Merge branch 'learning_rate-scheduling' into learnschedule
This commit is contained in:
AUTOMATIC1111
GitHub
commit
419e539fe3
33 changed files with 1955 additions and 271 deletions
2
.github/ISSUE_TEMPLATE/feature_request.md
vendored
2
.github/ISSUE_TEMPLATE/feature_request.md
vendored
|
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@ -2,7 +2,7 @@
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name: Feature request
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||||
about: Suggest an idea for this project
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title: ''
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labels: ''
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||||
labels: 'suggestion'
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||||
assignees: ''
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||||
|
||||
---
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|
|
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1
CODEOWNERS
Normal file
1
CODEOWNERS
Normal file
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|
@ -0,0 +1 @@
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* @AUTOMATIC1111
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11
README.md
11
README.md
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|
@ -28,10 +28,12 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
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- CodeFormer, face restoration tool as an alternative to GFPGAN
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- RealESRGAN, neural network upscaler
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- ESRGAN, neural network upscaler with a lot of third party models
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- SwinIR, neural network upscaler
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- SwinIR and Swin2SR([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
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- LDSR, Latent diffusion super resolution upscaling
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- Resizing aspect ratio options
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- Sampling method selection
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- Adjust sampler eta values (noise multiplier)
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- More advanced noise setting options
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- Interrupt processing at any time
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- 4GB video card support (also reports of 2GB working)
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- Correct seeds for batches
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@ -67,6 +69,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
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- also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
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- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
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- DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
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- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
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## Installation and Running
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Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
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@ -116,13 +119,17 @@ The documentation was moved from this README over to the project's [wiki](https:
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- CodeFormer - https://github.com/sczhou/CodeFormer
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- ESRGAN - https://github.com/xinntao/ESRGAN
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- SwinIR - https://github.com/JingyunLiang/SwinIR
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- Swin2SR - https://github.com/mv-lab/swin2sr
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- LDSR - https://github.com/Hafiidz/latent-diffusion
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- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
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- Doggettx - Cross Attention layer optimization - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
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- InvokeAI, lstein - Cross Attention layer optimization - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
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- Rinon Gal - Textual Inversion - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
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- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
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- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
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- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
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- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
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- xformers - https://github.com/facebookresearch/xformers
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- DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
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- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
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- DeepDanbooru - interrogator for anime diffusors https://github.com/KichangKim/DeepDanbooru
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- (You)
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|
|
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@ -16,7 +16,7 @@ contextMenuInit = function(){
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oldMenu.remove()
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}
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let tabButton = gradioApp().querySelector('button')
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let tabButton = uiCurrentTab
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let baseStyle = window.getComputedStyle(tabButton)
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const contextMenu = document.createElement('nav')
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@ -123,48 +123,53 @@ contextMenuInit = function(){
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return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
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}
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initResponse = contextMenuInit()
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appendContextMenuOption = initResponse[0]
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removeContextMenuOption = initResponse[1]
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addContextMenuEventListener = initResponse[2]
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initResponse = contextMenuInit();
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appendContextMenuOption = initResponse[0];
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removeContextMenuOption = initResponse[1];
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addContextMenuEventListener = initResponse[2];
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//Start example Context Menu Items
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generateOnRepeatId = appendContextMenuOption('#txt2img_generate','Generate forever',function(){
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let genbutton = gradioApp().querySelector('#txt2img_generate');
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let interruptbutton = gradioApp().querySelector('#txt2img_interrupt');
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if(!interruptbutton.offsetParent){
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genbutton.click();
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}
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clearInterval(window.generateOnRepeatInterval)
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window.generateOnRepeatInterval = setInterval(function(){
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(function(){
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//Start example Context Menu Items
|
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let generateOnRepeat = function(genbuttonid,interruptbuttonid){
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let genbutton = gradioApp().querySelector(genbuttonid);
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let interruptbutton = gradioApp().querySelector(interruptbuttonid);
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if(!interruptbutton.offsetParent){
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genbutton.click();
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}
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},
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500)}
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)
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cancelGenerateForever = function(){
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clearInterval(window.generateOnRepeatInterval)
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let interruptbutton = gradioApp().querySelector('#txt2img_interrupt');
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if(interruptbutton.offsetParent){
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interruptbutton.click();
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clearInterval(window.generateOnRepeatInterval)
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window.generateOnRepeatInterval = setInterval(function(){
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if(!interruptbutton.offsetParent){
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genbutton.click();
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}
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},
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500)
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}
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}
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appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
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appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever)
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appendContextMenuOption('#txt2img_generate','Generate forever',function(){
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generateOnRepeat('#txt2img_generate','#txt2img_interrupt');
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})
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appendContextMenuOption('#img2img_generate','Generate forever',function(){
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generateOnRepeat('#img2img_generate','#img2img_interrupt');
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})
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appendContextMenuOption('#roll','Roll three',
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function(){
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let rollbutton = gradioApp().querySelector('#roll');
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setTimeout(function(){rollbutton.click()},100)
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setTimeout(function(){rollbutton.click()},200)
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setTimeout(function(){rollbutton.click()},300)
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let cancelGenerateForever = function(){
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clearInterval(window.generateOnRepeatInterval)
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}
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)
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appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
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appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever)
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appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
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appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
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appendContextMenuOption('#roll','Roll three',
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function(){
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let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
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setTimeout(function(){rollbutton.click()},100)
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setTimeout(function(){rollbutton.click()},200)
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setTimeout(function(){rollbutton.click()},300)
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}
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)
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})();
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//End example Context Menu Items
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onUiUpdate(function(){
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|
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@ -79,6 +79,8 @@ titles = {
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"Highres. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",
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"Scale latent": "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.",
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"Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
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"Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be bevaing in an unethical manner.",
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}
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|
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@ -104,6 +104,7 @@ def prepare_enviroment():
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args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
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xformers = '--xformers' in args
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deepdanbooru = '--deepdanbooru' in args
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ngrok = '--ngrok' in args
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try:
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commit = run(f"{git} rev-parse HEAD").strip()
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@ -127,13 +128,16 @@ def prepare_enviroment():
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if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"):
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if platform.system() == "Windows":
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run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/a/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
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run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/c/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
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elif platform.system() == "Linux":
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run_pip("install xformers", "xformers")
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if not is_installed("deepdanbooru") and deepdanbooru:
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run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
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if not is_installed("pyngrok") and ngrok:
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run_pip("install pyngrok", "ngrok")
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os.makedirs(dir_repos, exist_ok=True)
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||||
git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
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@ -1,98 +0,0 @@
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|||
import glob
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||||
import os
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import sys
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import traceback
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||||
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||||
import torch
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||||
from ldm.util import default
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||||
from modules import devices, shared
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||||
import torch
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||||
from torch import einsum
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||||
from einops import rearrange, repeat
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||||
|
||||
|
||||
class HypernetworkModule(torch.nn.Module):
|
||||
def __init__(self, dim, state_dict):
|
||||
super().__init__()
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||||
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||||
self.linear1 = torch.nn.Linear(dim, dim * 2)
|
||||
self.linear2 = torch.nn.Linear(dim * 2, dim)
|
||||
|
||||
self.load_state_dict(state_dict, strict=True)
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self.to(devices.device)
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def forward(self, x):
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return x + (self.linear2(self.linear1(x)))
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||||
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class Hypernetwork:
|
||||
filename = None
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||||
name = None
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||||
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||||
def __init__(self, filename):
|
||||
self.filename = filename
|
||||
self.name = os.path.splitext(os.path.basename(filename))[0]
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||||
self.layers = {}
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||||
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||||
state_dict = torch.load(filename, map_location='cpu')
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for size, sd in state_dict.items():
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self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
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def list_hypernetworks(path):
|
||||
res = {}
|
||||
for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
|
||||
name = os.path.splitext(os.path.basename(filename))[0]
|
||||
res[name] = filename
|
||||
return res
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||||
|
||||
|
||||
def load_hypernetwork(filename):
|
||||
path = shared.hypernetworks.get(filename, None)
|
||||
if path is not None:
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print(f"Loading hypernetwork {filename}")
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||||
try:
|
||||
shared.loaded_hypernetwork = Hypernetwork(path)
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||||
except Exception:
|
||||
print(f"Error loading hypernetwork {path}", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
else:
|
||||
if shared.loaded_hypernetwork is not None:
|
||||
print(f"Unloading hypernetwork")
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||||
|
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shared.loaded_hypernetwork = None
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||||
|
||||
|
||||
def attention_CrossAttention_forward(self, x, context=None, mask=None):
|
||||
h = self.heads
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||||
|
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q = self.to_q(x)
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context = default(context, x)
|
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|
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hypernetwork = shared.loaded_hypernetwork
|
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hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
|
||||
|
||||
if hypernetwork_layers is not None:
|
||||
k = self.to_k(hypernetwork_layers[0](context))
|
||||
v = self.to_v(hypernetwork_layers[1](context))
|
||||
else:
|
||||
k = self.to_k(context)
|
||||
v = self.to_v(context)
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
||||
|
||||
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
||||
|
||||
if mask is not None:
|
||||
mask = rearrange(mask, 'b ... -> b (...)')
