feat: add metadata handling

False alarm, worked as intended before. Sorry for the fuzz.
This reverts commit d16a54edd6.

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -21,6 +21,7 @@ body:
         　5. Try a fresh installation of Fooocus in a different directory - see if a clean installation solves the issue
         Before making a issue report please, check that the issue hasn't been reported recently.
       options:
+        - label: The issue has not been resolved by following the [troubleshooting guide](https://github.com/lllyasviel/Fooocus/blob/main/troubleshoot.md)
         - label: The issue exists on a clean installation of Fooocus
         - label: The issue exists in the current version of Fooocus
         - label: The issue has not been reported before recently

Dockerfile

@@ -10,7 +10,7 @@ RUN apt-get update -y && \
 COPY requirements_docker.txt requirements_versions.txt /tmp/
 RUN pip install --no-cache-dir -r /tmp/requirements_docker.txt -r /tmp/requirements_versions.txt && \
 	rm -f /tmp/requirements_docker.txt /tmp/requirements_versions.txt
-RUN pip install --no-cache-dir xformers==0.0.22 --no-dependencies
+RUN pip install --no-cache-dir xformers==0.0.23 --no-dependencies
 RUN curl -fsL -o /usr/local/lib/python3.10/dist-packages/gradio/frpc_linux_amd64_v0.2 https://cdn-media.huggingface.co/frpc-gradio-0.2/frpc_linux_amd64 && \
 	chmod +x /usr/local/lib/python3.10/dist-packages/gradio/frpc_linux_amd64_v0.2
 

args_manager.py

@@ -4,7 +4,10 @@ import os
 from tempfile import gettempdir
 
 args_parser.parser.add_argument("--share", action='store_true', help="Set whether to share on Gradio.")
+
 args_parser.parser.add_argument("--preset", type=str, default=None, help="Apply specified UI preset.")
+args_parser.parser.add_argument("--disable-preset-selection", action='store_true',
+                                help="Disables preset selection in Gradio.")
 
 args_parser.parser.add_argument("--language", type=str, default='default',
                                 help="Translate UI using json files in [language] folder. "
@@ -49,7 +52,4 @@ if args_parser.args.disable_analytics:
 if args_parser.args.disable_in_browser:
     args_parser.args.in_browser = False
 
-if args_parser.args.temp_path is None:
-    args_parser.args.temp_path = os.path.join(gettempdir(), 'Fooocus')
-
 args = args_parser.args

css/style.css

@@ -1,5 +1,136 @@
 /* based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/style.css */
 
