    if task_data.show_only_filtered_image or filtered_images is images:
-        return filtered_images, seeds
-    else:
-        return images + filtered_images, seeds + seeds
-
-
-def generate_images_internal(
-    req: GenerateImageRequest,
-    task_data: TaskData,
-    data_queue: queue.Queue,
-    task_temp_images: list,
-    step_callback,
-    stream_image_progress: bool,
-    stream_image_progress_interval: int,
-):
-    context.temp_images.clear()
-
-    callback = make_step_callback(
-        req,
-        task_data,
-        data_queue,
-        task_temp_images,
-        step_callback,
-        stream_image_progress,
-        stream_image_progress_interval,
-    )
-
-    try:
-        if req.init_image is not None and not context.test_diffusers:
-            req.sampler_name = "ddim"
-
-        images = generate_images(context, callback=callback, **req.dict())
-        user_stopped = False
-    except UserInitiatedStop:
-        images = []
-        user_stopped = True
-        if context.partial_x_samples is not None:
-            if context.test_diffusers:
-                images = diffusers_latent_samples_to_images(context, context.partial_x_samples)
-            else:
-                images = latent_samples_to_images(context, context.partial_x_samples)
-    finally:
-        if hasattr(context, "partial_x_samples") and context.partial_x_samples is not None:
-            if not context.test_diffusers:
-                del context.partial_x_samples
-            context.partial_x_samples = None
-
-    return images, user_stopped
-
-
-def filter_images(req: GenerateImageRequest, task_data: TaskData, images: list, user_stopped):
-    if user_stopped:
-        return images
-
-    if task_data.block_nsfw:
-        images = apply_filters(context, "nsfw_checker", images)
-
-    if task_data.use_face_correction and "codeformer" in task_data.use_face_correction.lower():
-        default_realesrgan = DEFAULT_MODELS["realesrgan"][0]["file_name"]
-        prev_realesrgan_path = None
-        if task_data.codeformer_upscale_faces and default_realesrgan not in context.model_paths["realesrgan"]:
-            prev_realesrgan_path = context.model_paths["realesrgan"]
-            context.model_paths["realesrgan"] = resolve_model_to_use(default_realesrgan, "realesrgan")
-            load_model(context, "realesrgan")
-
-        try:
-            images = apply_filters(
-                context,
-                "codeformer",
-                images,
-                upscale_faces=task_data.codeformer_upscale_faces,
-                codeformer_fidelity=task_data.codeformer_fidelity,
-            )
-        finally:
-            if prev_realesrgan_path:
-                context.model_paths["realesrgan"] = prev_realesrgan_path
-                load_model(context, "realesrgan")
-    elif task_data.use_face_correction and "gfpgan" in task_data.use_face_correction.lower():
-        images = apply_filters(context, "gfpgan", images)
-
-    if task_data.use_upscale:
-        if "realesrgan" in task_data.use_upscale.lower():
-            images = apply_filters(context, "realesrgan", images, scale=task_data.upscale_amount)
-        elif task_data.use_upscale == "latent_upscaler":
-            images = apply_filters(
-                context,
-                "latent_upscaler",
-                images,
-                scale=task_data.upscale_amount,
-                latent_upscaler_options={
-                    "prompt": req.prompt,
-                    "negative_prompt": req.negative_prompt,
-                    "seed": req.seed,
-                    "num_inference_steps": task_data.latent_upscaler_steps,
-                    "guidance_scale": 0,
-                },
-            )
-
-    return images
-
-
-def construct_response(images: list, seeds: list, task_data: TaskData, base_seed: int):
-    return [
-        ResponseImage(
-            data=img_to_base64_str(
-                img,
-                task_data.output_format,
-                task_data.output_quality,
-                task_data.output_lossless,
-            ),
-            seed=seed,
-        )
-        for img, seed in zip(images, seeds)
-    ]
-
-
-def make_step_callback(
-    req: GenerateImageRequest,
-    task_data: TaskData,
-    data_queue: queue.Queue,
-    task_temp_images: list,
-    step_callback,
-    stream_image_progress: bool,
-    stream_image_progress_interval: int,
-):
-    n_steps = req.num_inference_steps if req.init_image is None else int(req.num_inference_steps * req.prompt_strength)
-    last_callback_time = -1
-
-    def update_temp_img(x_samples, task_temp_images: list):
-        partial_images = []
-
-        if context.test_diffusers:
-            images = diffusers_latent_samples_to_images(context, x_samples)
-        else:
-            images = latent_samples_to_images(context, x_samples)
-
-        if task_data.block_nsfw:
-            images = apply_filters(context, "nsfw_checker", images)
-
-        for i, img in enumerate(images):
-            buf = img_to_buffer(img, output_format="JPEG")
-
-            context.temp_images[f"{task_data.request_id}/{i}"] = buf
-            task_temp_images[i] = buf
-            partial_images.append({"path": f"/image/tmp/{task_data.request_id}/{i}"})
-        del images
-        return partial_images
-
-    def on_image_step(x_samples, i, *args):
-        nonlocal last_callback_time
-
-        if context.test_diffusers:
-            context.partial_x_samples = (x_samples, args[0])
-        else:
-            context.partial_x_samples = x_samples
-
-        step_time = time.time() - last_callback_time if last_callback_time != -1 else -1
-        last_callback_time = time.time()
-
-        progress = {"step": i, "step_time": step_time, "total_steps": n_steps}
-
-        if stream_image_progress and stream_image_progress_interval > 0 and i % stream_image_progress_interval == 0:
-            progress["output"] = update_temp_img(context.partial_x_samples, task_temp_images)
-
-        data_queue.put(json.dumps(progress))
-
-        step_callback()
-
-        if context.stop_processing:
-            raise UserInitiatedStop("User requested that we stop processing")
-
-    return on_image_step

ui/easydiffusion/runtime.py

@@ -0,0 +1,51 @@
+"""
+A runtime that runs on a specific device (in a thread).
+
+It can run various tasks like image generation, image filtering, model merge etc by using that thread-local context.
+
+This creates an `sdkit.Context` that's bound to the device specified while calling the `init()` function.
+"""
+
+from easydiffusion import device_manager
+from easydiffusion.utils import log
+from sdkit import Context
+from sdkit.utils import get_device_usage
+
+context = Context()  # thread-local
+"""
+runtime data (bound locally to this thread), for e.g. device, references to loaded models, optimization flags etc
+"""
+
+
+def init(device):
+    """
+    Initializes the fields that will be bound to this runtime's context, and sets the current torch device
+    """
+    context.stop_processing = False
+    context.temp_images = {}
+    context.partial_x_samples = None
+    context.model_load_errors = {}
+    context.enable_codeformer = True
+
+    from easydiffusion import app
+
+    app_config = app.getConfig()
+    context.test_diffusers = app_config.get("use_v3_engine", True)
+
+    log.info("Device usage during initialization:")
+    get_device_usage(device, log_info=True, process_usage_only=False)
+
+    device_manager.device_init(context, device)
+
+
+def set_vram_optimizations(context: Context):
+    from easydiffusion import app
+
+    config = app.getConfig()
+    vram_usage_level = config.get("vram_usage_level", "balanced")
+
+    if vram_usage_level != context.vram_usage_level:
+        context.vram_usage_level = vram_usage_level
+        return True
+
+    return False

ui/easydiffusion/server.py

@@ -8,8 +8,19 @@ import os
 import traceback
 from typing import List, Union
 
-from easydiffusion import app, model_manager, task_manager
-from easydiffusion.types import GenerateImageRequest, MergeRequest, TaskData
+from easydiffusion import app, model_manager, task_manager, package_manager
+from easydiffusion.tasks import RenderTask, FilterTask
+from easydiffusion.types import (
+    GenerateImageRequest,
+    FilterImageRequest,
+    MergeRequest,
+    TaskData,
+    RenderTaskData,
+    ModelsData,
+    OutputFormatData,
+    SaveToDiskData,
+    convert_legacy_render_req_to_new,
+)
 from easydiffusion.utils import log
 from fastapi import FastAPI, HTTPException
 from fastapi.staticfiles import StaticFiles
@@ -27,6 +38,7 @@ NOCACHE_HEADERS = {
     "Pragma": "no-cache",
     "Expires": "0",
 }
+PROTECTED_CONFIG_KEYS = ("block_nsfw",)  # can't change these via the HTTP API
 
 
 class NoCacheStaticFiles(StaticFiles):
@@ -54,7 +66,8 @@ class SetAppConfigRequest(BaseModel, extra=Extra.allow):
     ui_open_browser_on_start: bool = None
     listen_to_network: bool = None
     listen_port: int = None
-    test_diffusers: bool = False
+    use_v3_engine: bool = True
+    models_dir: str = None
 
 
 def init():
@@ -97,6 +110,10 @@ def init():
     def render(req: dict):
         return render_internal(req)
 
+    @server_api.post("/filter")
+    def render(req: dict):
+        return filter_internal(req)
+
     @server_api.post("/model/merge")
     def model_merge(req: dict):
         print(req)
@@ -122,6 +139,14 @@ def init():
     def stop_cloudflare_tunnel(req: dict):
         return stop_cloudflare_tunnel_internal(req)
 
+    @server_api.post("/package/{package_name:str}")
+    def modify_package(package_name: str, req: dict):
+        return modify_package_internal(package_name, req)
+
+    @server_api.get("/sha256/{obj_path:path}")
+    def get_sha256(obj_path: str):
+        return get_sha256_internal(obj_path)
+
     @server_api.get("/")
     def read_root():
         return FileResponse(os.path.join(app.SD_UI_DIR, "index.html"), headers=NOCACHE_HEADERS)
@@ -151,10 +176,11 @@ def set_app_config_internal(req: SetAppConfigRequest):
             config["net"] = {}
         config["net"]["listen_port"] = int(req.listen_port)
 
-    config["test_diffusers"] = req.test_diffusers
+    config["use_v3_engine"] = req.use_v3_engine
+    config["models_dir"] = req.models_dir
 
     for property, property_value in req.dict().items():
-        if property_value is not None and property not in req.__fields__:
+        if property_value is not None and property not in req.__fields__ and property not in PROTECTED_CONFIG_KEYS:
             config[property] = property_value
 
     try:
@@ -170,11 +196,13 @@ def set_app_config_internal(req: SetAppConfigRequest):
 
 
 def update_render_devices_in_config(config, render_devices):
-    if render_devices not in ("cpu", "auto") and not render_devices.startswith("cuda:"):
-        raise HTTPException(status_code=400, detail=f"Invalid render device requested: {render_devices}")
+    from easydiffusion.device_manager import validate_render_devices
 
-    if render_devices.startswith("cuda:"):
+    try:
         render_devices = render_devices.split(",")
+        validate_render_devices(render_devices)
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
 
     config["render_devices"] = render_devices
 
@@ -183,7 +211,12 @@ def read_web_data_internal(key: str = None, **kwargs):
     if not key:  # /get without parameters, stable-diffusion easter egg.
         raise HTTPException(status_code=418, detail="StableDiffusion is drawing a teapot!")  # HTTP418 I'm a teapot
     elif key == "app_config":
-        return JSONResponse(app.getConfig(), headers=NOCACHE_HEADERS)
+        config = app.getConfig()
+
+        if "models_dir" not in config:
+            config["models_dir"] = app.MODELS_DIR
+
+        return JSONResponse(config, headers=NOCACHE_HEADERS)
     elif key == "system_info":
         config = app.getConfig()
 
@@ -194,6 +227,7 @@ def read_web_data_internal(key: str = None, **kwargs):
             "hosts": app.getIPConfig(),
             "default_output_dir": output_dir,
             "enforce_output_dir": ("force_save_path" in config),
+            "enforce_output_metadata": ("force_save_metadata" in config),
         }
         system_info["devices"]["config"] = config.get("render_devices", "auto")
         return JSONResponse(system_info, headers=NOCACHE_HEADERS)
@@ -213,55 +247,94 @@ def ping_internal(session_id: str = None):
         if task_manager.current_state_error:
             raise HTTPException(status_code=500, detail=str(task_manager.current_state_error))
         raise HTTPException(status_code=500, detail="Render thread is dead.")
+
     if task_manager.current_state_error and not isinstance(task_manager.current_state_error, StopAsyncIteration):
         raise HTTPException(status_code=500, detail=str(task_manager.current_state_error))
+
     # Alive
     response = {"status": str(task_manager.current_state)}
+
     if session_id:
         session = task_manager.get_cached_session(session_id, update_ttl=True)
         response["tasks"] = {id(t): t.status for t in session.tasks}
+
     response["devices"] = task_manager.get_devices()
+    response["packages_installed"] = package_manager.get_installed_packages()
+    response["packages_installing"] = package_manager.installing
+
     if cloudflare.address != None:
         response["cloudflare"] = cloudflare.address
+
     return JSONResponse(response, headers=NOCACHE_HEADERS)
 
 
 def render_internal(req: dict):
     try:
+        req = convert_legacy_render_req_to_new(req)
+
         # separate out the request data into rendering and task-specific data
         render_req: GenerateImageRequest = GenerateImageRequest.parse_obj(req)
-        task_data: TaskData = TaskData.parse_obj(req)
+        task_data: RenderTaskData = RenderTaskData.parse_obj(req)
+        models_data: ModelsData = ModelsData.parse_obj(req)
+        output_format: OutputFormatData = OutputFormatData.parse_obj(req)
+        save_data: SaveToDiskData = SaveToDiskData.parse_obj(req)
 
         # Overwrite user specified save path
         config = app.getConfig()
         if "force_save_path" in config:
-            task_data.save_to_disk_path = config["force_save_path"]
+            save_data.save_to_disk_path = config["force_save_path"]
 
         render_req.init_image_mask = req.get("mask")  # hack: will rename this in the HTTP API in a future revision
 
         app.save_to_config(
-            task_data.use_stable_diffusion_model,
-            task_data.use_vae_model,
-            task_data.use_hypernetwork_model,
+            models_data.model_paths.get("stable-diffusion"),
+            models_data.model_paths.get("vae"),
+            models_data.model_paths.get("hypernetwork"),
             task_data.vram_usage_level,
         )
 
         # enqueue the task
-        new_task = task_manager.render(render_req, task_data)
+        task = RenderTask(render_req, task_data, models_data, output_format, save_data)
+        return enqueue_task(task)
+    except HTTPException as e:
+        raise e
+    except Exception as e:
+        log.error(traceback.format_exc())
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+def filter_internal(req: dict):
+    try:
+        filter_req: FilterImageRequest = FilterImageRequest.parse_obj(req)
+        task_data: TaskData = TaskData.parse_obj(req)
+        models_data: ModelsData = ModelsData.parse_obj(req)
+        output_format: OutputFormatData = OutputFormatData.parse_obj(req)
+        save_data: SaveToDiskData = SaveToDiskData.parse_obj(req)
+
+        # enqueue the task
+        task = FilterTask(filter_req, task_data, models_data, output_format, save_data)
+        return enqueue_task(task)
+    except HTTPException as e:
+        raise e
+    except Exception as e:
+        log.error(traceback.format_exc())
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+def enqueue_task(task):
+    try:
+        task_manager.enqueue_task(task)
         response = {
             "status": str(task_manager.current_state),
             "queue": len(task_manager.tasks_queue),
-            "stream": f"/image/stream/{id(new_task)}",
-            "task": id(new_task),
+            "stream": f"/image/stream/{task.id}",
+            "task": task.id,
         }
         return JSONResponse(response, headers=NOCACHE_HEADERS)
     except ChildProcessError as e:  # Render thread is dead
         raise HTTPException(status_code=500, detail=f"Rendering thread has died.")  # HTTP500 Internal Server Error
     except ConnectionRefusedError as e:  # Unstarted task pending limit reached, deny queueing too many.
         raise HTTPException(status_code=503, detail=str(e))  # HTTP503 Service Unavailable
-    except Exception as e:
-        log.error(traceback.format_exc())
-        raise HTTPException(status_code=500, detail=str(e))
 
 
 def model_merge_internal(req: dict):
@@ -381,3 +454,41 @@ def stop_cloudflare_tunnel_internal(req: dict):
         log.error(str(e))
         log.error(traceback.format_exc())
         return HTTPException(status_code=500, detail=str(e))
+
+
+def modify_package_internal(package_name: str, req: dict):
+    try:
+        cmd = req["command"]
+        if cmd not in ("install", "uninstall"):
+            raise RuntimeError(f"Unknown command: {cmd}")
+
+        cmd = getattr(package_manager, cmd)
+        cmd(package_name)
+
+        return JSONResponse({"status": "OK"}, headers=NOCACHE_HEADERS)
+    except Exception as e:
+        log.error(str(e))
+        log.error(traceback.format_exc())
+        return HTTPException(status_code=500, detail=str(e))
+
+
+def get_sha256_internal(obj_path):
+    from easydiffusion.utils import sha256sum
+
+    path = obj_path.split("/")
+    type = path.pop(0)
+
+    try:
+        model_path = model_manager.resolve_model_to_use("/".join(path), type)
+    except Exception as e:
+        log.error(str(e))
+        log.error(traceback.format_exc())
+
+        return HTTPException(status_code=404)
+    try:
+        digest = sha256sum(model_path)
+        return {"digest": digest}
+    except Exception as e:
+        log.error(str(e))
+        log.error(traceback.format_exc())
+        return HTTPException(status_code=500, detail=str(e))

ui/easydiffusion/task_manager.py

@@ -17,16 +17,20 @@ from typing import Any, Hashable
 
 import torch
 from easydiffusion import device_manager
-from easydiffusion.types import GenerateImageRequest, TaskData
+from easydiffusion.tasks import Task
 from easydiffusion.utils import log
 from sdkit.utils import gc
 
+from torchruntime.utils import get_device_count, get_device, get_device_name, get_installed_torch_platform
+from sdkit.utils import is_cpu_device, mem_get_info
+
 THREAD_NAME_PREFIX = ""
 ERR_LOCK_FAILED = " failed to acquire lock within timeout."
 LOCK_TIMEOUT = 15  # Maximum locking time in seconds before failing a task.
 # It's better to get an exception than a deadlock... ALWAYS use timeout in critical paths.
 
 DEVICE_START_TIMEOUT = 60  # seconds - Maximum time to wait for a render device to init.
+MAX_OVERLOAD_ALLOWED_RATIO = 2  # i.e. 2x pending tasks compared to the number of render threads
 
 
 class SymbolClass(type):  # Print nicely formatted Symbol names.
@@ -58,46 +62,6 @@ class ServerStates:
         pass
 
 
-class RenderTask:  # Task with output queue and completion lock.
-    def __init__(self, req: GenerateImageRequest, task_data: TaskData):
-        task_data.request_id = id(self)
-        self.render_request: GenerateImageRequest = req  # Initial Request
-        self.task_data: TaskData = task_data
-        self.response: Any = None  # Copy of the last reponse
-        self.render_device = None  # Select the task affinity. (Not used to change active devices).
-        self.temp_images: list = [None] * req.num_outputs * (1 if task_data.show_only_filtered_image else 2)
-        self.error: Exception = None
-        self.lock: threading.Lock = threading.Lock()  # Locks at task start and unlocks when task is completed
-        self.buffer_queue: queue.Queue = queue.Queue()  # Queue of JSON string segments
-
-    async def read_buffer_generator(self):
-        try:
-            while not self.buffer_queue.empty():
-                res = self.buffer_queue.get(block=False)
-                self.buffer_queue.task_done()
-                yield res
-        except queue.Empty as e:
-            yield
-
-    @property
-    def status(self):
-        if self.lock.locked():
-            return "running"
-        if isinstance(self.error, StopAsyncIteration):
-            return "stopped"
-        if self.error:
-            return "error"
-        if not self.buffer_queue.empty():
-            return "buffer"
-        if self.response:
-            return "completed"
-        return "pending"
-
-    @property
-    def is_pending(self):
-        return bool(not self.response and not self.error)
-
-
 # Temporary cache to allow to query tasks results for a short time after they are completed.
 class DataCache:
     def __init__(self):
@@ -123,8 +87,8 @@ class DataCache:
             # Remove Items
             for key in to_delete:
                 (_, val) = self._base[key]
-                if isinstance(val, RenderTask):
-                    log.debug(f"RenderTask {key} expired. Data removed.")
+                if isinstance(val, Task):
+                    log.debug(f"Task {key} expired. Data removed.")
                 elif isinstance(val, SessionState):
                     log.debug(f"Session {key} expired. Data removed.")
                 else:
@@ -220,8 +184,8 @@ class SessionState:
                 tasks.append(task)
         return tasks
 
-    def put(self, task, ttl=TASK_TTL):
-        task_id = id(task)
+    def put(self, task: Task, ttl=TASK_TTL):
+        task_id = task.id
         self._tasks_ids.append(task_id)
         if not task_cache.put(task_id, task, ttl):
             return False
@@ -230,11 +194,16 @@ class SessionState:
         return True
 
 
+def keep_task_alive(task: Task):
+    task_cache.keep(task.id, TASK_TTL)
+    session_cache.keep(task.session_id, TASK_TTL)
+
+
 def thread_get_next_task():
-    from easydiffusion import renderer
+    from easydiffusion import runtime
 
     if not manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT):
-        log.warn(f"Render thread on device: {renderer.context.device} failed to acquire manager lock.")
+        log.warn(f"Render thread on device: {runtime.context.device} failed to acquire manager lock.")
         return None
     if len(tasks_queue) <= 0:
         manager_lock.release()
@@ -242,7 +211,7 @@ def thread_get_next_task():
     task = None
     try:  # Select a render task.
         for queued_task in tasks_queue:
-            if queued_task.render_device and renderer.context.device != queued_task.render_device:
+            if queued_task.render_device and runtime.context.device != queued_task.render_device:
                 # Is asking for a specific render device.
                 if is_alive(queued_task.render_device) > 0:
                     continue  # requested device alive, skip current one.
@@ -251,7 +220,7 @@ def thread_get_next_task():
                     queued_task.error = Exception(queued_task.render_device + " is not currently active.")
                     task = queued_task
                     break
-            if not queued_task.render_device and renderer.context.device == "cpu" and is_alive() > 1:
+            if not queued_task.render_device and runtime.context.device == "cpu" and is_alive() > 1:
                 # not asking for any specific devices, cpu want to grab task but other render devices are alive.
                 continue  # Skip Tasks, don't run on CPU unless there is nothing else or user asked for it.
             task = queued_task
@@ -266,19 +235,19 @@ def thread_get_next_task():
 def thread_render(device):
     global current_state, current_state_error
 
-    from easydiffusion import model_manager, renderer
+    from easydiffusion import model_manager, runtime
 
     try:
-        renderer.init(device)
+        runtime.init(device)
 
         weak_thread_data[threading.current_thread()] = {
-            "device": renderer.context.device,
-            "device_name": renderer.context.device_name,
+            "device": runtime.context.device,
+            "device_name": runtime.context.device_name,
             "alive": True,
         }
 
         current_state = ServerStates.LoadingModel
-        model_manager.load_default_models(renderer.context)
+        model_manager.load_default_models(runtime.context)
 
         current_state = ServerStates.Online
     except Exception as e:
@@ -290,8 +259,8 @@ def thread_render(device):
         session_cache.clean()
         task_cache.clean()
         if not weak_thread_data[threading.current_thread()]["alive"]:
-            log.info(f"Shutting down thread for device {renderer.context.device}")
-            model_manager.unload_all(renderer.context)
+            log.info(f"Shutting down thread for device {runtime.context.device}")
+            model_manager.unload_all(runtime.context)
             return
         if isinstance(current_state_error, SystemExit):
             current_state = ServerStates.Unavailable
@@ -311,62 +280,31 @@ def thread_render(device):
             task.response = {"status": "failed", "detail": str(task.error)}
             task.buffer_queue.put(json.dumps(task.response))
             continue
-        log.info(f"Session {task.task_data.session_id} starting task {id(task)} on {renderer.context.device_name}")
+        log.info(f"Session {task.session_id} starting task {task.id} on {runtime.context.device_name}")
         if not task.lock.acquire(blocking=False):
             raise Exception("Got locked task from queue.")
         try:
+            task.run()
 
-            def step_callback():
-                global current_state_error
-
-                task_cache.keep(id(task), TASK_TTL)
-                session_cache.keep(task.task_data.session_id, TASK_TTL)
-
-                if (
-                    isinstance(current_state_error, SystemExit)
-                    or isinstance(current_state_error, StopAsyncIteration)
-                    or isinstance(task.error, StopAsyncIteration)
-                ):
-                    renderer.context.stop_processing = True
-                    if isinstance(current_state_error, StopAsyncIteration):
-                        task.error = current_state_error
-                        current_state_error = None
-                        log.info(f"Session {task.task_data.session_id} sent cancel signal for task {id(task)}")
-
-            current_state = ServerStates.LoadingModel
-            model_manager.resolve_model_paths(task.task_data)
-            model_manager.reload_models_if_necessary(renderer.context, task.task_data)
-            model_manager.fail_if_models_did_not_load(renderer.context)
-
-            current_state = ServerStates.Rendering
-            task.response = renderer.make_images(
-                task.render_request,
-                task.task_data,
-                task.buffer_queue,
-                task.temp_images,
-                step_callback,
-            )
             # Before looping back to the generator, mark cache as still alive.
-            task_cache.keep(id(task), TASK_TTL)
-            session_cache.keep(task.task_data.session_id, TASK_TTL)
+            keep_task_alive(task)
         except Exception as e:
             task.error = str(e)
             task.response = {"status": "failed", "detail": str(task.error)}
             task.buffer_queue.put(json.dumps(task.response))
             log.error(traceback.format_exc())
         finally:
-            gc(renderer.context)
+            gc(runtime.context)
             task.lock.release()
-        task_cache.keep(id(task), TASK_TTL)
-        session_cache.keep(task.task_data.session_id, TASK_TTL)
+
+        keep_task_alive(task)
+
         if isinstance(task.error, StopAsyncIteration):
-            log.info(f"Session {task.task_data.session_id} task {id(task)} cancelled!")
+            log.info(f"Session {task.session_id} task {task.id} cancelled!")
         elif task.error is not None:
-            log.info(f"Session {task.task_data.session_id} task {id(task)} failed!")
+            log.info(f"Session {task.session_id} task {task.id} failed!")
         else:
-            log.info(
-                f"Session {task.task_data.session_id} task {id(task)} completed by {renderer.context.device_name}."
-            )
+            log.info(f"Session {task.session_id} task {task.id} completed by {runtime.context.device_name}.")
         current_state = ServerStates.Online
 
 
@@ -394,34 +332,33 @@ def get_devices():
         "active": {},
     }
 
-    def get_device_info(device):
-        if device in ("cpu", "mps"):
+    def get_device_info(device_id):
+        if is_cpu_device(device_id):
             return {"name": device_manager.get_processor_name()}
 
-        mem_free, mem_total = torch.cuda.mem_get_info(device)
+        device = get_device(device_id)
+
+        mem_free, mem_total = mem_get_info(device)
         mem_free /= float(10**9)
         mem_total /= float(10**9)
 
         return {
-            "name": torch.cuda.get_device_name(device),
+            "name": get_device_name(device),
             "mem_free": mem_free,
             "mem_total": mem_total,
             "max_vram_usage_level": device_manager.get_max_vram_usage_level(device),
         }
 
     # list the compatible devices
-    cuda_count = torch.cuda.device_count()
-    for device in range(cuda_count):
-        device = f"cuda:{device}"
-        if not device_manager.is_device_compatible(device):
-            continue
+    torch_platform_name = get_installed_torch_platform()[0]
+    device_count = get_device_count()
+    for device_id in range(device_count):
+        device_id = f"{torch_platform_name}:{device_id}" if device_count > 1 else torch_platform_name
 
-        devices["all"].update({device: get_device_info(device)})
+        devices["all"].update({device_id: get_device_info(device_id)})
 
-    if device_manager.is_mps_available():
-        devices["all"].update({"mps": get_device_info("mps")})
-
-    devices["all"].update({"cpu": get_device_info("cpu")})
+    if torch_platform_name != "cpu":
+        devices["all"].update({"cpu": get_device_info("cpu")})
 
     # list the activated devices
     if not manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT):
@@ -433,11 +370,17 @@ def get_devices():
             weak_data = weak_thread_data.get(rthread)
             if not weak_data or not "device" in weak_data or not "device_name" in weak_data:
                 continue
-            device = weak_data["device"]
-            devices["active"].update({device: get_device_info(device)})
+            device_id = weak_data["device"]
+            devices["active"].update({device_id: get_device_info(device_id)})
     finally:
         manager_lock.release()
 
+    # temp until TRT releases
+    import os
+    from easydiffusion import app
+
+    devices["enable_trt"] = os.path.exists(os.path.join(app.ROOT_DIR, "tensorrt"))
+
     return devices
 
 
@@ -486,12 +429,6 @@ def start_render_thread(device):
 
 
 def stop_render_thread(device):
-    try:
-        device_manager.validate_device_id(device, log_prefix="stop_render_thread")
-    except:
-        log.error(traceback.format_exc())
-        return False
-
     if not manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT):
         raise Exception("stop_render_thread" + ERR_LOCK_FAILED)
     log.info(f"Stopping Rendering Thread on device: {device}")
@@ -548,28 +485,27 @@ def shutdown_event():  # Signal render thread to close on shutdown
     current_state_error = SystemExit("Application shutting down.")
 
