mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
Merge branch 'master' of https://github.com/AUTOMATIC1111/stable-diffusion-webui into upstream-master
This commit is contained in:
ssysm
commit
6fdad291bd
@ -66,6 +66,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
|
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- separate prompts using uppercase `AND`
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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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## 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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@ -123,4 +124,5 @@ The documentation was moved from this README over to the project's [wiki](https:
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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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- 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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@ -1,5 +1,4 @@
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// A full size 'lightbox' preview modal shown when left clicking on gallery previews
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function closeModal() {
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gradioApp().getElementById("lightboxModal").style.display = "none";
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}
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@ -21,6 +20,26 @@ function negmod(n, m) {
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return ((n % m) + m) % m;
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}
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function updateOnBackgroundChange() {
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const modalImage = gradioApp().getElementById("modalImage")
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if (modalImage && modalImage.offsetParent) {
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let allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2")
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let currentButton = null
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allcurrentButtons.forEach(function(elem) {
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if (elem.parentElement.offsetParent) {
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currentButton = elem;
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}
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})
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if (modalImage.src != currentButton.children[0].src) {
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modalImage.src = currentButton.children[0].src;
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if (modalImage.style.display === 'none') {
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modal.style.setProperty('background-image', `url(${modalImage.src})`)
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}
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}
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}
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}
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function modalImageSwitch(offset) {
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var allgalleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all")
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var galleryButtons = []
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@ -40,7 +59,11 @@ function modalImageSwitch(offset){
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})
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var result = -1
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galleryButtons.forEach(function(v, i){ if(v==currentButton) { result = i } })
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galleryButtons.forEach(function(v, i) {
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if (v == currentButton) {
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result = i
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}
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})
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if (result != -1) {
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nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]
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@ -51,7 +74,9 @@ function modalImageSwitch(offset){
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if (modalImage.style.display === 'none') {
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modal.style.setProperty('background-image', `url(${modalImage.src})`)
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}
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setTimeout( function(){modal.focus()},10)
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setTimeout(function() {
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modal.focus()
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}, 10)
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}
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}
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}
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@ -90,9 +115,7 @@ function showGalleryImage(){
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return;
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e.dataset.modded = true;
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if(e && e.parentElement.tagName == 'DIV'){
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e.style.cursor='pointer'
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e.addEventListener('click', function (evt) {
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if(!opts.js_modal_lightbox) return;
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modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
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@ -145,6 +168,7 @@ onUiUpdate(function(){
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if (fullImg_preview != null) {
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fullImg_preview.forEach(galleryImageHandler);
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}
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updateOnBackgroundChange();
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})
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document.addEventListener("DOMContentLoaded", function() {
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79
launch.py
79
launch.py
@ -7,38 +7,14 @@ import shlex
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import platform
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dir_repos = "repositories"
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dir_tmp = "tmp"
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python = sys.executable
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git = os.environ.get('GIT', "git")
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torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
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requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
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commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
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gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
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clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
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stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
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taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
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k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878")
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codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
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blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
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args = shlex.split(commandline_args)
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def extract_arg(args, name):
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return [x for x in args if x != name], name in args
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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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def repo_dir(name):
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return os.path.join(dir_repos, name)
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def run(command, desc=None, errdesc=None):
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if desc is not None:
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print(desc)
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@ -58,23 +34,11 @@ stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.st
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return result.stdout.decode(encoding="utf8", errors="ignore")
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def run_python(code, desc=None, errdesc=None):
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return run(f'"{python}" -c "{code}"', desc, errdesc)
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def run_pip(args, desc=None):
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return run(f'"{python}" -m pip {args} --prefer-binary', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
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def check_run(command):
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result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
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return result.returncode == 0
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def check_run_python(code):
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return check_run(f'"{python}" -c "{code}"')
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def is_installed(package):
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try:
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spec = importlib.util.find_spec(package)
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@ -84,6 +48,22 @@ def is_installed(package):
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return spec is not None
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def repo_dir(name):
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return os.path.join(dir_repos, name)
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def run_python(code, desc=None, errdesc=None):