+.loader-container {
+  display: flex; /* Use flex to align items horizontally */
+  align-items: center; /* Center items vertically within the container */
+  white-space: nowrap; /* Prevent line breaks within the container */
+}
+
+.loader {
+  border: 8px solid #f3f3f3; /* Light grey */
+  border-top: 8px solid #3498db; /* Blue */
+  border-radius: 50%;
+  width: 30px;
+  height: 30px;
+  animation: spin 2s linear infinite;
+}
+
+@keyframes spin {
+  0% { transform: rotate(0deg); }
+  100% { transform: rotate(360deg); }
+}
+
+/* Style the progress bar */
+progress {
+  appearance: none; /* Remove default styling */
+  height: 20px; /* Set the height of the progress bar */
+  border-radius: 5px; /* Round the corners of the progress bar */
+  background-color: #f3f3f3; /* Light grey background */
+  width: 100%;
+}
+
+/* Style the progress bar container */
+.progress-container {
+  margin-left: 20px;
+  margin-right: 20px;
+  flex-grow: 1; /* Allow the progress container to take up remaining space */
+}
+
+/* Set the color of the progress bar fill */
+progress::-webkit-progress-value {
+  background-color: #3498db; /* Blue color for the fill */
+}
+
+progress::-moz-progress-bar {
+  background-color: #3498db; /* Blue color for the fill in Firefox */
+}
+
+/* Style the text on the progress bar */
+progress::after {
+  content: attr(value '%'); /* Display the progress value followed by '%' */
+  position: absolute;
+  top: 50%;
+  left: 50%;
+  transform: translate(-50%, -50%);
+  color: white; /* Set text color */
+  font-size: 14px; /* Set font size */
+}
+
+/* Style other texts */
+.loader-container > span {
+  margin-left: 5px; /* Add spacing between the progress bar and the text */
+}
+
+.progress-bar > .generating {
+  display: none !important;
+}
+
+.progress-bar{
+  height: 30px !important;
+}
+
+.type_row{
+  height: 80px !important;
+}
+
+.type_row_half{
+  height: 32px !important;
+}
+
+.scroll-hide{
+  resize: none !important;
+}
+
+.refresh_button{
+  border: none !important;
+  background: none !important;
+  font-size: none !important;
+  box-shadow: none !important;
+}
+
+.advanced_check_row{
+  width: 250px !important;
+}
+
+.min_check{
+  min-width: min(1px, 100%) !important;
+}
+
+.resizable_area {
+  resize: vertical;
+  overflow: auto !important;
+}
+
+.aspect_ratios label {
+    width: 140px !important;
+}
+
+.aspect_ratios label span {
+    white-space: nowrap !important;
+}
+
+.aspect_ratios label input {
+    margin-left: -5px !important;
+}
+
+.lora_enable label {
+  height: 100%;
+}
+
+.lora_enable label input {
+  margin: auto;
+}
+
+.lora_enable label span {
+  display: none;
+}
+
+@-moz-document url-prefix() {
+  .lora_weight input[type=number] {
+    width: 80px;
+  }
+}
+
 #context-menu{
     z-index:9999;
     position:absolute;
@@ -218,3 +349,48 @@
 #stylePreviewOverlay.lower-half {
     transform: translate(-140px, -140px);
 }
+
+/* scrollable box for style selections */
+.contain .tabs {
+  height: 100%;
+}
+
+.contain .tabs .tabitem.style_selections_tab {
+  height: 100%;
+}
+
+.contain .tabs .tabitem.style_selections_tab > div:first-child {
+  height: 100%;
+}
+
+.contain .tabs .tabitem.style_selections_tab .style_selections {
+  min-height: 200px;
+  height: 100%;
+}
+
+.contain .tabs .tabitem.style_selections_tab .style_selections .wrap[data-testid="checkbox-group"] {
+  position: absolute; /* remove this to disable scrolling within the checkbox-group */
+  overflow: auto;
+  padding-right: 2px;
+  max-height: 100%;
+}
+
+.contain .tabs .tabitem.style_selections_tab .style_selections .wrap[data-testid="checkbox-group"] label {
+  /* max-width: calc(35% - 15px) !important; */ /* add this to enable 3 columns layout */
+  flex: calc(50% - 5px) !important;
+}
+
+.contain .tabs .tabitem.style_selections_tab .style_selections .wrap[data-testid="checkbox-group"] label span {
+  /* white-space:nowrap; */ /* add this to disable text wrapping (better choice for 3 columns layout) */
+  overflow: hidden;
+  text-overflow: ellipsis;
+}
+
+/* styles preview tooltip */
+.preview-tooltip {
+  background-color: #fff8;
+  font-family: monospace;
+  text-align: center;
+  border-radius-top: 5px;
+  display: none; /* remove this to enable tooltip in preview image */
+}

fooocus_colab.ipynb

@@ -12,7 +12,7 @@
     "%cd /content\n",
     "!git clone https://github.com/lllyasviel/Fooocus.git\n",
     "%cd /content/Fooocus\n",
-    "!python entry_with_update.py --share\n"
+    "!python entry_with_update.py --share --always-high-vram\n"
    ]
   }
  ],

fooocus_version.py

@@ -1 +1 @@
-version = '2.2.1'
+version = '2.3.1'

javascript/script.js

@@ -150,6 +150,9 @@ function initStylePreviewOverlay() {
     let overlayVisible = false;
     const samplesPath = document.querySelector("meta[name='samples-path']").getAttribute("content")
     const overlay = document.createElement('div');
+    const tooltip = document.createElement('div');
+    tooltip.className = 'preview-tooltip';
+    overlay.appendChild(tooltip);
     overlay.id = 'stylePreviewOverlay';
     document.body.appendChild(overlay);
     document.addEventListener('mouseover', function (e) {
@@ -165,6 +168,9 @@ function initStylePreviewOverlay() {
             "fooocus_v2",
             name.toLowerCase().replaceAll(" ", "_")
         ).replaceAll("\\", "\\\\")}")`;
+
+        tooltip.textContent = name;
+
         function onMouseLeave() {
             overlayVisible = false;
             overlay.style.opacity = "0";