 
-def render(render_req: GenerateImageRequest, task_data: TaskData):
+def enqueue_task(task: Task):
     current_thread_count = is_alive()
     if current_thread_count <= 0:  # Render thread is dead
         raise ChildProcessError("Rendering thread has died.")
 
     # Alive, check if task in cache
-    session = get_cached_session(task_data.session_id, update_ttl=True)
+    session = get_cached_session(task.session_id, update_ttl=True)
     pending_tasks = list(filter(lambda t: t.is_pending, session.tasks))
-    if current_thread_count < len(pending_tasks):
+    if len(pending_tasks) > current_thread_count * MAX_OVERLOAD_ALLOWED_RATIO:
         raise ConnectionRefusedError(
-            f"Session {task_data.session_id} already has {len(pending_tasks)} pending tasks out of {current_thread_count}."
+            f"Session {task.session_id} already has {len(pending_tasks)} pending tasks, with {current_thread_count} workers."
         )
 
-    new_task = RenderTask(render_req, task_data)
-    if session.put(new_task, TASK_TTL):
+    if session.put(task, TASK_TTL):
         # Use twice the normal timeout for adding user requests.
         # Tries to force session.put to fail before tasks_queue.put would.
         if manager_lock.acquire(blocking=True, timeout=LOCK_TIMEOUT * 2):
             try:
-                tasks_queue.append(new_task)
+                tasks_queue.append(task)
                 idle_event.set()
-                return new_task
+                return task
             finally:
                 manager_lock.release()
     raise RuntimeError("Failed to add task to cache.")

ui/easydiffusion/tasks/__init__.py

@@ -0,0 +1,3 @@
+from .task import Task
+from .render_images import RenderTask
+from .filter_images import FilterTask

ui/easydiffusion/tasks/filter_images.py

@@ -0,0 +1,164 @@
+import os
+import json
+import pprint
+import time
+
+from numpy import base_repr
+
+from sdkit.filter import apply_filters
+from sdkit.models import load_model
+from sdkit.utils import img_to_base64_str, get_image, log, save_images
+
+from easydiffusion import model_manager, runtime
+from easydiffusion.types import (
+    FilterImageRequest,
+    FilterImageResponse,
+    ModelsData,
+    OutputFormatData,
+    SaveToDiskData,
+    TaskData,
+    GenerateImageRequest,
+)
+from easydiffusion.utils.save_utils import format_folder_name
+
+from .task import Task
+
+
+class FilterTask(Task):
+    "For applying filters to input images"
+
+    def __init__(
+        self,
+        req: FilterImageRequest,
+        task_data: TaskData,
+        models_data: ModelsData,
+        output_format: OutputFormatData,
+        save_data: SaveToDiskData,
+    ):
+        super().__init__(task_data.session_id)
+
+        task_data.request_id = self.id
+
+        self.request = req
+        self.task_data = task_data
+        self.models_data = models_data
+        self.output_format = output_format
+        self.save_data = save_data
+
+        # convert to multi-filter format, if necessary
+        if isinstance(req.filter, str):
+            req.filter_params = {req.filter: req.filter_params}
+            req.filter = [req.filter]
+
+        if not isinstance(req.image, list):
+            req.image = [req.image]
+
+    def run(self):
+        "Runs the image filtering task on the assigned thread"
+
+        from easydiffusion import app
+
+        context = runtime.context
+
+        model_manager.resolve_model_paths(self.models_data)
+        model_manager.reload_models_if_necessary(context, self.models_data)
+        model_manager.fail_if_models_did_not_load(context)
+
+        print_task_info(self.request, self.models_data, self.output_format, self.save_data)
+
+        if isinstance(self.request.image, list):
+            images = [get_image(img) for img in self.request.image]
+        else:
+            images = get_image(self.request.image)
+
+        images = filter_images(context, images, self.request.filter, self.request.filter_params)
+
+        output_format = self.output_format
+
+        if self.save_data.save_to_disk_path is not None:
+            app_config = app.getConfig()
+            folder_format = app_config.get("folder_format", "$id")
+
+            dummy_req = GenerateImageRequest()
+            img_id = base_repr(int(time.time() * 10000), 36)[-7:]  # Base 36 conversion, 0-9, A-Z
+
+            save_dir_path = os.path.join(
+                self.save_data.save_to_disk_path, format_folder_name(folder_format, dummy_req, self.task_data)
+            )
+            save_images(
+                images,
+                save_dir_path,
+                file_name=img_id,
+                output_format=output_format.output_format,
+                output_quality=output_format.output_quality,
+                output_lossless=output_format.output_lossless,
+            )
+
+        images = [
+            img_to_base64_str(
+                img, output_format.output_format, output_format.output_quality, output_format.output_lossless
+            )
+            for img in images
+        ]
+
+        res = FilterImageResponse(self.request, self.models_data, images=images)
+        res = res.json()
+        self.buffer_queue.put(json.dumps(res))
+
+        log.info("Filter task completed")
+
+        self.response = res
+
+
+def filter_images(context, images, filters, filter_params={}):
+    filters = filters if isinstance(filters, list) else [filters]
+
+    for filter_name in filters:
+        params = filter_params.get(filter_name, {})
+
+        previous_state = before_filter(context, filter_name, params)
+
+        try:
+            images = apply_filters(context, filter_name, images, **params)
+        finally:
+            after_filter(context, filter_name, params, previous_state)
+
+    return images
+
+
+def before_filter(context, filter_name, filter_params):
+    if filter_name == "codeformer":
+        from easydiffusion.model_manager import DEFAULT_MODELS, resolve_model_to_use
+
+        default_realesrgan = DEFAULT_MODELS["realesrgan"][0]["file_name"]
+        prev_realesrgan_path = None
+
+        upscale_faces = filter_params.get("upscale_faces", False)
+        if upscale_faces and default_realesrgan not in context.model_paths["realesrgan"]:
+            prev_realesrgan_path = context.model_paths.get("realesrgan")
+            context.model_paths["realesrgan"] = resolve_model_to_use(default_realesrgan, "realesrgan")
+            load_model(context, "realesrgan")
+
+        return prev_realesrgan_path
+
+
+def after_filter(context, filter_name, filter_params, previous_state):
+    if filter_name == "codeformer":
+        prev_realesrgan_path = previous_state
+        if prev_realesrgan_path:
+            context.model_paths["realesrgan"] = prev_realesrgan_path
+            load_model(context, "realesrgan")
+
+
+def print_task_info(
+    req: FilterImageRequest, models_data: ModelsData, output_format: OutputFormatData, save_data: SaveToDiskData
+):
+    req_str = pprint.pformat({"filter": req.filter, "filter_params": req.filter_params}).replace("[", "\[")
+    models_data = pprint.pformat(models_data.dict()).replace("[", "\[")
+    output_format = pprint.pformat(output_format.dict()).replace("[", "\[")
+    save_data = pprint.pformat(save_data.dict()).replace("[", "\[")
+
+    log.info(f"request: {req_str}")
+    log.info(f"models data: {models_data}")
+    log.info(f"output format: {output_format}")
+    log.info(f"save data: {save_data}")

ui/easydiffusion/tasks/render_images.py

@@ -0,0 +1,378 @@
+import json
+import pprint
+import queue
+import time
+
+from easydiffusion import model_manager, runtime
+from easydiffusion.types import GenerateImageRequest, ModelsData, OutputFormatData, SaveToDiskData
+from easydiffusion.types import Image as ResponseImage
+from easydiffusion.types import GenerateImageResponse, RenderTaskData, UserInitiatedStop
+from easydiffusion.utils import get_printable_request, log, save_images_to_disk
+from sdkit.generate import generate_images
+from sdkit.utils import (
+    diffusers_latent_samples_to_images,
+    gc,
+    img_to_base64_str,
+    img_to_buffer,
+    latent_samples_to_images,
+    resize_img,
+    get_image,
+    log,
+)
+
+from .task import Task
+from .filter_images import filter_images
+
+
+class RenderTask(Task):
+    "For image generation"
+
+    def __init__(
+        self,
+        req: GenerateImageRequest,
+        task_data: RenderTaskData,
+        models_data: ModelsData,
+        output_format: OutputFormatData,
+        save_data: SaveToDiskData,
+    ):
+        super().__init__(task_data.session_id)
+
+        task_data.request_id = self.id
+
+        self.render_request = req  # Initial Request
+        self.task_data = task_data
+        self.models_data = models_data
+        self.output_format = output_format
+        self.save_data = save_data
+
+        self.temp_images: list = [None] * req.num_outputs * (1 if task_data.show_only_filtered_image else 2)
+
+    def run(self):
+        "Runs the image generation task on the assigned thread"
+
+        from easydiffusion import task_manager, app
+
+        context = runtime.context
+        config = app.getConfig()
+
+        if config.get("block_nsfw", False):  # override if set on the server
+            self.task_data.block_nsfw = True
+            if "nsfw_checker" not in self.task_data.filters:
+                self.task_data.filters.append("nsfw_checker")
+                self.models_data.model_paths["nsfw_checker"] = "nsfw_checker"
+
+        def step_callback():
+            task_manager.keep_task_alive(self)
+            task_manager.current_state = task_manager.ServerStates.Rendering
+
+            if isinstance(task_manager.current_state_error, (SystemExit, StopAsyncIteration)) or isinstance(
+                self.error, StopAsyncIteration
+            ):
+                context.stop_processing = True
+                if isinstance(task_manager.current_state_error, StopAsyncIteration):
+                    self.error = task_manager.current_state_error
+                    task_manager.current_state_error = None
+                    log.info(f"Session {self.session_id} sent cancel signal for task {self.id}")
+
+        task_manager.current_state = task_manager.ServerStates.LoadingModel
+        model_manager.resolve_model_paths(self.models_data)
+
+        models_to_force_reload = []
+        if (
+            runtime.set_vram_optimizations(context)
+            or self.has_param_changed(context, "clip_skip")
+            or self.trt_needs_reload(context)
+        ):
+            models_to_force_reload.append("stable-diffusion")
+
+        model_manager.reload_models_if_necessary(context, self.models_data, models_to_force_reload)
+        model_manager.fail_if_models_did_not_load(context)
+
+        task_manager.current_state = task_manager.ServerStates.Rendering
+        self.response = make_images(
+            context,
+            self.render_request,
+            self.task_data,
+            self.models_data,
+            self.output_format,
+            self.save_data,
+            self.buffer_queue,
+            self.temp_images,
+            step_callback,
+        )
+
+    def has_param_changed(self, context, param_name):
+        if not context.test_diffusers:
+            return False
+        if "stable-diffusion" not in context.models or "params" not in context.models["stable-diffusion"]:
+            return True
+
+        model = context.models["stable-diffusion"]
+        new_val = self.models_data.model_params.get("stable-diffusion", {}).get(param_name, False)
+        return model["params"].get(param_name) != new_val
+
+    def trt_needs_reload(self, context):
+        if not context.test_diffusers:
+            return False
+        if "stable-diffusion" not in context.models or "params" not in context.models["stable-diffusion"]:
+            return True
+
+        model = context.models["stable-diffusion"]
+
+        # curr_convert_to_trt = model["params"].get("convert_to_tensorrt")
+        new_convert_to_trt = self.models_data.model_params.get("stable-diffusion", {}).get("convert_to_tensorrt", False)
+
+        pipe = model["default"]
+        is_trt_loaded = hasattr(pipe.unet, "_allocate_trt_buffers") or hasattr(
+            pipe.unet, "_allocate_trt_buffers_backup"
+        )
+        if new_convert_to_trt and not is_trt_loaded:
+            return True
+
+        curr_build_config = model["params"].get("trt_build_config")
+        new_build_config = self.models_data.model_params.get("stable-diffusion", {}).get("trt_build_config", {})
+
+        return new_convert_to_trt and curr_build_config != new_build_config
+
+
+def make_images(
+    context,
+    req: GenerateImageRequest,
+    task_data: RenderTaskData,
+    models_data: ModelsData,
+    output_format: OutputFormatData,
+    save_data: SaveToDiskData,
+    data_queue: queue.Queue,
+    task_temp_images: list,
+    step_callback,
+):
+    context.stop_processing = False
+    print_task_info(req, task_data, models_data, output_format, save_data)
+
+    images, seeds = make_images_internal(
+        context, req, task_data, models_data, output_format, save_data, data_queue, task_temp_images, step_callback
+    )
+
+    res = GenerateImageResponse(
+        req, task_data, models_data, output_format, save_data, images=construct_response(images, seeds, output_format)
+    )
+    res = res.json()
+    data_queue.put(json.dumps(res))
+    log.info("Task completed")
+
+    return res
+
+
+def print_task_info(
+    req: GenerateImageRequest,
+    task_data: RenderTaskData,
+    models_data: ModelsData,
+    output_format: OutputFormatData,
+    save_data: SaveToDiskData,
+):
+    req_str = pprint.pformat(get_printable_request(req, task_data, models_data, output_format, save_data)).replace("[", "\[")
+    task_str = pprint.pformat(task_data.dict()).replace("[", "\[")
+    models_data = pprint.pformat(models_data.dict()).replace("[", "\[")
+    output_format = pprint.pformat(output_format.dict()).replace("[", "\[")
+    save_data = pprint.pformat(save_data.dict()).replace("[", "\[")
+
+    log.info(f"request: {req_str}")
+    log.info(f"task data: {task_str}")
+    # log.info(f"models data: {models_data}")
+    log.info(f"output format: {output_format}")
+    log.info(f"save data: {save_data}")
+
+
+def make_images_internal(
+    context,
+    req: GenerateImageRequest,
+    task_data: RenderTaskData,
+    models_data: ModelsData,
+    output_format: OutputFormatData,
+    save_data: SaveToDiskData,
+    data_queue: queue.Queue,
+    task_temp_images: list,
+    step_callback,
+):
+    images, user_stopped = generate_images_internal(
+        context,
+        req,
+        task_data,
+        models_data,
+        data_queue,
+        task_temp_images,
+        step_callback,
+        task_data.stream_image_progress,
+        task_data.stream_image_progress_interval,
+    )
+
+    gc(context)
+
+    filters, filter_params = task_data.filters, task_data.filter_params
+    filtered_images = filter_images(context, images, filters, filter_params) if not user_stopped else images
+
+    if save_data.save_to_disk_path is not None:
+        save_images_to_disk(images, filtered_images, req, task_data, models_data, output_format, save_data)
+
+    seeds = [*range(req.seed, req.seed + len(images))]
+    if task_data.show_only_filtered_image or filtered_images is images:
+        return filtered_images, seeds
+    else:
+        return images + filtered_images, seeds + seeds
+
+
+def generate_images_internal(
+    context,
+    req: GenerateImageRequest,
+    task_data: RenderTaskData,
+    models_data: ModelsData,
+    data_queue: queue.Queue,
+    task_temp_images: list,
+    step_callback,
+    stream_image_progress: bool,
+    stream_image_progress_interval: int,
+):
+    context.temp_images.clear()
+
+    callback = make_step_callback(
+        context,
+        req,
+        task_data,
+        data_queue,
+        task_temp_images,
+        step_callback,
+        stream_image_progress,
+        stream_image_progress_interval,
+    )
+
+    try:
+        if req.init_image is not None and not context.test_diffusers:
+            req.sampler_name = "ddim"
+
+        req.width, req.height = map(lambda x: x - x % 8, (req.width, req.height))  # clamp to 8
+
+        if req.control_image and task_data.control_filter_to_apply:
+            req.control_image = get_image(req.control_image)
+            req.control_image = resize_img(req.control_image.convert("RGB"), req.width, req.height, clamp_to_8=True)
+            req.control_image = filter_images(context, req.control_image, task_data.control_filter_to_apply)[0]
+
+        if req.init_image is not None and int(req.num_inference_steps * req.prompt_strength) == 0:
+            req.prompt_strength = 1 / req.num_inference_steps if req.num_inference_steps > 0 else 1
+
+        if context.test_diffusers:
+            pipe = context.models["stable-diffusion"]["default"]
+            if hasattr(pipe.unet, "_allocate_trt_buffers_backup"):
+                setattr(pipe.unet, "_allocate_trt_buffers", pipe.unet._allocate_trt_buffers_backup)
+                delattr(pipe.unet, "_allocate_trt_buffers_backup")
+
+            if hasattr(pipe.unet, "_allocate_trt_buffers"):
+                convert_to_trt = models_data.model_params["stable-diffusion"].get("convert_to_tensorrt", False)
+                if convert_to_trt:
+                    pipe.unet.forward = pipe.unet._trt_forward
+                    # pipe.vae.decoder.forward = pipe.vae.decoder._trt_forward
+                    log.info(f"Setting unet.forward to TensorRT")
+                else:
+                    log.info(f"Not using TensorRT for unet.forward")
+                    pipe.unet.forward = pipe.unet._non_trt_forward
+                    # pipe.vae.decoder.forward = pipe.vae.decoder._non_trt_forward
+                    setattr(pipe.unet, "_allocate_trt_buffers_backup", pipe.unet._allocate_trt_buffers)
+                    delattr(pipe.unet, "_allocate_trt_buffers")
+
+            if task_data.enable_vae_tiling:
+                if hasattr(pipe, "enable_vae_tiling"):
+                    pipe.enable_vae_tiling()
+            else:
+                if hasattr(pipe, "disable_vae_tiling"):
+                    pipe.disable_vae_tiling()
+
+        images = generate_images(context, callback=callback, **req.dict())
+        user_stopped = False
+    except UserInitiatedStop:
+        images = []
+        user_stopped = True
+        if context.partial_x_samples is not None:
+            if context.test_diffusers:
+                images = diffusers_latent_samples_to_images(context, context.partial_x_samples)
+            else:
+                images = latent_samples_to_images(context, context.partial_x_samples)
+    finally:
+        if hasattr(context, "partial_x_samples") and context.partial_x_samples is not None:
+            if not context.test_diffusers:
+                del context.partial_x_samples
+            context.partial_x_samples = None
+
+    return images, user_stopped
+
+
+def construct_response(images: list, seeds: list, output_format: OutputFormatData):
+    return [
+        ResponseImage(
+            data=img_to_base64_str(
+                img,
+                output_format.output_format,
+                output_format.output_quality,
+                output_format.output_lossless,
+            ),
+            seed=seed,
+        )
+        for img, seed in zip(images, seeds)
+    ]
+
+
+def make_step_callback(
+    context,
+    req: GenerateImageRequest,
+    task_data: RenderTaskData,
+    data_queue: queue.Queue,
+    task_temp_images: list,
+    step_callback,
+    stream_image_progress: bool,
+    stream_image_progress_interval: int,
+):
+    n_steps = req.num_inference_steps if req.init_image is None else int(req.num_inference_steps * req.prompt_strength)
+    last_callback_time = -1
+
+    def update_temp_img(x_samples, task_temp_images: list):
+        partial_images = []
+
+        if context.test_diffusers:
+            images = diffusers_latent_samples_to_images(context, x_samples)
+        else:
+            images = latent_samples_to_images(context, x_samples)
+
+        if task_data.block_nsfw:
+            images = filter_images(context, images, "nsfw_checker")
+
+        for i, img in enumerate(images):
+            buf = img_to_buffer(img, output_format="JPEG")
+
+            context.temp_images[f"{task_data.request_id}/{i}"] = buf
+            task_temp_images[i] = buf
+            partial_images.append({"path": f"/image/tmp/{task_data.request_id}/{i}"})
+        del images
+        return partial_images
+
+    def on_image_step(x_samples, i, *args):
+        nonlocal last_callback_time
+
+        if context.test_diffusers:
+            context.partial_x_samples = (x_samples, args[0])
+        else:
+            context.partial_x_samples = x_samples
+
+        step_time = time.time() - last_callback_time if last_callback_time != -1 else -1
+        last_callback_time = time.time()
+
+        progress = {"step": i, "step_time": step_time, "total_steps": n_steps}
+
+        if stream_image_progress and stream_image_progress_interval > 0 and i % stream_image_progress_interval == 0:
+            progress["output"] = update_temp_img(context.partial_x_samples, task_temp_images)
+
+        data_queue.put(json.dumps(progress))
+
+        step_callback()
+
+        if context.stop_processing:
+            raise UserInitiatedStop("User requested that we stop processing")
+
+    return on_image_step

ui/easydiffusion/tasks/task.py

@@ -0,0 +1,47 @@
+from threading import Lock
+from queue import Queue, Empty as EmptyQueueException
+from typing import Any
+
+
+class Task:
+    "Task with output queue and completion lock"
+
+    def __init__(self, session_id):
+        self.id = id(self)
+        self.session_id = session_id
+        self.render_device = None  # Select the task affinity. (Not used to change active devices).
+        self.error: Exception = None
+        self.lock: Lock = Lock()  # Locks at task start and unlocks when task is completed
+        self.buffer_queue: Queue = Queue()  # Queue of JSON string segments
+        self.response: Any = None  # Copy of the last reponse
+
+    async def read_buffer_generator(self):
+        try:
+            while not self.buffer_queue.empty():
+                res = self.buffer_queue.get(block=False)
+                self.buffer_queue.task_done()
+                yield res
+        except EmptyQueueException as e:
+            yield
+
+    @property
+    def status(self):
+        if self.lock.locked():
+            return "running"
+        if isinstance(self.error, StopAsyncIteration):
+            return "stopped"
+        if self.error:
+            return "error"
+        if not self.buffer_queue.empty():
+            return "buffer"
+        if self.response:
+            return "completed"
+        return "pending"
+
+    @property
+    def is_pending(self):
+        return bool(not self.response and not self.error)
+
+    def run(self):
+        "Override this to implement the task's behavior"
+        pass

ui/easydiffusion/types.py

@@ -1,4 +1,4 @@
-from typing import Any, List, Union
+from typing import Any, List, Dict, Union
 
 from pydantic import BaseModel
 
@@ -17,19 +17,58 @@ class GenerateImageRequest(BaseModel):
 
     init_image: Any = None
     init_image_mask: Any = None
+    control_image: Any = None
+    control_alpha: Union[float, List[float]] = None
     prompt_strength: float = 0.8
-    preserve_init_image_color_profile = False
+    preserve_init_image_color_profile: bool = False
+    strict_mask_border: bool = False
 
     sampler_name: str = None  # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"
     hypernetwork_strength: float = 0
     lora_alpha: Union[float, List[float]] = 0
-    tiling: str = "none"  # "none", "x", "y", "xy"
+    tiling: str = None  # None, "x", "y", "xy"
+
+
+class FilterImageRequest(BaseModel):
+    image: Any = None
+    filter: Union[str, List[str]] = None
+    filter_params: dict = {}
+
+
+class ModelsData(BaseModel):
+    """
+    Contains the information related to the models involved in a request.
+
+    - To load a model: set the relative path(s) to the model in `model_paths`. No effect if already loaded.
+    - To unload a model: set the model to `None` in `model_paths`. No effect if already unloaded.
+
+    Models that aren't present in `model_paths` will not be changed.
+    """
+
+    model_paths: Dict[str, Union[str, None, List[str]]] = None
+    "model_type to string path, or list of string paths"
+
+    model_params: Dict[str, Dict[str, Any]] = {}
+    "model_type to dict of parameters"
+
+
+class OutputFormatData(BaseModel):
+    output_format: str = "jpeg"  # or "png" or "webp"
+    output_quality: int = 75
+    output_lossless: bool = False
+
+
+class SaveToDiskData(BaseModel):
+    save_to_disk_path: str = None
+    metadata_output_format: str = "txt"  # or "json"
 
 
 class TaskData(BaseModel):
     request_id: str = None
     session_id: str = "session"
-    save_to_disk_path: str = None
+
+
+class RenderTaskData(TaskData):
     vram_usage_level: str = "balanced"  # or "low" or "medium"
 
     use_face_correction: Union[str, List[str]] = None  # or "GFPGANv1.3"
@@ -40,13 +79,15 @@ class TaskData(BaseModel):
     use_vae_model: Union[str, List[str]] = None
     use_hypernetwork_model: Union[str, List[str]] = None
     use_lora_model: Union[str, List[str]] = None
+    use_controlnet_model: Union[str, List[str]] = None
+    use_embeddings_model: Union[str, List[str]] = None
+    filters: List[str] = []
+    filter_params: Dict[str, Dict[str, Any]] = {}
+    control_filter_to_apply: Union[str, List[str]] = None
+    enable_vae_tiling: bool = True
 
     show_only_filtered_image: bool = False
     block_nsfw: bool = False
-    output_format: str = "jpeg"  # or "png" or "webp"
-    output_quality: int = 75
-    output_lossless: bool = False
-    metadata_output_format: str = "txt"  # or "json"
     stream_image_progress: bool = False
     stream_image_progress_interval: int = 5
     clip_skip: bool = False
@@ -59,7 +100,7 @@ class MergeRequest(BaseModel):
     model1: str = None
     ratio: float = None
     out_path: str = "mix"
-    use_fp16 = True
+    use_fp16: bool = True
 
 
 class Image:
@@ -80,24 +121,42 @@ class Image:
         }
 
 
-class Response:
+class GenerateImageResponse:
     render_request: GenerateImageRequest
     task_data: TaskData
+    models_data: ModelsData
     images: list
 
-    def __init__(self, render_request: GenerateImageRequest, task_data: TaskData, images: list):
+    def __init__(
+        self,
+        render_request: GenerateImageRequest,
+        task_data: TaskData,
+        models_data: ModelsData,
+        output_format: OutputFormatData,
+        save_data: SaveToDiskData,
+        images: list,
+    ):
         self.render_request = render_request
         self.task_data = task_data
+        self.models_data = models_data
+        self.output_format = output_format
+        self.save_data = save_data
         self.images = images
 
     def json(self):
         del self.render_request.init_image
         del self.render_request.init_image_mask
+        del self.render_request.control_image
+
+        task_data = self.task_data.dict()
+        task_data.update(self.output_format.dict())
+        task_data.update(self.save_data.dict())
 
         res = {
             "status": "succeeded",
             "render_request": self.render_request.dict(),
-            "task_data": self.task_data.dict(),
+            "task_data": task_data,
+            # "models_data": self.models_data.dict(), # haven't migrated the UI to the new format (yet)
             "output": [],
         }
 