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return run(f'"{python}" -c "{code}"', desc, errdesc)
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def run_pip(args, desc=None):
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return run(f'"{python}" -m pip {args} --prefer-binary', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
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||||
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||||
|
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def check_run_python(code):
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||||
return check_run(f'"{python}" -c "{code}"')
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def git_clone(url, dir, name, commithash=None):
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# TODO clone into temporary dir and move if successful
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@ -105,6 +85,26 @@ def git_clone(url, dir, name, commithash=None):
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run(f'"{git}" -C {dir} checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
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def prepare_enviroment():
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torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
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requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
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||||
commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
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||||
|
||||
gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
|
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clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
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|
||||
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
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taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
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k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878")
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codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
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blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
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|
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args = shlex.split(commandline_args)
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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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try:
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commit = run(f"{git} rev-parse HEAD").strip()
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except Exception:
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@ -113,7 +113,6 @@ except Exception:
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print(f"Python {sys.version}")
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print(f"Commit hash: {commit}")
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|
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if not is_installed("torch") or not is_installed("torchvision"):
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run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
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@ -132,6 +131,9 @@ if not is_installed("xformers") and xformers and platform.python_version().start
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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")
|
||||
|
||||
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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@ -151,10 +153,13 @@ if "--exit" in args:
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print("Exiting because of --exit argument")
|
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exit(0)
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|
||||
|
||||
def start_webui():
|
||||
print(f"Launching Web UI with arguments: {' '.join(sys.argv[1:])}")
|
||||
import webui
|
||||
webui.webui()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
prepare_enviroment()
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||||
start_webui()
|
||||
|
||||
@ -10,13 +10,11 @@ from basicsr.utils.download_util import load_file_from_url
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||||
import modules.upscaler
|
||||
from modules import devices, modelloader
|
||||
from modules.bsrgan_model_arch import RRDBNet
|
||||
from modules.paths import models_path
|
||||
|
||||
|
||||
class UpscalerBSRGAN(modules.upscaler.Upscaler):
|
||||
def __init__(self, dirname):
|
||||
self.name = "BSRGAN"
|
||||
self.model_path = os.path.join(models_path, self.name)
|
||||
self.model_name = "BSRGAN 4x"
|
||||
self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth"
|
||||
self.user_path = dirname
|
||||
|
||||
73
modules/deepbooru.py
Normal file
73
modules/deepbooru.py
Normal file
@ -0,0 +1,73 @@
|
||||
import os.path
|
||||
from concurrent.futures import ProcessPoolExecutor
|
||||
from multiprocessing import get_context
|
||||
|
||||
|
||||
def _load_tf_and_return_tags(pil_image, threshold):
|
||||
import deepdanbooru as dd
|
||||
import tensorflow as tf
|
||||
import numpy as np
|
||||
|
||||
this_folder = os.path.dirname(__file__)
|
||||
model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
|
||||
if not os.path.exists(os.path.join(model_path, 'project.json')):
|
||||
# there is no point importing these every time
|
||||
import zipfile
|
||||
from basicsr.utils.download_util import load_file_from_url
|
||||
load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
|
||||
model_path)
|
||||
with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
|
||||
zip_ref.extractall(model_path)
|
||||
os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
|
||||
|
||||
tags = dd.project.load_tags_from_project(model_path)
|
||||
model = dd.project.load_model_from_project(
|
||||
model_path, compile_model=True
|
||||
)
|
||||
|
||||
width = model.input_shape[2]
|
||||
height = model.input_shape[1]
|
||||
image = np.array(pil_image)
|
||||
image = tf.image.resize(
|
||||
image,
|
||||
size=(height, width),
|
||||
method=tf.image.ResizeMethod.AREA,
|
||||
preserve_aspect_ratio=True,
|
||||
)
|
||||
image = image.numpy() # EagerTensor to np.array
|
||||
image = dd.image.transform_and_pad_image(image, width, height)
|
||||
image = image / 255.0
|
||||
image_shape = image.shape
|
||||
image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
|
||||
|
||||
y = model.predict(image)[0]
|
||||
|
||||
result_dict = {}
|
||||
|
||||
for i, tag in enumerate(tags):
|
||||
result_dict[tag] = y[i]
|
||||
result_tags_out = []
|
||||
result_tags_print = []
|
||||
for tag in tags:
|
||||
if result_dict[tag] >= threshold:
|
||||
if tag.startswith("rating:"):
|
||||
continue
|
||||
result_tags_out.append(tag)
|
||||
result_tags_print.append(f'{result_dict[tag]} {tag}')
|
||||
|
||||
print('\n'.join(sorted(result_tags_print, reverse=True)))
|
||||
|
||||
return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
|
||||
|
||||
|
||||
def subprocess_init_no_cuda():
|
||||
import os
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
|
||||
|
||||
|
||||
def get_deepbooru_tags(pil_image, threshold=0.5):
|
||||
context = get_context('spawn')
|
||||
with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
|
||||
f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
|
||||
ret = f.result() # will rethrow any exceptions
|
||||
return ret
|
||||
@ -5,9 +5,8 @@ import torch
|
||||
from PIL import Image
|
||||
from basicsr.utils.download_util import load_file_from_url
|
||||
|
||||
import modules.esrgam_model_arch as arch
|
||||
import modules.esrgan_model_arch as arch
|
||||
from modules import shared, modelloader, images, devices
|
||||
from modules.paths import models_path
|
||||
from modules.upscaler import Upscaler, UpscalerData
|
||||
from modules.shared import opts
|
||||
|
||||
@ -76,7 +75,6 @@ class UpscalerESRGAN(Upscaler):
|
||||
self.model_name = "ESRGAN_4x"
|
||||
self.scalers = []
|
||||
self.user_path = dirname
|
||||
self.model_path = os.path.join(models_path, self.name)
|
||||
super().__init__()
|
||||
model_paths = self.find_models(ext_filter=[".pt", ".pth"])
|
||||
scalers = []
|
||||
|
||||
@ -29,7 +29,7 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
|
||||
if extras_mode == 1:
|
||||
#convert file to pillow image
|
||||
for img in image_folder:
|
||||
image = Image.fromarray(np.array(Image.open(img)))
|
||||
image = Image.open(img)
|
||||
imageArr.append(image)
|
||||
imageNameArr.append(os.path.splitext(img.orig_name)[0])
|
||||
else:
|
||||
@ -98,6 +98,10 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
|
||||
no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo,
|
||||
forced_filename=image_name if opts.use_original_name_batch else None)
|
||||
|
||||
if opts.enable_pnginfo:
|
||||
image.info = existing_pnginfo
|
||||
image.info["extras"] = info
|
||||
|
||||
outputs.append(image)
|
||||
|
||||
devices.torch_gc()
|
||||
@ -170,8 +174,8 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
|
||||
print(f"Loading {secondary_model_info.filename}...")
|
||||
secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
|
||||
|
||||
theta_0 = primary_model['state_dict']
|
||||
theta_1 = secondary_model['state_dict']
|
||||
theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
|
||||
theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)
|
||||
|
||||
theta_funcs = {
|
||||
"Weighted Sum": weighted_sum,
|
||||
|
||||
@ -40,27 +40,37 @@ class Hypernetwork:
|
||||
self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
|
||||
|
||||
|
||||
def load_hypernetworks(path):
|
||||
def list_hypernetworks(path):
|
||||
res = {}
|
||||
|
||||
for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
|
||||
try:
|
||||
hn = Hypernetwork(filename)
|
||||
res[hn.name] = hn
|
||||
except Exception:
|
||||
print(f"Error loading hypernetwork {filename}", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
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(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 attention_CrossAttention_forward(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
|
||||
q = self.to_q(x)
|
||||
context = default(context, x)
|
||||
|
||||
hypernetwork = shared.selected_hypernetwork()
|
||||
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:
|
||||
|
||||
@ -349,6 +349,38 @@ def get_next_sequence_number(path, basename):
|
||||
|
||||
|
||||
def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
|
||||
'''Save an image.