|
||||
max_neg_value = -torch.finfo(sim.dtype).max
|
||||
mask = repeat(mask, 'b j -> (b h) () j', h=h)
|
||||
sim.masked_fill_(~mask, max_neg_value)
|
||||
|
||||
# attention, what we cannot get enough of
|
||||
attn = sim.softmax(dim=-1)
|
||||
|
||||
out = einsum('b i j, b j d -> b i d', attn, v)
|
||||
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
|
||||
return self.to_out(out)
|
||||
294
modules/hypernetworks/hypernetwork.py
Normal file
294
modules/hypernetworks/hypernetwork.py
Normal file
|
|
@ -0,0 +1,294 @@
|
|||
import datetime
|
||||
import glob
|
||||
import html
|
||||
import os
|
||||
import sys
|
||||
import traceback
|
||||
import tqdm
|
||||
|
||||
import torch
|
||||
|
||||
from ldm.util import default
|
||||
from modules import devices, shared, processing, sd_models
|
||||
import torch
|
||||
from torch import einsum
|
||||
from einops import rearrange, repeat
|
||||
import modules.textual_inversion.dataset
|
||||
|
||||
|
||||
class HypernetworkModule(torch.nn.Module):
|
||||
def __init__(self, dim, state_dict=None):
|
||||
super().__init__()
|
||||
|
||||
self.linear1 = torch.nn.Linear(dim, dim * 2)
|
||||
self.linear2 = torch.nn.Linear(dim * 2, dim)
|
||||
|
||||
if state_dict is not None:
|
||||
self.load_state_dict(state_dict, strict=True)
|
||||
else:
|
||||
|
||||
self.linear1.weight.data.normal_(mean=0.0, std=0.01)
|
||||
self.linear1.bias.data.zero_()
|
||||
self.linear2.weight.data.normal_(mean=0.0, std=0.01)
|
||||
self.linear2.bias.data.zero_()
|
||||
|
||||
self.to(devices.device)
|
||||
|
||||
def forward(self, x):
|
||||
return x + (self.linear2(self.linear1(x)))
|
||||
|
||||
|
||||
class Hypernetwork:
|
||||
filename = None
|
||||
name = None
|
||||
|
||||
def __init__(self, name=None, enable_sizes=None):
|
||||
self.filename = None
|
||||
self.name = name
|
||||
self.layers = {}
|
||||
self.step = 0
|
||||
self.sd_checkpoint = None
|
||||
self.sd_checkpoint_name = None
|
||||
|
||||
for size in enable_sizes or []:
|
||||
self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
|
||||
|
||||
def weights(self):
|
||||
res = []
|
||||
|
||||
for k, layers in self.layers.items():
|
||||
for layer in layers:
|
||||
layer.train()
|
||||
res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias]
|
||||
|
||||
return res
|
||||
|
||||
def save(self, filename):
|
||||
state_dict = {}
|
||||
|
||||
for k, v in self.layers.items():
|
||||
state_dict[k] = (v[0].state_dict(), v[1].state_dict())
|
||||
|
||||
state_dict['step'] = self.step
|
||||
state_dict['name'] = self.name
|
||||
state_dict['sd_checkpoint'] = self.sd_checkpoint
|
||||
state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
|
||||
|
||||
torch.save(state_dict, filename)
|
||||
|
||||
def load(self, filename):
|
||||
self.filename = filename
|
||||
if self.name is None:
|
||||
self.name = os.path.splitext(os.path.basename(filename))[0]
|
||||
|
||||
state_dict = torch.load(filename, map_location='cpu')
|
||||
|
||||
for size, sd in state_dict.items():
|
||||
if type(size) == int:
|
||||
self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
|
||||
|
||||
self.name = state_dict.get('name', self.name)
|
||||
self.step = state_dict.get('step', 0)
|
||||
self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
|
||||
self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
|
||||
|
||||
|
||||
def list_hypernetworks(path):
|
||||
res = {}
|
||||
for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
|
||||
name = os.path.splitext(os.path.basename(filename))[0]
|
||||
res[name] = filename
|
||||
return res
|
||||
|
||||
|
||||
def load_hypernetwork(filename):
|
||||
path = shared.hypernetworks.get(filename, None)
|
||||
if path is not None:
|
||||
print(f"Loading hypernetwork {filename}")
|
||||
try:
|
||||
shared.loaded_hypernetwork = Hypernetwork()
|
||||
shared.loaded_hypernetwork.load(path)
|
||||
|
||||
except Exception:
|
||||
print(f"Error loading hypernetwork {path}", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
else:
|
||||
if shared.loaded_hypernetwork is not None:
|
||||
print(f"Unloading hypernetwork")
|
||||
|
||||
shared.loaded_hypernetwork = None
|
||||
|
||||
|
||||
def apply_hypernetwork(hypernetwork, context, layer=None):
|
||||
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
|
||||
|
||||
if hypernetwork_layers is None:
|
||||
return context, context
|
||||
|
||||
if layer is not None:
|
||||
layer.hyper_k = hypernetwork_layers[0]
|
||||
layer.hyper_v = hypernetwork_layers[1]
|
||||
|
||||
context_k = hypernetwork_layers[0](context)
|
||||
context_v = hypernetwork_layers[1](context)
|
||||
return context_k, context_v
|
||||
|
||||
|
||||
def attention_CrossAttention_forward(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
|
||||
q = self.to_q(x)
|
||||
context = default(context, x)
|
||||
|
||||
context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self)
|
||||
k = self.to_k(context_k)
|
||||
v = self.to_v(context_v)
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
||||
|
||||
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
||||
|
||||
if mask is not None:
|
||||
mask = rearrange(mask, 'b ... -> b (...)')
|
||||
max_neg_value = -torch.finfo(sim.dtype).max
|
||||
mask = repeat(mask, 'b j -> (b h) () j', h=h)
|
||||
sim.masked_fill_(~mask, max_neg_value)
|
||||
|
||||
# attention, what we cannot get enough of
|
||||
attn = sim.softmax(dim=-1)
|
||||
|
||||
out = einsum('b i j, b j d -> b i d', attn, v)
|
||||
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
|
||||
return self.to_out(out)
|
||||
|
||||
|
||||
def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
|
||||
assert hypernetwork_name, 'embedding not selected'
|
||||
|
||||
path = shared.hypernetworks.get(hypernetwork_name, None)
|
||||
shared.loaded_hypernetwork = Hypernetwork()
|
||||
shared.loaded_hypernetwork.load(path)
|
||||
|
||||
shared.state.textinfo = "Initializing hypernetwork training..."