@@ -107,5 +166,112 @@ class Response:
         return res
 
 
+class FilterImageResponse:
+    request: FilterImageRequest
+    models_data: ModelsData
+    images: list
+
+    def __init__(self, request: FilterImageRequest, models_data: ModelsData, images: list):
+        self.request = request
+        self.models_data = models_data
+        self.images = images
+
+    def json(self):
+        del self.request.image
+
+        res = {
+            "status": "succeeded",
+            "request": self.request.dict(),
+            "models_data": self.models_data.dict(),
+            "output": [],
+        }
+
+        for image in self.images:
+            res["output"].append(image)
+
+        return res
+
+
 class UserInitiatedStop(Exception):
     pass
+
+
+def convert_legacy_render_req_to_new(old_req: dict):
+    new_req = dict(old_req)
+
+    # new keys
+    model_paths = new_req["model_paths"] = {}
+    model_params = new_req["model_params"] = {}
+    filters = new_req["filters"] = []
+    filter_params = new_req["filter_params"] = {}
+
+    # move the model info
+    model_paths["stable-diffusion"] = old_req.get("use_stable_diffusion_model")
+    model_paths["vae"] = old_req.get("use_vae_model")
+    model_paths["hypernetwork"] = old_req.get("use_hypernetwork_model")
+    model_paths["lora"] = old_req.get("use_lora_model")
+    model_paths["controlnet"] = old_req.get("use_controlnet_model")
+    model_paths["embeddings"] = old_req.get("use_embeddings_model")
+
+    model_paths["gfpgan"] = old_req.get("use_face_correction", "")
+    model_paths["gfpgan"] = model_paths["gfpgan"] if "gfpgan" in model_paths["gfpgan"].lower() else None
+
+    model_paths["codeformer"] = old_req.get("use_face_correction", "")
+    model_paths["codeformer"] = model_paths["codeformer"] if "codeformer" in model_paths["codeformer"].lower() else None
+
+    model_paths["realesrgan"] = old_req.get("use_upscale", "")
+    model_paths["realesrgan"] = model_paths["realesrgan"] if "realesrgan" in model_paths["realesrgan"].lower() else None
+
+    model_paths["latent_upscaler"] = old_req.get("use_upscale", "")
+    model_paths["latent_upscaler"] = (
+        model_paths["latent_upscaler"] if "latent_upscaler" in model_paths["latent_upscaler"].lower() else None
+    )
+    if "control_filter_to_apply" in old_req:
+        filter_model = old_req["control_filter_to_apply"]
+        model_paths[filter_model] = filter_model
+
+    if old_req.get("block_nsfw"):
+        model_paths["nsfw_checker"] = "nsfw_checker"
+
+    # move the model params
+    if model_paths["stable-diffusion"]:
+        model_params["stable-diffusion"] = {
+            "clip_skip": bool(old_req.get("clip_skip", False)),
+            "convert_to_tensorrt": bool(old_req.get("convert_to_tensorrt", False)),
+            "trt_build_config": old_req.get(
+                "trt_build_config", {"batch_size_range": (1, 1), "dimensions_range": [(768, 1024)]}
+            ),
+        }
+
+    # move the filter params
+    if model_paths["realesrgan"]:
+        filter_params["realesrgan"] = {"scale": int(old_req.get("upscale_amount", 4))}
+    if model_paths["latent_upscaler"]:
+        filter_params["latent_upscaler"] = {
+            "prompt": old_req["prompt"],
+            "negative_prompt": old_req.get("negative_prompt"),
+            "seed": int(old_req.get("seed", 42)),
+            "num_inference_steps": int(old_req.get("latent_upscaler_steps", 10)),
+            "guidance_scale": 0,
+        }
+    if model_paths["codeformer"]:
+        filter_params["codeformer"] = {
+            "upscale_faces": bool(old_req.get("codeformer_upscale_faces", True)),
+            "codeformer_fidelity": float(old_req.get("codeformer_fidelity", 0.5)),
+        }
+
+    # set the filters
+    if old_req.get("block_nsfw"):
+        filters.append("nsfw_checker")
+
+    if model_paths["codeformer"]:
+        filters.append("codeformer")
+    elif model_paths["gfpgan"]:
+        filters.append("gfpgan")
+
+    if model_paths["realesrgan"]:
+        filters.append("realesrgan")
+    elif model_paths["latent_upscaler"]:
+        filters.append("latent_upscaler")
+
+    return new_req

ui/easydiffusion/utils/__init__.py

@@ -1,4 +1,5 @@
 import logging
+import hashlib
 
 log = logging.getLogger("easydiffusion")
 
@@ -6,3 +7,15 @@ from .save_utils import (
     save_images_to_disk,
     get_printable_request,
 )
+
+def sha256sum(filename):
+    sha256 = hashlib.sha256()
+    with open(filename, "rb") as f:
+        while True:
+            data = f.read(8192)  # Read in chunks of 8192 bytes
+            if not data:
+                break
+            sha256.update(data)
+
+    return sha256.hexdigest()
+

ui/easydiffusion/utils/save_utils.py

@@ -7,9 +7,17 @@ from datetime import datetime
 from functools import reduce
 
 from easydiffusion import app
-from easydiffusion.types import GenerateImageRequest, TaskData
+from easydiffusion.types import (
+    GenerateImageRequest,
+    TaskData,
+    RenderTaskData,
+    OutputFormatData,
+    SaveToDiskData,
+    ModelsData,
+)
 from numpy import base_repr
 from sdkit.utils import save_dicts, save_images
+from sdkit.models.model_loader.embeddings import get_embedding_token
 
 filename_regex = re.compile("[^a-zA-Z0-9._-]")
 img_number_regex = re.compile("([0-9]{5,})")
@@ -21,6 +29,9 @@ TASK_TEXT_MAPPING = {
     "seed": "Seed",
     "use_stable_diffusion_model": "Stable Diffusion model",
     "clip_skip": "Clip Skip",
+    "use_controlnet_model": "ControlNet model",
+    "control_filter_to_apply": "ControlNet Filter",
+    "control_alpha": "ControlNet Strength",
     "use_vae_model": "VAE model",
     "sampler_name": "Sampler",
     "width": "Width",
@@ -32,7 +43,7 @@ TASK_TEXT_MAPPING = {
     "lora_alpha": "LoRA Strength",
     "use_hypernetwork_model": "Hypernetwork model",
     "hypernetwork_strength": "Hypernetwork Strength",
-    "use_embedding_models": "Embedding models",
+    "use_embeddings_model": "Embedding models",
     "tiling": "Seamless Tiling",
     "use_face_correction": "Use Face Correction",
     "use_upscale": "Use Upscaling",
@@ -92,7 +103,7 @@ def format_folder_name(format: str, req: GenerateImageRequest, task_data: TaskDa
 def format_file_name(
     format: str,
     req: GenerateImageRequest,
-    task_data: TaskData,
+    task_data: RenderTaskData,
     now: float,
     batch_file_number: int,
     folder_img_number: ImageNumber,
@@ -114,12 +125,20 @@ def format_file_name(
     return filename_regex.sub("_", format)
 
 
-def save_images_to_disk(images: list, filtered_images: list, req: GenerateImageRequest, task_data: TaskData):
+def save_images_to_disk(
+    images: list,
+    filtered_images: list,
+    req: GenerateImageRequest,
+    task_data: RenderTaskData,
+    models_data: ModelsData,
+    output_format: OutputFormatData,
+    save_data: SaveToDiskData,
+):
     now = time.time()
     app_config = app.getConfig()
     folder_format = app_config.get("folder_format", "$id")
-    save_dir_path = os.path.join(task_data.save_to_disk_path, format_folder_name(folder_format, req, task_data))
-    metadata_entries = get_metadata_entries_for_request(req, task_data)
+    save_dir_path = os.path.join(save_data.save_to_disk_path, format_folder_name(folder_format, req, task_data))
+    metadata_entries = get_metadata_entries_for_request(req, task_data, models_data, output_format, save_data)
     file_number = calculate_img_number(save_dir_path, task_data)
     make_filename = make_filename_callback(
         app_config.get("filename_format", "$p_$tsb64"),
@@ -134,19 +153,19 @@ def save_images_to_disk(images: list, filtered_images: list, req: GenerateImageR
             filtered_images,
             save_dir_path,
             file_name=make_filename,
-            output_format=task_data.output_format,
-            output_quality=task_data.output_quality,
-            output_lossless=task_data.output_lossless,
+            output_format=output_format.output_format,
+            output_quality=output_format.output_quality,
+            output_lossless=output_format.output_lossless,
         )
-        if task_data.metadata_output_format:
-            for metadata_output_format in task_data.metadata_output_format.split(","):
+        if save_data.metadata_output_format:
+            for metadata_output_format in save_data.metadata_output_format.split(","):
                 if metadata_output_format.lower() in ["json", "txt", "embed"]:
                     save_dicts(
                         metadata_entries,
                         save_dir_path,
                         file_name=make_filename,
                         output_format=metadata_output_format,
-                        file_format=task_data.output_format,
+                        file_format=output_format.output_format,
                     )
     else:
         make_filter_filename = make_filename_callback(
@@ -162,39 +181,46 @@ def save_images_to_disk(images: list, filtered_images: list, req: GenerateImageR
             images,
             save_dir_path,
             file_name=make_filename,
-            output_format=task_data.output_format,
-            output_quality=task_data.output_quality,
-            output_lossless=task_data.output_lossless,
+            output_format=output_format.output_format,
+            output_quality=output_format.output_quality,
+            output_lossless=output_format.output_lossless,
         )
         save_images(
             filtered_images,
             save_dir_path,
             file_name=make_filter_filename,
-            output_format=task_data.output_format,
-            output_quality=task_data.output_quality,
-            output_lossless=task_data.output_lossless,
+            output_format=output_format.output_format,
+            output_quality=output_format.output_quality,
+            output_lossless=output_format.output_lossless,
         )
-        if task_data.metadata_output_format:
-            for metadata_output_format in task_data.metadata_output_format.split(","):
+        if save_data.metadata_output_format:
+            for metadata_output_format in save_data.metadata_output_format.split(","):
                 if metadata_output_format.lower() in ["json", "txt", "embed"]:
                     save_dicts(
                         metadata_entries,
                         save_dir_path,
                         file_name=make_filter_filename,
-                        output_format=task_data.metadata_output_format,
-                        file_format=task_data.output_format,
+                        output_format=metadata_output_format,
+                        file_format=output_format.output_format,
                     )
 
 
-def get_metadata_entries_for_request(req: GenerateImageRequest, task_data: TaskData):
-    metadata = get_printable_request(req, task_data)
+def get_metadata_entries_for_request(
+    req: GenerateImageRequest,
+    task_data: RenderTaskData,
+    models_data: ModelsData,
+    output_format: OutputFormatData,
+    save_data: SaveToDiskData,
+):
+    metadata = get_printable_request(req, task_data, models_data, output_format, save_data)
 
     # if text, format it in the text format expected by the UI
-    is_txt_format = task_data.metadata_output_format and "txt" in task_data.metadata_output_format.lower().split(",")
+    is_txt_format = save_data.metadata_output_format and "txt" in save_data.metadata_output_format.lower().split(",")
     if is_txt_format:
+
         def format_value(value):
             if isinstance(value, list):
-                return ", ".join([ str(it) for it in value ])
+                return ", ".join([str(it) for it in value])
             return value
 
         metadata = {
@@ -208,12 +234,20 @@ def get_metadata_entries_for_request(req: GenerateImageRequest, task_data: TaskD
     return entries
 
 
-def get_printable_request(req: GenerateImageRequest, task_data: TaskData):
+def get_printable_request(
+    req: GenerateImageRequest,
+    task_data: RenderTaskData,
+    models_data: ModelsData,
+    output_format: OutputFormatData,
+    save_data: SaveToDiskData,
+):
     req_metadata = req.dict()
     task_data_metadata = task_data.dict()
+    task_data_metadata.update(output_format.dict())
+    task_data_metadata.update(save_data.dict())
 
     app_config = app.getConfig()
-    using_diffusers = app_config.get("test_diffusers", False)
+    using_diffusers = app_config.get("use_v3_engine", True)
 
     # Save the metadata in the order defined in TASK_TEXT_MAPPING
     metadata = {}
@@ -222,25 +256,13 @@ def get_printable_request(req: GenerateImageRequest, task_data: TaskData):
             metadata[key] = req_metadata[key]
         elif key in task_data_metadata:
             metadata[key] = task_data_metadata[key]
-        elif key == "use_embedding_models" and using_diffusers:
-            embeddings_extensions = {".pt", ".bin", ".safetensors"}
-            def scan_directory(directory_path: str):
-                used_embeddings = []
-                for entry in os.scandir(directory_path):
-                    if entry.is_file():
-                        entry_extension = os.path.splitext(entry.name)[1]
-                        if entry_extension not in embeddings_extensions:
-                            continue
 
-                        embedding_name_regex = regex.compile(r"(^|[\s,])" + regex.escape(os.path.splitext(entry.name)[0]) + r"([+-]*$|[\s,]|[+-]+[\s,])")
-                        if embedding_name_regex.search(req.prompt) or embedding_name_regex.search(req.negative_prompt):
-                            used_embeddings.append(entry.path)
-                    elif entry.is_dir():
-                        used_embeddings.extend(scan_directory(entry.path))
-                return used_embeddings
-            used_embeddings = scan_directory(os.path.join(app.MODELS_DIR, "embeddings"))
-            metadata["use_embedding_models"] = used_embeddings if len(used_embeddings) > 0 else None
-    
+        if key == "use_embeddings_model" and task_data_metadata[key] and using_diffusers:
+            embeddings_used = models_data.model_paths["embeddings"]
+            embeddings_used = embeddings_used if isinstance(embeddings_used, list) else [embeddings_used]
+
+            metadata["use_embeddings_model"] = embeddings_used if len(embeddings_used) > 0 else None
+
     # Clean up the metadata
     if req.init_image is None and "prompt_strength" in metadata:
         del metadata["prompt_strength"]
@@ -252,9 +274,26 @@ def get_printable_request(req: GenerateImageRequest, task_data: TaskData):
         del metadata["lora_alpha"]
     if task_data.use_upscale != "latent_upscaler" and "latent_upscaler_steps" in metadata:
         del metadata["latent_upscaler_steps"]
+    if task_data.use_controlnet_model is None and "control_filter_to_apply" in metadata:
+        del metadata["control_filter_to_apply"]
 
-    if not using_diffusers:
-        for key in (x for x in ["use_lora_model", "lora_alpha", "clip_skip", "tiling", "latent_upscaler_steps"] if x in metadata):
+    if using_diffusers:
+        for key in (x for x in ["use_hypernetwork_model", "hypernetwork_strength"] if x in metadata):
+            del metadata[key]
+    else:
+        for key in (
+            x
+            for x in [
+                "use_lora_model",
+                "lora_alpha",
+                "clip_skip",
+                "tiling",
+                "latent_upscaler_steps",
+                "use_controlnet_model",
+                "control_filter_to_apply",
+            ]
+            if x in metadata
+        ):
             del metadata[key]
 
     return metadata
@@ -263,7 +302,7 @@ def get_printable_request(req: GenerateImageRequest, task_data: TaskData):
 def make_filename_callback(
     filename_format: str,
     req: GenerateImageRequest,
-    task_data: TaskData,
+    task_data: RenderTaskData,
     folder_img_number: int,
     suffix=None,
     now=None,
@@ -280,7 +319,7 @@ def make_filename_callback(
     return make_filename
 
 
-def _calculate_img_number(save_dir_path: str, task_data: TaskData):
+def _calculate_img_number(save_dir_path: str, task_data: RenderTaskData):
     def get_highest_img_number(accumulator: int, file: os.DirEntry) -> int:
         if not file.is_file:
             return accumulator
@@ -324,5 +363,5 @@ def _calculate_img_number(save_dir_path: str, task_data: TaskData):
 _calculate_img_number.session_img_numbers = {}
 
 
-def calculate_img_number(save_dir_path: str, task_data: TaskData):
+def calculate_img_number(save_dir_path: str, task_data: RenderTaskData):
     return ImageNumber(lambda: _calculate_img_number(save_dir_path, task_data))

ui/index.html

@@ -17,12 +17,16 @@
     <link rel="stylesheet" href="/media/css/searchable-models.css">
     <link rel="stylesheet" href="/media/css/image-modal.css">
     <link rel="stylesheet" href="/media/css/plugins.css">
+    <link rel="stylesheet" href="/media/css/animations.css">
+    <link rel="stylesheet" href="/media/css/croppr.css" rel="stylesheet"/>
     <link rel="manifest" href="/media/manifest.webmanifest">
     <script src="/media/js/jquery-3.6.1.min.js"></script>
     <script src="/media/js/jquery-confirm.min.js"></script>
     <script src="/media/js/jszip.min.js"></script>
     <script src="/media/js/FileSaver.min.js"></script>
     <script src="/media/js/marked.min.js"></script>
+    <script src="/media/js/croppr.js"></script>
+    <script src="/media/js/exif-reader.js"></script>
 </head>
 <body>
 <div id="container">
@@ -31,7 +35,7 @@
             <h1>
                 <img id="logo_img" src="/media/images/icon-512x512.png" >
                 Easy Diffusion
-                <small><span id="version">v2.5.46</span> <span id="updateBranchLabel"></span></small>
+                <small><span id="version">v3.0.9c</span> <span id="updateBranchLabel"></span></small>
             </h1>
         </div>
         <div id="server-status">
@@ -58,7 +62,14 @@
                 <div id="editor-inputs-prompt" class="row">
                     <div id="prompt-toolbar" class="split-toolbar">
                         <div id="prompt-toolbar-left" class="toolbar-left">
-                            <label for="prompt"><b>Enter Prompt</b></label> <small>or</small> <button id="promptsFromFileBtn" class="tertiaryButton smallButton">Load from a file</button>
+                            <label for="prompt"><b>Enter Prompt</b>
+                                <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip right">
+                                    You can type your prompts in the below textbox or load them from a file. You can also
+                                    reload tasks from metadata embedded in PNG, WEBP and JPEG images (enable embedding from the Settings).
+                                </span></i>
+                            </label>
+                            <small>or</small>
+                            <button id="promptsFromFileBtn" class="tertiaryButton smallButton">Load from a file</button>
                         </div>
                         <div id="prompt-toolbar-right" class="toolbar-right">
                             <button id="image-modifier-dropdown" class="tertiaryButton smallButton">+ Image Modifiers</button>
@@ -72,7 +83,7 @@
                         <a href="https://github.com/easydiffusion/easydiffusion/wiki/Writing-prompts#negative-prompts" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top">Click to learn more about Negative Prompts</span></i></a>
                         <small>(optional)</small>
                     </label>
-                    <button id="negative-embeddings-button" class="tertiaryButton smallButton displayNone">+ Embedding</button>
+                    <button id="negative-embeddings-button" class="tertiaryButton smallButton displayNone">+ Negative Embedding</button>
                     <div class="collapsible-content">
                         <textarea id="negative_prompt" name="negative_prompt" placeholder="list the things to remove from the image (e.g. fog, green)"></textarea>
                     </div>
@@ -80,10 +91,15 @@
 
                 <div id="editor-inputs-init-image" class="row">
                     <label for="init_image">Initial Image (img2img) <small>(optional)</small> </label>
+                    <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top">
+                        Add img2img source image using the Browse button, via drag & drop from external file or browser image (incl.
+                        rendered image) or by pasting an image from the clipboard using Ctrl+V.<br /><br />
+                        You may also reload the metadata embedded in a PNG, WEBP or JPEG image (enable embedding from the Settings).
+                    </span></i>
 
                     <div id="init_image_preview_container" class="image_preview_container">
-                        <div id="init_image_wrapper">
-                            <img id="init_image_preview" src="" crossorigin="anonymous" />
+                        <div id="init_image_wrapper" class="preview_image_wrapper">
+                            <img id="init_image_preview" class="image_preview" src="" crossorigin="anonymous" />
                             <span id="init_image_size_box" class="img_bottom_label"></span>
                             <button class="init_image_clear image_clear_btn"><i class="fa-solid fa-xmark"></i></button>
                         </div>
@@ -108,6 +124,7 @@
                     </div>
 
                     <div id="apply_color_correction_setting" class="pl-5"><input id="apply_color_correction" name="apply_color_correction" type="checkbox"> <label for="apply_color_correction">Preserve color profile <small>(helps during inpainting)</small></label></div>
+                    <div id="strict_mask_border_setting" class="pl-5"><input id="strict_mask_border" name="strict_mask_border" type="checkbox"> <label for="strict_mask_border">Strict Mask Border <small>(won't modify outside the mask, but the mask border might be visible)</small></label></div>
 
                 </div>
 
@@ -138,13 +155,25 @@
                 <div id="editor-settings-entries" class="collapsible-content">
                     <div><table>
                     <tr><b class="settings-subheader">Image Settings</b></tr>
-                    <tr class="pl-5"><td><label for="seed">Seed:</label></td><td><input id="seed" name="seed" size="10" value="0" onkeypress="preventNonNumericalInput(event)"> <input id="random_seed" name="random_seed" type="checkbox" checked><label for="random_seed">Random</label></td></tr>
-                    <tr class="pl-5"><td><label for="num_outputs_total">Number of Images:</label></td><td><input id="num_outputs_total" name="num_outputs_total" value="1" size="1" onkeypress="preventNonNumericalInput(event)"> <label><small>(total)</small></label> <input id="num_outputs_parallel" name="num_outputs_parallel" value="1" size="1" onkeypress="preventNonNumericalInput(event)"> <label for="num_outputs_parallel"><small>(in parallel)</small></label></td></tr>
+                    <tr class="pl-5"><td><label for="seed">Seed:</label></td><td><input id="seed" name="seed" size="10" value="0" onkeypress="preventNonNumericalInput(event)" inputmode="numeric"> <input id="random_seed" name="random_seed" type="checkbox" checked><label for="random_seed">Random</label></td></tr>
+                    <tr class="pl-5"><td><label for="num_outputs_total">Number of Images:</label></td>
+                        <td><input id="num_outputs_total" name="num_outputs_total" value="1" type="number" value="1" min="1" step="1" onkeypres"="preventNonNumericalInput(event)" inputmode="numeric">
+                        <label><small>(total)</small></label>
+                        <input id="num_outputs_parallel" name="num_outputs_parallel" value="1" type="number" value="1" min="1" step="1" onkeypress="preventNonNumericalInput(event)" inputmode="numeric">
+                        <label id="num_outputs_parallel_label" for="num_outputs_parallel"><small>(in parallel)</small></label></td>
+                    </tr>
                     <tr class="pl-5"><td><label for="stable_diffusion_model">Model:</label></td><td class="model-input">
                         <input id="stable_diffusion_model" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" />
                         <button id="reload-models" class="secondaryButton reloadModels"><i class='fa-solid fa-rotate'></i></button>
                         <a href="https://github.com/easydiffusion/easydiffusion/wiki/Custom-Models" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about custom models</span></i></a>
                     </td></tr>
+                    <tr class="pl-5 displayNone" id="enable_trt_config">
+                        <td><label for="convert_to_tensorrt">Enable TensorRT:</label></td>
+                        <td class="diffusers-restart-needed">
+                            <input id="convert_to_tensorrt" name="convert_to_tensorrt" type="checkbox">
+                            <!-- <label><small>Takes upto 20 mins the first time</small></label> -->
+                        </td>
+                    </tr>
                     <tr class="pl-5 displayNone" id="clip_skip_config">
                         <td><label for="clip_skip">Clip Skip:</label></td>
                         <td class="diffusers-restart-needed">
@@ -152,6 +181,65 @@
                             <a href="https://github.com/easydiffusion/easydiffusion/wiki/Clip-Skip" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about Clip Skip</span></i></a>
                         </td>
                     </tr>
+                    <tr id="controlnet_model_container" class="pl-5">
+                        <td><label for="controlnet_model">ControlNet Image:</label></td>
+                        <td class="diffusers-restart-needed">
+                            <div id="control_image_wrapper" class="preview_image_wrapper">
+                                <img id="control_image_preview" class="image_preview" src="" crossorigin="anonymous" />
+                                <span id="control_image_size_box" class="img_bottom_label"></span>
+                                <button class="control_image_clear image_clear_btn"><i class="fa-solid fa-xmark"></i></button>
+                            </div>
+                            <input id="control_image" name="control_image" type="file" />
+                            <a href="https://github.com/easydiffusion/easydiffusion/wiki/ControlNet" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about ControlNets</span></i></a>
+                            <div id="controlnet_config" class="displayNone">
+                                <label><small>Filter to apply:</small></label>
+                                <select id="control_image_filter">
+                                    <option value="">None</option>
+                                    <optgroup label="Pose">
+                                        <option value="openpose">OpenPose (*)</option>
+                                        <option value="openpose_face">OpenPose face</option>
+                                        <option value="openpose_faceonly">OpenPose face-only</option>
+                                        <option value="openpose_hand">OpenPose hand</option>
+                                        <option value="openpose_full">OpenPose full</option>
+                                    </optgroup>
+                                    <optgroup label="Outline">
+                                        <option value="canny">Canny (*)</option>
+                                        <option value="mlsd">Straight lines</option>
+                                        <option value="scribble_hed">Scribble hed (*)</option>
+                                        <option value="scribble_hedsafe">Scribble hedsafe</option>
+                                        <option value="scribble_pidinet">Scribble pidinet</option>
+                                        <option value="scribble_pidsafe">Scribble pidsafe</option>
+                                        <option value="softedge_hed">Softedge hed</option>
+                                        <option value="softedge_hedsafe">Softedge hedsafe</option>
+                                        <option value="softedge_pidinet">Softedge pidinet</option>
+                                        <option value="softedge_pidsafe">Softedge pidsafe</option>
+                                    </optgroup>
+                                    <optgroup label="Depth">
+                                        <option value="normal_bae">Normal bae (*)</option>
+                                        <option value="depth_midas">Depth midas</option>
+                                        <option value="depth_zoe">Depth zoe</option>
+                                        <option value="depth_leres">Depth leres</option>
+                                        <option value="depth_leres++">Depth leres++</option>
+                                    </optgroup>
+                                    <optgroup label="Line art">
+                                        <option value="lineart_coarse">Lineart coarse</option>
+                                        <option value="lineart_realistic">Lineart realistic</option>
+                                        <option value="lineart_anime">Lineart anime</option>
+                                    </optgroup>
+                                    <optgroup label="Misc">
+                                        <option value="shuffle">Shuffle</option>
+                                        <option value="segment">Segment</option>
+                                    </optgroup>
+                                </select>
+                                <br/>
+                                <label for="controlnet_model"><small>Model:</small></label> <input id="controlnet_model" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" />
+                                <br/>
+                                <label><small>Will download the necessary models, the first time.</small></label>
+                                <br/>
+                                <label for="controlnet_alpha_slider"><small>Strength:</small></label> <input id="controlnet_alpha_slider" name="controlnet_alpha_slider" class="editor-slider" value="10" type="range" min="0" max="10"> <input id="controlnet_alpha" name="controlnet_alpha" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="decimal">
+                            </div>
+                        </td>
+                    </tr>
                     <tr class="pl-5"><td><label for="vae_model">Custom VAE:</label></td><td>
                         <input id="vae_model" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" />
                         <a href="https://github.com/easydiffusion/easydiffusion/wiki/VAE-Variational-Auto-Encoder" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about VAEs</span></i></a>
@@ -180,17 +268,17 @@
                             <option value="unipc_tu_2" class="k_diffusion-only">UniPC TU 2</option>
                             <option value="unipc_tq" class="k_diffusion-only">UniPC TQ</option>
                         </select>
-                        <a href="https://github.com/easydiffusion/easydiffusion/wiki/How-to-Use#samplers" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about samplers</span></i></a>
+                        <a href="https://github.com/easydiffusion/easydiffusion/wiki/Samplers" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about samplers</span></i></a>
                     </td></tr>
-                    <tr class="pl-5"><td><label>Image Size: </label></td><td>
+                    <tr class="pl-5"><td><label>Image Size: </label></td><td id="image-size-options">
                         <select id="width" name="width" value="512">
-                            <option value="128">128 (*)</option>
+                            <option value="128">128</option>
                             <option value="192">192</option>
-                            <option value="256">256 (*)</option>
+                            <option value="256">256</option>
                             <option value="320">320</option>
                             <option value="384">384</option>
                             <option value="448">448</option>
-                            <option value="512" selected>512 (*)</option>
+                            <option value="512" selected="">512 (*)</option>
                             <option value="576">576</option>
                             <option value="640">640</option>
                             <option value="704">704</option>
@@ -204,15 +292,18 @@
                             <option value="1792">1792</option>
                             <option value="2048">2048</option>
                         </select>
-                        <label for="width"><small>(width)</small></label>
+                        <label id="widthLabel" for="width"><small><span>(width)</span></small></label>
+                        <div class="tooltip-container">
+                            <span id="swap-width-height" class="clickable smallButton" style="margin-left: 2px; margin-right:2px;"><i class="fa-solid fa-right-left"><span class="simple-tooltip top-left"> Swap width and height </span></i></span>
+                        </div>
                         <select id="height" name="height" value="512">
-                            <option value="128">128 (*)</option>
+                            <option value="128">128</option>
                             <option value="192">192</option>
-                            <option value="256">256 (*)</option>
+                            <option value="256">256</option>
                             <option value="320">320</option>
                             <option value="384">384</option>
                             <option value="448">448</option>
-                            <option value="512" selected>512 (*)</option>
+                            <option value="512" selected="">512 (*)</option>
                             <option value="576">576</option>
                             <option value="640">640</option>
                             <option value="704">704</option>
@@ -226,27 +317,52 @@
                             <option value="1792">1792</option>
                             <option value="2048">2048</option>
                         </select>
-                        <label for="height"><small>(height)</small></label>
+                        <label id="heightLabel" for="height"><small><span>(height)</span></small></label>
+                        <div id="recent-resolutions-container">
+                            <span id="recent-resolutions-button" class="clickable"><i class="fa-solid fa-sliders"><span class="simple-tooltip top-left"> Advanced sizes </span></i></span>
+                            <div id="recent-resolutions-popup" class="displayNone">
+                                <small>Custom size:</small><br>
+                                <input id="custom-width" name="custom-width" type="number" min="128" value="512" onkeypress="preventNonNumericalInput(event)" inputmode="numeric">
+                                &times;
+                                <input id="custom-height" name="custom-height" type="number" min="128" value="512" onkeypress="preventNonNumericalInput(event)" inputmode="numeric"><br>
+                                <small>Resize:</small><br>
+                                <input id="resize-slider" name="resize-slider" class="editor-slider" value="1" type="range" min="0.4" max="2" step="0.005" style="width:100%;"><br>
+                                <div id="enlarge-buttons"><button data-factor="0.5" class="tertiaryButton smallButton">×0.5</button>&nbsp;<button data-factor="1.2" class="tertiaryButton smallButton">×1.2</button>&nbsp;<button data-factor="1.5" class="tertiaryButton smallButton">×1.5</button>&nbsp;<button data-factor="2" class="tertiaryButton smallButton">×2</button>&nbsp;<button data-factor="3" class="tertiaryButton smallButton">×3</button></div>
+
+                                <div class="two-column">
+                                    <div class="left-column">
+                                        <small>Recently&nbsp;used:</small><br>
+                                        <div id="recent-resolution-list">
+                                        </div>
+                                    </div>
+                                    <div class="right-column">
+                                        <small>Common&nbsp;sizes:</small><br>
+                                        <div id="common-resolution-list">
+                                        </div>
+                                    </div>
+
+                                </div>
+                            </div>
+                        </div>
                         <div id="small_image_warning" class="displayNone">Small image sizes can cause bad image quality</div>
                     </td></tr>
-                    <tr class="pl-5"><td><label for="num_inference_steps">Inference Steps:</label></td><td> <input id="num_inference_steps" name="num_inference_steps" type="number" min="1" step="1" style="width: 42pt" value="25" onkeypress="preventNonNumericalInput(event)"></td></tr>
-                    <tr class="pl-5"><td><label for="guidance_scale_slider">Guidance Scale:</label></td><td> <input id="guidance_scale_slider" name="guidance_scale_slider" class="editor-slider" value="75" type="range" min="11" max="500"> <input id="guidance_scale" name="guidance_scale" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)"></td></tr>
-                    <tr id="prompt_strength_container" class="pl-5"><td><label for="prompt_strength_slider">Prompt Strength:</label></td><td> <input id="prompt_strength_slider" name="prompt_strength_slider" class="editor-slider" value="80" type="range" min="0" max="99"> <input id="prompt_strength" name="prompt_strength" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)"><br/></td></tr>
+                    <tr class="pl-5"><td><label for="num_inference_steps">Inference Steps:</label></td><td> <input id="num_inference_steps" name="num_inference_steps" type="number" min="1" step="1" style="width: 42pt" value="25" onkeypress="preventNonNumericalInput(event)" inputmode="numeric"></td></tr>
+                    <tr class="pl-5"><td><label for="guidance_scale_slider">Guidance Scale:</label></td><td> <input id="guidance_scale_slider" name="guidance_scale_slider" class="editor-slider" value="75" type="range" min="11" max="500"> <input id="guidance_scale" name="guidance_scale" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="decimal"></td></tr>
+                    <tr id="prompt_strength_container" class="pl-5"><td><label for="prompt_strength_slider">Prompt Strength:</label></td><td> <input id="prompt_strength_slider" name="prompt_strength_slider" class="editor-slider" value="80" type="range" min="0" max="99"> <input id="prompt_strength" name="prompt_strength" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="decimal"><br/></td></tr>
                     <tr id="lora_model_container" class="pl-5">
                         <td>
                             <label for="lora_model">LoRA:</label>
                         </td>
                         <td class="diffusers-restart-needed">
-                            <div class="model_entries"></div>
-                            <button class="add_model_entry"><i class="fa-solid fa-plus"></i> add another LoRA</button>
+                            <div id="lora_model" data-path=""></div>
                         </td>
                     </tr>
-                    <tr class="pl-5"><td><label for="hypernetwork_model">Hypernetwork:</label></td><td>
+                    <tr id="hypernetwork_model_container" class="pl-5"><td><label for="hypernetwork_model">Hypernetwork:</label></td><td>
                         <input id="hypernetwork_model" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" />
                     </td></tr>
                     <tr id="hypernetwork_strength_container" class="pl-5">
                         <td><label for="hypernetwork_strength_slider">Hypernetwork Strength:</label></td>
-                        <td> <input id="hypernetwork_strength_slider" name="hypernetwork_strength_slider" class="editor-slider" value="100" type="range" min="0" max="100"> <input id="hypernetwork_strength" name="hypernetwork_strength" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)"><br/></td>
+                        <td> <input id="hypernetwork_strength_slider" name="hypernetwork_strength_slider" class="editor-slider" value="100" type="range" min="0" max="100"> <input id="hypernetwork_strength" name="hypernetwork_strength" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="decimal"><br/></td>
                     </tr>
                     <tr id="tiling_container" class="pl-5">
                         <td><label for="tiling">Seamless Tiling:</label></td>
@@ -271,8 +387,15 @@
                         </span>
                     </td></tr>
                     <tr class="pl-5" id="output_quality_row"><td><label for="output_quality">Image Quality:</label></td><td>
-                    <input id="output_quality_slider" name="output_quality" class="editor-slider" value="75" type="range" min="10" max="95"> <input id="output_quality" name="output_quality" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)">
+                    <input id="output_quality_slider" name="output_quality" class="editor-slider" value="75" type="range" min="10" max="95"> <input id="output_quality" name="output_quality" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="numeric">
                     </td></tr>
+                    <tr class="pl-5">
+                        <td><label for="tiling">Enable VAE Tiling:</label></td>
+                        <td class="diffusers-restart-needed">
+                            <input id="enable_vae_tiling" name="enable_vae_tiling" type="checkbox" checked>
+                            <label><small>Optimizes memory for larger images</small></label>
+                        </td>
+                    </tr>
                     </table></div>
 
