Merge branch 'master' of https://github.com/AUTOMATIC1111/stable-diffusion-webui into upstream-master

2022-10-09 23:20:39 -04:00 · 2022-10-09 23:20:39 -04:00 · 6fdad291bd
commit 6fdad291bd
parent cc92dc1f8d 45fbd1c5fe
29 changed files with 628 additions and 247 deletions
--- a/README.md
+++ b/README.md
@ -66,6 +66,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
     - separate prompts using uppercase `AND`
     - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
 - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
+- DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)

 ## Installation and Running
 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.
@ -123,4 +124,5 @@ The documentation was moved from this README over to the project's [wiki](https:
 - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
 - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
+- DeepDanbooru - interrogator for anime diffusors https://github.com/KichangKim/DeepDanbooru
 - (You)
--- a/javascript/imageviewer.js
+++ b/javascript/imageviewer.js
@ -1,72 +1,97 @@
 // A full size 'lightbox' preview modal shown when left clicking on gallery previews
-
 function closeModal() {
-  gradioApp().getElementById("lightboxModal").style.display = "none";
+    gradioApp().getElementById("lightboxModal").style.display = "none";
 }

 function showModal(event) {
-  const source = event.target || event.srcElement;
-  const modalImage = gradioApp().getElementById("modalImage")
-  const lb = gradioApp().getElementById("lightboxModal")
-  modalImage.src = source.src
-  if (modalImage.style.display === 'none') {
-    lb.style.setProperty('background-image', 'url(' + source.src + ')');
-  }
-  lb.style.display = "block";
-  lb.focus()
-  event.stopPropagation()
+    const source = event.target || event.srcElement;
+    const modalImage = gradioApp().getElementById("modalImage")
+    const lb = gradioApp().getElementById("lightboxModal")
+    modalImage.src = source.src
+    if (modalImage.style.display === 'none') {
+        lb.style.setProperty('background-image', 'url(' + source.src + ')');
+    }
+    lb.style.display = "block";
+    lb.focus()
+    event.stopPropagation()
 }

 function negmod(n, m) {
-  return ((n % m) + m) % m;
+    return ((n % m) + m) % m;
 }

-function modalImageSwitch(offset){
-  var allgalleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all")
-  var galleryButtons = []
-  allgalleryButtons.forEach(function(elem){
-    if(elem.parentElement.offsetParent){
-        galleryButtons.push(elem);
+function updateOnBackgroundChange() {
+    const modalImage = gradioApp().getElementById("modalImage")
+    if (modalImage && modalImage.offsetParent) {
+        let allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2")
+        let currentButton = null
+        allcurrentButtons.forEach(function(elem) {
+            if (elem.parentElement.offsetParent) {
+                currentButton = elem;
+            }
+        })
+
+        if (modalImage.src != currentButton.children[0].src) {
+            modalImage.src = currentButton.children[0].src;
+            if (modalImage.style.display === 'none') {
+                modal.style.setProperty('background-image', `url(${modalImage.src})`)
+            }
+        }
    }
-  })
+}

-  if(galleryButtons.length>1){
-      var allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2")
-      var currentButton = null
-      allcurrentButtons.forEach(function(elem){
-        if(elem.parentElement.offsetParent){
-            currentButton = elem;
+function modalImageSwitch(offset) {
+    var allgalleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all")
+    var galleryButtons = []
+    allgalleryButtons.forEach(function(elem) {
+        if (elem.parentElement.offsetParent) {
+            galleryButtons.push(elem);
        }
-      })
+    })

-      var result = -1
-      galleryButtons.forEach(function(v, i){ if(v==currentButton) { result = i } })
+    if (galleryButtons.length > 1) {
+        var allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2")
+        var currentButton = null
+        allcurrentButtons.forEach(function(elem) {
+            if (elem.parentElement.offsetParent) {
+                currentButton = elem;
+            }
+        })

-      if(result != -1){
-        nextButton = galleryButtons[negmod((result+offset),galleryButtons.length)]
-        nextButton.click()
-        const modalImage = gradioApp().getElementById("modalImage");
-        const modal = gradioApp().getElementById("lightboxModal");
-        modalImage.src = nextButton.children[0].src;
-        if (modalImage.style.display === 'none') {
-            modal.style.setProperty('background-image', `url(${modalImage.src})`)
+        var result = -1
+        galleryButtons.forEach(function(v, i) {
+            if (v == currentButton) {
+                result = i
+            }
+        })
+
+        if (result != -1) {
+            nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]
+            nextButton.click()
+            const modalImage = gradioApp().getElementById("modalImage");
+            const modal = gradioApp().getElementById("lightboxModal");
+            modalImage.src = nextButton.children[0].src;
+            if (modalImage.style.display === 'none') {
+                modal.style.setProperty('background-image', `url(${modalImage.src})`)
+            }
+            setTimeout(function() {
+                modal.focus()
+            }, 10)
        }
-        setTimeout( function(){modal.focus()},10)
-      }
-  }
+    }
 }