|
||||
shared.state.job_count = steps
|
||||
|
||||
filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
|
||||
|
||||
log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
|
||||
unload = shared.opts.unload_models_when_training
|
||||
|
||||
if save_hypernetwork_every > 0:
|
||||
hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
|
||||
os.makedirs(hypernetwork_dir, exist_ok=True)
|
||||
else:
|
||||
hypernetwork_dir = None
|
||||
|
||||
if create_image_every > 0:
|
||||
images_dir = os.path.join(log_directory, "images")
|
||||
os.makedirs(images_dir, exist_ok=True)
|
||||
else:
|
||||
images_dir = None
|
||||
|
||||
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
|
||||
with torch.autocast("cuda"):
|
||||
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
|
||||
|
||||
if unload:
|
||||
shared.sd_model.cond_stage_model.to(devices.cpu)
|
||||
shared.sd_model.first_stage_model.to(devices.cpu)
|
||||
|
||||
hypernetwork = shared.loaded_hypernetwork
|
||||
weights = hypernetwork.weights()
|
||||
for weight in weights:
|
||||
weight.requires_grad = True
|
||||
|
||||
optimizer = torch.optim.AdamW(weights, lr=learn_rate)
|
||||
|
||||
losses = torch.zeros((32,))
|
||||
|
||||
last_saved_file = "<none>"
|
||||
last_saved_image = "<none>"
|
||||
|
||||
ititial_step = hypernetwork.step or 0
|
||||
if ititial_step > steps:
|
||||
return hypernetwork, filename
|
||||
|
||||
pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
|
||||
for i, (x, text, cond) in pbar:
|
||||
hypernetwork.step = i + ititial_step
|
||||
|
||||
if hypernetwork.step > steps:
|
||||
break
|
||||
|
||||
if shared.state.interrupted:
|
||||
break
|
||||
|
||||
with torch.autocast("cuda"):
|
||||
cond = cond.to(devices.device)
|
||||
x = x.to(devices.device)
|
||||
loss = shared.sd_model(x.unsqueeze(0), cond)[0]
|
||||
del x
|
||||
del cond
|
||||
|
||||
losses[hypernetwork.step % losses.shape[0]] = loss.item()
|
||||
|
||||
optimizer.zero_grad()
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
pbar.set_description(f"loss: {losses.mean():.7f}")
|
||||
|
||||
if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
|
||||
last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
|
||||
hypernetwork.save(last_saved_file)
|
||||
|
||||
if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
|
||||
last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
|
||||
|
||||
preview_text = text if preview_image_prompt == "" else preview_image_prompt
|
||||
|
||||
optimizer.zero_grad()
|
||||
shared.sd_model.cond_stage_model.to(devices.device)
|
||||
shared.sd_model.first_stage_model.to(devices.device)
|
||||
|
||||
p = processing.StableDiffusionProcessingTxt2Img(
|
||||
sd_model=shared.sd_model,
|
||||
prompt=preview_text,
|
||||
steps=20,
|
||||
do_not_save_grid=True,
|
||||
do_not_save_samples=True,
|
||||
)
|
||||
|
||||
processed = processing.process_images(p)
|
||||
image = processed.images[0]
|
||||
|
||||
if unload:
|
||||
shared.sd_model.cond_stage_model.to(devices.cpu)
|
||||
shared.sd_model.first_stage_model.to(devices.cpu)
|
||||
|
||||
shared.state.current_image = image
|
||||
image.save(last_saved_image)
|
||||
|
||||
last_saved_image += f", prompt: {preview_text}"
|
||||
|
||||
shared.state.job_no = hypernetwork.step
|
||||
|
||||
shared.state.textinfo = f"""
|
||||
<p>
|
||||
Loss: {losses.mean():.7f}<br/>
|
||||
Step: {hypernetwork.step}<br/>
|
||||
Last prompt: {html.escape(text)}<br/>
|
||||
Last saved embedding: {html.escape(last_saved_file)}<br/>
|
||||
Last saved image: {html.escape(last_saved_image)}<br/>
|
||||
</p>
|
||||
"""
|
||||
|
||||
checkpoint = sd_models.select_checkpoint()
|
||||
|
||||
hypernetwork.sd_checkpoint = checkpoint.hash
|
||||
hypernetwork.sd_checkpoint_name = checkpoint.model_name
|
||||
hypernetwork.save(filename)
|
||||
|
||||
return hypernetwork, filename
|
||||
|
||||
|
||||
47
modules/hypernetworks/ui.py
Normal file
47
modules/hypernetworks/ui.py
Normal file
|
|
@ -0,0 +1,47 @@
|
|||
import html
|
||||
import os
|
||||
|
||||
import gradio as gr
|
||||
|
||||
import modules.textual_inversion.textual_inversion
|
||||
import modules.textual_inversion.preprocess
|
||||
from modules import sd_hijack, shared, devices
|
||||
from modules.hypernetworks import hypernetwork
|
||||
|
||||
|
||||
def create_hypernetwork(name, enable_sizes):
|
||||
fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
|
||||
assert not os.path.exists(fn), f"file {fn} already exists"
|
||||
|
||||
hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name, enable_sizes=[int(x) for x in enable_sizes])
|
||||
hypernet.save(fn)
|
||||
|
||||
shared.reload_hypernetworks()
|
||||
|
||||
return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""
|
||||
|
||||
|
||||
def train_hypernetwork(*args):
|
||||
|
||||
initial_hypernetwork = shared.loaded_hypernetwork
|
||||
|
||||
assert not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram, 'Training models with lowvram or medvram is not possible'
|
||||
|
||||
try:
|
||||
sd_hijack.undo_optimizations()
|
||||
|
||||
hypernetwork, filename = modules.hypernetworks.hypernetwork.train_hypernetwork(*args)
|
||||
|
||||
res = f"""
|
||||
Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
|
||||
Hypernetwork saved to {html.escape(filename)}
|
||||
"""
|
||||
return res, ""
|
||||
except Exception:
|
||||
raise
|
||||
finally:
|
||||
shared.loaded_hypernetwork = initial_hypernetwork
|
||||
shared.sd_model.cond_stage_model.to(devices.device)
|
||||
shared.sd_model.first_stage_model.to(devices.device)
|
||||
sd_hijack.apply_optimizations()
|
||||
|
||||
15
modules/ngrok.py
Normal file
15
modules/ngrok.py
Normal file
|
|
@ -0,0 +1,15 @@
|
|||
from pyngrok import ngrok, conf, exception
|
||||
|
||||
|
||||
def connect(token, port):
|
||||
if token == None:
|
||||
token = 'None'
|
||||
conf.get_default().auth_token = token
|
||||
try:
|
||||
public_url = ngrok.connect(port).public_url
|
||||
except exception.PyngrokNgrokError:
|
||||
print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
|
||||
f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
|
||||
else:
|
||||
print(f'ngrok connected to localhost:{port}! URL: {public_url}\n'
|
||||
'You can use this link after the launch is complete.')
|
||||
|
|
@ -207,7 +207,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
|
|||
# enables the generation of additional tensors with noise that the sampler will use during its processing.
|
||||
# Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
|
||||
# produce the same images as with two batches [100], [101].
|
||||
if p is not None and p.sampler is not None and len(seeds) > 1 and opts.enable_batch_seeds:
|
||||
if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):
|
||||
sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
|
||||
else:
|
||||
sampler_noises = None
|
||||
|
|
@ -247,6 +247,9 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
|
|||
if sampler_noises is not None:
|
||||
cnt = p.sampler.number_of_needed_noises(p)
|
||||
|
||||
if opts.eta_noise_seed_delta > 0:
|
||||
torch.manual_seed(seed + opts.eta_noise_seed_delta)
|
||||
|
||||
for j in range(cnt):
|
||||
sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
|
||||
|
||||
|
|
@ -301,6 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
|
|||
"Denoising strength": getattr(p, 'denoising_strength', None),
|
||||
"Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
|
||||
"Clip skip": None if clip_skip <= 1 else clip_skip,
|
||||
"ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
|
||||
}
|
||||
|
||||
generation_params.update(p.extra_generation_params)
|
||||
|
|
|
|||
|
|
@ -10,6 +10,7 @@ import torch
|
|||
import numpy
|
||||
import _codecs
|
||||
import zipfile
|
||||
import re
|
||||
|
||||
|
||||
# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
|
||||
|
|
@ -54,11 +55,27 @@ class RestrictedUnpickler(pickle.Unpickler):
|
|||
raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")
|
||||
|
||||
|
||||
allowed_zip_names = ["archive/data.pkl", "archive/version"]
|
||||
allowed_zip_names_re = re.compile(r"^archive/data/\d+$")
|
||||
|
||||
|
||||
def check_zip_filenames(filename, names):
|
||||
for name in names:
|
||||
if name in allowed_zip_names:
|
||||
continue
|
||||
if allowed_zip_names_re.match(name):
|
||||
continue
|
||||
|
||||
raise Exception(f"bad file inside {filename}: {name}")
|
||||
|
||||
|
||||
def check_pt(filename):
|
||||
try:
|
||||
|
||||
# new pytorch format is a zip file
|
||||
with zipfile.ZipFile(filename) as z:
|
||||
check_zip_filenames(filename, z.namelist())
|
||||
|
||||
with z.open('archive/data.pkl') as file:
|
||||
unpickler = RestrictedUnpickler(file)
|
||||
unpickler.load()
|
||||
|
|
|
|||
|
|
@ -8,8 +8,9 @@ from torch import einsum
|
|||
from torch.nn.functional import silu
|
||||
|
||||
import modules.textual_inversion.textual_inversion
|
||||
from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork
|
||||
from modules import prompt_parser, devices, sd_hijack_optimizations, shared
|
||||
from modules.shared import opts, device, cmd_opts
|
||||
from modules.sd_hijack_optimizations import invokeAI_mps_available
|
||||
|
||||
import ldm.modules.attention
|
||||
import ldm.modules.diffusionmodules.model
|
||||
|
|
@ -23,30 +24,37 @@ def apply_optimizations():
|
|||
|
||||
ldm.modules.diffusionmodules.model.nonlinearity = silu
|
||||
|
||||
if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6)):
|
||||
if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):
|
||||
print("Applying xformers cross attention optimization.")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
|
||||
elif cmd_opts.opt_split_attention_v1:
|
||||
print("Applying v1 cross attention optimization.")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
|
||||
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
|
||||
if not invokeAI_mps_available and shared.device.type == 'mps':
|
||||
print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
|
||||
print("Applying v1 cross attention optimization.")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
|
||||
else:
|
||||
print("Applying cross attention optimization (InvokeAI).")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
|
||||
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
|
||||
print("Applying cross attention optimization.")