language/en.json

@@ -38,9 +38,12 @@
     "* \"Inpaint or Outpaint\" is powered by the sampler \"DPMPP Fooocus Seamless 2M SDE Karras Inpaint Sampler\" (beta)": "* \"Inpaint or Outpaint\" is powered by the sampler \"DPMPP Fooocus Seamless 2M SDE Karras Inpaint Sampler\" (beta)",
     "Setting": "Setting",
     "Style": "Style",
+    "Preset": "Preset",
     "Performance": "Performance",
     "Speed": "Speed",
     "Quality": "Quality",
+    "Extreme Speed": "Extreme Speed",
+    "Lightning": "Lightning",
     "Aspect Ratios": "Aspect Ratios",
     "width \u00d7 height": "width \u00d7 height",
     "Image Number": "Image Number",
@@ -50,6 +53,7 @@
     "Seed": "Seed",
     "Disable seed increment": "Disable seed increment",
     "Disable automatic seed increment when image number is > 1.": "Disable automatic seed increment when image number is > 1.",
+    "Read wildcards in order": "Read wildcards in order",
     "\ud83d\udcda History Log": "\uD83D\uDCDA History Log",
     "Image Style": "Image Style",
     "Fooocus V2": "Fooocus V2",
@@ -335,6 +339,8 @@
     "sgm_uniform": "sgm_uniform",
     "simple": "simple",
     "ddim_uniform": "ddim_uniform",
+    "VAE": "VAE",
+    "Default (model)": "Default (model)",
     "Forced Overwrite of Sampling Step": "Forced Overwrite of Sampling Step",
     "Set as -1 to disable. For developer debugging.": "Set as -1 to disable. For developer debugging.",
     "Forced Overwrite of Refiner Switch Step": "Forced Overwrite of Refiner Switch Step",
@@ -367,7 +373,6 @@
     "B2": "B2",
     "S1": "S1",
     "S2": "S2",
-    "Extreme Speed": "Extreme Speed",
     "\uD83D\uDD0E Type here to search styles ...": "\uD83D\uDD0E Type here to search styles ...",
     "Type prompt here.": "Type prompt here.",
     "Outpaint Expansion Direction:": "Outpaint Expansion Direction:",
@@ -381,5 +386,6 @@
     "Metadata Scheme": "Metadata Scheme",
     "Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.": "Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.",
     "fooocus (json)": "fooocus (json)",
-    "a1111 (plain text)": "a1111 (plain text)"
+    "a1111 (plain text)": "a1111 (plain text)",
+    "Unsupported image type in input": "Unsupported image type in input"
 }

launch.py

@@ -1,6 +1,6 @@
 import os
-import sys
 import ssl
+import sys
 
 print('[System ARGV] ' + str(sys.argv))
 
@@ -15,15 +15,13 @@ if "GRADIO_SERVER_PORT" not in os.environ:
 
 ssl._create_default_https_context = ssl._create_unverified_context
 
-
 import platform
 import fooocus_version
 
 from build_launcher import build_launcher
-from modules.launch_util import is_installed, run, python, run_pip, requirements_met
+from modules.launch_util import is_installed, run, python, run_pip, requirements_met, delete_folder_content
 from modules.model_loader import load_file_from_url
 
-
 REINSTALL_ALL = False
 TRY_INSTALL_XFORMERS = False
 
@@ -42,7 +40,7 @@ def prepare_environment():
 
     if TRY_INSTALL_XFORMERS:
         if REINSTALL_ALL or not is_installed("xformers"):
-            xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
+            xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23')
             if platform.system() == "Windows":
                 if platform.python_version().startswith("3.10"):
                     run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
@@ -68,6 +66,7 @@ vae_approx_filenames = [
      'https://huggingface.co/lllyasviel/misc/resolve/main/xl-to-v1_interposer-v3.1.safetensors')
 ]
 
+
 def ini_args():
     from args_manager import args
     return args
@@ -77,15 +76,24 @@ prepare_environment()
 build_launcher()
 args = ini_args()
 
-
 if args.gpu_device_id is not None:
     os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_device_id)
     print("Set device to:", args.gpu_device_id)
 