                     <div><ul>
@@ -281,7 +404,7 @@
                     <li class="pl-5" id="use_face_correction_container">
                         <input id="use_face_correction" name="use_face_correction" type="checkbox"> <label for="use_face_correction">Fix incorrect faces and eyes</label> <div style="display:inline-block;"><input id="gfpgan_model" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" /></div>
                         <table id="codeformer_settings" class="displayNone sub-settings">
-                            <tr class="pl-5"><td><label for="codeformer_fidelity_slider">Strength:</label></td><td><input id="codeformer_fidelity_slider" name="codeformer_fidelity_slider" class="editor-slider" value="5" type="range" min="0" max="10"> <input id="codeformer_fidelity" name="codeformer_fidelity" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)"></td></tr>
+                            <tr class="pl-5"><td><label for="codeformer_fidelity_slider">Strength:</label></td><td><input id="codeformer_fidelity_slider" name="codeformer_fidelity_slider" class="editor-slider" value="5" type="range" min="0" max="10"> <input id="codeformer_fidelity" name="codeformer_fidelity" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="decimal"></td></tr>
                             <tr class="pl-5"><td><label for="codeformer_upscale_faces">Upscale Faces:</label></td><td><input id="codeformer_upscale_faces" name="codeformer_upscale_faces" type="checkbox" checked> <label><small>(improves the resolution of faces)</small></label></td></tr>
                         </table>
                     </li>
@@ -298,7 +421,7 @@
                             <option value="latent_upscaler">Latent Upscaler 2x</option>
                         </select>
                         <table id="latent_upscaler_settings" class="displayNone sub-settings">
-                            <tr class="pl-5"><td><label for="latent_upscaler_steps_slider">Upscaling Steps:</label></td><td><input id="latent_upscaler_steps_slider" name="latent_upscaler_steps_slider" class="editor-slider" value="10" type="range" min="1" max="50"> <input id="latent_upscaler_steps" name="latent_upscaler_steps" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)"></td></tr>
+                            <tr class="pl-5"><td><label for="latent_upscaler_steps_slider">Upscaling Steps:</label></td><td><input id="latent_upscaler_steps_slider" name="latent_upscaler_steps_slider" class="editor-slider" value="10" type="range" min="1" max="50"> <input id="latent_upscaler_steps" name="latent_upscaler_steps" size="4" pattern="^[0-9\.]+$" onkeypress="preventNonNumericalInput(event)" inputmode="numeric"></td></tr>
                         </table>
                     </li>
                     <li class="pl-5"><input id="show_only_filtered_image" name="show_only_filtered_image" type="checkbox" checked> <label for="show_only_filtered_image">Show only the corrected/upscaled image</label></li>
@@ -306,7 +429,7 @@
                 </div>
             </div>
 
-            <label><small><b>Note:</b> The Image Modifiers section has moved to the <code>+ Image Modifiers</code> button at the top, just below the Prompt textbox.</small></label>
+            <label><small><b>Note:</b> The Image Modifiers section has moved to the <code>+ Image Modifiers</code> button at the top, just above the Prompt textbox.</small></label>
         </div>
 
         <div id="preview" class="col-free">
@@ -320,7 +443,7 @@
             <div id="preview-content">
                 <div id="preview-tools" class="displayNone">
                     <button id="clear-all-previews" class="secondaryButton"><i class="fa-solid fa-trash-can icon"></i> Clear All</button>
-                    <button class="tertiaryButton" id="show-download-popup"><i class="fa-solid fa-download"></i> Download images</button>
+                    <button class="tertiaryButton" id="show-download-popup"><i class="fa-solid fa-download"></i><span> Download images</span></button>
                     <div class="display-settings">
                         <button id="undo" class="displayNone primaryButton">
                             Undo <i class="fa-solid fa-rotate-left icon"></i>
@@ -343,12 +466,15 @@
                         <div class="dropdown-content">
                             <div class="dropdown-item">
                                 <input id="thumbnail_size" name="thumbnail_size" class="editor-slider" type="range" value="70" min="5" max="200" oninput="sliderUpdate(event)"> 
-                                <input id="thumbnail_size-input" name="thumbnail_size-input" size="3" value="70" pattern="^[0-9.]+$" onkeypress="preventNonNumericalInput(event)" oninput="sliderUpdate(event)">&nbsp;%
+                                <input id="thumbnail_size-input" name="thumbnail_size-input" size="3" value="70" pattern="^[0-9.]+$" onkeypress="preventNonNumericalInput(event)" oninput="sliderUpdate(event)" inputmode="numeric">&nbsp;%
                             </div>
                         </div>
                     </div>
                     <div class="clearfix" style="clear: both;"></div>
                 </div>
+                <div id="supportBanner" class="displayNone">
+                    If you found this project useful and want to help keep it alive, please consider <a href="https://ko-fi.com/easydiffusion" target="_blank">buying me a coffee</a> to help cover the cost of development and maintenance! Thanks for your support!
+                </div>
             </div>
         </div>
         </div>
@@ -359,6 +485,13 @@
                 <div class="parameters-table" id="system-settings-table"></div>
                 <br/>
                 <button id="save-system-settings-btn" class="primaryButton">Save</button>
+                <div id="install-extras-container" class="displayNone">
+                    <br/>
+                    <div id="install-extras">
+                        <h3><i class="fa fa-cubes-stacked"></i> Optional Packages</h3>
+                        <div class="parameters-table" id="system-settings-install-extras-table"></div>
+                    </div>
+                </div>
                 <br/><br/>
                 <div id="share-easy-diffusion">
                    <h3><i class="fa fa-user-group"></i> Share Easy Diffusion</h3>
@@ -386,28 +519,44 @@
                 <div class="float-container">
                     <div class="float-child">
                         <h1>Help</h1>
-                        <ul id="help-links">
-                            <li><span class="help-section">Using the software</span>
+                        <div id="help-links">
+                            <h4><span class="help-section"><b>Basics</b></span></h4>
                             <ul>
-                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/How-To-Use" target="_blank"><i class="fa-solid fa-book fa-fw"></i> How to use</a>
-                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/UI-Overview" target="_blank"><i class="fa-solid fa-list fa-fw"></i> UI Overview</a>
-                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Writing-Prompts" target="_blank"><i class="fa-solid fa-pen-to-square fa-fw"></i> Writing prompts</a>
-                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Inpainting" target="_blank"><i class="fa-solid fa-paintbrush fa-fw"></i> Inpainting</a>
-                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Run-on-Multiple-GPUs" target="_blank"><i class="fa-solid fa-paintbrush fa-fw"></i> Run on Multiple GPUs</a>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/How-To-Use" target="_blank">How to use</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Writing-Prompts" target="_blank">Writing prompts</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Image-Modifiers" target="_blank">Image Modifiers</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Inpainting" target="_blank">Inpainting</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Samplers" target="_blank">Samplers</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/UI-Overview" target="_blank">Summary of every UI option</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Troubleshooting" target="_blank">Common error messages (and solutions)</a></li>
                             </ul>
   
-                            <li><span class="help-section">Installation</span>
+                            <h4><span class="help-section"><b>Intermediate</b></span></h4>
                             <ul>
-                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Troubleshooting" target="_blank"><i class="fa-solid fa-circle-question fa-fw"></i> Troubleshooting</a>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Custom-Models" target="_blank">Custom Models</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Prompt-Syntax" target="_blank">Prompt Syntax (weights, emphasis etc)</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/UI-Plugins" target="_blank">UI Plugins</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Embeddings" target="_blank">Embeddings</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/LoRA" target="_blank">LoRA</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/SDXL" target="_blank">SDXL</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/ControlNet" target="_blank">ControlNet</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Seamless-Tiling" target="_blank">Seamless Tiling</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/xFormers" target="_blank">xFormers</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/The-beta-channel" target="_blank">The beta channel</a></li>
                             </ul>
   
-                            <li><span class="help-section">Downloadable Content</span>
+                            <h4><span class="help-section"><b>Advanced topics</b></span></h4>
                             <ul>
-                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Custom-Models" target="_blank"><i class="fa-solid fa-images fa-fw"></i> Custom Models</a>
-                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/UI-Plugins" target="_blank"><i class="fa-solid fa-puzzle-piece fa-fw"></i> UI Plugins</a>
-                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/VAE-Variational-Auto-Encoder" target="_blank"><i class="fa-solid fa-hand-sparkles fa-fw"></i> VAE Variational Auto Encoder</a>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Run-on-Multiple-GPUs" target="_blank">Run on Multiple GPUs</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Model-Merging" target="_blank">Model Merging</a></li>
+                                <li> <a href="https://github.com/easydiffusion/easydiffusion/wiki/Custom-Modifiers" target="_blank">Custom Modifiers</a></li>
                             </ul>
-                        </ul>
+
+                            <h4><span class="help-section"><b>Misc</b></span></h4>
+                            <ul>
+                                <li> <a href="https://theally.notion.site/The-Definitive-Stable-Diffusion-Glossary-1d1e6d15059c41e6a6b4306b4ecd9df9" target="_blank">Glossary of Stable Diffusion related terms</a></li>
+                            </ul>
+                        </div>
                     </div>
   
                     <div class="float-child">
@@ -579,6 +728,8 @@
                 <span>
             </div>
             <div id="embeddings-dialog-header-right">
+                <button id="add-embeddings-thumb" class="tertiaryButton smallButton" style="background-color: var(--background-color4);"><i class="fa-solid fa-folder-plus"></i> Add thumbnail</button>
+                <input id="add-embeddings-thumb-input" name="add-embeddings-thumb-input" type="file" class="displayNone">
                 <i id="embeddings-dialog-close-button" class="fa-solid fa-xmark fa-lg"></i>
             </div>
         </div>
@@ -588,7 +739,16 @@
                 <span class="embeddings-action-text">Expand Categories</span>
             </button>
             <i class="fa-solid fa-magnifying-glass"></i>
-            <input id="embeddings-search-box" type="text" spellcheck="false" autocomplete="off" placeholder="Search...">
+            <input id="embeddings-search-box" type="text" spellcheck="false" autocomplete="off" placeholder="Search..." inputmode="search">
+            <label for="embedding-card-size-selector"><small>Thumbnail Size:</small></label>
+            <select id="embedding-card-size-selector" name="embedding-card-size-selector">
+                <option value="-2">0</option>
+                <option value="-1" selected>1</option>
+                <option value="0">2</option>
+                <option value="1">3</option>
+                <option value="2">4</option>
+                <option value="3">5</option>
+            </select>
             <span style="float:right;"><label>Mode:</label>&nbsp;<select id="embeddings-mode"><option value="insert">Insert at cursor position</option><option value="append">Append at the end</option></select>
         </div>
         <div id="embeddings-list">
@@ -596,6 +756,34 @@
         </div>
     </dialog>
 
+    <dialog id="use-as-thumb-dialog">
+        <div id="use-as-thumb-dialog-header" class="dialog-header">
+            <div id="use-as-thumb-dialog-header-left" class="dialog-header-left">
+                <h4>Use as thumbnail</h4>
+                <span>Use a pictures as thumbnail for embeddings, LORAs, etc.</span>
+            </div>
+            <div id="use-as-thumb-dialog-header-right">
+                <i id="use-as-thumb-dialog-close-button" class="fa-solid fa-xmark fa-lg"></i>
+            </div>
+        </div>
+        <div>
+            <div class="use-as-thumb-grid">
+                <div class="use-as-thumb-preview">
+                    <div id="use-as-thumb-img-container"><img id="use-as-thumb-image" src="/media/images/noimg.png" width="512" height="512"></div>
+                </div>
+                <div class="use-as-thumb-select">
+                    <label for="use-as-thumb-select">Use the thumbnail for:</label><br>
+                    <select id="use-as-thumb-select" size="16" multiple>
+                    </select>
+                </div>
+                <div class="use-as-thumb-buttons">
+                    <button class="tertiaryButton" id="use-as-thumb-save">Save thumbnail</button>
+                    <button class="tertiaryButton" id="use-as-thumb-cancel">Cancel</button>
+                </div>
+            </div>
+        </div>
+    </dialog>
+
     <div id="image-editor" class="popup image-editor-popup">
         <div>
             <i class="close-button fa-solid fa-xmark"></i>
@@ -631,13 +819,13 @@
     <div id="footer-spacer"></div>
     <div id="footer">
         <div class="line-separator">&nbsp;</div>
-        <p>If you found this project useful and want to help keep it alive, please <a href="https://ko-fi.com/cmdr2_stablediffusion_ui" target="_blank"><img src="/media/images/kofi.png" id="coffeeButton"></a> to help cover the cost of development and maintenance! Thank you for your support!</p>
         <p>Please feel free to join the <a href="https://discord.com/invite/u9yhsFmEkB" target="_blank">discord community</a> or <a href="https://github.com/easydiffusion/easydiffusion/issues" target="_blank">file an issue</a> if you have any problems or suggestions in using this interface.</p>
         <div id="footer-legal">
             <p><b>Disclaimer:</b> The authors of this project are not responsible for any content generated using this interface.</p>
             <p>This license of this software forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, <br/>spread misinformation and target vulnerable groups. For the full list of restrictions please read <a href="https://github.com/easydiffusion/easydiffusion/blob/main/LICENSE" target="_blank">the license</a>.</p>
             <p>By using this software, you consent to the terms and conditions of the license.</p>
         </div>
+        <input id="test_diffusers" type="checkbox" style="display: none" checked />
     </div>
 </div>
 </body>
@@ -649,6 +837,8 @@
 <script src="media/js/auto-save.js"></script>
 
 <script src="media/js/searchable-models.js"></script>
+<script src="media/js/multi-model-selector.js"></script>
+<script src="media/js/task-manager.js"></script>
 <script src="media/js/main.js"></script>
 <script src="media/js/plugins.js"></script>
 <script src="media/js/themes.js"></script>
@@ -669,10 +859,10 @@ async function init() {
         events: {
             statusChange: setServerStatus,
             idle: onIdle,
-            ping: tunnelUpdate
+            ping: onPing
         }
     })
-    splashScreen()
+    // splashScreen()
 
     // load models again, but scan for malicious this time
     await getModels(true)

ui/main.py

@@ -1,12 +1,14 @@
-from easydiffusion import model_manager, app, server
+from easydiffusion import model_manager, app, server, bucket_manager
 from easydiffusion.server import server_api  # required for uvicorn
 
+app.init()
+
 server.init()
 
 # Init the app
 model_manager.init()
-app.init()
 app.init_render_threads()
+bucket_manager.init()
 
 # start the browser ui
 app.open_browser()

ui/media/css/animations.css

@@ -0,0 +1,68 @@
+@keyframes ldio-8f673ktaleu-1 {
+    0% { transform: rotate(0deg) }
+   50% { transform: rotate(-45deg) }
+  100% { transform: rotate(0deg) }
+}
+@keyframes ldio-8f673ktaleu-2 {
+    0% { transform: rotate(180deg) }
+   50% { transform: rotate(225deg) }
+  100% { transform: rotate(180deg) }
+}
+.ldio-8f673ktaleu > div:nth-child(2) {
+  transform: translate(-15px,0);
+}
+.ldio-8f673ktaleu > div:nth-child(2) div {
+  position: absolute;
+  top: 20px;
+  left: 20px;
+  width: 60px;
+  height: 30px;
+  border-radius: 60px 60px 0 0;
+  background: #f3b72e;
+  animation: ldio-8f673ktaleu-1 1s linear infinite;
+  transform-origin: 30px 30px
+}
+.ldio-8f673ktaleu > div:nth-child(2) div:nth-child(2) {
+  animation: ldio-8f673ktaleu-2 1s linear infinite
+}
+.ldio-8f673ktaleu > div:nth-child(2) div:nth-child(3) {
+  transform: rotate(-90deg);
+  animation: none;
+}@keyframes ldio-8f673ktaleu-3 {
+    0% { transform: translate(95px,0); opacity: 0 }
+   20% { opacity: 1 }
+  100% { transform: translate(35px,0); opacity: 1 }
+}
+.ldio-8f673ktaleu > div:nth-child(1) {
+  display: block;
+}
+.ldio-8f673ktaleu > div:nth-child(1) div {
+  position: absolute;
+  top: 46px;
+  left: -4px;
+  width: 8px;
+  height: 8px;
+  border-radius: 50%;
+  background: #3869c5;
+  animation: ldio-8f673ktaleu-3 1s linear infinite
+}
+.ldio-8f673ktaleu > div:nth-child(1) div:nth-child(1) { animation-delay: -0.67s }
+.ldio-8f673ktaleu > div:nth-child(1) div:nth-child(2) { animation-delay: -0.33s }
+.ldio-8f673ktaleu > div:nth-child(1) div:nth-child(3) { animation-delay: 0s }
+.loadingio-spinner-bean-eater-x0y3u8qky4n {
+  width: 58px;
+  height: 58px;
+  display: inline-block;
+  overflow: hidden;
+  background: none;
+}
+.ldio-8f673ktaleu {
+  width: 100%;
+  height: 100%;
+  position: relative;
+  transform: translateZ(0) scale(0.58);
+  backface-visibility: hidden;
+  transform-origin: 0 0; /* see note above */
+}
+.ldio-8f673ktaleu div { box-sizing: content-box; }
+/* generated by https://loading.io/ */

ui/media/css/croppr.css

@@ -0,0 +1,58 @@
+.croppr-container * {
+  user-select: none;
+  -moz-user-select: none;
+  -webkit-user-select: none;
+  -ms-user-select: none;
+  box-sizing: border-box;
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+}
+
+.croppr-container img {
+  vertical-align: middle;
+  max-width: 100%;
+}
+
+.croppr {
+  position: relative;
+  display: inline-block;
+}
+
+.croppr-overlay {
+  background: rgba(0,0,0,0.5);
+  position: absolute;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1;
+  cursor: crosshair;
+}
+
+.croppr-region {
+  border: 1px dashed rgba(0, 0, 0, 0.5);
+  position: absolute;
+  z-index: 3;
+  cursor: move;
+  top: 0;
+}
+
+.croppr-imageClipped {
+  position: absolute;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 2;
+  pointer-events: none;
+}
+
+.croppr-handle {
+  border: 1px solid black;
+  background-color: white;
+  width: 10px;
+  height: 10px;
+  position: absolute;
+  z-index: 4;
+  top: 0;
+}

ui/media/css/image-editor.css

@@ -229,4 +229,27 @@
 }
 .inpainter .load_mask {
 	display: flex;
-}
+}
+
+.editor-canvas-overlay {
+        cursor: none;
+}
+
+.image-brush-preview {
+        position: fixed;
+        background: black;
+        opacity: 0.3;
+        borderRadius: 50%;
+        cursor: none;
+        pointer-events: none;
+        transform: translate(-50%, -50%);
+}
+
+.editor-options-container > * > *:not(.active):not(.button) {
+        border: 1px dotted slategray;
+}
+
+.image_editor_opacity .editor-options-container > * > *:not(.active):not(.button) {
+        border: 1px dotted slategray;
+}
+

ui/media/css/main.css

@@ -34,6 +34,7 @@ code {
     width: 32px;
     height: 32px;
     transform: translateY(4px);
+    cursor: pointer;
 }
 #prompt {
     width: 100%;
@@ -476,6 +477,7 @@ dialog {
     background: var(--background-color2);
     color: var(--text-color);
     border-radius: 6px;
+    box-shadow: 0px 0px 30px black;
     border: 2px solid rgb(255 255 255 / 10%);
     padding: 0px;
 }
@@ -607,11 +609,18 @@ div.img-preview img {
     margin: auto;
     padding: 0px;
 }
+#help-links ul {
+    list-style-type: disc;
+    padding-left: 12pt;
+}
 #help-links li {
-    padding-bottom: 12pt;
+    padding-bottom: 6pt;
     display: block;
     font-size: 10pt;
 }
+#help-links ul li {
+    display: list-item;
+}
 #help-links li .fa-fw {
     padding-right: 2pt;
 }
@@ -794,7 +803,7 @@ div.img-preview img {
     margin-bottom: 8px;
 }
 
-#init_image_preview_container:not(.has-image) #init_image_wrapper,
+#init_image_preview_container:not(.has-image) .preview_image_wrapper,
 #init_image_preview_container:not(.has-image) #inpaint_button_container  {
     display: none;
 }
@@ -831,14 +840,14 @@ div.img-preview img {
     gap: 8px;
 }
 
-#init_image_wrapper {
+.preview_image_wrapper {
     grid-row: span 3;
     position: relative;
     width: fit-content;
     max-height: 150px;
 }
 
-#init_image_preview {
+.image_preview {
     max-height: 150px;
     height: 100%;
     width: 100%;
@@ -1088,7 +1097,7 @@ input::file-selector-button {
     .tab-content-inner {
         margin: 0px;
     }
-    .tab {
+    #top-nav .tab {
         font-size: 0;
     }
     .tab .icon {
@@ -1114,6 +1123,9 @@ input::file-selector-button {
     #preview-tools button .icon {
         font-size: 12pt;
     }
+    #show-download-popup .fa-solid {
+        font-size: 12pt;
+    }
 }
 
 @media screen and (max-width: 500px) {
@@ -1202,6 +1214,12 @@ input::file-selector-button {
         visibility: visible;
     }
 }
+
+.tooltip-container {
+    display: inline-block;
+    position: relative;
+}
+
 .simple-tooltip.right {
     right: 0px;
     top: 50%;
@@ -1418,6 +1436,10 @@ div.task-fs-initimage {
     display: none;
     position: absolute;
 }
+div.task-fs-initimage img {
+    max-height: 70vH;
+    max-width: 70vW;
+}
 div.task-initimg:hover div.task-fs-initimage {
     display: block;
     position: absolute;
@@ -1433,9 +1455,13 @@ div.top-right {
     right: 8px;
 }
 
+.task-fs-initimage .top-right button {
+    margin-top: 6px;
+}
+
 #small_image_warning {
-     font-size: smaller;
-     color: var(--status-orange);
+    font-size: smaller;
+    color: var(--status-orange);
 }
 
 button#save-system-settings-btn {
@@ -1460,6 +1486,9 @@ button#save-system-settings-btn {
     cursor: pointer;;
 }
 