|
||||
print("Applying cross attention optimization (Doggettx).")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
|
||||
|
||||
|
||||
def undo_optimizations():
|
||||
from modules.hypernetworks import hypernetwork
|
||||
|
||||
ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
|
||||
ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
|
||||
|
||||
|
||||
def get_target_prompt_token_count(token_count):
|
||||
if token_count < 75:
|
||||
return 75
|
||||
|
||||
return math.ceil(token_count / 10) * 10
|
||||
return math.ceil(max(token_count, 1) / 75) * 75
|
||||
|
||||
|
||||
class StableDiffusionModelHijack:
|
||||
|
|
@ -110,6 +118,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
self.tokenizer = wrapped.tokenizer
|
||||
self.token_mults = {}
|
||||
|
||||
self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
|
||||
|
||||
tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
|
||||
for text, ident in tokens_with_parens:
|
||||
mult = 1.0
|
||||
|
|
@ -127,7 +137,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
self.token_mults[ident] = mult
|
||||
|
||||
def tokenize_line(self, line, used_custom_terms, hijack_comments):
|
||||
id_start = self.wrapped.tokenizer.bos_token_id
|
||||
id_end = self.wrapped.tokenizer.eos_token_id
|
||||
|
||||
if opts.enable_emphasis:
|
||||
|
|
@ -140,6 +149,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
fixes = []
|
||||
remade_tokens = []
|
||||
multipliers = []
|
||||
last_comma = -1
|
||||
|
||||
for tokens, (text, weight) in zip(tokenized, parsed):
|
||||
i = 0
|
||||
|
|
@ -148,13 +158,33 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
|
||||
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
|
||||
|
||||
if token == self.comma_token:
|
||||
last_comma = len(remade_tokens)
|
||||
elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:
|
||||
last_comma += 1
|
||||
reloc_tokens = remade_tokens[last_comma:]
|
||||
reloc_mults = multipliers[last_comma:]
|
||||
|
||||
remade_tokens = remade_tokens[:last_comma]
|
||||
length = len(remade_tokens)
|
||||
|
||||
rem = int(math.ceil(length / 75)) * 75 - length
|
||||
remade_tokens += [id_end] * rem + reloc_tokens
|
||||
multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
|
||||
|
||||
if embedding is None:
|
||||
remade_tokens.append(token)
|
||||
multipliers.append(weight)
|
||||
i += 1
|
||||
else:
|
||||
emb_len = int(embedding.vec.shape[0])
|
||||
fixes.append((len(remade_tokens), embedding))
|
||||
iteration = len(remade_tokens) // 75
|
||||
if (len(remade_tokens) + emb_len) // 75 != iteration:
|
||||
rem = (75 * (iteration + 1) - len(remade_tokens))
|
||||
remade_tokens += [id_end] * rem
|
||||
multipliers += [1.0] * rem
|
||||
iteration += 1
|
||||
fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
|
||||
remade_tokens += [0] * emb_len
|
||||
multipliers += [weight] * emb_len
|
||||
used_custom_terms.append((embedding.name, embedding.checksum()))
|
||||
|
|
@ -162,10 +192,10 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
|
||||
token_count = len(remade_tokens)
|
||||
prompt_target_length = get_target_prompt_token_count(token_count)
|
||||
tokens_to_add = prompt_target_length - len(remade_tokens) + 1
|
||||
tokens_to_add = prompt_target_length - len(remade_tokens)
|
||||
|
||||
remade_tokens = [id_start] + remade_tokens + [id_end] * tokens_to_add
|
||||
multipliers = [1.0] + multipliers + [1.0] * tokens_to_add
|
||||
remade_tokens = remade_tokens + [id_end] * tokens_to_add
|
||||
multipliers = multipliers + [1.0] * tokens_to_add
|
||||
|
||||
return remade_tokens, fixes, multipliers, token_count
|
||||
|
||||
|
|
@ -260,29 +290,55 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
hijack_fixes.append(fixes)
|
||||
batch_multipliers.append(multipliers)
|
||||
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
|
||||
|
||||
|
||||
def forward(self, text):
|
||||
|
||||
if opts.use_old_emphasis_implementation:
|
||||
use_old = opts.use_old_emphasis_implementation
|
||||
if use_old:
|
||||
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
|
||||
else:
|
||||
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
|
||||
|
||||
self.hijack.fixes = hijack_fixes
|
||||
self.hijack.comments += hijack_comments
|
||||
|
||||
if len(used_custom_terms) > 0:
|
||||
self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
|
||||
|
||||
if use_old:
|
||||
self.hijack.fixes = hijack_fixes
|
||||
return self.process_tokens(remade_batch_tokens, batch_multipliers)
|
||||
|
||||
z = None
|
||||
i = 0
|
||||
while max(map(len, remade_batch_tokens)) != 0:
|
||||
rem_tokens = [x[75:] for x in remade_batch_tokens]
|
||||
rem_multipliers = [x[75:] for x in batch_multipliers]
|
||||
|
||||
target_token_count = get_target_prompt_token_count(token_count) + 2
|
||||
self.hijack.fixes = []
|
||||
for unfiltered in hijack_fixes:
|
||||
fixes = []
|
||||
for fix in unfiltered:
|
||||
if fix[0] == i:
|
||||
fixes.append(fix[1])
|
||||
self.hijack.fixes.append(fixes)
|
||||
|
||||
z1 = self.process_tokens([x[:75] for x in remade_batch_tokens], [x[:75] for x in batch_multipliers])
|
||||
z = z1 if z is None else torch.cat((z, z1), axis=-2)
|
||||
|
||||
remade_batch_tokens = rem_tokens
|
||||
batch_multipliers = rem_multipliers
|
||||
i += 1
|
||||
|
||||
return z
|
||||
|
||||
|
||||
def process_tokens(self, remade_batch_tokens, batch_multipliers):
|
||||
if not opts.use_old_emphasis_implementation:
|
||||
remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens]
|
||||
batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
|
||||
|
||||
tokens = torch.asarray(remade_batch_tokens).to(device)
|
||||
outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
|
||||
|
||||
position_ids_array = [min(x, 75) for x in range(target_token_count-1)] + [76]
|
||||
position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1))
|
||||
|
||||
remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
|
||||
tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
|
||||
|
||||
outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=-opts.CLIP_stop_at_last_layers)
|
||||
if opts.CLIP_stop_at_last_layers > 1:
|
||||
z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
|
||||
z = self.wrapped.transformer.text_model.final_layer_norm(z)
|
||||
|
|
@ -290,7 +346,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
z = outputs.last_hidden_state
|
||||
|
||||
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
|
||||
batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
|
||||
batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
|
||||
batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
|
||||
original_mean = z.mean()
|
||||
z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import math
|
||||
import sys
|
||||
import traceback
|
||||
import importlib
|
||||
|
||||
import torch
|
||||
from torch import einsum
|
||||
|
|
@ -9,12 +10,12 @@ from ldm.util import default
|
|||
from einops import rearrange
|
||||
|
||||
from modules import shared
|
||||
from modules.hypernetworks import hypernetwork
|
||||
|
||||
|
||||
if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
|
||||
try:
|
||||
import xformers.ops
|
||||
import functorch
|
||||
xformers._is_functorch_available = True
|
||||
shared.xformers_available = True
|
||||
except Exception:
|
||||
print("Cannot import xformers", file=sys.stderr)
|
||||
|
|
@ -28,16 +29,10 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
|
|||
q_in = self.to_q(x)
|
||||
context = default(context, x)
|
||||
|
||||
hypernetwork = shared.loaded_hypernetwork
|
||||
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
|
||||
|
||||
if hypernetwork_layers is not None:
|
||||
k_in = self.to_k(hypernetwork_layers[0](context))
|
||||
v_in = self.to_v(hypernetwork_layers[1](context))
|
||||
else:
|
||||
k_in = self.to_k(context)
|
||||
v_in = self.to_v(context)
|
||||
del context, x
|
||||
context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
|
||||
k_in = self.to_k(context_k)
|
||||
v_in = self.to_v(context_v)
|
||||
del context, context_k, context_v, x
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
|
||||
del q_in, k_in, v_in
|
||||
|
|
@ -61,22 +56,16 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
|
|||
return self.to_out(r2)
|
||||
|
||||
|
||||
# taken from https://github.com/Doggettx/stable-diffusion
|
||||
# taken from https://github.com/Doggettx/stable-diffusion and modified
|
||||
def split_cross_attention_forward(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
|
||||
q_in = self.to_q(x)
|
||||
context = default(context, x)
|
||||
|
||||
hypernetwork = shared.loaded_hypernetwork
|
||||
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
|
||||
|
||||
if hypernetwork_layers is not None:
|
||||
k_in = self.to_k(hypernetwork_layers[0](context))
|
||||
v_in = self.to_v(hypernetwork_layers[1](context))
|
||||
else:
|
||||
k_in = self.to_k(context)
|
||||
v_in = self.to_v(context)
|
||||
context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
|
||||
k_in = self.to_k(context_k)
|
||||
v_in = self.to_v(context_v)
|
||||
|
||||
k_in *= self.scale
|
||||
|
||||
|
|
@ -128,18 +117,111 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
|
|||
|
||||
return self.to_out(r2)
|
||||
|
||||
|
||||
def check_for_psutil():
|
||||
try:
|
||||
spec = importlib.util.find_spec('psutil')
|
||||
return spec is not None
|
||||
except ModuleNotFoundError:
|
||||
return False
|
||||
|
||||
invokeAI_mps_available = check_for_psutil()
|
||||
|
||||
# -- Taken from https://github.com/invoke-ai/InvokeAI --
|
||||
if invokeAI_mps_available:
|
||||
import psutil
|
||||
mem_total_gb = psutil.virtual_memory().total // (1 << 30)
|
||||
|
||||
def einsum_op_compvis(q, k, v):
|
||||
s = einsum('b i d, b j d -> b i j', q, k)
|
||||
s = s.softmax(dim=-1, dtype=s.dtype)
|
||||
return einsum('b i j, b j d -> b i d', s, v)
|
||||
|
||||
def einsum_op_slice_0(q, k, v, slice_size):