-
 from modules import config
 
-def download_models():
+os.environ['GRADIO_TEMP_DIR'] = config.temp_path
+
+if config.temp_path_cleanup_on_launch:
+    print(f'[Cleanup] Attempting to delete content of temp dir {config.temp_path}')
+    result = delete_folder_content(config.temp_path, '[Cleanup] ')
+    if result:
+        print("[Cleanup] Cleanup successful")
+    else:
+        print(f"[Cleanup] Failed to delete content of temp dir.")
+
+
+def download_models(default_model, previous_default_models, checkpoint_downloads, embeddings_downloads, lora_downloads):
     for file_name, url in vae_approx_filenames:
         load_file_from_url(url=url, model_dir=config.path_vae_approx, file_name=file_name)
 
@@ -97,31 +105,32 @@ def download_models():
 
     if args.disable_preset_download:
         print('Skipped model download.')
-        return
+        return default_model, checkpoint_downloads
 
     if not args.always_download_new_model:
-        if not os.path.exists(os.path.join(config.paths_checkpoints[0], config.default_base_model_name)):
+        if not os.path.exists(os.path.join(config.paths_checkpoints[0], default_model)):
-            for alternative_model_name in config.previous_default_models:
+            for alternative_model_name in previous_default_models:
                 if os.path.exists(os.path.join(config.paths_checkpoints[0], alternative_model_name)):
-                    print(f'You do not have [{config.default_base_model_name}] but you have [{alternative_model_name}].')
+                    print(f'You do not have [{default_model}] but you have [{alternative_model_name}].')
                     print(f'Fooocus will use [{alternative_model_name}] to avoid downloading new models, '
-                          f'but you are not using latest models.')
+                          f'but you are not using the latest models.')
                     print('Use --always-download-new-model to avoid fallback and always get new models.')
-                    config.checkpoint_downloads = {}
+                    checkpoint_downloads = {}
-                    config.default_base_model_name = alternative_model_name
+                    default_model = alternative_model_name
                     break
 
-    for file_name, url in config.checkpoint_downloads.items():
+    for file_name, url in checkpoint_downloads.items():
         load_file_from_url(url=url, model_dir=config.paths_checkpoints[0], file_name=file_name)
-    for file_name, url in config.embeddings_downloads.items():
+    for file_name, url in embeddings_downloads.items():
         load_file_from_url(url=url, model_dir=config.path_embeddings, file_name=file_name)
-    for file_name, url in config.lora_downloads.items():
+    for file_name, url in lora_downloads.items():
         load_file_from_url(url=url, model_dir=config.paths_loras[0], file_name=file_name)
 
-    return
+    return default_model, checkpoint_downloads
 
 
-download_models()
+config.default_base_model_name, config.checkpoint_downloads = download_models(
-
+    config.default_base_model_name, config.previous_default_models, config.checkpoint_downloads,
+    config.embeddings_downloads, config.lora_downloads)
 
 from webui import *

ldm_patched/modules/sd.py

@@ -427,12 +427,13 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
 
     return (ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae)
 
-def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True):
+def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True, vae_filename_param=None):
     sd = ldm_patched.modules.utils.load_torch_file(ckpt_path)
     sd_keys = sd.keys()
     clip = None
     clipvision = None
     vae = None
+    vae_filename = None
     model = None
     model_patcher = None
     clip_target = None
@@ -462,8 +463,12 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
         model.load_model_weights(sd, "model.diffusion_model.")
 
     if output_vae:
+        if vae_filename_param is None:
             vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True)
             vae_sd = model_config.process_vae_state_dict(vae_sd)
+        else:
+            vae_sd = ldm_patched.modules.utils.load_torch_file(vae_filename_param)
+            vae_filename = vae_filename_param
         vae = VAE(sd=vae_sd)
 
     if output_clip:
@@ -485,7 +490,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
             print("loaded straight to GPU")
             model_management.load_model_gpu(model_patcher)
 
-    return (model_patcher, clip, vae, clipvision)
+    return model_patcher, clip, vae, vae_filename, clipvision
 
 
 def load_unet_state_dict(sd): #load unet in diffusers format

modules/async_worker.py

@@ -1,4 +1,5 @@
 import threading
+import re
 from modules.patch import PatchSettings, patch_settings, patch_all
 
 patch_all()
@@ -45,8 +46,8 @@ def worker():
     from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays
     from modules.private_logger import log
     from extras.expansion import safe_str
-    from modules.util import remove_empty_str, HWC3, resize_image, \
+    from modules.util import remove_empty_str, HWC3, resize_image, get_image_shape_ceil, set_image_shape_ceil, \
-        get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix
+        get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix, get_enabled_loras
     from modules.upscaler import perform_upscale
     from modules.flags import Performance
     from modules.meta_parser import get_metadata_parser, MetadataScheme
@@ -123,14 +124,6 @@ def worker():
         async_task.results = async_task.results + [wall]
         return
 
