Merge pull request #1321 from cmdr2/beta

Tiling and other bug fixes

CHANGES.md

@@ -22,6 +22,7 @@
 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
+* 2.5.39 - 25 May 2023 - (beta-only) Seamless Tiling - make seamlessly tiled images, e.g. rock and grass textures. Thanks @JeLuf.
 * 2.5.38 - 24 May 2023 - Better reporting of errors, and show an explanation if the user cannot disable the "Use CPU" setting.
 * 2.5.38 - 23 May 2023 - Add Latent Upscaler as another option for upscaling images. Thanks @JeLuf for the implementation of the Latent Upscaler model.
 * 2.5.37 - 19 May 2023 - (beta-only) Two more samplers: DDPM and DEIS. Also disables the samplers that aren't working yet in the Diffusers version. Thanks @ogmaresca.

scripts/check_modules.py

@@ -18,7 +18,7 @@ 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": "1.0.97",
+    "sdkit": "1.0.98",
     "stable-diffusion-sdkit": "2.1.4",
     "rich": "12.6.0",
     "uvicorn": "0.19.0",

ui/easydiffusion/types.py

@@ -23,6 +23,7 @@ class GenerateImageRequest(BaseModel):
     sampler_name: str = None  # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"
     hypernetwork_strength: float = 0
     lora_alpha: float = 0
+    tiling: str = "none" # "none", "x", "y", "xy"
 
 
 class TaskData(BaseModel):

ui/easydiffusion/utils/save_utils.py

@@ -30,9 +30,11 @@ TASK_TEXT_MAPPING = {
     "lora_alpha": "LoRA Strength",
     "use_hypernetwork_model": "Hypernetwork model",
     "hypernetwork_strength": "Hypernetwork Strength",
+    "tiling": "Seamless Tiling",
     "use_face_correction": "Use Face Correction",
     "use_upscale": "Use Upscaling",
     "upscale_amount": "Upscale By",
+    "latent_upscaler_steps": "Latent Upscaler Steps"
 }
 
 time_placeholders = {
@@ -169,21 +171,23 @@ def save_images_to_disk(images: list, filtered_images: list, req: GenerateImageR
             output_quality=task_data.output_quality,
             output_lossless=task_data.output_lossless,
         )
-        if task_data.metadata_output_format.lower() in ["json", "txt", "embed"]:
+        if task_data.metadata_output_format:
-            save_dicts(
+            for metadata_output_format in task_data.metadata_output_format.split(","):
-                metadata_entries,
+                if metadata_output_format.lower() in ["json", "txt", "embed"]:
-                save_dir_path,
+                    save_dicts(
-                file_name=make_filter_filename,
+                        metadata_entries,
-                output_format=task_data.metadata_output_format,
+                        save_dir_path,
-                file_format=task_data.output_format,
+                        file_name=make_filter_filename,
-            )
+                        output_format=task_data.metadata_output_format,
+                        file_format=task_data.output_format,
+                    )
 
 
 def get_metadata_entries_for_request(req: GenerateImageRequest, task_data: TaskData):
     metadata = get_printable_request(req, task_data)
 
     # if text, format it in the text format expected by the UI
-    is_txt_format = task_data.metadata_output_format.lower() == "txt"
+    is_txt_format = task_data.metadata_output_format and "txt" in task_data.metadata_output_format.lower().split(",")
     if is_txt_format:
         metadata = {TASK_TEXT_MAPPING[key]: val for key, val in metadata.items() if key in TASK_TEXT_MAPPING}
 
@@ -215,10 +219,12 @@ def get_printable_request(req: GenerateImageRequest, task_data: TaskData):
         del metadata["hypernetwork_strength"]
     if task_data.use_lora_model is None and "lora_alpha" in metadata:
         del metadata["lora_alpha"]
+    if task_data.use_upscale != "latent_upscaler" and "latent_upscaler_steps" in metadata:
+        del metadata["latent_upscaler_steps"]
 
     app_config = app.getConfig()
     if not app_config.get("test_diffusers", False):
-        for key in (x for x in ["use_lora_model", "lora_alpha", "clip_skip"] if x in metadata):
+        for key in (x for x in ["use_lora_model", "lora_alpha", "clip_skip", "tiling", "latent_upscaler_steps"] if x in metadata):
             del metadata[key]
 