|
||||
r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
|
||||
for i in range(0, q.shape[0], slice_size):
|
||||
end = i + slice_size
|
||||
r[i:end] = einsum_op_compvis(q[i:end], k[i:end], v[i:end])
|
||||
return r
|
||||
|
||||
def einsum_op_slice_1(q, k, v, slice_size):
|
||||
r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
|
||||
for i in range(0, q.shape[1], slice_size):
|
||||
end = i + slice_size
|
||||
r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v)
|
||||
return r
|
||||
|
||||
def einsum_op_mps_v1(q, k, v):
|
||||
if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
|
||||
return einsum_op_compvis(q, k, v)
|
||||
else:
|
||||
slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
|
||||
return einsum_op_slice_1(q, k, v, slice_size)
|
||||
|
||||
def einsum_op_mps_v2(q, k, v):
|
||||
if mem_total_gb > 8 and q.shape[1] <= 4096:
|
||||
return einsum_op_compvis(q, k, v)
|
||||
else:
|
||||
return einsum_op_slice_0(q, k, v, 1)
|
||||
|
||||
def einsum_op_tensor_mem(q, k, v, max_tensor_mb):
|
||||
size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
|
||||
if size_mb <= max_tensor_mb:
|
||||
return einsum_op_compvis(q, k, v)
|
||||
div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
|
||||
if div <= q.shape[0]:
|
||||
return einsum_op_slice_0(q, k, v, q.shape[0] // div)
|
||||
return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
|
||||
|
||||
def einsum_op_cuda(q, k, v):
|
||||
stats = torch.cuda.memory_stats(q.device)
|
||||
mem_active = stats['active_bytes.all.current']
|
||||
mem_reserved = stats['reserved_bytes.all.current']
|
||||
mem_free_cuda, _ = torch.cuda.mem_get_info(q.device)
|
||||
mem_free_torch = mem_reserved - mem_active
|
||||
mem_free_total = mem_free_cuda + mem_free_torch
|
||||
# Divide factor of safety as there's copying and fragmentation
|
||||
return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
|
||||
|
||||
def einsum_op(q, k, v):
|
||||
if q.device.type == 'cuda':
|
||||
return einsum_op_cuda(q, k, v)
|
||||
|
||||
if q.device.type == 'mps':
|
||||
if mem_total_gb >= 32:
|
||||
return einsum_op_mps_v1(q, k, v)
|
||||
return einsum_op_mps_v2(q, k, v)
|
||||
|
||||
# Smaller slices are faster due to L2/L3/SLC caches.
|
||||
# Tested on i7 with 8MB L3 cache.
|
||||
return einsum_op_tensor_mem(q, k, v, 32)
|
||||
|
||||
def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
|
||||
q = self.to_q(x)
|
||||
context = default(context, x)
|
||||
|
||||
context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
|
||||
k = self.to_k(context_k) * self.scale
|
||||
v = self.to_v(context_v)
|
||||
del context, context_k, context_v, x
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
||||
r = einsum_op(q, k, v)
|
||||
return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
|
||||
|
||||
# -- End of code from https://github.com/invoke-ai/InvokeAI --
|
||||
|
||||
def xformers_attention_forward(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
q_in = self.to_q(x)
|
||||
context = default(context, x)
|
||||
hypernetwork = shared.loaded_hypernetwork
|
||||
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
|
||||
if hypernetwork_layers is not None:
|
||||
k_in = self.to_k(hypernetwork_layers[0](context))
|
||||
v_in = self.to_v(hypernetwork_layers[1](context))
|
||||
else:
|
||||
k_in = self.to_k(context)
|
||||
v_in = self.to_v(context)
|
||||
|
||||
context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
|
||||
k_in = self.to_k(context_k)
|
||||
v_in = self.to_v(context_v)
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
|
||||
del q_in, k_in, v_in
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
|
||||
|
|
|
|||
|
|
@ -152,6 +152,10 @@ def load_model_weights(model, checkpoint_info):
|
|||
devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
|
||||
|
||||
vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
|
||||
|
||||
if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
|
||||
vae_file = shared.cmd_opts.vae_path
|
||||
|
||||
if os.path.exists(vae_file):
|
||||
print(f"Loading VAE weights from: {vae_file}")
|
||||
vae_ckpt = torch.load(vae_file, map_location="cpu")
|
||||
|
|
|
|||
|
|
@ -57,7 +57,7 @@ def set_samplers():
|
|||
global samplers, samplers_for_img2img
|
||||
|
||||
hidden = set(opts.hide_samplers)
|
||||
hidden_img2img = set(opts.hide_samplers + ['PLMS', 'DPM fast', 'DPM adaptive'])
|
||||
hidden_img2img = set(opts.hide_samplers + ['PLMS'])
|
||||
|
||||
samplers = [x for x in all_samplers if x.name not in hidden]
|
||||
samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
|
||||
|
|
@ -365,16 +365,26 @@ class KDiffusionSampler:
|
|||
else:
|
||||
sigmas = self.model_wrap.get_sigmas(steps)
|
||||
|
||||
noise = noise * sigmas[steps - t_enc - 1]
|
||||
xi = x + noise
|
||||
|
||||
extra_params_kwargs = self.initialize(p)
|
||||
|
||||
sigma_sched = sigmas[steps - t_enc - 1:]
|
||||
xi = x + noise * sigma_sched[0]
|
||||
|
||||
extra_params_kwargs = self.initialize(p)
|
||||
if 'sigma_min' in inspect.signature(self.func).parameters:
|
||||
## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
|
||||
extra_params_kwargs['sigma_min'] = sigma_sched[-2]
|
||||
if 'sigma_max' in inspect.signature(self.func).parameters:
|
||||
extra_params_kwargs['sigma_max'] = sigma_sched[0]
|
||||
if 'n' in inspect.signature(self.func).parameters:
|
||||
extra_params_kwargs['n'] = len(sigma_sched) - 1
|
||||
if 'sigma_sched' in inspect.signature(self.func).parameters:
|
||||
extra_params_kwargs['sigma_sched'] = sigma_sched
|
||||
if 'sigmas' in inspect.signature(self.func).parameters:
|
||||
extra_params_kwargs['sigmas'] = sigma_sched
|
||||
|
||||
self.model_wrap_cfg.init_latent = x
|
||||
|
||||
return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
|
||||
return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
|
||||
|
||||
|
||||
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
|
||||
steps = steps or p.steps
|
||||
|
|
|
|||
|
|
@ -13,7 +13,8 @@ import modules.memmon
|
|||
import modules.sd_models
|
||||
import modules.styles
|
||||
import modules.devices as devices
|
||||
from modules import sd_samplers, hypernetwork
|
||||
from modules import sd_samplers
|
||||
from modules.hypernetworks import hypernetwork
|
||||
from modules.paths import models_path, script_path, sd_path
|
||||
|
||||
sd_model_file = os.path.join(script_path, 'model.ckpt')
|
||||
|
|
@ -29,6 +30,7 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th
|
|||
parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
|
||||
parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
|
||||
parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
|
||||
parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
|
||||
parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
|
||||
parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
|
||||
parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
|
||||
|
|
@ -36,6 +38,7 @@ parser.add_argument("--always-batch-cond-uncond", action='store_true', help="dis
|
|||
parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
|
||||
parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
|
||||
parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
|
||||
parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
|
||||
parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
|
||||
parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
|
||||
parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
|
||||
|
|
@ -47,9 +50,10 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with
|
|||
parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
|
||||
parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
|
||||
parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
|
||||
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
|
||||
parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
|
||||
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
|
||||
parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
|
||||
parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
|
||||
parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
|
||||
parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[])
|
||||
parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
|
||||
parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
|
||||
|
|
@ -66,6 +70,7 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR
|
|||
parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
|
||||
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
|
||||
parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
|
||||
parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
|
||||
parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
|
||||
|
||||
|
||||
|
|
@ -81,10 +86,17 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
|
|||
xformers_available = False
|
||||
config_filename = cmd_opts.ui_settings_file
|
||||
|
||||
hypernetworks = hypernetwork.list_hypernetworks(os.path.join(models_path, 'hypernetworks'))
|
||||
hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
|
||||
loaded_hypernetwork = None
|
||||
|
||||
|
||||
def reload_hypernetworks():
|
||||
global hypernetworks
|
||||
|
||||
hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
|
||||
hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
|
||||
|
||||
|
||||
class State:
|
||||
skipped = False
|
||||
interrupted = False
|
||||
|
|
@ -172,6 +184,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
|
|||
|
||||
"use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
|
||||
"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
|
||||
"do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('saving-paths', "Paths for saving"), {
|
||||
|
|
@ -215,6 +228,10 @@ options_templates.update(options_section(('system', "System"), {
|
|||
"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('training', "Training"), {