-    def apply_enabled_loras(loras):
-        enabled_loras = []
-        for lora_enabled, lora_model, lora_weight in loras:
-            if lora_enabled:
-                enabled_loras.append([lora_model, lora_weight])
-
-        return enabled_loras
-
     @torch.no_grad()
     @torch.inference_mode()
     def handler(async_task):
@@ -148,12 +141,13 @@ def worker():
         image_number = args.pop()
         output_format = args.pop()
         image_seed = args.pop()
+        read_wildcards_in_order = args.pop()
         sharpness = args.pop()
         guidance_scale = args.pop()
         base_model_name = args.pop()
         refiner_model_name = args.pop()
         refiner_switch = args.pop()
-        loras = apply_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop()), ] for _ in range(modules.config.default_max_lora_number)])
+        loras = get_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop())] for _ in range(modules.config.default_max_lora_number)])
         input_image_checkbox = args.pop()
         current_tab = args.pop()
         uov_method = args.pop()
@@ -172,6 +166,7 @@ def worker():
         adaptive_cfg = args.pop()
         sampler_name = args.pop()
         scheduler_name = args.pop()
+        vae_name = args.pop()
         overwrite_step = args.pop()
         overwrite_switch = args.pop()
         overwrite_width = args.pop()
@@ -250,6 +245,25 @@ def worker():
             adm_scaler_negative = 1.0
             adm_scaler_end = 0.0
 
+        elif performance_selection == Performance.LIGHTNING:
+            print('Enter Lightning mode.')
+            progressbar(async_task, 1, 'Downloading Lightning components ...')
+            loras += [(modules.config.downloading_sdxl_lightning_lora(), 1.0)]
+
+            if refiner_model_name != 'None':
+                print(f'Refiner disabled in Lightning mode.')
+
+            refiner_model_name = 'None'
+            sampler_name = 'euler'
+            scheduler_name = 'sgm_uniform'
+            sharpness = 0.0
+            guidance_scale = 1.0
+            adaptive_cfg = 1.0
+            refiner_switch = 1.0
+            adm_scaler_positive = 1.0
+            adm_scaler_negative = 1.0
+            adm_scaler_end = 0.0
+
         print(f'[Parameters] Adaptive CFG = {adaptive_cfg}')
         print(f'[Parameters] Sharpness = {sharpness}')
         print(f'[Parameters] ControlNet Softness = {controlnet_softness}')
@@ -347,7 +361,7 @@ def worker():
                         print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}')
                         if refiner_model_name == 'None':
                             use_synthetic_refiner = True
-                            refiner_switch = 0.5
+                            refiner_switch = 0.8
                     else:
                         inpaint_head_model_path, inpaint_patch_model_path = None, None
                         print(f'[Inpaint] Parameterized inpaint is disabled.')
@@ -415,23 +429,23 @@ def worker():
             progressbar(async_task, 3, 'Loading models ...')
             pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name,
                                         loras=loras, base_model_additional_loras=base_model_additional_loras,
-                                        use_synthetic_refiner=use_synthetic_refiner)
+                                        use_synthetic_refiner=use_synthetic_refiner, vae_name=vae_name)
 
             progressbar(async_task, 3, 'Processing prompts ...')
             tasks = []
             
             for i in range(image_number):
                 if disable_seed_increment:
-                    task_seed = seed
+                    task_seed = seed % (constants.MAX_SEED + 1)
                 else:
                     task_seed = (seed + i) % (constants.MAX_SEED + 1)  # randint is inclusive, % is not
 
                 task_rng = random.Random(task_seed)  # may bind to inpaint noise in the future
-                task_prompt = apply_wildcards(prompt, task_rng)
+                task_prompt = apply_wildcards(prompt, task_rng, i, read_wildcards_in_order)
                 task_prompt = apply_arrays(task_prompt, i)
-                task_negative_prompt = apply_wildcards(negative_prompt, task_rng)
+                task_negative_prompt = apply_wildcards(negative_prompt, task_rng, i, read_wildcards_in_order)
-                task_extra_positive_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_positive_prompts]
+                task_extra_positive_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_positive_prompts]
-                task_extra_negative_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_negative_prompts]
+                task_extra_negative_prompts = [apply_wildcards(pmt, task_rng, i, read_wildcards_in_order) for pmt in extra_negative_prompts]
 
                 positive_basic_workloads = []
                 negative_basic_workloads = []
@@ -601,12 +615,12 @@ def worker():
 