     return metadata

ui/index.html

@@ -30,7 +30,7 @@
             <h1>
                 <img id="logo_img" src="/media/images/icon-512x512.png" >
                 Easy Diffusion
-                <small>v2.5.38 <span id="updateBranchLabel"></span></small>
+                <small>v2.5.39 <span id="updateBranchLabel"></span></small>
             </h1>
         </div>
         <div id="server-status">
@@ -167,7 +167,7 @@
                             <option value="unipc_snr" class="k_diffusion-only">UniPC SNR</option>
                             <option value="unipc_tu">UniPC TU</option>
                             <option value="unipc_snr_2" class="k_diffusion-only">UniPC SNR 2</option>
-                            <option value="unipc_tu_2">UniPC TU 2</option>
+                            <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/cmdr2/stable-diffusion-ui/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>
@@ -236,6 +236,15 @@
                         <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>
                     </tr>
+                    <tr id="tiling_container" class="pl-5"><td><label for="tiling">Seamless Tiling:</label></td><td>
+                        <select id="tiling" name="tiling">
+                            <option value="none" selected>None</option>
+                            <option value="x">Horizontal</option>
+                            <option value="y">Vertical</option>
+                            <option value="xy">Both</option>
+                        </select>
+                        <a href="https://github.com/cmdr2/stable-diffusion-ui/wiki/Seamless-Tiling" target="_blank"><i class="fa-solid fa-circle-question help-btn"><span class="simple-tooltip top-left">Click to learn more about Seamless Tiling</span></i></a>
+                    </td></tr>
                     <tr class="pl-5"><td><label for="output_format">Output Format:</label></td><td>
                         <select id="output_format" name="output_format">
                             <option value="jpeg" selected>jpeg</option>

ui/media/js/auto-save.js

@@ -25,6 +25,7 @@ const SETTINGS_IDS_LIST = [
     "prompt_strength",
     "hypernetwork_strength",
     "lora_alpha",
+    "tiling",
     "output_format",
     "output_quality",
     "output_lossless",
@@ -34,6 +35,7 @@ const SETTINGS_IDS_LIST = [
     "gfpgan_model",
     "use_upscale",
     "upscale_amount",
+    "latent_upscaler_steps",
     "block_nsfw",
     "show_only_filtered_image",
     "upscale_model",

ui/media/js/dnd.js

@@ -79,6 +79,7 @@ const TASK_MAPPING = {
             if (!widthField.value) {
                 widthField.value = oldVal
             }
+            widthField.dispatchEvent(new Event("change"))
         },
         readUI: () => parseInt(widthField.value),
         parse: (val) => parseInt(val),
@@ -91,6 +92,7 @@ const TASK_MAPPING = {
             if (!heightField.value) {
                 heightField.value = oldVal
             }
+            heightField.dispatchEvent(new Event("change"))
         },
         readUI: () => parseInt(heightField.value),
         parse: (val) => parseInt(val),
@@ -172,16 +174,22 @@ const TASK_MAPPING = {
         name: "Use Face Correction",
         setUI: (use_face_correction) => {
             const oldVal = gfpganModelField.value
-            gfpganModelField.value = getModelPath(use_face_correction, [".pth"])
+            console.log("use face correction", use_face_correction)
-            if (gfpganModelField.value) {
+            if (use_face_correction == null || use_face_correction == "None") {
-                // Is a valid value for the field.
-                useFaceCorrectionField.checked = true
-                gfpganModelField.disabled = false
-            } else {
-                // Not a valid value, restore the old value and disable the filter.
                 gfpganModelField.disabled = true
-                gfpganModelField.value = oldVal
                 useFaceCorrectionField.checked = false
+            } else {
+                gfpganModelField.value = getModelPath(use_face_correction, [".pth"])
+                if (gfpganModelField.value) {
+                    // Is a valid value for the field.
+                    useFaceCorrectionField.checked = true
+                    gfpganModelField.disabled = false
+                } else {
+                    // Not a valid value, restore the old value and disable the filter.
+                    gfpganModelField.disabled = true
+                    gfpganModelField.value = oldVal
+                    useFaceCorrectionField.checked = false
+                }
             }
 
             //useFaceCorrectionField.checked = parseBoolean(use_face_correction)
@@ -218,6 +226,14 @@ const TASK_MAPPING = {
         readUI: () => upscaleAmountField.value,
         parse: (val) => val,
     },
+    latent_upscaler_steps: {
+        name: "Latent Upscaler Steps",
+        setUI: (latent_upscaler_steps) => {
+            latentUpscalerStepsField.value = latent_upscaler_steps
+        },
+        readUI: () => latentUpscalerStepsField.value,
+        parse: (val) => val,
+    },
     sampler_name: {
         name: "Sampler",
         setUI: (sampler_name) => {
@@ -249,6 +265,14 @@ const TASK_MAPPING = {
         readUI: () => clip_skip.checked,
         parse: (val) => Boolean(val),
     },
+    tiling: {
+        name: "Tiling",
+        setUI: (val) => {
+            tilingField.value = val
+        },
+        readUI: () => tilingField.value,
+        parse: (val) => val,
+    },
     use_vae_model: {
         name: "VAE model",
         setUI: (use_vae_model) => {
@@ -411,6 +435,7 @@ function restoreTaskToUI(task, fieldsToSkip) {
     if (!("original_prompt" in task.reqBody)) {
         promptField.value = task.reqBody.prompt
     }
+    promptField.dispatchEvent(new Event("input"))
 