-function modalNextImage(event){
-  modalImageSwitch(1)
-  event.stopPropagation()
+function modalNextImage(event) {
+    modalImageSwitch(1)
+    event.stopPropagation()
 }

-function modalPrevImage(event){
-  modalImageSwitch(-1)  
-  event.stopPropagation()
+function modalPrevImage(event) {
+    modalImageSwitch(-1)
+    event.stopPropagation()
 }

-function modalKeyHandler(event){
+function modalKeyHandler(event) {
    switch (event.key) {
        case "ArrowLeft":
            modalPrevImage(event)
@ -80,24 +105,22 @@ function modalKeyHandler(event){
    }
 }

-function showGalleryImage(){
+function showGalleryImage() {
    setTimeout(function() {
        fullImg_preview = gradioApp().querySelectorAll('img.w-full.object-contain')
-        
-        if(fullImg_preview != null){
+
+        if (fullImg_preview != null) {
            fullImg_preview.forEach(function function_name(e) {
                if (e.dataset.modded)
                    return;
                e.dataset.modded = true;
                if(e && e.parentElement.tagName == 'DIV'){
-
                    e.style.cursor='pointer'
-
                    e.addEventListener('click', function (evt) {
                        if(!opts.js_modal_lightbox) return;
                        modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
                        showModal(evt)
-                    },true);
+                    }, true);
                }
            });
        }
@ -105,21 +128,21 @@ function showGalleryImage(){
    }, 100);
 }

-function modalZoomSet(modalImage, enable){
-    if( enable ){
+function modalZoomSet(modalImage, enable) {
+    if (enable) {
        modalImage.classList.add('modalImageFullscreen');
-    } else{
+    } else {
        modalImage.classList.remove('modalImageFullscreen');
    }
 }

-function modalZoomToggle(event){
+function modalZoomToggle(event) {
    modalImage = gradioApp().getElementById("modalImage");
    modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen'))
    event.stopPropagation()
 }

-function modalTileImageToggle(event){
+function modalTileImageToggle(event) {
    const modalImage = gradioApp().getElementById("modalImage");
    const modal = gradioApp().getElementById("lightboxModal");
    const isTiling = modalImage.style.display === 'none';
@ -134,17 +157,18 @@ function modalTileImageToggle(event){
    event.stopPropagation()
 }

-function galleryImageHandler(e){
-    if(e && e.parentElement.tagName == 'BUTTON'){
+function galleryImageHandler(e) {
+    if (e && e.parentElement.tagName == 'BUTTON') {
        e.onclick = showGalleryImage;
    }
 }

-onUiUpdate(function(){
+onUiUpdate(function() {
    fullImg_preview = gradioApp().querySelectorAll('img.w-full')
-        if(fullImg_preview != null){
-	    fullImg_preview.forEach(galleryImageHandler);
+    if (fullImg_preview != null) {
+        fullImg_preview.forEach(galleryImageHandler);
    }
+    updateOnBackgroundChange();
 })

 document.addEventListener("DOMContentLoaded", function() {
@ -152,13 +176,13 @@ document.addEventListener("DOMContentLoaded", function() {
    const modal = document.createElement('div')
    modal.onclick = closeModal;
    modal.id = "lightboxModal";
-    modal.tabIndex=0
+    modal.tabIndex = 0
    modal.addEventListener('keydown', modalKeyHandler, true)

    const modalControls = document.createElement('div')
    modalControls.className = 'modalControls gradio-container';
    modal.append(modalControls);
-    
+
    const modalZoom = document.createElement('span')
    modalZoom.className = 'modalZoom cursor';
    modalZoom.innerHTML = '&#10529;'
@ -183,30 +207,30 @@ document.addEventListener("DOMContentLoaded", function() {
    const modalImage = document.createElement('img')
    modalImage.id = 'modalImage';
    modalImage.onclick = closeModal;
-    modalImage.tabIndex=0
+    modalImage.tabIndex = 0
    modalImage.addEventListener('keydown', modalKeyHandler, true)
    modal.appendChild(modalImage)