+.validation-failed {
+    border: solid 2px red;
+}
 /* SCROLLBARS */
 :root {
     --scrollbar-width: 14px;
@@ -1650,6 +1679,35 @@ body.wait-pause {
     }
 }
 
+.spinner-container {
+    width: 80px;
+    height: 100px;
+    margin: 100px auto;
+    margin-top: 30vH;
+}
+
+.spinner-block {
+    position: relative;
+    box-sizing: border-box;
+    float: left;
+    margin: 0 10px 10px 0;
+    width: 12px;
+    height: 12px;
+    border-radius: 3px;
+    background: var(--accent-color);
+}
+
+.spinner-block:nth-child(4n+1) { animation: spinner-wave 2s ease .0s infinite; }
+.spinner-block:nth-child(4n+2) { animation: spinner-wave 2s ease .2s infinite; }
+.spinner-block:nth-child(4n+3) { animation: spinner-wave 2s ease .4s infinite; }
+.spinner-block:nth-child(4n+4) { animation: spinner-wave 2s ease .6s infinite; margin-right: 0; }
+
+@keyframes spinner-wave {
+    0%   { top: 0;     opacity: 1; }
+    50%  { top: 30px;  opacity: .2; }
+    100% { top: 0;     opacity: 1; }
+}
+
 #embeddings-dialog {
     overflow: clip;
 }
@@ -1664,6 +1722,12 @@ body.wait-pause {
     overflow-y: scroll;
 }
 
+@media screen and (max-width: 1400px) {
+    #embeddings-list {
+        width: 80vW;
+    }
+}
+
 #embeddings-list button {
     margin: 2px;
     color: var(--button-color);
@@ -1741,6 +1805,32 @@ body.wait-pause {
     float: right;
 }
 
+.use-as-thumb-grid {  display: grid;
+  grid-template-columns: 1fr auto;
+  grid-template-rows: 1fr auto;
+  gap: 8px 8px;
+  grid-auto-flow: row;
+  grid-template-areas:
+    "uat-preview uat-select"
+    "uat-preview uat-buttons";
+}
+
+.use-as-thumb-preview {
+  justify-self: center;
+  align-self: center;
+  grid-area: uat-preview;
+}
+
+.use-as-thumb-select { 
+    grid-area: uat-select;
+}
+
+.use-as-thumb-buttons {
+  justify-self: center;
+  grid-area: uat-buttons;
+}
+
+
 .diffusers-disabled-on-startup .diffusers-restart-needed {
     font-size: 0;
 }
@@ -1753,3 +1843,200 @@ body.wait-pause {
     content: "Please restart Easy Diffusion!";
     font-size: 10pt;
 }
+
+input#custom-width, input#custom-height {
+    width: 47pt;
+}
+
+div#recent-resolutions-container {
+    position: relative;
+    display:inline-block;
+}
+
+div#recent-resolutions-popup {
+    position: absolute;
+    right: 0px;
+    margin: 3px;
+    padding: 0.2em 1em 0.4em 1em;
+    z-index: 1;
+    background: var(--background-color3);
+    border-radius: 4px;
+    box-shadow: 0 20px 28px 0 rgba(0, 0, 0, 0.15), 0 6px 20px 0 rgba(0, 0, 0, 0.15);
+}
+
+div#recent-resolutions-popup small {
+    opacity: 0.7;
+}
+
+div#common-resolution-list button {
+    background: var(--background-color1);
+}
+
+td#image-size-options small {
+    margin-right: 0px !important;
+}
+
+td#image-size-options {
+    white-space: nowrap;
+}
+
+div#recent-resolution-list {
+    text-align: center;
+}
+
+div#enlarge-buttons {
+    text-align: center;
+}
+
+.two-column {  display: grid;
+  grid-template-columns: 1fr 1fr;
+  grid-template-rows: 1fr;
+  gap: 0px 0.5em;
+  grid-auto-flow: row;
+  grid-template-areas:
+    "left-column right-column";
+}
+
+.left-column {
+  justify-self: center;
+  align-self: center;
+  grid-area: left-column;
+}
+
+.right-column {
+  justify-self: center;
+  align-self: center;
+  grid-area: right-column;
+}
+
+.clickable {
+    cursor: pointer;
+}
+
+.imgContainer .spinner {
+    position: absolute;
+    left: 50%;
+    top: 50%;
+    transform: translate(-50%, -50%);
+    margin: 0;
+    padding: 0;
+
+    background: var(--background-color3);
+    opacity: 0.95;
+    border-radius: 5px;
+    padding: 4pt;
+    border: 1px solid var(--button-color);
+    box-shadow: 0px 0px 4px black;
+}
+
+.imgContainer .spinnerStatus {
+    font-size: 10pt;
+}
+
+#controlnet_model_container small {
+    color: var(--text-color)
+}
+#control_image {
+    width: 130pt;
+}
+#controlnet_model {
+    width: 77%;
+}
+
+.drop-area {
+    width: 45%;
+    height: 50px;
+    border: 2px dashed #ccc;
+    text-align: center;
+    line-height: 50px;
+    font-size: small;
+    color: #ccc;
+    border-radius: 10px;
+    display: none;
+    margin: 12px 10px;
+}
+
+#num_outputs_total {
+    width: 42pt;
+}
+#num_outputs_parallel {
+    width: 42pt;
+}
+.model_entry .model_weight {
+    width: 50pt;
+}
+
+/* hack for fixing Image Modifier Improvements plugin */
+#imageTagPopupContainer {
+    position: absolute;
+}
+
+@media screen and (max-width: 400px) {
+    .editor-slider {
+        width: 40%;
+    }
+    input::-webkit-outer-spin-button,
+    input::-webkit-inner-spin-button {
+        -webkit-appearance: none;
+        margin: 0;
+    }
+    input[type=number] {
+        -moz-appearance: textfield;
+        /* Firefox */
+    }
+    #num_outputs_total {
+        width: 27pt;
+    }
+    #num_outputs_parallel {
+        width: 27pt;
+        margin-left: -4pt;
+    }
+    .model_entry .model_weight {
+        width: 30pt;
+    }
+    #width {
+        width: 50pt;
+    }
+    #height {
+        width: 50pt;
+    }
+}
+@media screen and (max-width: 460px) {
+    #widthLabel small span {
+        display: none;
+    }
+    #widthLabel small:after {
+        content: "(w)";
+    }
+    #heightLabel small span {
+        display: none;
+    }
+    #heightLabel small:after {
+        content: "(h)";
+    }
+    #prompt-toolbar-right {
+        text-align: right;
+    }
+    #editor-settings label {
+        font-size: 9pt;
+    }
+    #editor-settings .model-filter {
+        width: 56%;
+    }
+    #vae_model {
+        width: 65% !important;
+    }
+    .model_entry .model_name {
+        width: 60% !important;
+    }
+}
+
+#supportBanner {
+    font-size: 9pt;
+    padding: 5pt;
+    border: 1px solid var(--background-color2);
+    margin-bottom: 5pt;
+    border-radius: 4pt;
+    padding-top: 6pt;
+    color: var(--small-label-color);
+}

ui/media/js/auto-save.js

@@ -15,14 +15,12 @@ const SETTINGS_IDS_LIST = [
     "stable_diffusion_model",
     "clip_skip",
     "vae_model",
-    "hypernetwork_model",
     "sampler_name",
     "width",
     "height",
     "num_inference_steps",
     "guidance_scale",
     "prompt_strength",
-    "hypernetwork_strength",
     "tiling",
     "output_format",
     "output_quality",
@@ -45,6 +43,7 @@ const SETTINGS_IDS_LIST = [
     "sound_toggle",
     "vram_usage_level",
     "confirm_dangerous_actions",
+    "profileName",
     "metadata_output_format",
     "auto_save_settings",
     "apply_color_correction",
@@ -54,10 +53,20 @@ const SETTINGS_IDS_LIST = [
     "zip_toggle",
     "tree_toggle",
     "json_toggle",
+    "extract_lora_from_prompt",
+    "embedding-card-size-selector",
+    "lora_model",
+    "enable_vae_tiling",
+    "controlnet_alpha",
 ]
 
 const IGNORE_BY_DEFAULT = ["prompt"]
 
+if (!testDiffusers.checked) {
+    SETTINGS_IDS_LIST.push("hypernetwork_model")
+    SETTINGS_IDS_LIST.push("hypernetwork_strength")
+}
+
 const SETTINGS_SECTIONS = [
     // gets the "keys" property filled in with an ordered list of settings in this section via initSettings
     { id: "editor-inputs", name: "Prompt" },
@@ -169,23 +178,6 @@ function loadSettings() {
             }
         })
         CURRENTLY_LOADING_SETTINGS = false
-    } else if (localStorage.length < 2) {
-        // localStorage is too short for OldSettings
-        // So this is likely the first time Easy Diffusion is running.
-        // Initialize vram_usage_level based on the available VRAM
-        function initGPUProfile(event) {
-            if (
-                "detail" in event &&
-                "active" in event.detail &&
-                "cuda:0" in event.detail.active &&
-                event.detail.active["cuda:0"].mem_total < 4.5
-            ) {
-                vramUsageLevelField.value = "low"
-                vramUsageLevelField.dispatchEvent(new Event("change"))
-            }
-            document.removeEventListener("system_info_update", initGPUProfile)
-        }
-        document.addEventListener("system_info_update", initGPUProfile)
     } else {
         CURRENTLY_LOADING_SETTINGS = true
         tryLoadOldSettings()

ui/media/js/dnd.js

@@ -268,7 +268,11 @@ const TASK_MAPPING = {
     tiling: {
         name: "Tiling",
         setUI: (val) => {
-            tilingField.value = val
+            if (val === null || val === "None") {
+                tilingField.value = "none"
+            } else {
+                tilingField.value = val
+            }
         },
         readUI: () => tilingField.value,
         parse: (val) => val,
@@ -289,42 +293,67 @@ const TASK_MAPPING = {
         readUI: () => vaeModelField.value,
         parse: (val) => val,
     },
+    use_controlnet_model: {
+        name: "ControlNet model",
+        setUI: (use_controlnet_model) => {
+            controlnetModelField.value = getModelPath(use_controlnet_model, [".pth", ".safetensors"])
+        },
+        readUI: () => controlnetModelField.value,
+        parse: (val) => val,
+    },
+    control_filter_to_apply: {
+        name: "ControlNet Filter",
+        setUI: (control_filter_to_apply) => {
+            controlImageFilterField.value = control_filter_to_apply
+        },
+        readUI: () => controlImageFilterField.value,
+        parse: (val) => val,
+    },
+    control_alpha: {
+        name: "ControlNet Strength",
+        setUI: (control_alpha) => {
+            control_alpha = control_alpha || 1.0
+            controlAlphaField.value = control_alpha
+            updateControlAlphaSlider()
+        },
+        readUI: () => parseFloat(controlAlphaField.value),
+        parse: (val) => val === null ? 1.0 : parseFloat(val),
+    },
     use_lora_model: {
         name: "LoRA model",
         setUI: (use_lora_model) => {
-            // create rows
-            for (let i = loraModels.length; i < use_lora_model.length; i++) {
-                createLoraEntry()
-            }
-
-            use_lora_model.forEach((model_name, i) => {
-                let field = loraModels[i][0]
-                const oldVal = field.value
-
-                if (model_name !== "") {
-                    model_name = getModelPath(model_name, [".ckpt", ".safetensors"])
-                    model_name = model_name !== "" ? model_name : oldVal
+            let modelPaths = []
+            use_lora_model = use_lora_model === null ? "" : use_lora_model
+            use_lora_model = Array.isArray(use_lora_model) ? use_lora_model : [use_lora_model]
+            use_lora_model.forEach((m) => {
+                if (m.includes("models\\lora\\")) {
+                    m = m.split("models\\lora\\")[1]
+                } else if (m.includes("models\\\\lora\\\\")) {
+                    m = m.split("models\\\\lora\\\\")[1]
+                } else if (m.includes("models/lora/")) {
+                    m = m.split("models/lora/")[1]
                 }
-                field.value = model_name
+                m = m.replaceAll("\\\\", "/")
+                m = getModelPath(m, [".ckpt", ".safetensors"])
+                modelPaths.push(m)
             })
-
-            // clear the remaining entries
-            let container = document.querySelector("#lora_model_container .model_entries")
-            for (let i = use_lora_model.length; i < loraModels.length; i++) {
-                let modelEntry = loraModels[i][2]
-                container.removeChild(modelEntry)
-            }
-
-            loraModels.splice(use_lora_model.length)
+            loraModelField.modelNames = modelPaths
         },
         readUI: () => {
-            let values = loraModels.map((e) => e[0].value)
-            values = values.filter((e) => e.trim() !== "")
-            values = values.length > 0 ? values : "None"
-            return values
+            return loraModelField.modelNames
         },
         parse: (val) => {
             val = !val || val === "None" ? "" : val
+            if (typeof val === "string" && val.includes(",")) {
+                val = val.split(",")
+                val = val.map((v) => v.trim())
+                val = val.map((v) => v.replaceAll("\\", "\\\\"))
+                val = val.map((v) => v.replaceAll('"', ""))
+                val = val.map((v) => v.replaceAll("'", ""))
+                val = val.map((v) => '"' + v + '"')
+                val = "[" + val + "]"
+                val = JSON.parse(val)
+            }
             val = Array.isArray(val) ? val : [val]
             return val
         },
@@ -332,31 +361,17 @@ const TASK_MAPPING = {
     lora_alpha: {
         name: "LoRA Strength",
         setUI: (lora_alpha) => {
-            for (let i = loraModels.length; i < lora_alpha.length; i++) {
-                createLoraEntry()
-            }
-
-            lora_alpha.forEach((model_strength, i) => {
-                let field = loraModels[i][1]
-                field.value = model_strength
-            })
-
-            // clear the remaining entries
-            let container = document.querySelector("#lora_model_container .model_entries")
-            for (let i = lora_alpha.length; i < loraModels.length; i++) {
-                let modelEntry = loraModels[i][2]
-                container.removeChild(modelEntry)
-            }
-
-            loraModels.splice(lora_alpha.length)
+            lora_alpha = Array.isArray(lora_alpha) ? lora_alpha : [lora_alpha]
+            loraModelField.modelWeights = lora_alpha
         },
         readUI: () => {
-            let models = loraModels.filter((e) => e[0].value.trim() !== "")
-            let values = models.map((e) => e[1].value)
-            values = values.length > 0 ? values : 0
-            return values
+            return loraModelField.modelWeights
         },
         parse: (val) => {
+            if (typeof val === "string" && val.includes(",")) {
+                val = "[" + val.replaceAll("'", '"') + "]"
+                val = JSON.parse(val)
+            }
             val = Array.isArray(val) ? val : [val]
             val = val.map((e) => parseFloat(e))
             return val
@@ -472,11 +487,8 @@ function restoreTaskToUI(task, fieldsToSkip) {
     }
 
     if (!("use_lora_model" in task.reqBody)) {
-        loraModels.forEach((e) => {
-            e[0].value = ""
-            e[1].value = 0
-            e[0].dispatchEvent(new Event("change"))
-        })
+        loraModelField.modelNames = []
+        loraModelField.modelWeights = []
     }
 
     // restore the original prompt if provided (e.g. use settings), fallback to prompt as needed (e.g. copy/paste or d&d)
@@ -519,10 +531,28 @@ function restoreTaskToUI(task, fieldsToSkip) {
         )
         initImagePreview.src = task.reqBody.init_image
     }
+
+    // hide/show controlnet picture as needed
+    if (IMAGE_REGEX.test(controlImagePreview.src) && task.reqBody.control_image == undefined) {
+        // hide source image
+        controlImageClearBtn.dispatchEvent(new Event("click"))
+    } else if (task.reqBody.control_image !== undefined) {
+        // listen for inpainter loading event, which happens AFTER the main image loads (which reloads the inpai
+        controlImagePreview.src = task.reqBody.control_image
+    }
+
+    if ("use_controlnet_model" in task.reqBody && task.reqBody.use_controlnet_model && !("control_alpha" in task.reqBody)) {
+        controlAlphaField.value = 1.0
+        updateControlAlphaSlider()
+    }
 }
 function readUI() {
     const reqBody = {}
     for (const key in TASK_MAPPING) {
+        if (testDiffusers.checked && (key === "use_hypernetwork_model" || key === "hypernetwork_strength")) {
+            continue
+        }
+
         reqBody[key] = TASK_MAPPING[key].readUI()
     }
     return {
@@ -569,6 +599,12 @@ const TASK_TEXT_MAPPING = {
     use_stable_diffusion_model: "Stable Diffusion model",
     use_hypernetwork_model: "Hypernetwork model",
     hypernetwork_strength: "Hypernetwork Strength",
+    use_lora_model: "LoRA model",
+    lora_alpha: "LoRA Strength",
+    use_controlnet_model: "ControlNet model",
+    control_filter_to_apply: "ControlNet Filter",
+    control_alpha: "ControlNet Strength",
+    tiling: "Seamless Tiling",
 }
 function parseTaskFromText(str) {
     const taskReqBody = {}

ui/media/js/engine.js

@@ -1047,7 +1047,9 @@
         }
     }
     class FilterTask extends Task {
-        constructor(options = {}) {}
+        constructor(options = {}) {
+            super(options)
+        }
         /** Send current task to server.
          * @param {*} [timeout=-1] Optional timeout value in ms
          * @returns the response from the render request.
@@ -1055,9 +1057,27 @@
          */
         async post(timeout = -1) {
             let jsonResponse = await super.post("/filter", timeout)
-            //this._setId(jsonResponse.task)
+            if (typeof jsonResponse?.task !== "number") {
+                console.warn("Endpoint error response: ", jsonResponse)
+                const event = Object.assign({ task: this }, jsonResponse)
+                await eventSource.fireEvent(EVENT_UNEXPECTED_RESPONSE, event)
+                if ("continueWith" in event) {
+                    jsonResponse = await Promise.resolve(event.continueWith)
+                }
+                if (typeof jsonResponse?.task !== "number") {
+                    const err = new Error(jsonResponse?.detail || "Endpoint response does not contains a task ID.")
+                    this.abort(err)
+                    throw err
+                }
+            }
+            this._setId(jsonResponse.task)
+            if (jsonResponse.stream) {
+                this.streamUrl = jsonResponse.stream
+            }
             this._setStatus(TaskStatus.waiting)
+            return jsonResponse
         }
+        checkReqBody() {}
         enqueue(progressCallback) {
             return Task.enqueueNew(this, FilterTask, progressCallback)
         }
@@ -1068,6 +1088,65 @@
             if (this.isStopped) {
                 return
             }
+
+            this._setStatus(TaskStatus.pending)
+            progressCallback?.call(this, { reqBody: this._reqBody })
+            Object.freeze(this._reqBody)
+
+            // Post task request to backend
+            let renderRes = undefined
+            try {
+                renderRes = yield this.post()
+                yield progressCallback?.call(this, { renderResponse: renderRes })
+            } catch (e) {
+                yield progressCallback?.call(this, { detail: e.message })
+                throw e
+            }
+
+            try {
+                // Wait for task to start on server.
+                yield this.waitUntil({
+                    callback: function() {
+                        return progressCallback?.call(this, {})
+                    },
+                    status: TaskStatus.processing,
+                })
+            } catch (e) {
+                this.abort(err)
+                throw e
+            }
+
+            // Task started!
+            // Open the reader.
+            const reader = this.reader
+            const task = this
+            reader.onError = function(response) {
+                if (progressCallback) {
+                    task.abort(new Error(response.statusText))
+                    return progressCallback.call(task, { response, reader })
+                }
+                return Task.prototype.onError.call(task, response)
+            }
+            yield progressCallback?.call(this, { reader })
+
+            //Start streaming the results.
+            const streamGenerator = reader.open()
+            let value = undefined
+            let done = undefined
+            yield progressCallback?.call(this, { stream: streamGenerator })
+            do {
+                ;({ value, done } = yield streamGenerator.next())
+                if (typeof value !== "object") {
+                    continue
+                }
+                if (value.status !== undefined) {
+                    yield progressCallback?.call(this, value)
+                    if (value.status === "succeeded" || value.status === "failed") {
+                        done = true
+                    }
+                }
+            } while (!done)
+            return value
         }
         static start(task, progressCallback) {
             if (typeof task !== "object") {

ui/media/js/image-editor.js

@@ -626,6 +626,7 @@ class ImageEditor {
                 .getImageData(0, 0, this.width, this.height)
                 .data.some((channel) => channel !== 0)
             maskSetting.checked = !is_blank
+            maskSetting.dispatchEvent(new Event("change"))
         }
         this.hide()
     }

ui/media/js/multi-model-selector.js

@@ -0,0 +1,256 @@
+/**
+ * A component consisting of multiple model dropdowns, along with a "weight" field per model.
+ *
+ * Behaves like a single input element, giving an object in response to the .value field.
+ *
+ * Inspired by the design of the ModelDropdown component (searchable-models.js).
+ */
+
+class MultiModelSelector {
+    root
+    modelType
+    modelNameFriendly
+    defaultWeight
+    weightStep
+
+    modelContainer
+    addNewButton
+
+    counter = 0
+
+    /* MIMIC A REGULAR INPUT FIELD */
+    get id() {
+        return this.root.id
+    }
+    get parentElement() {
+        return this.root.parentElement
+    }
+    get parentNode() {
+        return this.root.parentNode
+    }
+    get value() {
+        return { modelNames: this.modelNames, modelWeights: this.modelWeights }
+    }
+    set value(modelData) {
+        if (typeof modelData !== "object") {
+            throw new Error("Multi-model selector expects an object containing modelNames and modelWeights as keys!")
+        }
+        if (!("modelNames" in modelData) || !("modelWeights" in modelData)) {
+            throw new Error("modelNames or modelWeights not present in the data passed to the multi-model selector")
+        }
+
+        let newModelNames = modelData["modelNames"]
+        let newModelWeights = modelData["modelWeights"]
+        if (newModelNames.length !== newModelWeights.length) {
+            throw new Error("Need to pass an equal number of modelNames and modelWeights!")
+        }
+
+        // update weight first, name second.
+        // for some unholy reason this order matters for dispatch chains
+        // the root of all this unholiness is because searchable-models automatically dispatches an update event
+        // as soon as the value is updated via JS, which is against the DOM pattern of not dispatching an event automatically
+        // unless the caller explicitly dispatches the event.
+        this.modelWeights = newModelWeights
+        this.modelNames = newModelNames
+    }
+    get disabled() {
+        return false
+    }
+    set disabled(state) {
+        // do nothing
+    }
+    getModelElements(ignoreEmpty = false) {
+        let entries = this.root.querySelectorAll(".model_entry")
+        entries = [...entries]
+        let elements = entries.map((e) => {
+            let modelName = e.querySelector(".model_name").field
+            let modelWeight = e.querySelector(".model_weight")
+            if (ignoreEmpty && modelName.value.trim() === "") {
+                return null
+            }
+
+            return { name: modelName, weight: modelWeight }
+        })
+        elements = elements.filter((e) => e !== null)
+        return elements
+    }
+    addEventListener(type, listener, options) {
+        // do nothing
+    }
+    dispatchEvent(event) {
+        // do nothing
+    }
+    appendChild(option) {
+        // do nothing
+    }
+
+    // remember 'this' - http://blog.niftysnippets.org/2008/04/you-must-remember-this.html
+    bind(f, obj) {
+        return function() {
+            return f.apply(obj, arguments)
+        }
+    }
+
+    constructor(root, modelType, modelNameFriendly = undefined, defaultWeight = 0.5, weightStep = 0.02) {
+        this.root = root
+        this.modelType = modelType
+        this.modelNameFriendly = modelNameFriendly || modelType
+        this.defaultWeight = defaultWeight
+        this.weightStep = weightStep
+
+        let self = this
+        document.addEventListener("refreshModels", function() {
+            setTimeout(self.bind(self.populateModels, self), 1)
+        })
+
+        this.createStructure()
+        this.populateModels()
+    }
+
+    createStructure() {
+        this.modelContainer = document.createElement("div")
+        this.modelContainer.className = "model_entries"
+        this.root.appendChild(this.modelContainer)
+
+        this.addNewButton = document.createElement("button")
+        this.addNewButton.className = "add_model_entry"
+        this.addNewButton.innerHTML = '<i class="fa-solid fa-plus"></i> add another ' + this.modelNameFriendly
+        this.addNewButton.addEventListener("click", this.bind(this.addModelEntry, this))
+        this.root.appendChild(this.addNewButton)
+    }
+
+    populateModels() {
+        if (this.root.dataset.path === "") {
+            if (this.length === 0) {
+                this.addModelEntry() // create a single blank entry
+            }
+        } else {
+            this.value = JSON.parse(this.root.dataset.path)
+        }
+    }
+
+    addModelEntry() {
+        let idx = this.counter++
+        let currLength = this.length
+
+        const modelElement = document.createElement("div")
+        modelElement.className = "model_entry"
+        modelElement.innerHTML = `
+            <input id="${this.modelType}_${idx}" class="model_name model-filter" type="text" spellcheck="false" autocomplete="off" data-path="" />
+            <input class="model_weight" type="number" step="${this.weightStep}" value="${this.defaultWeight}" pattern="^-?[0-9]*\.?[0-9]*$" onkeypress="preventNonNumericalInput(event)">
+        `
+        this.modelContainer.appendChild(modelElement)
+
+        let modelNameEl = modelElement.querySelector(".model_name")
+        modelNameEl.field = new ModelDropdown(modelNameEl, this.modelType, "None")
+        let modelWeightEl = modelElement.querySelector(".model_weight")
+
+        let self = this
+
+        function makeUpdateEvent(type) {
+            return function(e) {
+                e.stopPropagation()
+
+                let modelData = self.value
+                self.root.dataset.path = JSON.stringify(modelData)
+
+                self.root.dispatchEvent(new Event(type))
+            }
+        }
+
+        modelNameEl.addEventListener("change", makeUpdateEvent("change"))
+        modelNameEl.addEventListener("input", makeUpdateEvent("input"))
+        modelWeightEl.addEventListener("change", makeUpdateEvent("change"))
+        modelWeightEl.addEventListener("input", makeUpdateEvent("input"))
+
+        let removeBtn = document.createElement("button")
+        removeBtn.className = "remove_model_btn"
+        removeBtn.setAttribute("title", "Remove model")
+        removeBtn.innerHTML = '<i class="fa-solid fa-minus"></i>'
+
+        if (currLength === 0) {
+            removeBtn.classList.add("displayNone")
+        }
+
+        removeBtn.addEventListener(
+            "click",
+            this.bind(function(e) {
+                this.modelContainer.removeChild(modelElement)
+
+                makeUpdateEvent("change")(e)
+            }, this)
+        )
+
+        modelElement.appendChild(removeBtn)
+    }
+
+    removeModelEntry() {
+        if (this.length === 0) {
+            return
+        }
+
+        let lastEntry = this.modelContainer.lastElementChild
+        lastEntry.remove()
+    }
+
+    get length() {
+        return this.getModelElements().length
+    }
+
+    get modelNames() {
+        return this.getModelElements(true).map((e) => e.name.value)
+    }
+
+    set modelNames(newModelNames) {
+        this.resizeEntryList(newModelNames.length)
+
+        if (newModelNames.length === 0) {
+            this.getModelElements()[0].name.value = ""
+        }
+
+        // assign to the corresponding elements
+        let currElements = this.getModelElements()
+        for (let i = 0; i < newModelNames.length; i++) {
+            let curr = currElements[i]
+
+            curr.name.value = newModelNames[i]
+        }
+    }
+
+    get modelWeights() {
+        return this.getModelElements(true).map((e) => e.weight.value)
+    }
+
+    set modelWeights(newModelWeights) {
+        this.resizeEntryList(newModelWeights.length)
+
+        if (newModelWeights.length === 0) {
+            this.getModelElements()[0].weight.value = this.defaultWeight
+        }
+
+        // assign to the corresponding elements
+        let currElements = this.getModelElements()
+        for (let i = 0; i < newModelWeights.length; i++) {
+            let curr = currElements[i]
+
+            curr.weight.value = newModelWeights[i]
+        }
+    }
+
+    resizeEntryList(newLength) {
+        if (newLength === 0) {
+            newLength = 1
+        }
+
+        let currLength = this.length
+        if (currLength < newLength) {
+            for (let i = currLength; i < newLength; i++) {
+                this.addModelEntry()
+            }
+        } else {
+            for (let i = newLength; i < currLength; i++) {
+                this.removeModelEntry()
+            }
+        }
+    }
+}

ui/media/js/parameters.js

@@ -16,6 +16,7 @@ var ParameterType = {
  */
 let parametersTable = document.querySelector("#system-settings-table")
 let networkParametersTable = document.querySelector("#system-settings-network-table")
+let installExtrasTable = document.querySelector("#system-settings-install-extras-table")
 