     // properly reset checkboxes
     if (!("use_face_correction" in task.reqBody)) {

ui/media/js/engine.js

@@ -789,9 +789,10 @@
         use_hypernetwork_model: "string",
         hypernetwork_strength: "number",
         output_lossless: "boolean",
+        tiling: "string",
     }
 
-    // Higer values will result in...
+    // Higher values will result in...
     // pytorch_lightning/utilities/seed.py:60: UserWarning: X is not in bounds, numpy accepts from 0 to 4294967295
     const MAX_SEED_VALUE = 4294967295
 

ui/media/js/main.js

@@ -18,6 +18,11 @@ const taskConfigSetup = {
             visible: ({ reqBody }) => reqBody?.clip_skip,
             value: ({ reqBody }) => "yes",
         },
+        tiling: {
+            label: "Tiling",
+            visible: ({ reqBody }) => reqBody?.tiling != "none",
+            value: ({ reqBody }) => reqBody?.tiling,
+        },
         use_vae_model: {
             label: "VAE",
             visible: ({ reqBody }) => reqBody?.use_vae_model !== undefined && reqBody?.use_vae_model.trim() !== "",
@@ -91,6 +96,7 @@ let latentUpscalerStepsSlider = document.querySelector("#latent_upscaler_steps_s
 let latentUpscalerStepsField = document.querySelector("#latent_upscaler_steps")
 let stableDiffusionModelField = new ModelDropdown(document.querySelector("#stable_diffusion_model"), "stable-diffusion")
 let clipSkipField = document.querySelector("#clip_skip")
+let tilingField = document.querySelector("#tiling")
 let vaeModelField = new ModelDropdown(document.querySelector("#vae_model"), "vae", "None")
 let hypernetworkModelField = new ModelDropdown(document.querySelector("#hypernetwork_model"), "hypernetwork", "None")
 let hypernetworkStrengthSlider = document.querySelector("#hypernetwork_strength_slider")
@@ -1221,6 +1227,7 @@ function getCurrentUserRequest() {
             //render_device: undefined, // Set device affinity. Prefer this device, but wont activate.
             use_stable_diffusion_model: stableDiffusionModelField.value,
             clip_skip: clipSkipField.checked,
+            tiling: tilingField.value,
             use_vae_model: vaeModelField.value,
             stream_progress_updates: true,
             stream_image_progress: numOutputsTotal > 50 ? false : streamImageProgressField.checked,

ui/media/js/parameters.js

@@ -396,6 +396,7 @@ async function getAppConfig() {
         if (!testDiffusersEnabled) {
             document.querySelector("#lora_model_container").style.display = "none"
             document.querySelector("#lora_alpha_container").style.display = "none"
+            document.querySelector("#tiling_container").style.display = "none"
 
             document.querySelectorAll("#sampler_name option.diffusers-only").forEach((option) => {
                 option.style.display = "none"
@@ -403,6 +404,7 @@ async function getAppConfig() {
         } else {
             document.querySelector("#lora_model_container").style.display = ""
             document.querySelector("#lora_alpha_container").style.display = loraModelField.value ? "" : "none"
+            document.querySelector("#tiling_container").style.display = ""
 
             document.querySelectorAll("#sampler_name option.k_diffusion-only").forEach((option) => {
                 option.disabled = true

ui/plugins/ui/merge.plugin.js

@@ -403,16 +403,19 @@
                 // Batch main loop
                 for (let i = 0; i < iterations; i++) {
                     let alpha = (start + i * step) / 100
-                    switch (document.querySelector("#merge-interpolation").value) {
+
-                        case "SmoothStep":
+                    if (isTabActive(tabSettingsBatch)) {
-                            alpha = smoothstep(alpha)
+                        switch (document.querySelector("#merge-interpolation").value) {
-                            break
+                            case "SmoothStep":
-                        case "SmootherStep":
+                                alpha = smoothstep(alpha)
-                            alpha = smootherstep(alpha)
+                                break
-                            break
+                            case "SmootherStep":
-                        case "SmoothestStep":
+                                alpha = smootherstep(alpha)
-                            alpha = smootheststep(alpha)
+                                break
-                            break
+                            case "SmoothestStep":
+                                alpha = smootheststep(alpha)
+                                break
+                        }
                     }
                     addLogMessage(`merging batch job ${i + 1}/${iterations}, alpha = ${alpha.toFixed(5)}...`)
 
@@ -420,7 +423,8 @@
                     request["out_path"] += "-" + alpha.toFixed(5) + "." + document.querySelector("#merge-format").value
                     addLogMessage(`&nbsp;&nbsp;filename: ${request["out_path"]}`)
 
-                    request["ratio"] = alpha
+                    // 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" },