|
||||
"unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP form VRAM when training"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
||||
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True),
|
||||
"sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
|
||||
|
|
@ -225,6 +242,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
|||
"enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
|
||||
"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
|
||||
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
|
||||
"comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
|
||||
"filter_nsfw": OptionInfo(False, "Filter NSFW content"),
|
||||
'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
|
||||
"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
|
||||
|
|
@ -237,6 +255,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
|
|||
"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
|
||||
"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
|
||||
"interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
|
||||
"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "User interface"), {
|
||||
|
|
@ -260,6 +279,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
|
|||
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
|
||||
}))
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -10,6 +10,7 @@ from tqdm import tqdm
|
|||
from modules import modelloader
|
||||
from modules.shared import cmd_opts, opts, device
|
||||
from modules.swinir_model_arch import SwinIR as net
|
||||
from modules.swinir_model_arch_v2 import Swin2SR as net2
|
||||
from modules.upscaler import Upscaler, UpscalerData
|
||||
|
||||
precision_scope = (
|
||||
|
|
@ -57,22 +58,42 @@ class UpscalerSwinIR(Upscaler):
|
|||
filename = path
|
||||
if filename is None or not os.path.exists(filename):
|
||||
return None
|
||||
model = net(
|
||||
if filename.endswith(".v2.pth"):
|
||||
model = net2(
|
||||
upscale=scale,
|
||||
in_chans=3,
|
||||
img_size=64,
|
||||
window_size=8,
|
||||
img_range=1.0,
|
||||
depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
|
||||
embed_dim=240,
|
||||
num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
|
||||
depths=[6, 6, 6, 6, 6, 6],
|
||||
embed_dim=180,
|
||||
num_heads=[6, 6, 6, 6, 6, 6],
|
||||
mlp_ratio=2,
|
||||
upsampler="nearest+conv",
|
||||
resi_connection="3conv",
|
||||
)
|
||||
resi_connection="1conv",
|
||||
)
|
||||
params = None
|
||||
else:
|
||||
model = net(
|
||||
upscale=scale,
|
||||
in_chans=3,
|
||||
img_size=64,
|
||||
window_size=8,
|
||||
img_range=1.0,
|
||||
depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
|
||||
embed_dim=240,
|
||||
num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
|
||||
mlp_ratio=2,
|
||||
upsampler="nearest+conv",
|
||||
resi_connection="3conv",
|
||||
)
|
||||
params = "params_ema"
|
||||
|
||||
pretrained_model = torch.load(filename)
|
||||
model.load_state_dict(pretrained_model["params_ema"], strict=True)
|
||||
if params is not None:
|
||||
model.load_state_dict(pretrained_model[params], strict=True)
|
||||
else:
|
||||
model.load_state_dict(pretrained_model, strict=True)
|
||||
if not cmd_opts.no_half:
|
||||
model = model.half()
|
||||
return model
|
||||
|
|
|
|||
1017
modules/swinir_model_arch_v2.py
Normal file
1017
modules/swinir_model_arch_v2.py
Normal file
File diff suppressed because it is too large
Load diff
|
|
@ -8,14 +8,14 @@ from torchvision import transforms
|
|||
|
||||
import random
|
||||
import tqdm
|
||||
from modules import devices
|
||||
from modules import devices, shared
|
||||
import re
|
||||
|
||||
re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
|
||||
|
||||
|
||||
class PersonalizedBase(Dataset):
|
||||
def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
|
||||
def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False):
|
||||
|
||||
self.placeholder_token = placeholder_token
|
||||
|
||||
|
|
@ -32,12 +32,15 @@ class PersonalizedBase(Dataset):
|
|||
|
||||
assert data_root, 'dataset directory not specified'
|
||||
|
||||
cond_model = shared.sd_model.cond_stage_model
|
||||
|
||||
self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
|
||||
print("Preparing dataset...")
|
||||
for path in tqdm.tqdm(self.image_paths):
|
||||
image = Image.open(path)
|
||||
image = image.convert('RGB')
|
||||
image = image.resize((self.width, self.height), PIL.Image.BICUBIC)
|
||||
try:
|
||||
image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
filename = os.path.basename(path)
|
||||
filename_tokens = os.path.splitext(filename)[0]
|
||||
|
|
@ -52,7 +55,13 @@ class PersonalizedBase(Dataset):
|
|||
init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
|
||||
init_latent = init_latent.to(devices.cpu)
|
||||
|
||||
self.dataset.append((init_latent, filename_tokens))
|
||||
if include_cond:
|
||||
text = self.create_text(filename_tokens)
|
||||
cond = cond_model([text]).to(devices.cpu)
|
||||
else:
|
||||
cond = None
|
||||
|
||||
self.dataset.append((init_latent, filename_tokens, cond))
|
||||
|
||||
self.length = len(self.dataset) * repeats
|
||||
|
||||
|
|
@ -63,6 +72,12 @@ class PersonalizedBase(Dataset):
|
|||
def shuffle(self):
|
||||
self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
|
||||
|
||||
def create_text(self, filename_tokens):
|
||||
text = random.choice(self.lines)
|
||||
text = text.replace("[name]", self.placeholder_token)
|
||||
text = text.replace("[filewords]", ' '.join(filename_tokens))
|
||||
return text
|
||||
|
||||
def __len__(self):
|
||||
return self.length
|
||||
|
||||
|
|
@ -71,10 +86,7 @@ class PersonalizedBase(Dataset):
|
|||
self.shuffle()
|
||||
|
||||
index = self.indexes[i % len(self.indexes)]
|
||||
x, filename_tokens = self.dataset[index]
|
||||
x, filename_tokens, cond = self.dataset[index]
|
||||
|
||||
text = random.choice(self.lines)
|
||||
text = text.replace("[name]", self.placeholder_token)
|
||||
text = text.replace("[filewords]", ' '.join(filename_tokens))
|
||||
|
||||
return x, text
|
||||
text = self.create_text(filename_tokens)
|
||||
return x, text, cond
|
||||
|
|
|
|||
|
|
@ -46,7 +46,10 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
|
|||
for index, imagefile in enumerate(tqdm.tqdm(files)):
|
||||
subindex = [0]
|
||||
filename = os.path.join(src, imagefile)
|
||||
img = Image.open(filename).convert("RGB")
|
||||
try:
|
||||
img = Image.open(filename).convert("RGB")
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
if shared.state.interrupted:
|
||||
break
|
||||
|
|
|
|||
|
|
@ -156,7 +156,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
|
|||
return fn
|
||||
|
||||
|
||||
def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file):
|
||||
def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, preview_image_prompt):
|
||||
assert embedding_name, 'embedding not selected'
|
||||
|
||||
shared.state.textinfo = "Initializing textual inversion training..."
|
||||
|
|
@ -208,7 +208,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
|
|||
optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
|
||||
|
||||
pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
|
||||
for i, (x, text) in pbar:
|
||||
for i, (x, text, _) in pbar:
|
||||
embedding.step = i + ititial_step
|
||||
|
||||
if embedding.step > end_step:
|
||||
|
|
@ -236,10 +236,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
|
|||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
epoch_num = embedding.step // epoch_len
|
||||
epoch_step = embedding.step - (epoch_num * epoch_len) + 1
|
||||
epoch_num = embedding.step // len(ds)
|
||||
epoch_step = embedding.step - (epoch_num * len(ds)) + 1
|
||||
|
||||
pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
|
||||
pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}")
|
||||
|
||||
if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
|
||||
last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
|
||||
|
|
@ -248,12 +248,14 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
|
|||
if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
|
||||
last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
|
||||
|
||||
preview_text = text if preview_image_prompt == "" else preview_image_prompt
|
||||
|
||||
p = processing.StableDiffusionProcessingTxt2Img(
|
||||
sd_model=shared.sd_model,
|
||||
prompt=text,
|
||||
prompt=preview_text,
|
||||
steps=20,
|
||||
height=training_height,
|
||||
width=training_width,
|
||||
height=training_height,
|
||||
width=training_width,
|
||||
do_not_save_grid=True,
|
||||
do_not_save_samples=True,
|
||||
)
|
||||
|
|
@ -264,7 +266,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
|
|||
shared.state.current_image = image
|
||||
image.save(last_saved_image)
|
||||
|
||||
last_saved_image += f", prompt: {text}"
|
||||
last_saved_image += f", prompt: {preview_text}"
|
||||
|
||||
shared.state.job_no = embedding.step
|
||||
|
||||
|
|
|
|||
|
|
@ -23,6 +23,8 @@ def preprocess(*args):
|
|||
|
||||
def train_embedding(*args):
|
||||
|
||||
assert not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram, 'Training models with lowvram or medvram is not possible'
|
||||
|
||||
try:
|
||||
sd_hijack.undo_optimizations()
|
||||
|
||||
|
|
|
|||
112
modules/ui.py
112
modules/ui.py
|
|
@ -39,6 +39,7 @@ import modules.generation_parameters_copypaste
|
|||
from modules import prompt_parser
|
||||
from modules.images import save_image
|
||||
import modules.textual_inversion.ui
|
||||
import modules.hypernetworks.ui
|
||||
|
||||
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
|
||||
mimetypes.init()
|
||||
|
|
@ -50,6 +51,11 @@ if not cmd_opts.share and not cmd_opts.listen:
|
|||
gradio.utils.version_check = lambda: None
|
||||
gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
|
||||
|
||||
if cmd_opts.ngrok != None:
|
||||
import modules.ngrok as ngrok
|
||||
print('ngrok authtoken detected, trying to connect...')