 /**
  * JSDoc style
@@ -96,6 +97,17 @@ var PARAMETERS = [
             },
         ],
     },
+    {
+        id: "models_dir",
+        type: ParameterType.custom,
+        icon: "fa-folder-tree",
+        label: "Models Folder",
+        note: "Path to the 'models' folder. Please save and refresh the page after changing this.",
+        saveInAppConfig: true,
+        render: (parameter) => {
+            return `<input id="${parameter.id}" name="${parameter.id}" size="30">`
+        },
+    },
     {
         id: "block_nsfw",
         type: ParameterType.checkbox,
@@ -120,6 +132,15 @@ var PARAMETERS = [
         icon: "fa-arrow-down-short-wide",
         default: false,
     },
+    {
+        id: "extract_lora_from_prompt",
+        type: ParameterType.checkbox,
+        label: "Extract LoRA tags from the prompt",
+        note:
+            "Automatically extract lora tags like &lt;lora:name:0.4&gt; from the prompt, and apply the correct LoRA (if present)",
+        icon: "fa-code",
+        default: true,
+    },
     {
         id: "ui_open_browser_on_start",
         type: ParameterType.checkbox,
@@ -184,6 +205,17 @@ var PARAMETERS = [
         icon: "fa-check-double",
         default: true,
     },
+    {
+        id: "profileName",
+        type: ParameterType.custom,
+        label: "Profile Name",
+        note:
+            "Name of the profile for model manager settings, e.g. thumbnails for embeddings. Use this to have different settings for different users.",
+        render: (parameter) => {
+            return `<input id="${parameter.id}" name="${parameter.id}" value="default" size="12">`
+        },
+        icon: "fa-user-gear",
+    },
     {
         id: "listen_to_network",
         type: ParameterType.checkbox,
@@ -217,13 +249,13 @@ var PARAMETERS = [
         default: false,
     },
     {
-        id: "test_diffusers",
+        id: "use_v3_engine",
         type: ParameterType.checkbox,
-        label: "Test Diffusers",
+        label: "Use the new v3 engine (diffusers)",
         note:
-            "<b>Experimental! Can have bugs!</b> Use upcoming features (like LoRA) in our new engine. Please press Save, then restart the program after changing this.",
+            "Use our new v3 engine, with additional features like LoRA, ControlNet, SDXL, Embeddings, Tiling and lots more! Please press Save, then restart the program after changing this.",
         icon: "fa-bolt",
-        default: false,
+        default: true,
         saveInAppConfig: true,
     },
     {
@@ -241,6 +273,29 @@ var PARAMETERS = [
         render: () => '<button id="toggle-cloudflare-tunnel" class="primaryButton">Start</button>',
         table: networkParametersTable,
     },
+    {
+        id: "nvidia_tensorrt",
+        type: ParameterType.custom,
+        label: "NVIDIA TensorRT",
+        note: `Faster image generation by converting your Stable Diffusion models to the NVIDIA TensorRT format. You can choose the
+               models to convert. Download size: approximately 2 GB.<br/><br/>
+               <b>Early access version:</b> support for LoRA is still under development.
+               <div id="trt-build-config" class="displayNone">
+                    <h3>Build Config:</h3>
+                    Batch size range:
+                    <label>Min:</label> <input id="trt-build-min-batch" type="number" min="1" value="1" style="width: 40pt" />
+                    <label>Max:</label> <input id="trt-build-max-batch" type="number" min="1" value="1" style="width: 40pt" /><br/><br/>
+                    <b>Build for resolutions</b>:<br/>
+                    <input id="trt-build-res-512" type="checkbox" value="1" /> 512x512 to 768x768<br/>
+                    <input id="trt-build-res-768" type="checkbox" value="1" checked /> 768x768 to 1024x1024<br/>
+                    <input id="trt-build-res-1024" type="checkbox" value="1" /> 1024x1024 to 1280x1280<br/>
+                    <input id="trt-build-res-1280" type="checkbox" value="1" /> 1280x1280 to 1536x1536<br/>
+                    <input id="trt-build-res-1536" type="checkbox" value="1" /> 1536x1536 to 1792x1792<br/>
+               </div>`,
+        icon: "fa-angles-up",
+        render: () => '<button id="toggle-tensorrt-install" class="primaryButton">Install</button>',
+        table: installExtrasTable,
+    },
 ]
 
 function getParameterSettingsEntry(id) {
@@ -376,7 +431,9 @@ let listenPortField = document.querySelector("#listen_port")
 let useBetaChannelField = document.querySelector("#use_beta_channel")
 let uiOpenBrowserOnStartField = document.querySelector("#ui_open_browser_on_start")
 let confirmDangerousActionsField = document.querySelector("#confirm_dangerous_actions")
-let testDiffusers = document.querySelector("#test_diffusers")
+let testDiffusers = document.querySelector("#use_v3_engine")
+let profileNameField = document.querySelector("#profileName")
+let modelsDirField = document.querySelector("#models_dir")
 
 let saveSettingsBtn = document.querySelector("#save-system-settings-btn")
 
@@ -408,8 +465,6 @@ async function getAppConfig() {
         if (config.update_branch === "beta") {
             useBetaChannelField.checked = true
             document.querySelector("#updateBranchLabel").innerText = "(beta)"
-        } else {
-            getParameterSettingsEntry("test_diffusers").classList.add("displayNone")
         }
         if (config.ui && config.ui.open_browser_on_start === false) {
             uiOpenBrowserOnStartField.checked = false
@@ -420,12 +475,17 @@ async function getAppConfig() {
         if (config.net && config.net.listen_port !== undefined) {
             listenPortField.value = config.net.listen_port
         }
+        modelsDirField.value = config.models_dir
 
-        const testDiffusersEnabled = config.test_diffusers && config.update_branch !== "main"
+        let testDiffusersEnabled = true
+        if (config.use_v3_engine === false) {
+            testDiffusersEnabled = false
+        }
         testDiffusers.checked = testDiffusersEnabled
+        document.querySelector("#test_diffusers").checked = testDiffusers.checked // don't break plugins
 
         if (config.config_on_startup) {
-            if (config.config_on_startup?.test_diffusers && config.update_branch !== "main") {
+            if (config.config_on_startup?.use_v3_engine) {
                 document.body.classList.add("diffusers-enabled-on-startup")
                 document.body.classList.remove("diffusers-disabled-on-startup")
             } else {
@@ -437,20 +497,36 @@ async function getAppConfig() {
         if (!testDiffusersEnabled) {
             document.querySelector("#lora_model_container").style.display = "none"
             document.querySelector("#tiling_container").style.display = "none"
+            document.querySelector("#controlnet_model_container").style.display = "none"
+            document.querySelector("#hypernetwork_model_container").style.display = ""
+            document.querySelector("#hypernetwork_strength_container").style.display = ""
+            document.querySelector("#negative-embeddings-button").style.display = "none"
 
             document.querySelectorAll("#sampler_name option.diffusers-only").forEach((option) => {
                 option.style.display = "none"
             })
+            IMAGE_STEP_SIZE = 64
+            customWidthField.step = IMAGE_STEP_SIZE
+            customHeightField.step = IMAGE_STEP_SIZE
         } else {
             document.querySelector("#lora_model_container").style.display = ""
             document.querySelector("#tiling_container").style.display = ""
+            document.querySelector("#controlnet_model_container").style.display = ""
+            document.querySelector("#hypernetwork_model_container").style.display = "none"
+            document.querySelector("#hypernetwork_strength_container").style.display = "none"
 
             document.querySelectorAll("#sampler_name option.k_diffusion-only").forEach((option) => {
                 option.style.display = "none"
             })
             document.querySelector("#clip_skip_config").classList.remove("displayNone")
             document.querySelector("#embeddings-button").classList.remove("displayNone")
-            document.querySelector("#negative-embeddings-button").classList.remove("displayNone")
+            IMAGE_STEP_SIZE = 8
+            customWidthField.step = IMAGE_STEP_SIZE
+            customHeightField.step = IMAGE_STEP_SIZE
+        }
+
+        if (config.force_save_metadata) {
+            metadataOutputFormatField.value = config.force_save_metadata
         }
 
         console.log("get config status response", config)
@@ -566,7 +642,7 @@ function setDeviceInfo(devices) {
 
     function ID_TO_TEXT(d) {
         let info = devices.all[d]
-        if ("mem_free" in info && "mem_total" in info) {
+        if ("mem_free" in info && "mem_total" in info && info["mem_total"] > 0) {
             return `${info.name} <small>(${d}) (${info.mem_free.toFixed(1)}Gb free / ${info.mem_total.toFixed(
                 1
             )} Gb total)</small>`
@@ -582,6 +658,23 @@ function setDeviceInfo(devices) {
     systemInfoEl.querySelector("#system-info-cpu").innerText = cpu
     systemInfoEl.querySelector("#system-info-gpus-all").innerHTML = allGPUs.join("</br>")
     systemInfoEl.querySelector("#system-info-rendering-devices").innerHTML = activeGPUs.join("</br>")
+
+    // tensorRT
+    if (devices.active && testDiffusers.checked && devices.enable_trt === true) {
+        let nvidiaGPUs = Object.keys(devices.active).filter((d) => {
+            let gpuName = devices.active[d].name
+            gpuName = gpuName.toLowerCase()
+            return (
+                gpuName.includes("nvidia") ||
+                gpuName.includes("geforce") ||
+                gpuName.includes("quadro") ||
+                gpuName.includes("tesla")
+            )
+        })
+        if (nvidiaGPUs.length > 0) {
+            document.querySelector("#install-extras-container").classList.remove("displayNone")
+        }
+    }
 }
 
 function setHostInfo(hosts) {
@@ -647,10 +740,13 @@ async function getSystemInfo() {
             force = res["enforce_output_dir"]
             if (force == true) {
                 saveToDiskField.checked = true
-                metadataOutputFormatField.disabled = false
+                metadataOutputFormatField.disabled = res["enforce_output_metadata"]
+                diskPathField.disabled = true
             }
             saveToDiskField.disabled = force
-            diskPathField.disabled = force
+        } else {
+            diskPathField.disabled = !saveToDiskField.checked
+            metadataOutputFormatField.disabled = !saveToDiskField.checked
         }
         setDiskPath(res["default_output_dir"], force)
     } catch (e) {
@@ -743,11 +839,3 @@ navigator.permissions.query({ name: "clipboard-write" }).then(function(result) {
 })
 
 document.addEventListener("system_info_update", (e) => setDeviceInfo(e.detail))
-
-useBetaChannelField.addEventListener('change', (e) => {
-    if (e.target.checked) { 
-        getParameterSettingsEntry("test_diffusers").classList.remove('displayNone') 
-    } else { 
-        getParameterSettingsEntry("test_diffusers").classList.add('displayNone')
-    }
-})

ui/media/js/searchable-models.js

@@ -118,13 +118,16 @@ class ModelDropdown {
         )
     }
 
-    saveCurrentSelection(elem, value, path) {
+    saveCurrentSelection(elem, value, path, dispatchEvent = true) {
         this.currentSelection.elem = elem
         this.currentSelection.value = value
         this.currentSelection.path = path
         this.modelFilter.dataset.path = path
         this.modelFilter.value = value
-        this.modelFilter.dispatchEvent(new Event("change"))
+
+        if (dispatchEvent) {
+            this.modelFilter.dispatchEvent(new Event("change"))
+        }
     }
 
     processClick(e) {
@@ -348,13 +351,13 @@ class ModelDropdown {
         }
     }
 
-    selectEntry(path) {
+    selectEntry(path, dispatchEvent = true) {
         if (path !== undefined) {
             const entries = this.modelElements
 
             for (const elem of entries) {
                 if (elem.dataset.path == path) {
-                    this.saveCurrentSelection(elem, elem.innerText, elem.dataset.path)
+                    this.saveCurrentSelection(elem, elem.innerText, elem.dataset.path, dispatchEvent)
                     this.highlightedModelEntry = elem
                     elem.scrollIntoView({ block: "nearest" })
                     break
@@ -529,7 +532,7 @@ class ModelDropdown {
             rootModelList.style.minWidth = modelFilterStyle.width
         })
 
-        this.selectEntry(this.activeModel)
+        this.selectEntry(this.activeModel, false)
     }
 
     /**
@@ -552,17 +555,23 @@ class ModelDropdown {
                     this.createModelNodeList(`${folderName || ""}/${childFolderName}`, childModels, false)
                 )
             } else {
+                let modelId = model
+                let modelName = model
+                if (typeof model === "object") {
+                    modelId = Object.keys(model)[0]
+                    modelName = model[modelId]
+                }
                 const classes = ["model-file"]
                 if (isRootFolder) {
                     classes.push("in-root-folder")
                 }
                 // Remove the leading slash from the model path
-                const fullPath = folderName ? `${folderName.substring(1)}/${model}` : model
+                const fullPath = folderName ? `${folderName.substring(1)}/${modelId}` : modelId
                 modelsMap.set(
-                    model,
+                    modelId,
                     createElement("li", { "data-path": fullPath }, classes, [
                         createElement("i", undefined, ["fa-regular", "fa-file", "icon"]),
-                        model,
+                        modelName,
                     ])
                 )
             }
@@ -643,22 +652,6 @@ async function getModels(scanForMalicious = true) {
             makeImageBtn.disabled = true
         }
 
-        /* This code should no longer be needed. Commenting out for now, will cleanup later.
-        const sd_model_setting_key = "stable_diffusion_model"
-        const vae_model_setting_key = "vae_model"
-        const hypernetwork_model_key = "hypernetwork_model"
-
-        const stableDiffusionOptions = modelsOptions['stable-diffusion']
-        const vaeOptions = modelsOptions['vae']
-        const hypernetworkOptions = modelsOptions['hypernetwork']
-
-        // TODO: set default for model here too
-        SETTINGS[sd_model_setting_key].default = stableDiffusionOptions[0]
-        if (getSetting(sd_model_setting_key) == '' || SETTINGS[sd_model_setting_key].value == '') {
-            setSetting(sd_model_setting_key, stableDiffusionOptions[0])
-        }
-        */
-
         // notify ModelDropdown objects to refresh
         document.dispatchEvent(new Event("refreshModels"))
     } catch (e) {
@@ -667,4 +660,7 @@ async function getModels(scanForMalicious = true) {
 }
 
 // reload models button
-document.querySelector("#reload-models").addEventListener("click", () => getModels())
+document.querySelector("#reload-models").addEventListener("click", (e) => {
+    e.stopPropagation()
+    getModels()
+})

ui/media/js/task-manager.js

@@ -0,0 +1,409 @@
+const htmlTaskMap = new WeakMap()
+
+const pauseBtn = document.querySelector("#pause")
+const resumeBtn = document.querySelector("#resume")
+const processOrder = document.querySelector("#process_order_toggle")
+
+let pauseClient = false
+
+async function onIdle() {
+    const serverCapacity = SD.serverCapacity
+    if (pauseClient === true) {
+        await resumeClient()
+    }
+
+    for (const taskEntry of getUncompletedTaskEntries()) {
+        if (SD.activeTasks.size >= serverCapacity) {
+            break
+        }
+        const task = htmlTaskMap.get(taskEntry)
+        if (!task) {
+            const taskStatusLabel = taskEntry.querySelector(".taskStatusLabel")
+            taskStatusLabel.style.display = "none"
+            continue
+        }
+        await onTaskStart(task)
+    }
+}
+
+function getUncompletedTaskEntries() {
+    const taskEntries = Array.from(document.querySelectorAll("#preview .imageTaskContainer .taskStatusLabel"))
+        .filter((taskLabel) => taskLabel.style.display !== "none")
+        .map(function(taskLabel) {
+            let imageTaskContainer = taskLabel.parentNode
+            while (!imageTaskContainer.classList.contains("imageTaskContainer") && imageTaskContainer.parentNode) {
+                imageTaskContainer = imageTaskContainer.parentNode
+            }
+            return imageTaskContainer
+        })
+    if (!processOrder.checked) {
+        taskEntries.reverse()
+    }
+    return taskEntries
+}
+
+async function onTaskStart(task) {
+    if (!task.isProcessing || task.batchesDone >= task.batchCount) {
+        return
+    }
+
+    if (typeof task.startTime !== "number") {
+        task.startTime = Date.now()
+    }
+    if (!("instances" in task)) {
+        task["instances"] = []
+    }
+
+    task["stopTask"].innerHTML = '<i class="fa-solid fa-circle-stop"></i> Stop'
+    task["taskStatusLabel"].innerText = "Starting"
+    task["taskStatusLabel"].classList.add("waitingTaskLabel")
+
+    if (task.previewTaskReq !== undefined) {
+        let controlImagePreview = task.taskConfig.querySelector(".controlnet-img-preview > img")
+        try {
+            let result = await SD.filter(task.previewTaskReq)
+
+            controlImagePreview.src = result.output[0]
+            let controlImageLargePreview = task.taskConfig.querySelector(
+                ".controlnet-img-preview .task-fs-initimage img"
+            )
+            controlImageLargePreview.src = controlImagePreview.src
+        } catch (error) {
+            console.log("filter error", error)
+        }
+
+        delete task.previewTaskReq
+    }
+
+    let newTaskReqBody = task.reqBody
+    if (task.batchCount > 1) {
+        // Each output render batch needs it's own task reqBody instance to avoid altering the other runs after they are completed.
+        newTaskReqBody = Object.assign({}, task.reqBody)
+        if (task.batchesDone == task.batchCount - 1) {
+            // Last batch of the task
+            // If the number of parallel jobs is no factor of the total number of images, the last batch must create less than "parallel jobs count" images
+            // E.g. with numOutputsTotal = 6 and num_outputs = 5, the last batch shall only generate 1 image.
+            newTaskReqBody.num_outputs = task.numOutputsTotal - task.reqBody.num_outputs * (task.batchCount - 1)
+        }
+    }
+
+    const startSeed = task.seed || newTaskReqBody.seed
+    const genSeeds = Boolean(
+        typeof newTaskReqBody.seed !== "number" || (newTaskReqBody.seed === task.seed && task.numOutputsTotal > 1)
+    )
+    if (genSeeds) {
+        newTaskReqBody.seed = parseInt(startSeed) + task.batchesDone * task.reqBody.num_outputs
+    }
+
+    const outputContainer = document.createElement("div")
+    outputContainer.className = "img-batch"
+    task.outputContainer.insertBefore(outputContainer, task.outputContainer.firstChild)
+
+    const eventInfo = { reqBody: newTaskReqBody }
+    const callbacksPromises = PLUGINS["TASK_CREATE"].map((hook) => {
+        if (typeof hook !== "function") {
+            console.error("The provided TASK_CREATE hook is not a function. Hook: %o", hook)
+            return Promise.reject(new Error("hook is not a function."))
+        }
+        try {
+            return Promise.resolve(hook.call(task, eventInfo))
+        } catch (err) {
+            console.error(err)
+            return Promise.reject(err)
+        }
+    })
+    await Promise.allSettled(callbacksPromises)
+    let instance = eventInfo.instance
+    if (!instance) {
+        const factory = PLUGINS.OUTPUTS_FORMATS.get(eventInfo.reqBody?.output_format || newTaskReqBody.output_format)
+        if (factory) {
+            instance = await Promise.resolve(factory(eventInfo.reqBody || newTaskReqBody))
+        }
+        if (!instance) {
+            console.error(
+                `${factory ? "Factory " + String(factory) : "No factory defined"} for output format ${eventInfo.reqBody
+                    ?.output_format || newTaskReqBody.output_format}. Instance is ${instance ||
+                    "undefined"}. Using default renderer.`
+            )
+            instance = new SD.RenderTask(eventInfo.reqBody || newTaskReqBody)
+        }
+    }
+
+    task["instances"].push(instance)
+    task.batchesDone++
+
+    document.dispatchEvent(new CustomEvent("before_task_start", { detail: { task: task } }))
+
+    instance.enqueue(getTaskUpdater(task, newTaskReqBody, outputContainer)).then(
+        (renderResult) => {
+            onRenderTaskCompleted(task, newTaskReqBody, instance, outputContainer, renderResult)
+        },
+        (reason) => {
+            onTaskErrorHandler(task, newTaskReqBody, instance, reason)
+        }
+    )
+
+    document.dispatchEvent(new CustomEvent("after_task_start", { detail: { task: task } }))
+}
+
+function getTaskUpdater(task, reqBody, outputContainer) {
+    const outputMsg = task["outputMsg"]
+    const progressBar = task["progressBar"]
+    const progressBarInner = progressBar.querySelector("div")
+
+    const batchCount = task.batchCount
+    let lastStatus = undefined
+    return async function(event) {
+        if (this.status !== lastStatus) {
+            lastStatus = this.status
+            switch (this.status) {
+                case SD.TaskStatus.pending:
+                    task["taskStatusLabel"].innerText = "Pending"
+                    task["taskStatusLabel"].classList.add("waitingTaskLabel")
+                    break
+                case SD.TaskStatus.waiting:
+                    task["taskStatusLabel"].innerText = "Waiting"
+                    task["taskStatusLabel"].classList.add("waitingTaskLabel")
+                    task["taskStatusLabel"].classList.remove("activeTaskLabel")
+                    break
+                case SD.TaskStatus.processing:
+                case SD.TaskStatus.completed:
+                    task["taskStatusLabel"].innerText = "Processing"
+                    task["taskStatusLabel"].classList.add("activeTaskLabel")
+                    task["taskStatusLabel"].classList.remove("waitingTaskLabel")
+                    break
+                case SD.TaskStatus.stopped:
+                    break
+                case SD.TaskStatus.failed:
+                    if (!SD.isServerAvailable()) {
+                        logError(
+                            "Stable Diffusion is still starting up, please wait. If this goes on beyond a few minutes, Stable Diffusion has probably crashed. Please check the error message in the command-line window.",
+                            event,
+                            outputMsg
+                        )
+                    } else if (typeof event?.response === "object") {
+                        let msg = "Stable Diffusion had an error reading the response:<br/><pre>"
+                        if (this.exception) {
+                            msg += `Error: ${this.exception.message}<br/>`
+                        }
+                        try {
+                            // 'Response': body stream already read
+                            msg += "Read: " + (await event.response.text())
+                        } catch (e) {
+                            msg += "Unexpected end of stream. "
+                        }
+                        const bufferString = event.reader.bufferedString
+                        if (bufferString) {
+                            msg += "Buffered data: " + bufferString
+                        }
+                        msg += "</pre>"
+                        logError(msg, event, outputMsg)
+                    }
+                    break
+            }
+        }
+        if ("update" in event) {
+            const stepUpdate = event.update
+            if (!("step" in stepUpdate)) {
+                return
+            }
+            // task.instances can be a mix of different tasks with uneven number of steps (Render Vs Filter Tasks)
+            const instancesWithProgressUpdates = task.instances.filter((instance) => instance.step !== undefined)
+            const overallStepCount =
+                instancesWithProgressUpdates.reduce(
+                    (sum, instance) =>
+                        sum +
+                        (instance.isPending
+                            ? Math.max(0, instance.step || stepUpdate.step) /
+                              (instance.total_steps || stepUpdate.total_steps)
+                            : 1),
+                    0 // Initial value
+                ) * stepUpdate.total_steps // Scale to current number of steps.
+            const totalSteps = instancesWithProgressUpdates.reduce(
+                (sum, instance) => sum + (instance.total_steps || stepUpdate.total_steps),
+                stepUpdate.total_steps * (batchCount - task.batchesDone) // Initial value at (unstarted task count * Nbr of steps)
+            )
+            const percent = Math.min(100, 100 * (overallStepCount / totalSteps)).toFixed(0)
+
+            const timeTaken = stepUpdate.step_time // sec
+            const stepsRemaining = Math.max(0, totalSteps - overallStepCount)
+            const timeRemaining = timeTaken < 0 ? "" : millisecondsToStr(stepsRemaining * timeTaken * 1000)
+            outputMsg.innerHTML = `Batch ${task.batchesDone} of ${batchCount}. Generating image(s): ${percent}%. Time remaining (approx): ${timeRemaining}`
+            outputMsg.style.display = "block"
+            progressBarInner.style.width = `${percent}%`
+
+            if (stepUpdate.output) {
+                document.dispatchEvent(
+                    new CustomEvent("on_task_step", {
+                        detail: {
+                            task: task,
+                            reqBody: reqBody,
+                            stepUpdate: stepUpdate,
+                            outputContainer: outputContainer,
+                        },
+                    })
+                )
+            }
+        }
+    }
+}
+
+function onRenderTaskCompleted(task, reqBody, instance, outputContainer, stepUpdate) {
+    if (typeof stepUpdate === "object") {
+        if (stepUpdate.status === "succeeded") {
+            document.dispatchEvent(
+                new CustomEvent("on_render_task_success", {
+                    detail: {
+                        task: task,
+                        reqBody: reqBody,
+                        stepUpdate: stepUpdate,
+                        outputContainer: outputContainer,
+                    },
+                })
+            )
+        } else {
+            task.isProcessing = false
+            document.dispatchEvent(
+                new CustomEvent("on_render_task_fail", {
+                    detail: {
+                        task: task,
+                        reqBody: reqBody,
+                        stepUpdate: stepUpdate,
+                        outputContainer: outputContainer,
+                    },
+                })
+            )
+        }
+    }
+    if (task.isProcessing && task.batchesDone < task.batchCount) {
+        task["taskStatusLabel"].innerText = "Pending"
+        task["taskStatusLabel"].classList.add("waitingTaskLabel")
+        task["taskStatusLabel"].classList.remove("activeTaskLabel")
+        return
+    }
+    if ("instances" in task && task.instances.some((ins) => ins != instance && ins.isPending)) {
+        return
+    }
+
+    task.isProcessing = false
+    task["stopTask"].innerHTML = '<i class="fa-solid fa-trash-can"></i> Remove'
+    task["taskStatusLabel"].style.display = "none"
+
+    let time = millisecondsToStr(Date.now() - task.startTime)
+
+    if (task.batchesDone == task.batchCount) {
+        if (!task.outputMsg.innerText.toLowerCase().includes("error")) {
+            task.outputMsg.innerText = `Processed ${task.numOutputsTotal} images in ${time}`
+        }
+        task.progressBar.style.height = "0px"
+        task.progressBar.style.border = "0px solid var(--background-color3)"
+        task.progressBar.classList.remove("active")
+        // setStatus("request", "done", "success")
+    } else {
+        task.outputMsg.innerText += `. Task ended after ${time}`
+    }
+
+    // if (randomSeedField.checked) { // we already update this before the task starts
+    //     seedField.value = task.seed
+    // }
+
+    if (SD.activeTasks.size > 0) {
+        return
+    }
+    const uncompletedTasks = getUncompletedTaskEntries()
+    if (uncompletedTasks && uncompletedTasks.length > 0) {
+        return
+    }
+
+    if (pauseClient) {
+        resumeBtn.click()
+    }
+
+    document.dispatchEvent(
+        new CustomEvent("on_all_tasks_complete", {
+            detail: {},
+        })
+    )
+}
+
+function resumeClient() {
+    if (pauseClient) {
+        document.body.classList.remove("wait-pause")
+        document.body.classList.add("pause")
+    }
+    return new Promise((resolve) => {
+        let playbuttonclick = function() {
+            resumeBtn.removeEventListener("click", playbuttonclick)
+            resolve("resolved")
+        }
+        resumeBtn.addEventListener("click", playbuttonclick)
+    })
+}
+
+function abortTask(task) {
+    if (!task.isProcessing) {
+        return false
+    }
+    task.isProcessing = false
+    task.progressBar.classList.remove("active")
+    task["taskStatusLabel"].style.display = "none"
+    task["stopTask"].innerHTML = '<i class="fa-solid fa-trash-can"></i> Remove'
+    if (!task.instances?.some((r) => r.isPending)) {
+        return
+    }
+    task.instances.forEach((instance) => {
+        try {
+            instance.abort()
+        } catch (e) {
+            console.error(e)
+        }
+    })
+}
+
+async function stopAllTasks() {
+    getUncompletedTaskEntries().forEach((taskEntry) => {
+        const taskStatusLabel = taskEntry.querySelector(".taskStatusLabel")
+        if (taskStatusLabel) {
+            taskStatusLabel.style.display = "none"
+        }
+        const task = htmlTaskMap.get(taskEntry)
+        if (!task) {
+            return
+        }
+        abortTask(task)
+    })
+}
+
+function onTaskErrorHandler(task, reqBody, instance, reason) {
+    if (!task.isProcessing) {
+        return
+    }
+    console.log("Render request %o, Instance: %o, Error: %s", reqBody, instance, reason)
+    abortTask(task)
+    const outputMsg = task["outputMsg"]
+    logError(
+        "Stable Diffusion had an error. Please check the logs in the command-line window. <br/><br/>" +
+            reason +
+            "<br/><pre>" +
+            reason.stack +
+            "</pre>",
+        task,
+        outputMsg
+    )
+    // setStatus("request", "error", "error")
+}
+
+pauseBtn.addEventListener("click", function() {
+    pauseClient = true
+    pauseBtn.style.display = "none"
+    resumeBtn.style.display = "inline"
+    document.body.classList.add("wait-pause")
+})
+
+resumeBtn.addEventListener("click", function() {
+    pauseClient = false
+    resumeBtn.style.display = "none"
+    pauseBtn.style.display = "inline"
+    document.body.classList.remove("pause")
+    document.body.classList.remove("wait-pause")
+})

ui/media/js/utils.js

@@ -1097,6 +1097,48 @@ async function deleteKeys(keyToDelete) {
     }
 }
 
+/**
+ * @param {String} Data URL of the image
+ * @param {Integer} Top left X-coordinate of the crop area
+ * @param {Integer} Top left Y-coordinate of the crop area
+ * @param {Integer} Width of the crop area
+ * @param {Integer} Height of the crop area
+ * @return {String}
+ */
+function cropImageDataUrl(dataUrl, x, y, width, height) {
+    return new Promise((resolve, reject) => {
+        const image = new Image()
+        image.src = dataUrl
+
+        image.onload = () => {
+            const canvas = document.createElement('canvas')
+            canvas.width = width
+            canvas.height = height
+
+            const ctx = canvas.getContext('2d')
+            ctx.drawImage(image, x, y, width, height, 0, 0, width, height)
+
+            const croppedDataUrl = canvas.toDataURL('image/png')
+            resolve(croppedDataUrl)
+        }
+
+        image.onerror = (error) => {
+            reject(error)
+        }
+    })
+}
+
+/**
+ * @param {String} HTML representing a single element
+ * @return {Element}
+ */
+function htmlToElement(html) {
+    var template = document.createElement('template');
+    html = html.trim(); // Never return a text node of whitespace as the result
+    template.innerHTML = html;
+    return template.content.firstChild;
+}
+
 function modalDialogCloseOnBackdropClick(dialog) {
     dialog.addEventListener('mousedown', function (event) {
         // Firefox creates an event with clientX|Y = 0|0 when choosing an <option>.
@@ -1156,4 +1198,37 @@ function makeDialogDraggable(element) {
     })() )
 }
 