|
||||
|
||||
Args:
|
||||
image (`PIL.Image`):
|
||||
The image to be saved.
|
||||
path (`str`):
|
||||
The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory.
|
||||
basename (`str`):
|
||||
The base filename which will be applied to `filename pattern`.
|
||||
seed, prompt, short_filename,
|
||||
extension (`str`):
|
||||
Image file extension, default is `png`.
|
||||
pngsectionname (`str`):
|
||||
Specify the name of the section which `info` will be saved in.
|
||||
info (`str` or `PngImagePlugin.iTXt`):
|
||||
PNG info chunks.
|
||||
existing_info (`dict`):
|
||||
Additional PNG info. `existing_info == {pngsectionname: info, ...}`
|
||||
no_prompt:
|
||||
TODO I don't know its meaning.
|
||||
p (`StableDiffusionProcessing`)
|
||||
forced_filename (`str`):
|
||||
If specified, `basename` and filename pattern will be ignored.
|
||||
save_to_dirs (bool):
|
||||
If true, the image will be saved into a subdirectory of `path`.
|
||||
|
||||
Returns: (fullfn, txt_fullfn)
|
||||
fullfn (`str`):
|
||||
The full path of the saved imaged.
|
||||
txt_fullfn (`str` or None):
|
||||
If a text file is saved for this image, this will be its full path. Otherwise None.
|
||||
'''
|
||||
if short_filename or prompt is None or seed is None:
|
||||
file_decoration = ""
|
||||
elif opts.save_to_dirs:
|
||||
@ -424,7 +456,10 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
|
||||
piexif.insert(exif_bytes(), fullfn_without_extension + ".jpg")
|
||||
|
||||
if opts.save_txt and info is not None:
|
||||
with open(f"{fullfn_without_extension}.txt", "w", encoding="utf8") as file:
|
||||
txt_fullfn = f"{fullfn_without_extension}.txt"
|
||||
with open(txt_fullfn, "w", encoding="utf8") as file:
|
||||
file.write(info + "\n")
|
||||
else:
|
||||
txt_fullfn = None
|
||||
|
||||
return fullfn
|
||||
return fullfn, txt_fullfn
|
||||
|
||||
@ -7,13 +7,11 @@ from basicsr.utils.download_util import load_file_from_url
|
||||
from modules.upscaler import Upscaler, UpscalerData
|
||||
from modules.ldsr_model_arch import LDSR
|
||||
from modules import shared
|
||||
from modules.paths import models_path
|
||||
|
||||
|
||||
class UpscalerLDSR(Upscaler):
|
||||
def __init__(self, user_path):
|
||||
self.name = "LDSR"
|
||||
self.model_path = os.path.join(models_path, self.name)
|
||||
self.user_path = user_path
|
||||
self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
|
||||
self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
import modules.safe
|
||||
|
||||
script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
|
||||
models_path = os.path.join(script_path, "models")
|
||||
|
||||
@ -46,6 +46,12 @@ def apply_color_correction(correction, image):
|
||||
return image
|
||||
|
||||
|
||||
def get_correct_sampler(p):
|
||||
if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):
|
||||
return sd_samplers.samplers
|
||||
elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
|
||||
return sd_samplers.samplers_for_img2img
|
||||
|
||||
class StableDiffusionProcessing:
|
||||
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
|
||||
self.sd_model = sd_model
|
||||
@ -123,7 +129,7 @@ class Processed:
|
||||
self.index_of_first_image = index_of_first_image
|
||||
self.styles = p.styles
|
||||
self.job_timestamp = state.job_timestamp
|
||||
self.clip_skip = opts.CLIP_ignore_last_layers
|
||||
self.clip_skip = opts.CLIP_stop_at_last_layers
|
||||
|
||||
self.eta = p.eta
|
||||
self.ddim_discretize = p.ddim_discretize
|
||||
@ -268,16 +274,18 @@ def fix_seed(p):
|
||||
def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
|
||||
index = position_in_batch + iteration * p.batch_size
|
||||
|
||||
clip_skip = getattr(p, 'clip_skip', opts.CLIP_ignore_last_layers)
|
||||
clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
|
||||
|
||||
generation_params = {
|
||||
"Steps": p.steps,
|
||||
"Sampler": sd_samplers.samplers[p.sampler_index].name,
|
||||
"Sampler": get_correct_sampler(p)[p.sampler_index].name,
|
||||
"CFG scale": p.cfg_scale,
|
||||
"Seed": all_seeds[index],
|
||||
"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
|
||||
"Size": f"{p.width}x{p.height}",
|
||||
"Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
|
||||
"Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
|
||||
"Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')),
|
||||
"Batch size": (None if p.batch_size < 2 else p.batch_size),
|
||||
"Batch pos": (None if p.batch_size < 2 else position_in_batch),
|
||||
"Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
|
||||
@ -285,7 +293,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
|
||||
"Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
|
||||
"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==0 else clip_skip,
|
||||
"Clip skip": None if clip_skip <= 1 else clip_skip,
|
||||
}
|
||||
|
||||
generation_params.update(p.extra_generation_params)
|
||||
@ -445,6 +453,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
||||
|
||||
text = infotext(n, i)
|
||||
infotexts.append(text)
|
||||
if opts.enable_pnginfo:
|
||||
image.info["parameters"] = text
|
||||
output_images.append(image)
|
||||
|
||||
@ -464,6 +473,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
||||
if opts.return_grid:
|
||||
text = infotext()
|
||||
infotexts.insert(0, text)
|
||||
if opts.enable_pnginfo:
|
||||
grid.info["parameters"] = text
|
||||
output_images.insert(0, grid)
|
||||
index_of_first_image = 1
|
||||
|
||||
@ -8,14 +8,12 @@ from basicsr.utils.download_util import load_file_from_url
|
||||
from realesrgan import RealESRGANer
|
||||
|
||||
from modules.upscaler import Upscaler, UpscalerData
