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Merge branch 'beta' into ddpm_deis_samplers

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cmdr2 2023-05-19 18:21:03 +05:30 committed by GitHub
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11 changed files with 54 additions and 17 deletions
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1
CHANGES.md
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@ -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
* 2.5.37 - 19 May 2023 - (beta-only) More VRAM optimizations for all modes in diffusers. The VRAM usage for diffusers in "low" and "balanced" should now be equal or less than the non-diffusers version. Performs softmax in half precision, like sdkit does.
* 2.5.36 - 16 May 2023 - (beta-only) More VRAM optimizations for "balanced" VRAM usage mode.
* 2.5.36 - 11 May 2023 - (beta-only) More VRAM optimizations for "low" VRAM usage mode.
* 2.5.36 - 10 May 2023 - (beta-only) Bug fix for "meta" error when using a LoRA in 'low' VRAM usage mode.

13
scripts/check_modules.py
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@ -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": "1.0.93",
"sdkit": "1.0.95",
"stable-diffusion-sdkit": "2.1.4",
"rich": "12.6.0",
"uvicorn": "0.19.0",
@ -130,10 +130,13 @@ def include_cuda_versions(module_versions: tuple) -> tuple:
def is_amd_on_linux():
if os_name == "Linux":
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
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

12
ui/easydiffusion/device_manager.py
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@ -98,8 +98,8 @@ def auto_pick_devices(currently_active_devices):
continue
mem_free, mem_total = torch.cuda.mem_get_info(device)
mem_free /= float(10 ** 9)
mem_total /= float(10 ** 9)
mem_free /= float(10**9)
mem_total /= float(10**9)
device_name = torch.cuda.get_device_name(device)
log.debug(
f"{device} detected: {device_name} - Memory (free/total): {round(mem_free, 2)}Gb / {round(mem_total, 2)}Gb"
@ -182,7 +182,7 @@ def get_max_vram_usage_level(device):
else:
return "high"
mem_total /= float(10 ** 9)
mem_total /= float(10**9)
if mem_total < 4.5:
return "low"
elif mem_total < 6.5:
@ -224,10 +224,10 @@ def is_device_compatible(device):
# Memory check
try:
_, mem_total = torch.cuda.mem_get_info(device)
mem_total /= float(10 ** 9)
if mem_total < 3.0:
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 3 GB of VRAM is not compatible with Stable Diffusion")
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:

12
ui/easydiffusion/model_manager.py
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@ -122,7 +122,7 @@ def reload_models_if_necessary(context: Context, task_data: TaskData):
if context.model_paths.get(model_type) != path
}
if set_vram_optimizations(context): # reload SD
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, model_path_in_req in models_to_reload.items():
@ -157,6 +157,16 @@ def set_vram_optimizations(context: Context):
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)

1
ui/easydiffusion/types.py
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@ -48,6 +48,7 @@ class TaskData(BaseModel):
metadata_output_format: str = "txt" # or "json"
stream_image_progress: bool = False
stream_image_progress_interval: int = 5
clip_skip: bool = False
class MergeRequest(BaseModel):

13
ui/index.html
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@ -30,7 +30,7 @@
<h1>
<img id="logo_img" src="/media/images/icon-512x512.png" >
Easy Diffusion
<small>v2.5.36 <span id="updateBranchLabel"></span></small>
<small>v2.5.37 <span id="updateBranchLabel"></span></small>
</h1>
</div>
<div id="server-status">
@ -135,10 +135,13 @@
<button id="reload-models" class="secondaryButton reloadModels"><i class='fa-solid fa-rotate'></i></button>
<a href="https://github.com/cmdr2/stable-diffusion-ui/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 id="modelConfigSelection" class="pl-5"><td><label for="model_config">Model Config:</label></td><td>
<select id="model_config" name="model_config">
</select>
</td></tr> -->
<tr class="pl-5 displayNone" id="clip_skip_config">
<td><label for="clip_skip">Clip Skip:</label></td>
<td>
<input id="clip_skip" name="clip_skip" type="checkbox">
<a href="https://github.com/cmdr2/stable-diffusion-ui/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 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/cmdr2/stable-diffusion-ui/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>

1
ui/media/js/auto-save.js
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@ -13,6 +13,7 @@ const SETTINGS_IDS_LIST = [
"num_outputs_total",
"num_outputs_parallel",
"stable_diffusion_model",
"clip_skip",
"vae_model",
"hypernetwork_model",
"lora_model",

8
ui/media/js/dnd.js
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@ -240,6 +240,14 @@ const TASK_MAPPING = {
readUI: () => stableDiffusionModelField.value,
parse: (val) => val,
},
clip_skip: {
name: "Clip Skip",
setUI: (value) => {
clip_skip.checked = value
},
readUI: () => clip_skip.checked,
parse: (val) => Boolean(val),
},
use_vae_model: {
name: "VAE model",
setUI: (use_vae_model) => {

2
ui/media/js/engine.js
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@ -750,6 +750,7 @@
sampler_name: "string",
use_stable_diffusion_model: "string",
clip_skip: "boolean",
num_inference_steps: "number",
guidance_scale: "number",
@ -763,6 +764,7 @@
const TASK_DEFAULTS = {
sampler_name: "plms",
use_stable_diffusion_model: "sd-v1-4",
clip_skip: false,
num_inference_steps: 50,
guidance_scale: 7.5,
negative_prompt: "",

7
ui/media/js/main.js
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@ -13,6 +13,11 @@ const taskConfigSetup = {
num_inference_steps: "Inference Steps",
guidance_scale: "Guidance Scale",
use_stable_diffusion_model: "Model",
clip_skip: {
label: "Clip Skip",
visible: ({ reqBody }) => reqBody?.clip_skip,
value: ({ reqBody }) => "yes",
},
use_vae_model: {
label: "VAE",
visible: ({ reqBody }) => reqBody?.use_vae_model !== undefined && reqBody?.use_vae_model.trim() !== "",
@ -82,6 +87,7 @@ let useUpscalingField = document.querySelector("#use_upscale")
let upscaleModelField = document.querySelector("#upscale_model")
let upscaleAmountField = document.querySelector("#upscale_amount")
let stableDiffusionModelField = new ModelDropdown(document.querySelector("#stable_diffusion_model"), "stable-diffusion")
let clipSkipField = document.querySelector("#clip_skip")
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")
@ -1224,6 +1230,7 @@ function getCurrentUserRequest() {
sampler_name: samplerField.value,
//render_device: undefined, // Set device affinity. Prefer this device, but wont activate.
use_stable_diffusion_model: stableDiffusionModelField.value,
clip_skip: clipSkipField.checked,
use_vae_model: vaeModelField.value,
stream_progress_updates: true,
stream_image_progress: numOutputsTotal > 50 ? false : streamImageProgressField.checked,

1
ui/media/js/parameters.js
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@ -406,6 +406,7 @@ async function getAppConfig() {
document.querySelectorAll("#sampler_name option.k_diffusion-only").forEach(option => {
option.disabled = true
})
document.querySelector("#clip_skip_config").classList.remove("displayNone")
}
console.log("get config status response", config)