+function logMsg(msg, level, outputMsg) {
+    if (outputMsg.hasChildNodes()) {
+        outputMsg.appendChild(document.createElement("br"))
+    }
+    if (level === "error") {
+        outputMsg.innerHTML += '<span style="color: red">Error: ' + msg + "</span>"
+    } else if (level === "warn") {
+        outputMsg.innerHTML += '<span style="color: orange">Warning: ' + msg + "</span>"
+    } else {
+        outputMsg.innerText += msg
+    }
+    console.log(level, msg)
+}
 
+function logError(msg, res, outputMsg) {
+    logMsg(msg, "error", outputMsg)
+
+    console.log("request error", res)
+    console.trace()
+    // setStatus("request", "error", "error")
+}
+
+function playSound() {
+    const audio = new Audio("/media/ding.mp3")
+    audio.volume = 0.2
+    var promise = audio.play()
+    if (promise !== undefined) {
+        promise
+            .then((_) => {})
+            .catch((error) => {
+                console.warn("browser blocked autoplay")
+            })
+    }
+}

ui/plugins/ui/lora-prompt-parser.plugin.js

@@ -0,0 +1,94 @@
+/*
+    LoRA Prompt Parser 1.0
+    by Patrice
+
+    Copying and pasting a prompt with a LoRA tag will automatically select the corresponding option in the Easy Diffusion dropdown and remove the LoRA tag from the prompt. The LoRA must be already available in the corresponding Easy Diffusion dropdown (this is not a LoRA downloader).
+*/
+(function() {
+    "use strict"
+    
+    promptField.addEventListener('input', function(e) {
+        let loraExtractSetting = document.getElementById("extract_lora_from_prompt")
+        if (!loraExtractSetting.checked) {
+            return
+        }
+
+        const { LoRA, prompt } = extractLoraTags(e.target.value);
+		//console.log('e.target: ' + JSON.stringify(LoRA));
+    
+        if (LoRA !== null && LoRA.length > 0) {
+            promptField.value = prompt.replace(/,+$/, ''); // remove any trailing ,
+    
+            if (testDiffusers?.checked === false) {
+                showToast("LoRA's are only supported with diffusers. Just stripping the LoRA tag from the prompt.")
+            }
+        }
+                                 
+        if (LoRA !== null && LoRA.length > 0 && testDiffusers?.checked) {
+            let modelNames = LoRA.map(e => e.lora_model_0)
+            let modelWeights = LoRA.map(e => e.lora_alpha_0)
+            loraModelField.value = {modelNames: modelNames, modelWeights: modelWeights}
+
+            showToast("Prompt successfully processed")
+			
+        }
+            
+        //promptField.dispatchEvent(new Event('change'));
+    });
+    
+    // extract LoRA tags from strings
+    function extractLoraTags(prompt) {
+        // Define the regular expression for the tags
+        const regex = /<(?:lora|lyco):([^:>]+)(?::([^:>]*))?(?::([^:>]*))?>/gi
+
+        // Initialize an array to hold the matches
+        let matches = []
+
+        // Iterate over the string, finding matches
+        for (const match of prompt.matchAll(regex)) {
+            const modelFileName = match[1].trim()
+            const loraPathes = getAllModelPathes("lora", modelFileName)
+            if (loraPathes.length > 0) {
+                const loraPath = loraPathes[0]
+                // Initialize an object to hold a match
+                let loraTag = {
+                    lora_model_0: loraPath,
+                }
+				//console.log("Model:" +  modelFileName);
+        
+                // If weight is provided, add it to the loraTag object
+                if (match[2] !== undefined && match[2] !== '') {
+                    loraTag.lora_alpha_0 = parseFloat(match[2].trim())
+                }
+                else
+                {
+                    loraTag.lora_alpha_0 = 0.5
+                }
+				
+        
+                // If blockweights are provided, add them to the loraTag object
+                if (match[3] !== undefined && match[3] !== '') {
+                    loraTag.blockweights = match[3].trim()
+                }
+        
+                // Add the loraTag object to the array of matches
+                matches.push(loraTag);
+				//console.log(JSON.stringify(matches));
+            }
+            else
+            {
+                showToast("LoRA not found: " + match[1].trim(), 5000, true)            
+            }
+        }
+
+        // Clean up the prompt string, e.g. from "apple, banana, <lora:...>, orange, <lora:...>  , pear <lora:...>, <lora:...>" to "apple, banana, orange, pear"
+        let cleanedPrompt = prompt.replace(regex, '').replace(/(\s*,\s*(?=\s*,|$))|(^\s*,\s*)|\s+/g, ' ').trim();
+		//console.log('Matches: ' + JSON.stringify(matches));
+
+        // Return the array of matches and cleaned prompt string
+        return {
+            LoRA: matches,
+            prompt: cleanedPrompt
+        }
+    }
+})()

ui/plugins/ui/merge.plugin.js

@@ -1,454 +0,0 @@
-;(function() {
-    "use strict"
-
-    ///////////////////// Function section
-    function smoothstep(x) {
-        return x * x * (3 - 2 * x)
-    }
-
-    function smootherstep(x) {
-        return x * x * x * (x * (x * 6 - 15) + 10)
-    }
-
-    function smootheststep(x) {
-        let y = -20 * Math.pow(x, 7)
-        y += 70 * Math.pow(x, 6)
-        y -= 84 * Math.pow(x, 5)
-        y += 35 * Math.pow(x, 4)
-        return y
-    }
-    function getCurrentTime() {
-        const now = new Date()
-        let hours = now.getHours()
-        let minutes = now.getMinutes()
-        let seconds = now.getSeconds()
-
-        hours = hours < 10 ? `0${hours}` : hours
-        minutes = minutes < 10 ? `0${minutes}` : minutes
-        seconds = seconds < 10 ? `0${seconds}` : seconds
-
-        return `${hours}:${minutes}:${seconds}`
-    }
-
-    function addLogMessage(message) {
-        const logContainer = document.getElementById("merge-log")
-        logContainer.innerHTML += `<i>${getCurrentTime()}</i> ${message}<br>`
-
-        // Scroll to the bottom of the log
-        logContainer.scrollTop = logContainer.scrollHeight
-
-        document.querySelector("#merge-log-container").style.display = "block"
-    }
-
-    function addLogSeparator() {
-        const logContainer = document.getElementById("merge-log")
-        logContainer.innerHTML += "<hr>"
-
-        logContainer.scrollTop = logContainer.scrollHeight
-    }
-
-    function drawDiagram(fn) {
-        const SIZE = 300
-        const canvas = document.getElementById("merge-canvas")
-        canvas.height = canvas.width = SIZE
-        const ctx = canvas.getContext("2d")
-
-        // Draw coordinate system
-        ctx.scale(1, -1)
-        ctx.translate(0, -canvas.height)
-        ctx.lineWidth = 1
-        ctx.beginPath()
-
-        ctx.strokeStyle = "white"
-        ctx.moveTo(0, 0)
-        ctx.lineTo(0, SIZE)
-        ctx.lineTo(SIZE, SIZE)
-        ctx.lineTo(SIZE, 0)
-        ctx.lineTo(0, 0)
-        ctx.lineTo(SIZE, SIZE)
-        ctx.stroke()
-        ctx.beginPath()
-        ctx.setLineDash([1, 2])
-        const n = SIZE / 10
-        for (let i = n; i < SIZE; i += n) {
-            ctx.moveTo(0, i)
-            ctx.lineTo(SIZE, i)
-            ctx.moveTo(i, 0)
-            ctx.lineTo(i, SIZE)
-        }
-        ctx.stroke()
-        ctx.beginPath()
-        ctx.setLineDash([])
-        ctx.beginPath()
-        ctx.strokeStyle = "black"
-        ctx.lineWidth = 3
-        // Plot function
-        const numSamples = 20
-        for (let i = 0; i <= numSamples; i++) {
-            const x = i / numSamples
-            const y = fn(x)
-
-            const canvasX = x * SIZE
-            const canvasY = y * SIZE
-
-            if (i === 0) {
-                ctx.moveTo(canvasX, canvasY)
-            } else {
-                ctx.lineTo(canvasX, canvasY)
-            }
-        }
-        ctx.stroke()
-        // Plot alpha values (yellow boxes)
-        let start = parseFloat(document.querySelector("#merge-start").value)
-        let step = parseFloat(document.querySelector("#merge-step").value)
-        let iterations = document.querySelector("#merge-count").value >> 0
-        ctx.beginPath()
-        ctx.fillStyle = "yellow"
-        for (let i = 0; i < iterations; i++) {
-            const alpha = (start + i * step) / 100
-            const x = alpha * SIZE
-            const y = fn(alpha) * SIZE
-            if (x <= SIZE) {
-                ctx.rect(x - 3, y - 3, 6, 6)
-                ctx.fill()
-            } else {
-                ctx.strokeStyle = "red"
-                ctx.moveTo(0, 0)
-                ctx.lineTo(0, SIZE)
-                ctx.lineTo(SIZE, SIZE)
-                ctx.lineTo(SIZE, 0)
-                ctx.lineTo(0, 0)
-                ctx.lineTo(SIZE, SIZE)
-                ctx.stroke()
-                addLogMessage("<i>Warning: maximum ratio is &#8805; 100%</i>")
-            }
-        }
-    }
-
-    function updateChart() {
-        let fn = (x) => x
-        switch (document.querySelector("#merge-interpolation").value) {
-            case "SmoothStep":
-                fn = smoothstep
-                break
-            case "SmootherStep":
-                fn = smootherstep
-                break
-            case "SmoothestStep":
-                fn = smootheststep
-                break
-        }
-        drawDiagram(fn)
-    }
-    createTab({
-        id: "merge",
-        icon: "fa-code-merge",
-        label: "Merge models",
-        css: `
-        #tab-content-merge .tab-content-inner {
-            max-width: 100%;
-            padding: 10pt;
-        }
-        .merge-container {  
-            margin-left: 15%;
-            margin-right: 15%;
-            text-align: left;
-            display: inline-grid;
-            grid-template-columns: 1fr 1fr;
-            grid-template-rows: auto auto auto;
-            gap: 0px 0px;
-            grid-auto-flow: row;
-            grid-template-areas:
-              "merge-input merge-config"
-              "merge-buttons merge-buttons";
-        }
-        .merge-container p {
-            margin-top: 3pt;
-            margin-bottom: 3pt;
-        }
-        .merge-config .tab-content {
-            background: var(--background-color1);
-            border-radius: 3pt;
-        }
-        .merge-config .tab-content-inner {
-            text-align: left;
-        }
-
-        .merge-input { 
-            grid-area: merge-input; 
-            padding-left:1em;
-        }
-        .merge-config { 
-            grid-area: merge-config; 
-            padding:1em;
-        }
-        .merge-config input { 
-            margin-bottom: 3px; 
-        }
-        .merge-config select { 
-            margin-bottom: 3px; 
-        }
-        .merge-buttons { 
-            grid-area: merge-buttons; 
-            padding:1em; 
-            text-align: center;
-        }
-        #merge-button  { 
-            padding: 8px; 
-            width:20em; 
-        }
-        div#merge-log {
-            height:150px; 
-            overflow-x:hidden;
-            overflow-y:scroll;
-            background:var(--background-color1);
-            border-radius: 3pt;
-        }
-        div#merge-log i {
-            color: hsl(var(--accent-hue), 100%, calc(2*var(--accent-lightness))); 
-            font-family: monospace;
-        }
-        .disabled { 
-            background: var(--background-color4); 
-            color: var(--text-color); 
-        }
-        #merge-type-tabs {
-            border-bottom: 1px solid black;
-        }
-        #merge-log-container {
-            display: none;
-        }
-        .merge-container #merge-warning {
-            color: rgb(153, 153, 153);
-        }`,
-        content: `
-        <div class="merge-container panel-box">
-          <div class="merge-input">
-             <p><label for="#mergeModelA">Select Model A:</label></p>
-             <input id="mergeModelA" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" />
-             <p><label for="#mergeModelB">Select Model B:</label></p>
-             <input id="mergeModelB" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" />
-             <br/><br/>
-             <p id="merge-warning"><small><b>Important:</b> Please merge models of similar type.<br/>For e.g. <code>SD 1.4</code> models with only <code>SD 1.4/1.5</code> models,<br/><code>SD 2.0</code> with <code>SD 2.0</code>-type, and <code>SD 2.1</code> with <code>SD 2.1</code>-type models.</small></p>
-             <br/>
-             <table>
-                <tr>
-                    <td><label for="#merge-filename">Output file name:</label></td>
-                    <td><input id="merge-filename" size=24> <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Base name of the output file.<br>Mix ratio and file suffix will be appended to this.</span></i></td>
-                </tr>
-                <tr>
-                    <td><label for="#merge-fp">Output precision:</label></td>
-                    <td><select id="merge-fp">
-                        <option value="fp16">fp16 (smaller file size)</option>
-                        <option value="fp32">fp32 (larger file size)</option>
-                    </select>
-                    <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Image generation uses fp16, so it's a good choice.<br>Use fp32 if you want to use the result models for more mixes</span></i>
-                    </td>
-                </tr>
-                <tr>
-                    <td><label for="#merge-format">Output file format:</label></td>
-                    <td><select id="merge-format">
-                        <option value="safetensors">Safetensors (recommended)</option>
-                        <option value="ckpt">CKPT/Pickle (legacy format)</option>
-                    </select>
-                    </td>
-                </tr>
-             </table>
-             <br/>
-             <div id="merge-log-container">
-                <p><label for="#merge-log">Log messages:</label></p>
-                <div id="merge-log"></div>
-             </div>
-          </div>
-          <div class="merge-config">
-             <div class="tab-container">
-                <span id="tab-merge-opts-single" class="tab active">
-                    <span>Make a single file</small></span>
-                </span>
-                <span id="tab-merge-opts-batch" class="tab">
-                    <span>Make multiple variations</small></span>
-                </span>
-             </div>
-             <div>
-                <div id="tab-content-merge-opts-single" class="tab-content active">
-                    <div class="tab-content-inner">
-                        <small>Saves a single merged model file, at the specified merge ratio.</small><br/><br/>
-                        <label for="#single-merge-ratio-slider">Merge ratio:</label>
-                        <input id="single-merge-ratio-slider" name="single-merge-ratio-slider" class="editor-slider" value="50" type="range" min="1" max="1000">
-                        <input id="single-merge-ratio" size=2 value="5">%
-                        <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Model A's contribution to the mix. The rest will be from Model B.</span></i>
-                    </div>
-                </div>
-                <div id="tab-content-merge-opts-batch" class="tab-content">
-                    <div class="tab-content-inner">
-                        <small>Saves multiple variations of the model, at different merge ratios.<br/>Each variation will be saved as a separate file.</small><br/><br/>
-                        <table>
-                            <tr><td><label for="#merge-count">Number of variations:</label></td>
-                                <td> <input id="merge-count" size=2 value="5"></td>
-                                <td> <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Number of models to create</span></i></td></tr>
-                            <tr><td><label for="#merge-start">Starting merge ratio:</label></td>
-                                <td> <input id="merge-start" size=2 value="5">%</td>
-                                <td> <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Smallest share of model A in the mix</span></i></td></tr>
-                            <tr><td><label for="#merge-step">Increment each step:</label></td>
-                                <td> <input id="merge-step" size=2 value="10">%</td>
-                                <td> <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Share of model A added into the mix per step</span></i></td></tr>
-                            <tr><td><label for="#merge-interpolation">Interpolation model:</label></td>
-                                <td> <select id="merge-interpolation">
-                                    <option>Exact</option>
-                                    <option>SmoothStep</option>
-                                    <option>SmootherStep</option>
-                                    <option>SmoothestStep</option>
-                                </select></td>
-                                <td> <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Sigmoid function to be applied to the model share before mixing</span></i></td></tr>
-                        </table>
-                        <br/>
-                        <small>Preview of variation ratios:</small><br/>
-                        <canvas id="merge-canvas" width="400" height="400"></canvas>
-                    </div>
-                </div>
-             </div>
-          </div>
-          <div class="merge-buttons">
-             <button id="merge-button" class="primaryButton">Merge models</button>
-          </div>
-        </div>`,
-        onOpen: ({ firstOpen }) => {
-            if (!firstOpen) {
-                return
-            }
-
-            const tabSettingsSingle = document.querySelector("#tab-merge-opts-single")
-            const tabSettingsBatch = document.querySelector("#tab-merge-opts-batch")
-            linkTabContents(tabSettingsSingle)
-            linkTabContents(tabSettingsBatch)
-
-            console.log("Activate")
-            let mergeModelAField = new ModelDropdown(document.querySelector("#mergeModelA"), "stable-diffusion")
-            let mergeModelBField = new ModelDropdown(document.querySelector("#mergeModelB"), "stable-diffusion")
-            updateChart()
-
-            // slider
-            const singleMergeRatioField = document.querySelector("#single-merge-ratio")
-            const singleMergeRatioSlider = document.querySelector("#single-merge-ratio-slider")
-
-            function updateSingleMergeRatio() {
-                singleMergeRatioField.value = singleMergeRatioSlider.value / 10
-                singleMergeRatioField.dispatchEvent(new Event("change"))
-            }
-
-            function updateSingleMergeRatioSlider() {
-                if (singleMergeRatioField.value < 0) {
-                    singleMergeRatioField.value = 0
-                } else if (singleMergeRatioField.value > 100) {
-                    singleMergeRatioField.value = 100
-                }
-
-                singleMergeRatioSlider.value = singleMergeRatioField.value * 10
-                singleMergeRatioSlider.dispatchEvent(new Event("change"))
-            }
-
-            singleMergeRatioSlider.addEventListener("input", updateSingleMergeRatio)
-            singleMergeRatioField.addEventListener("input", updateSingleMergeRatioSlider)
-            updateSingleMergeRatio()
-
-            document.querySelector(".merge-config").addEventListener("change", updateChart)
-
-            document.querySelector("#merge-button").addEventListener("click", async function(e) {
-                // Build request template
-                let model0 = mergeModelAField.value
-                let model1 = mergeModelBField.value
-                let request = { model0: model0, model1: model1 }
-                request["use_fp16"] = document.querySelector("#merge-fp").value == "fp16"
-                let iterations = document.querySelector("#merge-count").value >> 0
-                let start = parseFloat(document.querySelector("#merge-start").value)
-                let step = parseFloat(document.querySelector("#merge-step").value)
-
-                if (isTabActive(tabSettingsSingle)) {
-                    start = parseFloat(singleMergeRatioField.value)
-                    step = 0
-                    iterations = 1
-                    addLogMessage(`merge ratio = ${start}%`)
-                } else {
-                    addLogMessage(`start = ${start}%`)
-                    addLogMessage(`step  = ${step}%`)
-                }
-
-                if (start + (iterations - 1) * step >= 100) {
-                    addLogMessage("<i>Aborting: maximum ratio is &#8805; 100%</i>")
-                    addLogMessage("Reduce the number of variations or the step size")
-                    addLogSeparator()
-                    document.querySelector("#merge-count").focus()
-                    return
-                }
-
-                if (document.querySelector("#merge-filename").value == "") {
-                    addLogMessage("<i>Aborting: No output file name specified</i>")
-                    addLogSeparator()
-                    document.querySelector("#merge-filename").focus()
-                    return
-                }
-
-                // Disable merge button
-                e.target.disabled = true
-                e.target.classList.add("disabled")
-                let cursor = $("body").css("cursor")
-                let label = document.querySelector("#merge-button").innerHTML
-                $("body").css("cursor", "progress")
-                document.querySelector("#merge-button").innerHTML = "Merging models ..."
-
-                addLogMessage("Merging models")
-                addLogMessage("Model A: " + model0)
-                addLogMessage("Model B: " + model1)
-
-                // Batch main loop
-                for (let i = 0; i < iterations; i++) {
-                    let alpha = (start + i * step) / 100
-
-                    if (isTabActive(tabSettingsBatch)) {
-                        switch (document.querySelector("#merge-interpolation").value) {
-                            case "SmoothStep":
-                                alpha = smoothstep(alpha)
-                                break
-                            case "SmootherStep":
-                                alpha = smootherstep(alpha)
-                                break
-                            case "SmoothestStep":
-                                alpha = smootheststep(alpha)
-                                break
-                        }
-                    }
-                    addLogMessage(`merging batch job ${i + 1}/${iterations}, alpha = ${alpha.toFixed(5)}...`)
-
-                    request["out_path"] = document.querySelector("#merge-filename").value
-                    request["out_path"] += "-" + alpha.toFixed(5) + "." + document.querySelector("#merge-format").value
-                    addLogMessage(`&nbsp;&nbsp;filename: ${request["out_path"]}`)
-
-                    // sdkit documentation: "ratio - the ratio of the second model. 1 means only the second model will be used."
-                    request["ratio"] = 1-alpha 
-                    let res = await fetch("/model/merge", {
-                        method: "POST",
-                        headers: { "Content-Type": "application/json" },
-                        body: JSON.stringify(request),
-                    })
-                    const data = await res.json()
-                    addLogMessage(JSON.stringify(data))
-                }
-                addLogMessage(
-                    "<b>Done.</b> The models have been saved to your <tt>models/stable-diffusion</tt> folder."
-                )
-                addLogSeparator()
-                // Re-enable merge button
-                $("body").css("cursor", cursor)
-                document.querySelector("#merge-button").innerHTML = label
-                e.target.disabled = false
-                e.target.classList.remove("disabled")
-
-                // Update model list
-                stableDiffusionModelField.innerHTML = ""
-                vaeModelField.innerHTML = ""
-                hypernetworkModelField.innerHTML = ""
-                await getModels()
-            })
-        },
-    })
-})()