|
||||
from modules.paths import models_path
|
||||
from modules.shared import cmd_opts, opts
|
||||
|
||||
|
||||
class UpscalerRealESRGAN(Upscaler):
|
||||
def __init__(self, path):
|
||||
self.name = "RealESRGAN"
|
||||
self.model_path = os.path.join(models_path, self.name)
|
||||
self.user_path = path
|
||||
super().__init__()
|
||||
try:
|
||||
|
||||
89
modules/safe.py
Normal file
89
modules/safe.py
Normal file
@ -0,0 +1,89 @@
|
||||
# this code is adapted from the script contributed by anon from /h/
|
||||
|
||||
import io
|
||||
import pickle
|
||||
import collections
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
import torch
|
||||
import numpy
|
||||
import _codecs
|
||||
import zipfile
|
||||
|
||||
|
||||
def encode(*args):
|
||||
out = _codecs.encode(*args)
|
||||
return out
|
||||
|
||||
|
||||
class RestrictedUnpickler(pickle.Unpickler):
|
||||
def persistent_load(self, saved_id):
|
||||
assert saved_id[0] == 'storage'
|
||||
return torch.storage._TypedStorage()
|
||||
|
||||
def find_class(self, module, name):
|
||||
if module == 'collections' and name == 'OrderedDict':
|
||||
return getattr(collections, name)
|
||||
if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
|
||||
return getattr(torch._utils, name)
|
||||
if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage']:
|
||||
return getattr(torch, name)
|
||||
if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
|
||||
return getattr(torch.nn.modules.container, name)
|
||||
if module == 'numpy.core.multiarray' and name == 'scalar':
|
||||
return numpy.core.multiarray.scalar
|
||||
if module == 'numpy' and name == 'dtype':
|
||||
return numpy.dtype
|
||||
if module == '_codecs' and name == 'encode':
|
||||
return encode
|
||||
if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint':
|
||||
import pytorch_lightning.callbacks
|
||||
return pytorch_lightning.callbacks.model_checkpoint
|
||||
if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint':
|
||||
import pytorch_lightning.callbacks.model_checkpoint
|
||||
return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint
|
||||
if module == "__builtin__" and name == 'set':
|
||||
return set
|
||||
|
||||
# Forbid everything else.
|
||||
raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")
|
||||
|
||||
|
||||
def check_pt(filename):
|
||||
try:
|
||||
|
||||
# new pytorch format is a zip file
|
||||
with zipfile.ZipFile(filename) as z:
|
||||
with z.open('archive/data.pkl') as file:
|
||||
unpickler = RestrictedUnpickler(file)
|
||||
unpickler.load()
|
||||
|
||||
except zipfile.BadZipfile:
|
||||
|
||||
# if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
|
||||
with open(filename, "rb") as file:
|
||||
unpickler = RestrictedUnpickler(file)
|
||||
for i in range(5):
|
||||
unpickler.load()
|
||||
|
||||
|
||||
def load(filename, *args, **kwargs):
|
||||
from modules import shared
|
||||
|
||||
try:
|
||||
if not shared.cmd_opts.disable_safe_unpickle:
|
||||
check_pt(filename)
|
||||
|
||||
except Exception:
|
||||
print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
|
||||
print(f"You can skip this check with --disable-safe-unpickle commandline argument.", file=sys.stderr)
|
||||
return None
|
||||
|
||||
return unsafe_torch_load(filename, *args, **kwargs)
|
||||
|
||||
|
||||
unsafe_torch_load = torch.load
|
||||
torch.load = load
|
||||
@ -9,14 +9,12 @@ from basicsr.utils.download_util import load_file_from_url
|
||||
|
||||
import modules.upscaler
|
||||
from modules import devices, modelloader
|
||||
from modules.paths import models_path
|
||||
from modules.scunet_model_arch import SCUNet as net
|
||||
|
||||
|
||||
class UpscalerScuNET(modules.upscaler.Upscaler):
|
||||
def __init__(self, dirname):
|
||||
self.name = "ScuNET"
|
||||
self.model_path = os.path.join(models_path, self.name)
|
||||
self.model_name = "ScuNET GAN"
|
||||
self.model_name2 = "ScuNET PSNR"
|
||||
self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
|
||||
|
||||
@ -282,14 +282,12 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
||||
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)
|
||||
|
||||
tmp = -opts.CLIP_ignore_last_layers
|
||||
if (opts.CLIP_ignore_last_layers == 0):
|
||||
outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids)
|
||||
z = outputs.last_hidden_state
|
||||
else:
|
||||
outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=tmp)
|
||||
z = outputs.hidden_states[tmp]
|
||||
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)
|
||||
else:
|
||||
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]
|
||||
|
||||
@ -28,7 +28,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
|
||||
q_in = self.to_q(x)
|
||||
context = default(context, x)
|
||||
|
||||
hypernetwork = shared.selected_hypernetwork()
|
||||
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:
|
||||
@ -68,7 +68,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
|
||||
q_in = self.to_q(x)
|
||||
context = default(context, x)
|
||||
|
||||
hypernetwork = shared.selected_hypernetwork()
|
||||
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:
|
||||
@ -132,7 +132,7 @@ 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.selected_hypernetwork()
|
||||
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))
|
||||
|
||||
@ -5,7 +5,6 @@ from collections import namedtuple
|
||||
import torch
|
||||
from omegaconf import OmegaConf
|
||||
|
||||
|
||||
from ldm.util import instantiate_from_config
|
||||
|
||||
from modules import shared, modelloader, devices
|
||||
@ -122,6 +121,13 @@ def select_checkpoint():
|
||||