|
||||
ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860)
|
||||
|
||||
|
||||
def gr_show(visible=True):
|
||||
return {"visible": visible, "__type__": "update"}
|
||||
|
|
@ -311,7 +317,7 @@ def interrogate(image):
|
|||
|
||||
|
||||
def interrogate_deepbooru(image):
|
||||
prompt = get_deepbooru_tags(image)
|
||||
prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold)
|
||||
return gr_show(True) if prompt is None else prompt
|
||||
|
||||
|
||||
|
|
@ -428,7 +434,10 @@ def create_toprow(is_img2img):
|
|||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=8):
|
||||
negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
|
||||
with gr.Row():
|
||||
negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
|
||||
with gr.Column(scale=1, elem_id="roll_col"):
|
||||
sh = gr.Button(elem_id="sh", visible=True)
|
||||
|
||||
with gr.Column(scale=1, elem_id="style_neg_col"):
|
||||
prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)
|
||||
|
|
@ -524,7 +533,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
|
||||
|
||||
with gr.Row():
|
||||
batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
|
||||
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
|
||||
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
|
||||
|
||||
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
|
||||
|
|
@ -549,15 +558,15 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
|
||||
open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)
|
||||
|
||||
with gr.Row():
|
||||
do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
|
||||
with gr.Row():
|
||||
do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
|
||||
|
||||
with gr.Row():
|
||||
download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
|
||||
with gr.Row():
|
||||
download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
|
||||
|
||||
with gr.Group():
|
||||
html_info = gr.HTML()
|
||||
generation_info = gr.Textbox(visible=False)
|
||||
with gr.Group():
|
||||
html_info = gr.HTML()
|
||||
generation_info = gr.Textbox(visible=False)
|
||||
|
||||
connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
|
||||
connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
|
||||
|
|
@ -710,7 +719,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
tiling = gr.Checkbox(label='Tiling', value=False)
|
||||
|
||||
with gr.Row():
|
||||
batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
|
||||
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
|
||||
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
|
||||
|
||||
with gr.Group():
|
||||
|
|
@ -737,15 +746,15 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
|
||||
open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)
|
||||
|
||||
with gr.Row():
|
||||
do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
|
||||
with gr.Row():
|
||||
do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
|
||||
|
||||
with gr.Row():
|
||||
download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
|
||||
with gr.Row():
|
||||
download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
|
||||
|
||||
with gr.Group():
|
||||
html_info = gr.HTML()
|
||||
generation_info = gr.Textbox(visible=False)
|
||||
with gr.Group():
|
||||
html_info = gr.HTML()
|
||||
generation_info = gr.Textbox(visible=False)
|
||||
|
||||
connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
|
||||
connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
|
||||
|
|
@ -961,7 +970,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
|
||||
extras_send_to_inpaint.click(
|
||||
fn=lambda x: image_from_url_text(x),
|
||||
_js="extract_image_from_gallery_img2img",
|
||||
_js="extract_image_from_gallery_inpaint",
|
||||
inputs=[result_images],
|
||||
outputs=[init_img_with_mask],
|
||||
)
|
||||
|
|
@ -1022,7 +1031,20 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
gr.HTML(value="")
|
||||
|
||||
with gr.Column():
|
||||
create_embedding = gr.Button(value="Create", variant='primary')
|
||||
create_embedding = gr.Button(value="Create embedding", variant='primary')
|
||||
|
||||
with gr.Group():
|
||||
gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new hypernetwork</p>")
|
||||
|
||||
new_hypernetwork_name = gr.Textbox(label="Name")
|
||||
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=3):
|
||||
gr.HTML(value="")
|
||||
|
||||
with gr.Column():
|
||||
create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
|
||||
|
||||
with gr.Group():
|
||||
gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
|
||||
|
|
@ -1047,6 +1069,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
with gr.Group():
|
||||
gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
|
||||
train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
|
||||
train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
|
||||
learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value = "5.0e-03")
|
||||
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
|
||||
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
|
||||
|
|
@ -1057,15 +1080,12 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
|
||||
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
|
||||
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
|
||||
preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=2):
|
||||
gr.HTML(value="")
|
||||
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
interrupt_training = gr.Button(value="Interrupt")
|
||||
train_embedding = gr.Button(value="Train", variant='primary')
|
||||
interrupt_training = gr.Button(value="Interrupt")
|
||||
train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
|
||||
train_embedding = gr.Button(value="Train Embedding", variant='primary')
|
||||
|
||||
with gr.Column():
|
||||
progressbar = gr.HTML(elem_id="ti_progressbar")
|
||||
|
|
@ -1091,6 +1111,19 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
]
|
||||
)
|
||||
|
||||
create_hypernetwork.click(
|
||||
fn=modules.hypernetworks.ui.create_hypernetwork,
|
||||
inputs=[
|
||||
new_hypernetwork_name,
|
||||
new_hypernetwork_sizes,
|
||||
],
|
||||
outputs=[
|
||||
train_hypernetwork_name,
|
||||
ti_output,
|
||||
ti_outcome,
|
||||
]
|
||||
)
|
||||
|
||||
run_preprocess.click(
|
||||
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
|
||||
_js="start_training_textual_inversion",
|
||||
|
|
@ -1124,6 +1157,27 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
create_image_every,
|
||||
save_embedding_every,
|
||||
template_file,
|
||||
preview_image_prompt,
|
||||
],
|
||||
outputs=[
|
||||
ti_output,
|
||||
ti_outcome,
|
||||
]
|
||||
)
|
||||
|
||||
train_hypernetwork.click(
|
||||
fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
|
||||
_js="start_training_textual_inversion",
|
||||
inputs=[
|
||||
train_hypernetwork_name,
|
||||
learn_rate,
|
||||
dataset_directory,
|
||||
log_directory,
|
||||
steps,
|
||||
create_image_every,
|
||||
save_embedding_every,
|
||||
template_file,
|
||||
preview_image_prompt,
|
||||
],
|
||||
outputs=[
|
||||
ti_output,
|
||||
|
|
@ -1137,6 +1191,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
outputs=[],
|
||||
)
|
||||
|
||||
|
||||
def create_setting_component(key):
|
||||
def fun():
|
||||
return opts.data[key] if key in opts.data else opts.data_labels[key].default
|
||||
|
|
@ -1290,6 +1345,7 @@ Requested path was: {f}
|
|||
shared.state.interrupt()
|
||||
settings_interface.gradio_ref.do_restart = True
|
||||
|
||||
|
||||
restart_gradio.click(
|
||||
fn=request_restart,
|
||||
inputs=[],
|
||||
|
|
@ -1331,7 +1387,7 @@ Requested path was: {f}
|
|||
|
||||
with gr.Tabs() as tabs:
|
||||
for interface, label, ifid in interfaces:
|
||||
with gr.TabItem(label, id=ifid):
|
||||
with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
|
||||
interface.render()
|
||||
|
||||
if os.path.exists(os.path.join(script_path, "notification.mp3")):
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ fairscale==0.4.4
|
|||
fonts
|
||||
font-roboto
|
||||
gfpgan
|
||||
gradio==3.4b3
|
||||
gradio==3.4.1
|
||||
invisible-watermark
|
||||
numpy
|
||||
omegaconf
|
||||
|
|
@ -23,4 +23,3 @@ resize-right
|
|||
torchdiffeq
|
||||
kornia
|
||||
lark
|
||||
functorch