ui/plugins/ui/model-tools.plugin.js

@@ -0,0 +1,770 @@
+;(function() {
+    "use strict"
+
+    let mergeCSS = `
+        /*********** Main tab ***********/
+        .tab-centered {
+            justify-content: center;
+        }
+
+        #model-tool-tab-content {
+            background-color: var(--background-color3);
+        }
+
+        #model-tool-tab-content .tab-content-inner {
+            text-align: initial;
+        }
+
+        #model-tool-tab-bar .tab {
+            margin-bottom: 0px;
+            border-top-left-radius: var(--input-border-radius);
+            background-color: var(--background-color3);
+            padding: 6px 6px 0.8em 6px;
+        }
+        #tab-content-merge .tab-content-inner {
+            max-width: 100%;
+            padding: 10pt;
+        }
+
+        /*********** Merge UI ***********/
+        .merge-model-container {  
+            margin-left: 15%;
+            margin-right: 15%;
+            text-align: left;
+            display: inline-grid;
+            grid-template-columns: 1fr 1fr;
+            grid-template-rows: auto auto auto;
+            gap: 0px 0px;
+            grid-auto-flow: row;
+            grid-template-areas:
+              "merge-input merge-config"
+              "merge-buttons merge-buttons";
+        }
+        .merge-model-container p {
+            margin-top: 3pt;
+            margin-bottom: 3pt;
+        }
+        .merge-config .tab-content {
+            background: var(--background-color1);
+            border-radius: 3pt;
+        }
+        .merge-config .tab-content-inner {
+            text-align: left;
+        }
+
+        .merge-input { 
+            grid-area: merge-input; 
+            padding-left:1em;
+        }
+        .merge-config { 
+            grid-area: merge-config; 
+            padding:1em;
+        }
+        .merge-config input { 
+            margin-bottom: 3px; 
+        }
+        .merge-config select { 
+            margin-bottom: 3px; 
+        }
+        .merge-buttons { 
+            grid-area: merge-buttons; 
+            padding:1em; 
+            text-align: center;
+        }
+        #merge-button  { 
+            padding: 8px; 
+            width:20em; 
+        }
+        div#merge-log {
+            height:150px; 
+            overflow-x:hidden;
+            overflow-y:scroll;
+            background:var(--background-color1);
+            border-radius: 3pt;
+        }
+        div#merge-log i {
+            color: hsl(var(--accent-hue), 100%, calc(2*var(--accent-lightness))); 
+            font-family: monospace;
+        }
+        .disabled { 
+            background: var(--background-color4); 
+            color: var(--text-color); 
+        }
+        #merge-type-tabs {
+            border-bottom: 1px solid black;
+        }
+        #merge-log-container {
+            display: none;
+        }
+        .merge-model-container #merge-warning {
+            color: var(--small-label-color);
+        }
+
+        /*********** LORA UI ***********/
+        .lora-manager-grid {  
+            display: grid;
+            gap: 0px 8px;
+            grid-auto-flow: row;
+        }
+
+        @media screen and (min-width: 1501px) {
+            .lora-manager-grid textarea {
+                height:350px;
+            }
+
+            .lora-manager-grid {  
+                grid-template-columns: auto 1fr 1fr;
+                grid-template-rows: auto 1fr;
+                grid-template-areas:
+                    "selector  selector selector"
+                    "thumbnail keywords notes";
+            }
+        }
+
+        @media screen and (min-width: 1001px) and (max-width: 1500px) {
+            .lora-manager-grid textarea {
+                height:250px;
+            }
+
+            .lora-manager-grid {  
+                grid-template-columns: auto auto;
+                grid-template-rows: auto auto auto;
+                grid-template-areas:
+                    "selector  selector"
+                    "thumbnail keywords"
+                    "thumbnail notes";
+            }
+
+        }
+
+        @media screen and (max-width: 1000px) {
+            .lora-manager-grid textarea {
+                height:200px;
+            }
+
+            .lora-manager-grid {  
+                grid-template-columns: auto;
+                grid-template-rows: auto auto auto auto;
+                grid-template-areas:
+                    "selector"
+                    "keywords"
+                    "thumbnail"
+                    "notes";
+            }
+
+        }
+
+        .lora-manager-grid-selector { 
+            grid-area: selector; 
+            justify-self: start;
+        }
+
+        .lora-manager-grid-thumbnail { 
+            grid-area: thumbnail; 
+            justify-self: center;
+        }
+
+        .lora-manager-grid-keywords { 
+            grid-area: keywords; 
+        }
+
+        .lora-manager-grid-notes { 
+            grid-area: notes; 
+        }
+        
+        .lora-manager-grid p {
+            margin-bottom: 2px;
+        }
+
+
+        `
+
+    let mergeUI = `
+        <div class="merge-model-container panel-box">
+          <div class="merge-input">
+             <p><label for="#mergeModelA">Select Model A:</label></p>
+             <input id="mergeModelA" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" />
+             <p><label for="#mergeModelB">Select Model B:</label></p>
+             <input id="mergeModelB" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" />
+             <br/><br/>
+             <p id="merge-warning"><small><b>Important:</b> Please merge models of similar type.<br/>For e.g. <code>SD 1.4</code> models with only <code>SD 1.4/1.5</code> models,<br/><code>SD 2.0</code> with <code>SD 2.0</code>-type, and <code>SD 2.1</code> with <code>SD 2.1</code>-type models.</small></p>
+             <br/>
+             <table>
+                <tr>
+                    <td><label for="#merge-filename">Output file name:</label></td>
+                    <td><input id="merge-filename" size=24> <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Base name of the output file.<br>Mix ratio and file suffix will be appended to this.</span></i></td>
+                </tr>
+                <tr>
+                    <td><label for="#merge-fp">Output precision:</label></td>
+                    <td><select id="merge-fp">
+                        <option value="fp16">fp16 (smaller file size)</option>
+                        <option value="fp32">fp32 (larger file size)</option>
+                    </select>
+                    <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Image generation uses fp16, so it's a good choice.<br>Use fp32 if you want to use the result models for more mixes</span></i>
+                    </td>
+                </tr>
+                <tr>
+                    <td><label for="#merge-format">Output file format:</label></td>
+                    <td><select id="merge-format">
+                        <option value="safetensors">Safetensors (recommended)</option>
+                        <option value="ckpt">CKPT/Pickle (legacy format)</option>
+                    </select>
+                    </td>
+                </tr>
+             </table>
+             <br/>
+             <div id="merge-log-container">
+                <p><label for="#merge-log">Log messages:</label></p>
+                <div id="merge-log"></div>
+             </div>
+          </div>
+          <div class="merge-config">
+             <div class="tab-container">
+                <span id="tab-merge-opts-single" class="tab active">
+                    <span>Make a single file</small></span>
+                </span>
+                <span id="tab-merge-opts-batch" class="tab">
+                    <span>Make multiple variations</small></span>
+                </span>
+             </div>
+             <div>
+                <div id="tab-content-merge-opts-single" class="tab-content active">
+                    <div class="tab-content-inner">
+                        <small>Saves a single merged model file, at the specified merge ratio.</small><br/><br/>
+                        <label for="#single-merge-ratio-slider">Merge ratio:</label>
+                        <input id="single-merge-ratio-slider" name="single-merge-ratio-slider" class="editor-slider" value="50" type="range" min="1" max="1000">
+                        <input id="single-merge-ratio" size=2 value="5">%
+                        <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Model A's contribution to the mix. The rest will be from Model B.</span></i>
+                    </div>
+                </div>
+                <div id="tab-content-merge-opts-batch" class="tab-content">
+                    <div class="tab-content-inner">
+                        <small>Saves multiple variations of the model, at different merge ratios.<br/>Each variation will be saved as a separate file.</small><br/><br/>
+                        <table>
+                            <tr><td><label for="#merge-count">Number of variations:</label></td>
+                                <td> <input id="merge-count" size=2 value="5"></td>
+                                <td> <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Number of models to create</span></i></td></tr>
+                            <tr><td><label for="#merge-start">Starting merge ratio:</label></td>
+                                <td> <input id="merge-start" size=2 value="5">%</td>
+                                <td> <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Smallest share of model A in the mix</span></i></td></tr>
+                            <tr><td><label for="#merge-step">Increment each step:</label></td>
+                                <td> <input id="merge-step" size=2 value="10">%</td>
+                                <td> <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Share of model A added into the mix per step</span></i></td></tr>
+                            <tr><td><label for="#merge-interpolation">Interpolation model:</label></td>
+                                <td> <select id="merge-interpolation">
+                                    <option>Exact</option>
+                                    <option>SmoothStep</option>
+                                    <option>SmootherStep</option>
+                                    <option>SmoothestStep</option>
+                                </select></td>
+                                <td> <i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Sigmoid function to be applied to the model share before mixing</span></i></td></tr>
+                        </table>
+                        <br/>
+                        <small>Preview of variation ratios:</small><br/>
+                        <canvas id="merge-canvas" width="400" height="400"></canvas>
+                    </div>
+                </div>
+             </div>
+          </div>
+          <div class="merge-buttons">
+             <button id="merge-button" class="primaryButton">Merge models</button>
+          </div>
+        </div>`
+
+        
+    let loraUI=`
+        <div class="panel-box lora-manager-grid">
+            <div class="lora-manager-grid-selector">
+                <label for="#loraModel">Select Lora:</label>
+                <input id="loraModel" type="text" spellcheck="false" autocomplete="off" class="model-filter" data-path="" />
+            </div>
+            <div class="lora-manager-grid-thumbnail">
+                <p style="height:2em;">Thumbnail:</p>
+                <div style="position:relative; height:256px; width:256px;background-color:#222;border-radius:1em;margin-bottom:1em;">
+                    <i id="lora-manager-image-placeholder" class="fa-regular fa-image" style="font-size:500%;color:#555;position:absolute; top: 50%; left: 50%; transform: translate(-50%,-50%);"></i>
+                    <img id="lora-manager-image" class="displayNone" style="border-radius:6px;max-height:256px;max-width:256px;"/>
+                </div>
+                <div style="text-align:center;">
+                    <button class="tertiaryButton" id="lora-manager-upload-button"><i class="fa-solid fa-upload"></i> Upload new thumbnail</button>
+                    <input id="lora-manager-upload-input" name="lora-manager-upload-input" type="file" class="displayNone">
+                    <!-- button class="tertiaryButton"><i class="fa-solid fa-trash-can"></i> Remove</button -->
+                </div>
+            </div>
+            <div class="lora-manager-grid-keywords">
+                <p style="height:2em;">Keywords:
+                <span style="float:right;margin-bottom:4px;"><button id="lora-keyword-from-civitai" class="tertiaryButton smallButton">Import from Civitai</button></span></p>
+                <textarea style="width:100%;resize:vertical;" id="lora-manager-keywords" placeholder="Put LORA specific keywords here..."></textarea>
+                <p style="color:var(--small-label-color);">
+                    <b>LORA model keywords</b> can be used via the <code>+&nbsp;Embeddings</code> button. They get added to the embedding 
+                    keyword menu when the LORA has been selected in the image settings.
+                </p>
+            </div>
+            <div class="lora-manager-grid-notes">
+                <p style="height:2em;">Notes:</p>
+                <textarea style="width:100%;resize:vertical;"  id="lora-manager-notes" placeholder="Place for things you want to remember..."></textarea>
+                <p id="civitai-section" class="displayNone">
+                    <b>Civitai model page:</b>
+                    <a id="civitai-model-page" target="_blank"></a>
+                </p>
+            </div>
+        </div>`
+
+    let tabHTML=`
+        <div id="model-tool-tab-bar" class="tab-container tab-centered">
+            <span id="tab-model-loraUI" class="tab active">
+                <span><i class="fa-solid fa-key"></i> Lora Keywords</small></span>
+            </span>
+            <span id="tab-model-mergeUI" class="tab">
+                <span><i class="fa-solid fa-code-merge"></i> Merge Models</small></span>
+            </span>
+        </div>
+        <div id="model-tool-tab-content" class="panel-box">
+            <div id="tab-content-model-loraUI" class="tab-content active">
+                <div class="tab-content-inner">
+                    ${loraUI}
+                </div>
+            </div>
+
+            <div id="tab-content-model-mergeUI" class="tab-content">
+                <div class="tab-content-inner">
+                    ${mergeUI}
+                </div>
+            </div>
+        </div>`
+
+
+    ///////////////////// Function section
+    function smoothstep(x) {
+        return x * x * (3 - 2 * x)
+    }
+
+    function smootherstep(x) {
+        return x * x * x * (x * (x * 6 - 15) + 10)
+    }
+
+    function smootheststep(x) {
+        let y = -20 * Math.pow(x, 7)
+        y += 70 * Math.pow(x, 6)
+        y -= 84 * Math.pow(x, 5)
+        y += 35 * Math.pow(x, 4)
+        return y
+    }
+    function getCurrentTime() {
+        const now = new Date()
+        let hours = now.getHours()
+        let minutes = now.getMinutes()
+        let seconds = now.getSeconds()
+
+        hours = hours < 10 ? `0${hours}` : hours
+        minutes = minutes < 10 ? `0${minutes}` : minutes
+        seconds = seconds < 10 ? `0${seconds}` : seconds
+
+        return `${hours}:${minutes}:${seconds}`
+    }
+
+    function addLogMessage(message) {
+        const logContainer = document.getElementById("merge-log")
+        logContainer.innerHTML += `<i>${getCurrentTime()}</i> ${message}<br>`
+
+        // Scroll to the bottom of the log
+        logContainer.scrollTop = logContainer.scrollHeight
+
+        document.querySelector("#merge-log-container").style.display = "block"
+    }
+
+    function addLogSeparator() {
+        const logContainer = document.getElementById("merge-log")
+        logContainer.innerHTML += "<hr>"
+
+        logContainer.scrollTop = logContainer.scrollHeight
+    }
+
+    function drawDiagram(fn) {
+        const SIZE = 300
+        const canvas = document.getElementById("merge-canvas")
+        canvas.height = canvas.width = SIZE
+        const ctx = canvas.getContext("2d")
+
+        // Draw coordinate system
+        ctx.scale(1, -1)
+        ctx.translate(0, -canvas.height)
+        ctx.lineWidth = 1
+        ctx.beginPath()
+
+        ctx.strokeStyle = "white"
+        ctx.moveTo(0, 0)
+        ctx.lineTo(0, SIZE)
+        ctx.lineTo(SIZE, SIZE)
+        ctx.lineTo(SIZE, 0)
+        ctx.lineTo(0, 0)
+        ctx.lineTo(SIZE, SIZE)
+        ctx.stroke()
+        ctx.beginPath()
+        ctx.setLineDash([1, 2])
+        const n = SIZE / 10
+        for (let i = n; i < SIZE; i += n) {
+            ctx.moveTo(0, i)
+            ctx.lineTo(SIZE, i)
+            ctx.moveTo(i, 0)
+            ctx.lineTo(i, SIZE)
+        }
+        ctx.stroke()
+        ctx.beginPath()
+        ctx.setLineDash([])
+        ctx.beginPath()
+        ctx.strokeStyle = "black"
+        ctx.lineWidth = 3
+        // Plot function
+        const numSamples = 20
+        for (let i = 0; i <= numSamples; i++) {
+            const x = i / numSamples
+            const y = fn(x)
+
+            const canvasX = x * SIZE
+            const canvasY = y * SIZE
+
+            if (i === 0) {
+                ctx.moveTo(canvasX, canvasY)
+            } else {
+                ctx.lineTo(canvasX, canvasY)
+            }
+        }
+        ctx.stroke()
+        // Plot alpha values (yellow boxes)
+        let start = parseFloat(document.querySelector("#merge-start").value)
+        let step = parseFloat(document.querySelector("#merge-step").value)
+        let iterations = document.querySelector("#merge-count").value >> 0
+        ctx.beginPath()
+        ctx.fillStyle = "yellow"
+        for (let i = 0; i < iterations; i++) {
+            const alpha = (start + i * step) / 100
+            const x = alpha * SIZE
+            const y = fn(alpha) * SIZE
+            if (x <= SIZE) {
+                ctx.rect(x - 3, y - 3, 6, 6)
+                ctx.fill()
+            } else {
+                ctx.strokeStyle = "red"
+                ctx.moveTo(0, 0)
+                ctx.lineTo(0, SIZE)
+                ctx.lineTo(SIZE, SIZE)
+                ctx.lineTo(SIZE, 0)
+                ctx.lineTo(0, 0)
+                ctx.lineTo(SIZE, SIZE)
+                ctx.stroke()
+                addLogMessage("<i>Warning: maximum ratio is &#8805; 100%</i>")
+            }
+        }
+    }
+
+    function updateChart() {
+        let fn = (x) => x
+        switch (document.querySelector("#merge-interpolation").value) {
+            case "SmoothStep":
+                fn = smoothstep
+                break
+            case "SmootherStep":
+                fn = smootherstep
+                break
+            case "SmoothestStep":
+                fn = smootheststep
+                break
+        }
+        drawDiagram(fn)
+    }
+
+    function initMergeUI() {
+        const tabSettingsSingle = document.querySelector("#tab-merge-opts-single")
+        const tabSettingsBatch = document.querySelector("#tab-merge-opts-batch")
+        linkTabContents(tabSettingsSingle)
+        linkTabContents(tabSettingsBatch)
+
+        let mergeModelAField = new ModelDropdown(document.querySelector("#mergeModelA"), "stable-diffusion")
+        let mergeModelBField = new ModelDropdown(document.querySelector("#mergeModelB"), "stable-diffusion")
+        updateChart()
+
+        // slider
+        const singleMergeRatioField = document.querySelector("#single-merge-ratio")
+        const singleMergeRatioSlider = document.querySelector("#single-merge-ratio-slider")
+
+        function updateSingleMergeRatio() {
+            singleMergeRatioField.value = singleMergeRatioSlider.value / 10
+            singleMergeRatioField.dispatchEvent(new Event("change"))
+        }
+
+        function updateSingleMergeRatioSlider() {
+            if (singleMergeRatioField.value < 0) {
+                singleMergeRatioField.value = 0
+            } else if (singleMergeRatioField.value > 100) {
+                singleMergeRatioField.value = 100
+            }
+
+            singleMergeRatioSlider.value = singleMergeRatioField.value * 10
+            singleMergeRatioSlider.dispatchEvent(new Event("change"))
+        }
+
+        singleMergeRatioSlider.addEventListener("input", updateSingleMergeRatio)
+        singleMergeRatioField.addEventListener("input", updateSingleMergeRatioSlider)
+        updateSingleMergeRatio()
+
+        document.querySelector(".merge-config").addEventListener("change", updateChart)
+
+        document.querySelector("#merge-button").addEventListener("click", async function(e) {
+            // Build request template
+            let model0 = mergeModelAField.value
+            let model1 = mergeModelBField.value
+            let request = { model0: model0, model1: model1 }
+            request["use_fp16"] = document.querySelector("#merge-fp").value == "fp16"
+            let iterations = document.querySelector("#merge-count").value >> 0
+            let start = parseFloat(document.querySelector("#merge-start").value)
+            let step = parseFloat(document.querySelector("#merge-step").value)
+
+            if (isTabActive(tabSettingsSingle)) {
+                start = parseFloat(singleMergeRatioField.value)
+                step = 0
+                iterations = 1
+                addLogMessage(`merge ratio = ${start}%`)
+            } else {
+                addLogMessage(`start = ${start}%`)
+                addLogMessage(`step  = ${step}%`)
+            }
+
+            if (start + (iterations - 1) * step >= 100) {
+                addLogMessage("<i>Aborting: maximum ratio is &#8805; 100%</i>")
+                addLogMessage("Reduce the number of variations or the step size")
+                addLogSeparator()
+                document.querySelector("#merge-count").focus()
+                return
+            }
+
+            if (document.querySelector("#merge-filename").value == "") {
+                addLogMessage("<i>Aborting: No output file name specified</i>")
+                addLogSeparator()
+                document.querySelector("#merge-filename").focus()
+                return
+            }
+
+            // Disable merge button
+            e.target.disabled = true
+            e.target.classList.add("disabled")
+            let cursor = $("body").css("cursor")
+            let label = document.querySelector("#merge-button").innerHTML
+            $("body").css("cursor", "progress")
+            document.querySelector("#merge-button").innerHTML = "Merging models ..."
+
+            addLogMessage("Merging models")
+            addLogMessage("Model A: " + model0)
+            addLogMessage("Model B: " + model1)
+
+            // Batch main loop
+            for (let i = 0; i < iterations; i++) {
+                let alpha = (start + i * step) / 100
+
+                if (isTabActive(tabSettingsBatch)) {
+                    switch (document.querySelector("#merge-interpolation").value) {
+                        case "SmoothStep":
+                            alpha = smoothstep(alpha)
+                            break
+                        case "SmootherStep":
+                            alpha = smootherstep(alpha)
+                            break
+                        case "SmoothestStep":
+                            alpha = smootheststep(alpha)
+                            break
+                    }
+                }
+                addLogMessage(`merging batch job ${i + 1}/${iterations}, alpha = ${alpha.toFixed(5)}...`)
+
+                request["out_path"] = document.querySelector("#merge-filename").value
+                request["out_path"] += "-" + alpha.toFixed(5) + "." + document.querySelector("#merge-format").value
+                addLogMessage(`&nbsp;&nbsp;filename: ${request["out_path"]}`)
+
+                // sdkit documentation: "ratio - the ratio of the second model. 1 means only the second model will be used."
+                request["ratio"] = 1-alpha 
+                let res = await fetch("/model/merge", {
+                    method: "POST",
+                    headers: { "Content-Type": "application/json" },
+                    body: JSON.stringify(request),
+                })
+                const data = await res.json()
+                addLogMessage(JSON.stringify(data))
+            }
+            addLogMessage(
+                "<b>Done.</b> The models have been saved to your <tt>models/stable-diffusion</tt> folder."
+            )
+            addLogSeparator()
+            // Re-enable merge button
+            $("body").css("cursor", cursor)
+            document.querySelector("#merge-button").innerHTML = label
+            e.target.disabled = false
+            e.target.classList.remove("disabled")
+
+            // Update model list
+            stableDiffusionModelField.innerHTML = ""
+            vaeModelField.innerHTML = ""
+            hypernetworkModelField.innerHTML = ""
+            await getModels()
+        })
+    }
+
+    const LoraUI = {
+        modelField: undefined,
+        keywordsField: undefined,
+        notesField: undefined,
+        civitaiImportBtn: undefined,
+        civitaiSecion: undefined,
+        civitaiAnchor: undefined,
+        image: undefined,
+        imagePlaceholder: undefined,
+
+        init() {
+            LoraUI.modelField = new ModelDropdown(document.querySelector("#loraModel"), "lora", "None")
+            LoraUI.keywordsField = document.querySelector("#lora-manager-keywords")
+            LoraUI.notesField = document.querySelector("#lora-manager-notes")
+            LoraUI.civitaiImportBtn = document.querySelector("#lora-keyword-from-civitai")
+            LoraUI.civitaiSection = document.querySelector("#civitai-section")
+            LoraUI.civitaiAnchor = document.querySelector("#civitai-model-page")
+            LoraUI.image = document.querySelector("#lora-manager-image")
+            LoraUI.imagePlaceholder = document.querySelector("#lora-manager-image-placeholder")
+            LoraUI.uploadBtn = document.querySelector("#lora-manager-upload-button")
+            LoraUI.uploadInput = document.querySelector("#lora-manager-upload-input")
+
+            LoraUI.modelField.addEventListener("change", LoraUI.updateFields)
+            LoraUI.keywordsField.addEventListener("focusout", LoraUI.saveInfos)
+            LoraUI.notesField.addEventListener("focusout", LoraUI.saveInfos)
+            LoraUI.civitaiImportBtn.addEventListener("click", LoraUI.importFromCivitai)
+
+            LoraUI.uploadBtn.addEventListener("click", (e) => LoraUI.uploadInput.click())
+            LoraUI.uploadInput.addEventListener("change", LoraUI.uploadLoraThumb)
+
+            document.addEventListener("saveThumb", LoraUI.updateFields)
+
+            LoraUI.updateFields()
+        },
+
+        uploadLoraThumb(e) {
+            console.log(e)
+            if (LoraUI.uploadInput.files.length === 0) {
+                return
+            }
+
+            let reader = new FileReader()
+            let file = LoraUI.uploadInput.files[0]
+
+            reader.addEventListener("load", (event) => {
+                let img = document.createElement("img")
+                img.src = reader.result
+                onUseAsThumbnailClick(
+                    {
+                        use_lora_model: LoraUI.modelField.value,
+                    },
+                    img
+                )
+            })
+
+            if (file) {
+                reader.readAsDataURL(file)
+            }
+        },
+
+        updateFields() {
+            document.getElementById("civitai-section").classList.add("displayNone")
+            Bucket.retrieve(`modelinfo/lora/${LoraUI.modelField.value}`)
+                .then((info) => {
+                    if (info == null) {
+                        LoraUI.keywordsField.value = ""
+                        LoraUI.notesField.value = ""
+                        LoraUI.hideCivitaiLink()
+                    } else {
+                        LoraUI.keywordsField.value = info.keywords.join("\n")
+                        LoraUI.notesField.value = info.notes
+                        if ("civitai" in info && info["civitai"] != null) {
+                            LoraUI.showCivitaiLink(info.civitai)
+                        } else {
+                            LoraUI.hideCivitaiLink()
+                        }
+                    }
+                })
+            Bucket.getImageAsDataURL(`${profileNameField.value}/lora/${LoraUI.modelField.value}.png`)
+                .then((data) => {
+                    LoraUI.image.src=data
+                    LoraUI.image.classList.remove("displayNone")
+                    LoraUI.imagePlaceholder.classList.add("displayNone")
+                })
+                .catch((error) => {
+                    LoraUI.image.classList.add("displayNone")
+                    LoraUI.imagePlaceholder.classList.remove("displayNone")
+                })
+        },
+
+        saveInfos() {
+            let info = {
+                keywords: LoraUI.keywordsField.value
+                            .split("\n")
+                            .filter((x) => (x != "")),
+                notes: LoraUI.notesField.value,
+                civitai: LoraUI.civitaiSection.checkVisibility() ? LoraUI.civitaiAnchor.href : null, 
+            }
+            Bucket.store(`modelinfo/lora/${LoraUI.modelField.value}`, info)
+        },
+
+        importFromCivitai() {
+            document.body.style["cursor"] = "progress"
+            fetch("/sha256/lora/"+LoraUI.modelField.value)
+                .then((result) => result.json())
+                .then((json) => fetch("https://civitai.com/api/v1/model-versions/by-hash/" + json.digest))
+                .then((result) => result.json())
+                .then((json) => {
+                    document.body.style["cursor"] = "default"
+                    if (json == null) {
+                        return
+                    }
+                    if ("trainedWords" in json) {
+                        LoraUI.keywordsField.value = json["trainedWords"].join("\n")
+                    } else {
+                        showToast("No keyword info found.")
+                    }
+                    if ("modelId" in json) {
+                        LoraUI.showCivitaiLink("https://civitai.com/models/" + json.modelId)
+                    } else {
+                        LoraUI.hideCivitaiLink()
+                    }
+
+                    LoraUI.saveInfos()
+                })
+        },
+
+        showCivitaiLink(href) {
+            LoraUI.civitaiSection.classList.remove("displayNone")
+            LoraUI.civitaiAnchor.href = href
+            LoraUI.civitaiAnchor.innerHTML = LoraUI.civitaiAnchor.href
+        },
+
+        hideCivitaiLink() {
+            LoraUI.civitaiSection.classList.add("displayNone")
+        }
+    }
+
+    createTab({
+        id: "merge",
+        icon: "fa-toolbox",
+        label: "Model tools",
+        css: mergeCSS,
+        content: tabHTML, 
+        onOpen: ({ firstOpen }) => {
+            if (!firstOpen) {
+                return
+            }
+            initMergeUI()
+            LoraUI.init()
+            const tabMergeUI = document.querySelector("#tab-model-mergeUI")
+            const tabLoraUI = document.querySelector("#tab-model-loraUI")
+            linkTabContents(tabMergeUI)
+            linkTabContents(tabLoraUI)
+        },
+    })
+})()
+async function getLoraKeywords(model) {
+    return Bucket.retrieve(`modelinfo/lora/${model}`)
+        .then((info) => info ? info.keywords : [])
+}

ui/plugins/ui/snow.plugin.js

@@ -0,0 +1,80 @@
+// christmas hack, courtesy: https://pajasevi.github.io/CSSnowflakes/
+
+;(function(){
+    "use strict";
+
+    function makeItSnow() {
+        const styleSheet = document.createElement("style")
+        styleSheet.textContent = `
+        /* customizable snowflake styling */
+        .snowflake {
+            color: #fff;
+            font-size: 1em;
+            font-family: Arial, sans-serif;
+            text-shadow: 0 0 5px #000;
+        }
+
+        .snowflake,.snowflake .inner{animation-iteration-count:infinite;animation-play-state:running}@keyframes snowflakes-fall{0%{transform:translateY(0)}100%{transform:translateY(110vh)}}@keyframes snowflakes-shake{0%,100%{transform:translateX(0)}50%{transform:translateX(80px)}}.snowflake{position:fixed;top:-10%;z-index:9999;-webkit-user-select:none;user-select:none;cursor:default;animation-name:snowflakes-shake;animation-duration:3s;animation-timing-function:ease-in-out}.snowflake .inner{animation-duration:10s;animation-name:snowflakes-fall;animation-timing-function:linear}.snowflake:nth-of-type(0){left:1%;animation-delay:0s}.snowflake:nth-of-type(0) .inner{animation-delay:0s}.snowflake:first-of-type{left:10%;animation-delay:1s}.snowflake:first-of-type .inner,.snowflake:nth-of-type(8) .inner{animation-delay:1s}.snowflake:nth-of-type(2){left:20%;animation-delay:.5s}.snowflake:nth-of-type(2) .inner,.snowflake:nth-of-type(6) .inner{animation-delay:6s}.snowflake:nth-of-type(3){left:30%;animation-delay:2s}.snowflake:nth-of-type(11) .inner,.snowflake:nth-of-type(3) .inner{animation-delay:4s}.snowflake:nth-of-type(4){left:40%;animation-delay:2s}.snowflake:nth-of-type(10) .inner,.snowflake:nth-of-type(4) .inner{animation-delay:2s}.snowflake:nth-of-type(5){left:50%;animation-delay:3s}.snowflake:nth-of-type(5) .inner{animation-delay:8s}.snowflake:nth-of-type(6){left:60%;animation-delay:2s}.snowflake:nth-of-type(7){left:70%;animation-delay:1s}.snowflake:nth-of-type(7) .inner{animation-delay:2.5s}.snowflake:nth-of-type(8){left:80%;animation-delay:0s}.snowflake:nth-of-type(9){left:90%;animation-delay:1.5s}.snowflake:nth-of-type(9) .inner{animation-delay:3s}.snowflake:nth-of-type(10){left:25%;animation-delay:0s}.snowflake:nth-of-type(11){left:65%;animation-delay:2.5s}
+        `
+        document.head.appendChild(styleSheet)
+
+        const snowflakes = document.createElement("div")
+        snowflakes.id = "snowflakes-container"
+        snowflakes.innerHTML = `
+            <div class="snowflakes" aria-hidden="true">
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+                <div class="snowflake">
+                <div class="inner">❅</div>
+                </div>
+            </div>`
+
+        document.body.appendChild(snowflakes)
+
+        const script = document.createElement("script")
+        script.innerHTML = `
+            $(document).ready(function() {
+                setTimeout(function() {
+                    $("#snowflakes-container").fadeOut("slow", function() {$(this).remove()})
+                }, 10 * 1000)
+            })
+        `
+        document.body.appendChild(script)
+    }
+
+    let date = new Date()
+    if (date.getMonth() === 11 && date.getDate() >= 12) {
+        makeItSnow()
+    }
+})()