return checkpoint_info
|
||||
|
||||
|
||||
def get_state_dict_from_checkpoint(pl_sd):
|
||||
if "state_dict" in pl_sd:
|
||||
return pl_sd["state_dict"]
|
||||
|
||||
return pl_sd
|
||||
|
||||
|
||||
def load_model_weights(model, checkpoint_info):
|
||||
checkpoint_file = checkpoint_info.filename
|
||||
sd_model_hash = checkpoint_info.hash
|
||||
@ -132,10 +138,7 @@ def load_model_weights(model, checkpoint_info):
|
||||
if "global_step" in pl_sd:
|
||||
print(f"Global Step: {pl_sd['global_step']}")
|
||||
|
||||
if "state_dict" in pl_sd:
|
||||
sd = pl_sd["state_dict"]
|
||||
else:
|
||||
sd = pl_sd
|
||||
sd = get_state_dict_from_checkpoint(pl_sd)
|
||||
|
||||
model.load_state_dict(sd, strict=False)
|
||||
|
||||
@ -165,7 +168,7 @@ def load_model():
|
||||
checkpoint_info = select_checkpoint()
|
||||
|
||||
if checkpoint_info.config != shared.cmd_opts.config:
|
||||
print(f"Loading config from: {shared.cmd_opts.config}")
|
||||
print(f"Loading config from: {checkpoint_info.config}")
|
||||
|
||||
sd_config = OmegaConf.load(checkpoint_info.config)
|
||||
sd_model = instantiate_from_config(sd_config.model)
|
||||
@ -192,7 +195,8 @@ def reload_model_weights(sd_model, info=None):
|
||||
return
|
||||
|
||||
if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
|
||||
return load_model()
|
||||
shared.sd_model = load_model()
|
||||
return shared.sd_model
|
||||
|
||||
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
|
||||
lowvram.send_everything_to_cpu()
|
||||
|
||||
@ -45,6 +45,7 @@ parser.add_argument("--swinir-models-path", type=str, help="Path to directory wi
|
||||
parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
|
||||
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-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
|
||||
@ -65,6 +66,8 @@ parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox
|
||||
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)
|
||||
|
||||
|
||||
cmd_opts = parser.parse_args()
|
||||
|
||||
@ -78,11 +81,8 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
|
||||
xformers_available = False
|
||||
config_filename = cmd_opts.ui_settings_file
|
||||
|
||||
hypernetworks = hypernetwork.load_hypernetworks(os.path.join(models_path, 'hypernetworks'))
|
||||
|
||||
|
||||
def selected_hypernetwork():
|
||||
return hypernetworks.get(opts.sd_hypernetwork, None)
|
||||
hypernetworks = hypernetwork.list_hypernetworks(os.path.join(models_path, 'hypernetworks'))
|
||||
loaded_hypernetwork = None
|
||||
|
||||
|
||||
class State:
|
||||
@ -132,13 +132,14 @@ def realesrgan_models_names():
|
||||
|
||||
|
||||
class OptionInfo:
|
||||
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None):
|
||||
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False):
|
||||
self.default = default
|
||||
self.label = label
|
||||
self.component = component
|
||||
self.component_args = component_args
|
||||
self.onchange = onchange
|
||||
self.section = None
|
||||
self.show_on_main_page = show_on_main_page
|
||||
|
||||
|
||||
def options_section(section_identifier, options_dict):
|
||||
@ -215,7 +216,7 @@ options_templates.update(options_section(('system', "System"), {
|
||||
}))
|
||||
|
||||
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()}),
|
||||
"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()]}),
|
||||
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
|
||||
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
|
||||
@ -225,7 +226,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
||||
"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"),
|
||||
"filter_nsfw": OptionInfo(False, "Filter NSFW content"),
|
||||
'CLIP_ignore_last_layers': OptionInfo(0, "Ignore last layers of CLIP model", gr.Slider, {"minimum": 0, "maximum": 5, "step": 1}),
|
||||
'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()}),
|
||||
}))
|
||||
|
||||
@ -240,10 +241,11 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
|
||||
|
||||
options_templates.update(options_section(('ui', "User interface"), {
|
||||
"show_progressbar": OptionInfo(True, "Show progressbar"),
|
||||
"show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
|
||||
"show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
|
||||
"return_grid": OptionInfo(True, "Show grid in results for web"),
|
||||
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
|
||||
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
|
||||
"add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
|
||||
"font": OptionInfo("", "Font for image grids that have text"),
|
||||
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
|
||||
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
|
||||
|
||||
@ -8,7 +8,6 @@ from basicsr.utils.download_util import load_file_from_url
|
||||
from tqdm import tqdm
|
||||
|
||||
from modules import modelloader
|
||||
from modules.paths import models_path
|
||||
from modules.shared import cmd_opts, opts, device
|
||||
from modules.swinir_model_arch import SwinIR as net
|
||||
from modules.upscaler import Upscaler, UpscalerData
|
||||
@ -25,7 +24,6 @@ class UpscalerSwinIR(Upscaler):
|
||||
"/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
|
||||
"-L_x4_GAN.pth "
|
||||
self.model_name = "SwinIR 4x"
|
||||
self.model_path = os.path.join(models_path, self.name)
|
||||
self.user_path = dirname
|
||||
super().__init__()
|
||||
scalers = []
|
||||
|
||||
124
modules/ui.py
124
modules/ui.py
@ -25,6 +25,8 @@ import gradio.routes
|
||||
from modules import sd_hijack
|
||||
from modules.paths import script_path
|
||||
from modules.shared import opts, cmd_opts
|