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ transformers==4.19.2
|
|||
diffusers==0.3.0
|
||||
basicsr==1.4.2
|
||||
gfpgan==1.3.8
|
||||
gradio==3.4b3
|
||||
gradio==3.4.1
|
||||
numpy==1.23.3
|
||||
Pillow==9.2.0
|
||||
realesrgan==0.3.0
|
||||
|
|
@ -22,4 +22,3 @@ resize-right==0.0.2
|
|||
torchdiffeq==0.2.3
|
||||
kornia==0.6.7
|
||||
lark==1.1.2
|
||||
functorch==0.2.1
|
||||
|
|
|
|||
23
script.js
23
script.js
|
|
@ -6,6 +6,10 @@ function get_uiCurrentTab() {
|
|||
return gradioApp().querySelector('.tabs button:not(.border-transparent)')
|
||||
}
|
||||
|
||||
function get_uiCurrentTabContent() {
|
||||
return gradioApp().querySelector('.tabitem[id^=tab_]:not([style*="display: none"])')
|
||||
}
|
||||
|
||||
uiUpdateCallbacks = []
|
||||
uiTabChangeCallbacks = []
|
||||
let uiCurrentTab = null
|
||||
|
|
@ -40,6 +44,25 @@ document.addEventListener("DOMContentLoaded", function() {
|
|||
mutationObserver.observe( gradioApp(), { childList:true, subtree:true })
|
||||
});
|
||||
|
||||
/**
|
||||
* Add a ctrl+enter as a shortcut to start a generation
|
||||
*/
|
||||
document.addEventListener('keydown', function(e) {
|
||||
var handled = false;
|
||||
if (e.key !== undefined) {
|
||||
if((e.key == "Enter" && (e.metaKey || e.ctrlKey))) handled = true;
|
||||
} else if (e.keyCode !== undefined) {
|
||||
if((e.keyCode == 13 && (e.metaKey || e.ctrlKey))) handled = true;
|
||||
}
|
||||
if (handled) {
|
||||
button = get_uiCurrentTabContent().querySelector('button[id$=_generate]');
|
||||
if (button) {
|
||||
button.click();
|
||||
}
|
||||
e.preventDefault();
|
||||
}
|
||||
})
|
||||
|
||||
/**
|
||||
* checks that a UI element is not in another hidden element or tab content
|
||||
*/
|
||||
|
|
|
|||
|
|
@ -38,6 +38,7 @@ class Script(scripts.Script):
|
|||
|
||||
grids = []
|
||||
all_images = []
|
||||
original_init_image = p.init_images
|
||||
state.job_count = loops * batch_count
|
||||
|
||||
initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
|
||||
|
|
@ -45,6 +46,9 @@ class Script(scripts.Script):
|
|||
for n in range(batch_count):
|
||||
history = []
|
||||
|
||||
# Reset to original init image at the start of each batch
|
||||
p.init_images = original_init_image
|
||||
|
||||
for i in range(loops):
|
||||
p.n_iter = 1
|
||||
p.batch_size = 1
|
||||
|
|
|
|||
|
|
@ -10,7 +10,8 @@ import numpy as np
|
|||
import modules.scripts as scripts
|
||||
import gradio as gr
|
||||
|
||||
from modules import images, hypernetwork
|
||||
from modules import images
|
||||
from modules.hypernetworks import hypernetwork
|
||||
from modules.processing import process_images, Processed, get_correct_sampler
|
||||
from modules.shared import opts, cmd_opts, state
|
||||
import modules.shared as shared
|
||||
|
|
@ -27,6 +28,9 @@ def apply_field(field):
|
|||
|
||||
|
||||
def apply_prompt(p, x, xs):
|
||||
if xs[0] not in p.prompt and xs[0] not in p.negative_prompt:
|
||||
raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.")
|
||||
|
||||
p.prompt = p.prompt.replace(xs[0], x)
|
||||
p.negative_prompt = p.negative_prompt.replace(xs[0], x)
|
||||
|
||||
|
|
@ -193,7 +197,7 @@ class Script(scripts.Script):
|
|||
x_values = gr.Textbox(label="X values", visible=False, lines=1)
|
||||
|
||||
with gr.Row():
|
||||
y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[4].label, visible=False, type="index", elem_id="y_type")
|
||||
y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[0].label, visible=False, type="index", elem_id="y_type")
|
||||
y_values = gr.Textbox(label="Y values", visible=False, lines=1)
|
||||
|
||||
draw_legend = gr.Checkbox(label='Draw legend', value=True)
|
||||
|
|
@ -205,7 +209,10 @@ class Script(scripts.Script):
|
|||
if not no_fixed_seeds:
|
||||
modules.processing.fix_seed(p)
|
||||
|
||||
p.batch_size = 1
|
||||
if not opts.return_grid:
|
||||
p.batch_size = 1
|
||||
|
||||
|
||||
CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
|
||||
|
||||
def process_axis(opt, vals):
|
||||
|
|
|
|||
39
style.css
39
style.css
|
|
@ -2,6 +2,27 @@
|
|||
max-width: 100%;
|
||||
}
|
||||
|
||||
#txt2img_token_counter {
|
||||
height: 0px;
|
||||
}
|
||||
|
||||
#img2img_token_counter {
|
||||
height: 0px;
|
||||
}
|
||||
|
||||
#sh{
|
||||
min-width: 2em;
|
||||
min-height: 2em;
|
||||
max-width: 2em;
|
||||
max-height: 2em;
|
||||
flex-grow: 0;
|
||||
padding-left: 0.25em;
|
||||
padding-right: 0.25em;
|
||||
margin: 0.1em 0;
|
||||
opacity: 0%;
|
||||
cursor: default;
|
||||
}
|
||||
|
||||
.output-html p {margin: 0 0.5em;}
|
||||
|
||||
.row > *,
|
||||
|
|
@ -219,6 +240,7 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block s
|
|||
#settings fieldset span.text-gray-500, #settings .gr-block.gr-box span.text-gray-500, #settings label.block span{
|
||||
position: relative;
|
||||
border: none;
|
||||
margin-right: 8em;
|
||||
}
|
||||
|
||||
.gr-panel div.flex-col div.justify-between label span{
|
||||
|
|
@ -467,3 +489,20 @@ input[type="range"]{
|
|||
max-width: 32em;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
canvas[key="mask"] {
|
||||
z-index: 12 !important;
|
||||
filter: invert();
|
||||
mix-blend-mode: multiply;
|
||||
pointer-events: none;
|
||||
}
|
||||
|
||||
|
||||
/* gradio 3.4.1 stuff for editable scrollbar values */
|
||||
.gr-box > div > div > input.gr-text-input{
|
||||
position: absolute;
|
||||
right: 0.5em;
|
||||
top: -0.6em;
|
||||
z-index: 200;
|
||||
width: 8em;
|
||||
}
|
||||
|
|
|
|||
27
textual_inversion_templates/hypernetwork.txt
Normal file
27
textual_inversion_templates/hypernetwork.txt
Normal file
|
|
@ -0,0 +1,27 @@
|
|||
a photo of a [filewords]
|
||||
a rendering of a [filewords]
|
||||
a cropped photo of the [filewords]
|
||||
the photo of a [filewords]
|
||||
a photo of a clean [filewords]
|
||||
a photo of a dirty [filewords]
|
||||
a dark photo of the [filewords]
|
||||
a photo of my [filewords]
|
||||
a photo of the cool [filewords]
|
||||
a close-up photo of a [filewords]
|
||||
a bright photo of the [filewords]
|
||||
a cropped photo of a [filewords]
|
||||
a photo of the [filewords]
|
||||
a good photo of the [filewords]
|
||||
a photo of one [filewords]
|
||||
a close-up photo of the [filewords]
|
||||
a rendition of the [filewords]
|
||||
a photo of the clean [filewords]
|
||||
a rendition of a [filewords]
|
||||
a photo of a nice [filewords]
|
||||
a good photo of a [filewords]
|
||||
a photo of the nice [filewords]
|
||||
a photo of the small [filewords]
|
||||
a photo of the weird [filewords]
|
||||
a photo of the large [filewords]
|
||||
a photo of a cool [filewords]
|
||||
a photo of a small [filewords]
|
||||
1
textual_inversion_templates/none.txt
Normal file
1
textual_inversion_templates/none.txt
Normal file
|
|
@ -0,0 +1 @@
|
|||
picture
|
||||
8
webui.py
8
webui.py
|
|
@ -29,6 +29,7 @@ from modules import devices
|
|||
from modules import modelloader
|
||||
from modules.paths import script_path
|
||||
from modules.shared import cmd_opts
|
||||
import modules.hypernetworks.hypernetwork
|
||||
|
||||
modelloader.cleanup_models()
|
||||
modules.sd_models.setup_model()
|
||||
|
|
@ -82,8 +83,7 @@ modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
|
|||
shared.sd_model = modules.sd_models.load_model()
|
||||
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
|
||||
|
||||
loaded_hypernetwork = modules.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)
|
||||
shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
|
||||
shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
|
||||
|
||||
|
||||
def webui():
|
||||
|
|
@ -108,7 +108,7 @@ def webui():
|
|||
prevent_thread_lock=True
|
||||
)
|
||||
|
||||
app.add_middleware(GZipMiddleware,minimum_size=1000)
|
||||
app.add_middleware(GZipMiddleware, minimum_size=1000)
|
||||
|
||||
while 1:
|
||||
time.sleep(0.5)
|
||||
|
|
@ -124,6 +124,8 @@ def webui():
|
|||
modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
|
||||
print('Reloading modules: modules.ui')
|
||||
importlib.reload(modules.ui)
|
||||
print('Refreshing Model List')
|
||||
modules.sd_models.list_models()
|
||||
print('Restarting Gradio')
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue