Merge remote-tracking branch 'a1111/dev' into hf_endpoint

.eslintrc.js

@@ -88,6 +88,7 @@ module.exports = {
         // imageviewer.js
         modalPrevImage: "readonly",
         modalNextImage: "readonly",
+        updateModalImageIfVisible: "readonly",
         // localStorage.js
         localSet: "readonly",
         localGet: "readonly",

README.md

@@ -133,7 +133,7 @@ If your system is very new, you need to install python3.11 or python3.10:
 # Ubuntu 24.04
 sudo add-apt-repository ppa:deadsnakes/ppa
 sudo apt update
-sudo apt install python3.11
+sudo apt install python3.11 python3.11-venv
 
 # Manjaro/Arch
 sudo pacman -S yay

javascript/imageviewer.js

@@ -54,6 +54,7 @@ function updateOnBackgroundChange() {
         updateModalImage();
     }
 }
+const updateModalImageIfVisible = updateOnBackgroundChange;
 
 function modalImageSwitch(offset) {
     var galleryButtons = all_gallery_buttons();
@@ -164,6 +165,7 @@ function modalLivePreviewToggle(event) {
     const modalToggleLivePreview = gradioApp().getElementById("modal_toggle_live_preview");
     opts.js_live_preview_in_modal_lightbox = !opts.js_live_preview_in_modal_lightbox;
     modalToggleLivePreview.innerHTML = opts.js_live_preview_in_modal_lightbox ? "🗇" : "🗆";
+    updateModalImageIfVisible();
     event.stopPropagation();
 }
 

javascript/progressbar.js

@@ -190,7 +190,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
                         livePreview.className = 'livePreview';
                         gallery.insertBefore(livePreview, gallery.firstElementChild);
                     }
-
+                    updateModalImageIfVisible();
                     livePreview.appendChild(img);
                     if (livePreview.childElementCount > 2) {
                         livePreview.removeChild(livePreview.firstElementChild);

modules/images.py

@@ -409,6 +409,7 @@ class FilenameGenerator:
         'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if (self.p.n_iter == 1 and self.p.batch_size == 1) or self.zip else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
         'hasprompt': lambda self, *args: self.hasprompt(*args),  # accepts formats:[hasprompt<prompt1|default><prompt2>..]
         'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
+        'randn_source': lambda self: opts.data["randn_source"],
         'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
         'user': lambda self: self.p.user,
         'vae_filename': lambda self: self.get_vae_filename(),

modules/launch_utils.py

@@ -330,6 +330,7 @@ def get_cuda_comp_cap():
 
 def early_access_blackwell_wheels():
     """For Blackwell GPUs, use Early Access PyTorch Wheels provided by Nvidia"""
+    print('deprecated early_access_blackwell_wheels')
     if all([
             os.environ.get('TORCH_INDEX_URL') is None,
             sys.version_info.major == 3,
@@ -347,8 +348,8 @@ def early_access_blackwell_wheels():
 
 
 def prepare_environment():
-    torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu121")
+    torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu128")
-    torch_command = os.environ.get('TORCH_COMMAND', early_access_blackwell_wheels() or f"pip install torch==2.1.2 torchvision==0.16.2 --extra-index-url {torch_index_url}")
+    torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.7.0 torchvision==0.22.0 --extra-index-url {torch_index_url}")
     if args.use_ipex:
         if platform.system() == "Windows":
             # The "Nuullll/intel-extension-for-pytorch" wheels were built from IPEX source for Intel Arc GPU: https://github.com/intel/intel-extension-for-pytorch/tree/xpu-main
@@ -372,7 +373,7 @@ def prepare_environment():
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
     requirements_file_for_npu = os.environ.get('REQS_FILE_FOR_NPU', "requirements_npu.txt")
 
-    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23.post1')
+    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.30')
     clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
     openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
 

modules/sd_schedulers.py

@@ -117,12 +117,15 @@ def ddim_scheduler(n, sigma_min, sigma_max, inner_model, device):
 
 
 def beta_scheduler(n, sigma_min, sigma_max, inner_model, device):
-    # From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024) """
+    # From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024)
     alpha = shared.opts.beta_dist_alpha
     beta = shared.opts.beta_dist_beta
-    timesteps = 1 - np.linspace(0, 1, n)
+    curve = [stats.beta.ppf(x, alpha, beta) for x in np.linspace(1, 0, n)]
-    timesteps = [stats.beta.ppf(x, alpha, beta) for x in timesteps]
+
-    sigmas = [sigma_min + (x * (sigma_max-sigma_min)) for x in timesteps]
+    start = inner_model.sigma_to_t(torch.tensor(sigma_max))
+    end = inner_model.sigma_to_t(torch.tensor(sigma_min))
+    timesteps = [end + x * (start - end) for x in curve]
+    sigmas = [inner_model.t_to_sigma(ts) for ts in timesteps]
     sigmas += [0.0]
     return torch.FloatTensor(sigmas).to(device)
 

modules/shared_options.py

@@ -407,8 +407,8 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", infotext='UniPC lower order final'),
     'sd_noise_schedule': OptionInfo("Default", "Noise schedule for sampling", gr.Radio, {"choices": ["Default", "Zero Terminal SNR"]}, infotext="Noise Schedule").info("for use with zero terminal SNR trained models"),
     'skip_early_cond': OptionInfo(0.0, "Ignore negative prompt during early sampling", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext="Skip Early CFG").info("disables CFG on a proportion of steps at the beginning of generation; 0=skip none; 1=skip all; can both improve sample diversity/quality and speed up sampling; XYZ plot: Skip Early CFG"),
-    'beta_dist_alpha': OptionInfo(0.6, "Beta scheduler - alpha", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}, infotext='Beta scheduler alpha').info('Default = 0.6; the alpha parameter of the beta distribution used in Beta sampling'),
+    'beta_dist_alpha': OptionInfo(0.6, "Beta scheduler - alpha", gr.Slider, {"minimum": 0.01, "maximum": 5.0, "step": 0.01}, infotext='Beta scheduler alpha').info('Default = 0.6; the alpha parameter of the beta distribution used in Beta sampling'),
-    'beta_dist_beta': OptionInfo(0.6, "Beta scheduler - beta", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}, infotext='Beta scheduler beta').info('Default = 0.6; the beta parameter of the beta distribution used in Beta sampling'),
+    'beta_dist_beta': OptionInfo(0.6, "Beta scheduler - beta", gr.Slider, {"minimum": 0.01, "maximum": 5.0, "step": 0.01}, infotext='Beta scheduler beta').info('Default = 0.6; the beta parameter of the beta distribution used in Beta sampling'),
 }))
 
 options_templates.update(options_section(('postprocessing', "Postprocessing", "postprocessing"), {

style.css

@@ -602,6 +602,7 @@ table.popup-table .link{
     background: var(--background-fill-primary);
     width: 100%;
     height: 100%;
+    pointer-events: none;
 }
 
 .livePreview img{