||||
if cmd_opts.deepdanbooru:
|
||||
from modules.deepbooru import get_deepbooru_tags
|
||||
import modules.shared as shared
|
||||
from modules.sd_samplers import samplers, samplers_for_img2img
|
||||
from modules.sd_hijack import model_hijack
|
||||
@ -98,9 +100,10 @@ def send_gradio_gallery_to_image(x):
|
||||
return image_from_url_text(x[0])
|
||||
|
||||
|
||||
def save_files(js_data, images, index):
|
||||
def save_files(js_data, images, do_make_zip, index):
|
||||
import csv
|
||||
filenames = []
|
||||
fullfns = []
|
||||
|
||||
#quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
|
||||
class MyObject:
|
||||
@ -137,14 +140,29 @@ def save_files(js_data, images, index):
|
||||
is_grid = image_index < p.index_of_first_image
|
||||
i = 0 if is_grid else (image_index - p.index_of_first_image)
|
||||
|
||||
fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
|
||||
fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
|
||||
|
||||
filename = os.path.relpath(fullfn, path)
|
||||
filenames.append(filename)
|
||||
fullfns.append(fullfn)
|
||||
if txt_fullfn:
|
||||
filenames.append(os.path.basename(txt_fullfn))
|
||||
fullfns.append(txt_fullfn)
|
||||
|
||||
writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
|
||||
|
||||
return '', '', plaintext_to_html(f"Saved: {filenames[0]}")
|
||||
# Make Zip
|
||||
if do_make_zip:
|
||||
zip_filepath = os.path.join(path, "images.zip")
|
||||
|
||||
from zipfile import ZipFile
|
||||
with ZipFile(zip_filepath, "w") as zip_file:
|
||||
for i in range(len(fullfns)):
|
||||
with open(fullfns[i], mode="rb") as f:
|
||||
zip_file.writestr(filenames[i], f.read())
|
||||
fullfns.insert(0, zip_filepath)
|
||||
|
||||
return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
|
||||
|
||||
|
||||
def wrap_gradio_call(func, extra_outputs=None):
|
||||
@ -292,6 +310,11 @@ def interrogate(image):
|
||||
return gr_show(True) if prompt is None else prompt
|
||||
|
||||
|
||||
def interrogate_deepbooru(image):
|
||||
prompt = get_deepbooru_tags(image)
|
||||
return gr_show(True) if prompt is None else prompt
|
||||
|
||||
|
||||
def create_seed_inputs():
|
||||
with gr.Row():
|
||||
with gr.Box():
|
||||
@ -428,15 +451,20 @@ def create_toprow(is_img2img):
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
with gr.Row(scale=1):
|
||||
if is_img2img:
|
||||
interrogate = gr.Button('Interrogate', elem_id="interrogate")
|
||||
interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
|
||||
if cmd_opts.deepdanbooru:
|
||||
deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
|
||||
else:
|
||||
deepbooru = None
|
||||
else:
|
||||
interrogate = None
|
||||
deepbooru = None
|
||||
prompt_style_apply = gr.Button('Apply style', elem_id="style_apply")
|
||||
save_style = gr.Button('Create style', elem_id="style_create")
|
||||
|
||||
return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste, token_counter, token_button
|
||||
return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
|
||||
|
||||
|
||||
def setup_progressbar(progressbar, preview, id_part, textinfo=None):
|
||||
@ -465,7 +493,7 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
import modules.txt2img
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
|
||||
txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
|
||||
txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
|
||||
dummy_component = gr.Label(visible=False)
|
||||
|
||||
with gr.Row(elem_id='txt2img_progress_row'):
|
||||
@ -521,6 +549,12 @@ 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():
|
||||
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)
|
||||
@ -570,13 +604,15 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
|
||||
save.click(
|
||||
fn=wrap_gradio_call(save_files),
|
||||
_js="(x, y, z) => [x, y, selected_gallery_index()]",
|
||||
_js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
|
||||
inputs=[
|
||||
generation_info,
|
||||
txt2img_gallery,
|
||||
do_make_zip,
|
||||
html_info,
|
||||
],
|
||||
outputs=[
|
||||
download_files,
|
||||
html_info,
|
||||
html_info,
|
||||
html_info,
|
||||
@ -617,7 +653,7 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as img2img_interface:
|
||||
img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
|
||||
img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
|
||||
|
||||
with gr.Row(elem_id='img2img_progress_row'):
|
||||
with gr.Column(scale=1):
|
||||
@ -701,6 +737,12 @@ 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():
|
||||
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)
|
||||
@ -774,15 +816,24 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
outputs=[img2img_prompt],
|
||||
)
|
||||
|
||||
if cmd_opts.deepdanbooru:
|
||||
img2img_deepbooru.click(
|
||||
fn=interrogate_deepbooru,
|
||||
inputs=[init_img],
|
||||
outputs=[img2img_prompt],
|
||||
)
|
||||
|
||||
save.click(
|
||||
fn=wrap_gradio_call(save_files),
|
||||
_js="(x, y, z) => [x, y, selected_gallery_index()]",
|
||||
_js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
|
||||
inputs=[
|
||||
generation_info,
|
||||
img2img_gallery,
|
||||
html_info
|
||||
do_make_zip,
|
||||
html_info,
|
||||
],
|
||||
outputs=[
|
||||
download_files,
|
||||
html_info,
|
||||
html_info,
|
||||
html_info,
|
||||
@ -1102,6 +1153,15 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
component_dict = {}
|
||||
|
||||
def open_folder(f):
|
||||
if not os.path.isdir(f):
|
||||
print(f"""
|
||||
WARNING
|
||||
An open_folder request was made with an argument that is not a folder.
|
||||
This could be an error or a malicious attempt to run code on your computer.
|
||||
Requested path was: {f}
|
||||
""", file=sys.stderr)
|
||||
return
|
||||
|
||||
if not shared.cmd_opts.hide_ui_dir_config:
|
||||
path = os.path.normpath(f)
|
||||
if platform.system() == "Windows":
|
||||
@ -1115,10 +1175,13 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
changed = 0
|
||||
|
||||
for key, value, comp in zip(opts.data_labels.keys(), args, components):
|
||||
if not opts.same_type(value, opts.data_labels[key].default):
|
||||
return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
|
||||
if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):
|
||||
return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()
|
||||
|
||||
for key, value, comp in zip(opts.data_labels.keys(), args, components):
|
||||
if comp == dummy_component:
|
||||
continue
|
||||
|
||||
comp_args = opts.data_labels[key].component_args
|
||||
if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
|
||||
continue
|
||||
@ -1136,6 +1199,21 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
|
||||
return f'{changed} settings changed.', opts.dumpjson()
|
||||
|
||||
def run_settings_single(value, key):
|
||||
if not opts.same_type(value, opts.data_labels[key].default):
|
||||
return gr.update(visible=True), opts.dumpjson()
|
||||
|
||||
oldval = opts.data.get(key, None)
|
||||
opts.data[key] = value
|
||||
|
||||
if oldval != value:
|
||||
if opts.data_labels[key].onchange is not None:
|
||||
opts.data_labels[key].onchange()
|
||||
|
||||
opts.save(shared.config_filename)
|
||||
|
||||
return gr.update(value=value), opts.dumpjson()
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as settings_interface:
|
||||
settings_submit = gr.Button(value="Apply settings", variant='primary')
|
||||
result = gr.HTML()
|
||||
@ -1143,6 +1221,8 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
settings_cols = 3
|
||||
items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
|
||||
|
||||
quicksettings_list = []
|
||||
|
||||
cols_displayed = 0
|
||||
items_displayed = 0
|
||||
previous_section = None
|
||||
@ -1165,6 +1245,10 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
|
||||
gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
|
||||
|
||||
if item.show_on_main_page:
|
||||
quicksettings_list.append((i, k, item))
|
||||
components.append(dummy_component)
|
||||
else:
|
||||
component = create_setting_component(k)
|
||||
component_dict[k] = component
|
||||
components.append(component)
|
||||
@ -1182,7 +1266,6 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
|
||||
restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
|
||||
|
||||
|
||||
def reload_scripts():
|
||||
modules.scripts.reload_script_body_only()
|
||||
|
||||
@ -1229,6 +1312,10 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
css += css_hide_progressbar
|
||||
|
||||
with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
|
||||
with gr.Row(elem_id="quicksettings"):
|
||||
for i, k, item in quicksettings_list:
|
||||
component = create_setting_component(k)
|
||||
component_dict[k] = component
|
||||
|
||||
settings_interface.gradio_ref = demo
|
||||
|
||||
@ -1247,6 +1334,15 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
outputs=[result, text_settings],
|
||||
)
|
||||
|
||||
for i, k, item in quicksettings_list:
|
||||
component = component_dict[k]
|
||||
|
||||
component.change(
|
||||
fn=lambda value, k=k: run_settings_single(value, key=k),
|
||||
inputs=[component],
|
||||
outputs=[component, text_settings],
|
||||
)
|
||||
|
||||
def modelmerger(*args):
|
||||
try:
|
||||
results = modules.extras.run_modelmerger(*args)
|
||||
|
||||
@ -36,10 +36,11 @@ class Upscaler:
|
||||
self.half = not modules.shared.cmd_opts.no_half
|
||||
self.pre_pad = 0
|
||||
self.mod_scale = None
|
||||
if self.name is not None and create_dirs:
|
||||
|
||||
if self.model_path is None and self.name:
|
||||
self.model_path = os.path.join(models_path, self.name)
|
||||
if not os.path.exists(self.model_path):
|
||||
os.makedirs(self.model_path)
|
||||
if self.model_path and create_dirs:
|
||||
os.makedirs(self.model_path, exist_ok=True)
|
||||
|
||||
try:
|
||||
import cv2
|
||||
|
||||
@ -10,7 +10,6 @@ from modules.processing import Processed, process_images
|
||||
from PIL import Image
|
||||
from modules.shared import opts, cmd_opts, state
|
||||
|
||||
|
||||
class Script(scripts.Script):
|
||||
def title(self):
|
||||
return "Prompts from file or textbox"
|
||||
@ -29,6 +28,9 @@ class Script(scripts.Script):
|
||||
checkbox_txt.change(fn=lambda x: [gr.File.update(visible = not x), gr.TextArea.update(visible = x)], inputs=[checkbox_txt], outputs=[file, prompt_txt])
|
||||
return [checkbox_txt, file, prompt_txt]
|
||||
|
||||
def on_show(self, checkbox_txt, file, prompt_txt):
|
||||
return [ gr.Checkbox.update(visible = True), gr.File.update(visible = not checkbox_txt), gr.TextArea.update(visible = checkbox_txt) ]
|
||||
|
||||
def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
|
||||
if (checkbox_txt):
|
||||
lines = [x.strip() for x in prompt_txt.splitlines()]
|
||||
|
||||
@ -10,8 +10,8 @@ import numpy as np
|
||||
import modules.scripts as scripts
|
||||
import gradio as gr
|
||||
|
||||
from modules import images
|
||||
from modules.processing import process_images, Processed
|
||||
from modules import images, hypernetwork
|
||||
from modules.processing import process_images, Processed, get_correct_sampler
|
||||
from modules.shared import opts, cmd_opts, state
|
||||
import modules.shared as shared
|
||||
import modules.sd_samplers
|
||||
@ -56,15 +56,17 @@ def apply_order(p, x, xs):
|
||||
p.prompt = prompt_tmp + p.prompt
|
||||
|
||||
|
||||
def build_samplers_dict(p):
|
||||
samplers_dict = {}
|
||||
for i, sampler in enumerate(modules.sd_samplers.samplers):
|
||||
for i, sampler in enumerate(get_correct_sampler(p)):
|
||||
samplers_dict[sampler.name.lower()] = i
|
||||
for alias in sampler.aliases:
|
||||
samplers_dict[alias.lower()] = i
|
||||
return samplers_dict
|
||||
|
||||
|
||||
def apply_sampler(p, x, xs):
|
||||
sampler_index = samplers_dict.get(x.lower(), None)
|
||||
sampler_index = build_samplers_dict(p).get(x.lower(), None)
|
||||
if sampler_index is None:
|
||||
raise RuntimeError(f"Unknown sampler: {x}")
|
||||
|
||||
@ -78,8 +80,11 @@ def apply_checkpoint(p, x, xs):
|
||||
|
||||
|
||||
def apply_hypernetwork(p, x, xs):
|
||||
hn = shared.hypernetworks.get(x, None)
|
||||
opts.data["sd_hypernetwork"] = hn.name if hn is not None else 'None'
|
||||
hypernetwork.load_hypernetwork(x)
|
||||
|
||||
|
||||
def apply_clip_skip(p, x, xs):
|
||||
opts.data["CLIP_stop_at_last_layers"] = x
|
||||
|
||||
|
||||
def format_value_add_label(p, opt, x):
|
||||
@ -133,6 +138,7 @@ axis_options = [
|
||||
AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
|
||||
AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
|
||||
AxisOption("Eta", float, apply_field("eta"), format_value_add_label),
|
||||
AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label),
|
||||
AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label), # as it is now all AxisOptionImg2Img items must go after AxisOption ones
|
||||
]
|
||||
|
||||
@ -143,7 +149,7 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
|
||||
ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
|
||||
hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
|
||||
|
||||
first_pocessed = None
|
||||
first_processed = None
|
||||
|
||||
state.job_count = len(xs) * len(ys) * p.n_iter
|
||||
|
||||
@ -152,8 +158,8 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
|
||||
state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
|
||||
|
||||
processed = cell(x, y)
|
||||
if first_pocessed is None:
|
||||
first_pocessed = processed
|
||||
if first_processed is None:
|
||||
first_processed = processed
|
||||
|
||||
try:
|
||||
res.append(processed.images[0])
|
||||
@ -164,9 +170,9 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
|
||||
if draw_legend:
|
||||
grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
|
||||
|
||||
first_pocessed.images = [grid]
|
||||
first_processed.images = [grid]
|
||||
|
||||
return first_pocessed
|
||||
return first_processed
|
||||
|
||||
|
||||
re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
|
||||
@ -196,10 +202,11 @@ class Script(scripts.Script):
|
||||
return [x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds]
|
||||
|
||||
def run(self, p, x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds):
|
||||
if not no_fixed_seeds:
|
||||
modules.processing.fix_seed(p)
|
||||
p.batch_size = 1
|
||||
|
||||
initial_hn = opts.sd_hypernetwork
|
||||
p.batch_size = 1
|
||||
CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
|
||||
|
||||
def process_axis(opt, vals):
|
||||
if opt.label == 'Nothing':
|
||||
@ -214,7 +221,6 @@ class Script(scripts.Script):
|
||||
m = re_range.fullmatch(val)
|
||||
mc = re_range_count.fullmatch(val)
|
||||
if m is not None:
|
||||
|
||||
start = int(m.group(1))
|
||||
end = int(m.group(2))+1
|
||||
step = int(m.group(3)) if m.group(3) is not None else 1
|
||||
@ -257,6 +263,17 @@ class Script(scripts.Script):
|
||||
|
||||
valslist = [opt.type(x) for x in valslist]
|
||||
|
||||
# Confirm options are valid before starting
|
||||
if opt.label == "Sampler":
|
||||
samplers_dict = build_samplers_dict(p)
|
||||
for sampler_val in valslist:
|
||||
if sampler_val.lower() not in samplers_dict.keys():
|
||||
raise RuntimeError(f"Unknown sampler: {sampler_val}")
|
||||
elif opt.label == "Checkpoint name":
|
||||
for ckpt_val in valslist:
|
||||
if modules.sd_models.get_closet_checkpoint_match(ckpt_val) is None:
|
||||
raise RuntimeError(f"Checkpoint for {ckpt_val} not found")
|
||||
|
||||
return valslist
|
||||
|
||||
x_opt = axis_options[x_type]
|
||||
@ -308,6 +325,8 @@ class Script(scripts.Script):
|
||||
# restore checkpoint in case it was changed by axes
|
||||
modules.sd_models.reload_model_weights(shared.sd_model)
|
||||
|
||||
opts.data["sd_hypernetwork"] = initial_hn
|
||||
hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
|
||||
|
||||
opts.data["CLIP_stop_at_last_layers"] = CLIP_stop_at_last_layers
|
||||
|
||||
return processed
|
||||
|
||||
17
style.css
17
style.css
@ -103,7 +103,12 @@
|
||||
|
||||
#style_apply, #style_create, #interrogate{
|
||||
margin: 0.75em 0.25em 0.25em 0.25em;
|
||||
min-width: 3em;
|
||||
min-width: 5em;
|
||||
}
|
||||
|
||||
#style_apply, #style_create, #deepbooru{
|
||||
margin: 0.75em 0.25em 0.25em 0.25em;
|
||||
min-width: 5em;
|
||||
}
|
||||
|
||||
#style_pos_col, #style_neg_col{
|
||||
@ -448,3 +453,13 @@ input[type="range"]{
|
||||
.context-menu-items a:hover{
|
||||
background: #a55000;
|
||||
}
|
||||
|
||||
#quicksettings > div{
|
||||
border: none;
|
||||
background: none;
|
||||
}
|
||||
|
||||
#quicksettings > div > div{
|
||||
max-width: 32em;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 526 KiB After Width: | Height: | Size: 329 KiB |
3
webui.py
3
webui.py
@ -82,6 +82,9 @@ 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)))
|
||||
|
||||
|
||||
def webui():
|
||||
# make the program just exit at ctrl+c without waiting for anything
|
||||
|
||||
Loading…
Reference in New Issue
Block a user