    const modalPrev = document.createElement('a')
    modalPrev.className = 'modalPrev';
    modalPrev.innerHTML = '&#10094;'
-    modalPrev.tabIndex=0
-    modalPrev.addEventListener('click',modalPrevImage,true);
+    modalPrev.tabIndex = 0
+    modalPrev.addEventListener('click', modalPrevImage, true);
    modalPrev.addEventListener('keydown', modalKeyHandler, true)
    modal.appendChild(modalPrev)

    const modalNext = document.createElement('a')
    modalNext.className = 'modalNext';
    modalNext.innerHTML = '&#10095;'
-    modalNext.tabIndex=0
-    modalNext.addEventListener('click',modalNextImage,true);
+    modalNext.tabIndex = 0
+    modalNext.addEventListener('click', modalNextImage, true);
    modalNext.addEventListener('keydown', modalKeyHandler, true)

    modal.appendChild(modalNext)


    gradioApp().getRootNode().appendChild(modal)
-    
+
    document.body.appendChild(modalFragment);
-	
+
 });
--- a/launch.py
+++ b/launch.py
@ -7,38 +7,14 @@ import shlex
 import platform

 dir_repos = "repositories"
-dir_tmp = "tmp"
-
 python = sys.executable
 git = os.environ.get('GIT', "git")
-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")
-requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
-commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
-
-gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
-clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
-
-stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
-taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
-k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878")
-codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
-blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
-
-args = shlex.split(commandline_args)


 def extract_arg(args, name):
    return [x for x in args if x != name], name in args


-args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
-xformers = '--xformers' in args
-
-
-def repo_dir(name):
-    return os.path.join(dir_repos, name)
-
-
 def run(command, desc=None, errdesc=None):
    if desc is not None:
        print(desc)
@ -58,23 +34,11 @@ stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.st
    return result.stdout.decode(encoding="utf8", errors="ignore")


-def run_python(code, desc=None, errdesc=None):
-    return run(f'"{python}" -c "{code}"', desc, errdesc)
-
-
-def run_pip(args, desc=None):
-    return run(f'"{python}" -m pip {args} --prefer-binary', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
-
-
 def check_run(command):
    result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
    return result.returncode == 0


-def check_run_python(code):
-    return check_run(f'"{python}" -c "{code}"')
-
-
 def is_installed(package):
    try:
        spec = importlib.util.find_spec(package)
@ -84,6 +48,22 @@ def is_installed(package):
    return spec is not None


+def repo_dir(name):
+    return os.path.join(dir_repos, name)
+
+
+def run_python(code, desc=None, errdesc=None):
+    return run(f'"{python}" -c "{code}"', desc, errdesc)
+
+
+def run_pip(args, desc=None):
+    return run(f'"{python}" -m pip {args} --prefer-binary', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
+
+
+def check_run_python(code):
+    return check_run(f'"{python}" -c "{code}"')
+
+
 def git_clone(url, dir, name, commithash=None):
    # TODO clone into temporary dir and move if successful

@ -105,56 +85,81 @@ def git_clone(url, dir, name, commithash=None):
        run(f'"{git}" -C {dir} checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")


-try:
-    commit = run(f"{git} rev-parse HEAD").strip()
-except Exception:
-    commit = "<none>"
+def prepare_enviroment():
+    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")
+    requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
+    commandline_args = os.environ.get('COMMANDLINE_ARGS', "")

-print(f"Python {sys.version}")
-print(f"Commit hash: {commit}")
+    gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
+    clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")

+    stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
+    taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
+    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878")
+    codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
+    blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")

-if not is_installed("torch") or not is_installed("torchvision"):
-    run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
+    args = shlex.split(commandline_args)

-if not skip_torch_cuda_test:
-    run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
+    args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
+    xformers = '--xformers' in args
+    deepdanbooru = '--deepdanbooru' in args

-if not is_installed("gfpgan"):
-    run_pip(f"install {gfpgan_package}", "gfpgan")
+    try:
+        commit = run(f"{git} rev-parse HEAD").strip()
+    except Exception:
+        commit = "<none>"

-if not is_installed("clip"):
-    run_pip(f"install {clip_package}", "clip")
+    print(f"Python {sys.version}")
+    print(f"Commit hash: {commit}")

-if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"):
-    if platform.system() == "Windows":
-        run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/a/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
-    elif platform.system() == "Linux":
-        run_pip("install xformers", "xformers")
+    if not is_installed("torch") or not is_installed("torchvision"):
+        run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")

-os.makedirs(dir_repos, exist_ok=True)
+    if not skip_torch_cuda_test:
+        run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")

-git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
-git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
-git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
-git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
-git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
+    if not is_installed("gfpgan"):
+        run_pip(f"install {gfpgan_package}", "gfpgan")

-if not is_installed("lpips"):
-    run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
+    if not is_installed("clip"):
+        run_pip(f"install {clip_package}", "clip")

-run_pip(f"install -r {requirements_file}", "requirements for Web UI")
+    if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"):
+        if platform.system() == "Windows":
+            run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/a/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
+        elif platform.system() == "Linux":
+            run_pip("install xformers", "xformers")

-sys.argv += args
+    if not is_installed("deepdanbooru") and deepdanbooru:
+        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)
+
+    git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
+    git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
+    git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
+    git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
+    git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
+
+    if not is_installed("lpips"):
+        run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
+
+    run_pip(f"install -r {requirements_file}", "requirements for Web UI")
+
+    sys.argv += args
+
+    if "--exit" in args:
+        print("Exiting because of --exit argument")
+        exit(0)

-if "--exit" in args:
-    print("Exiting because of --exit argument")
-    exit(0)

 def start_webui():
    print(f"Launching Web UI with arguments: {' '.join(sys.argv[1:])}")
    import webui
    webui.webui()

+
 if __name__ == "__main__":
+    prepare_enviroment()
    start_webui()
--- a/models/deepbooru/Put
+++ b/models/deepbooru/Put
--- a/modules/bsrgan_model.py
+++ b/modules/bsrgan_model.py
@ -10,13 +10,11 @@ from basicsr.utils.download_util import load_file_from_url
 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
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@ -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
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@ -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 = []
--- a/modules/esrgan_model_arch.py
+++ b/modules/esrgan_model_arch.py
--- a/modules/extras.py
+++ b/modules/extras.py
@ -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()
@ -169,9 +173,9 @@ 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,
--- a/modules/hypernetwork.py
+++ b/modules/hypernetwork.py
@ -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:
--- a/modules/images.py
+++ b/modules/images.py
@ -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
--- a/modules/ldsr_model.py
+++ b/modules/ldsr_model.py
@ -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"
--- a/modules/paths.py
+++ b/modules/paths.py
@ -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")
--- a/modules/processing.py
+++ b/modules/processing.py
@ -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)
@ -445,7 +453,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:

                text = infotext(n, i)
                infotexts.append(text)
-                image.info["parameters"] = text
+                if opts.enable_pnginfo:
+                    image.info["parameters"] = text
                output_images.append(image)

            del x_samples_ddim 
@ -464,7 +473,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
            if opts.return_grid:
                text = infotext()
                infotexts.insert(0, text)
-                grid.info["parameters"] = text
+                if opts.enable_pnginfo:
+                    grid.info["parameters"] = text
                output_images.insert(0, grid)
                index_of_first_image = 1

--- a/modules/realesrgan_model.py
+++ b/modules/realesrgan_model.py
@ -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:
--- a/modules/safe.py
+++ b/modules/safe.py
@ -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
--- a/modules/scunet_model.py
+++ b/modules/scunet_model.py
@ -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"
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@ -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]
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@ -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))
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@ -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
@ -131,11 +137,8 @@ def load_model_weights(model, checkpoint_info):
    pl_sd = torch.load(checkpoint_file, map_location="cpu")
    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()
--- a/modules/shared.py
+++ b/modules/shared.py
@ -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"),
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@ -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 = []
--- a/modules/ui.py
+++ b/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
@ -1165,10 +1245,14 @@ 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]))

-                component = create_setting_component(k)
-                component_dict[k] = component
-                components.append(component)
-                items_displayed += 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)
+                    items_displayed += 1

        request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
        request_notifications.click(
@ -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()

@ -1229,7 +1312,11 @@ 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
        
        with gr.Tabs() as tabs:
@ -1246,7 +1333,16 @@ def create_ui(wrap_gradio_gpu_call):
            inputs=components,
            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)
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@ -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
--- a/scripts/prompts_from_file.py
+++ b/scripts/prompts_from_file.py
@ -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()]
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@ -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
    

-samplers_dict = {}
-for i, sampler in enumerate(modules.sd_samplers.samplers):
-    samplers_dict[sampler.name.lower()] = i
-    for alias in sampler.aliases:
-        samplers_dict[alias.lower()] = i
+def build_samplers_dict(p):
+    samplers_dict = {}
+    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):
-        modules.processing.fix_seed(p)
-        p.batch_size = 1
+        if not no_fixed_seeds:
+            modules.processing.fix_seed(p)

-        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
@ -256,6 +262,17 @@ class Script(scripts.Script):
                valslist = list(permutations(valslist))

            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

@ -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
--- a/style.css
+++ b/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;
+}
--- a/txt2img_Screenshot.png
+++ b/txt2img_Screenshot.png
--- a/webui.py
+++ b/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