                 H, W, C = inpaint_image.shape
                 if 'left' in outpaint_selections:
-                    inpaint_image = np.pad(inpaint_image, [[0, 0], [int(H * 0.3), 0], [0, 0]], mode='edge')
+                    inpaint_image = np.pad(inpaint_image, [[0, 0], [int(W * 0.3), 0], [0, 0]], mode='edge')
-                    inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(H * 0.3), 0]], mode='constant',
+                    inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(W * 0.3), 0]], mode='constant',
                                           constant_values=255)
                 if 'right' in outpaint_selections:
-                    inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(H * 0.3)], [0, 0]], mode='edge')
+                    inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(W * 0.3)], [0, 0]], mode='edge')
-                    inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(H * 0.3)]], mode='constant',
+                    inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(W * 0.3)]], mode='constant',
                                           constant_values=255)
 
                 inpaint_image = np.ascontiguousarray(inpaint_image.copy())
@@ -787,7 +801,7 @@ def worker():
 
             try:
                 if async_task.last_stop is not False:
-                    ldm_patched.model_management.interrupt_current_processing()
+                    ldm_patched.modules.model_management.interrupt_current_processing()
                 positive_cond, negative_cond = task['c'], task['uc']
 
                 if 'cn' in goals:
@@ -856,7 +870,8 @@ def worker():
 
                     d.append(('Sampler', 'sampler', sampler_name))
                     d.append(('Scheduler', 'scheduler', scheduler_name))
-                    d.append(('Seed', 'seed', task['task_seed']))
+                    d.append(('VAE', 'vae', vae_name))
+                    d.append(('Seed', 'seed', str(task['task_seed'])))
 
                     if freeu_enabled:
                         d.append(('FreeU', 'freeu', str((freeu_b1, freeu_b2, freeu_s1, freeu_s2))))
@@ -870,7 +885,7 @@ def worker():
                         metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
                         metadata_parser.set_data(task['log_positive_prompt'], task['positive'],
                                                  task['log_negative_prompt'], task['negative'],
-                                                 steps, base_model_name, refiner_model_name, loras)
+                                                 steps, base_model_name, refiner_model_name, loras, vae_name)
                     d.append(('Metadata Scheme', 'metadata_scheme', metadata_scheme.value if save_metadata_to_images else save_metadata_to_images))
                     d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version))
                     img_paths.append(log(x, d, metadata_parser, output_format))

modules/config.py

@@ -3,12 +3,14 @@ import json
 import math
 import numbers
 import args_manager
+import tempfile
 import modules.flags
 import modules.sdxl_styles
 
 from modules.model_loader import load_file_from_url
 from modules.util import get_files_from_folder, makedirs_with_log
-from modules.flags import Performance, MetadataScheme
+from modules.flags import OutputFormat, Performance, MetadataScheme
+
 
 def get_config_path(key, default_value):
     env = os.getenv(key)
@@ -18,6 +20,7 @@ def get_config_path(key, default_value):
     else:
         return os.path.abspath(default_value)
 
+
 config_path = get_config_path('config_path', "./config.txt")
 config_example_path = get_config_path('config_example_path', "config_modification_tutorial.txt")
 config_dict = {}
@@ -94,21 +97,36 @@ def try_load_deprecated_user_path_config():
 
 try_load_deprecated_user_path_config()
 
-preset = args_manager.args.preset
 
+def get_presets():
+    preset_folder = 'presets'
+    presets = ['initial']
+    if not os.path.exists(preset_folder):
+        print('No presets found.')
+        return presets
+
+    return presets + [f[:f.index('.json')] for f in os.listdir(preset_folder) if f.endswith('.json')]
+
+
+def try_get_preset_content(preset):
     if isinstance(preset, str):
         preset_path = os.path.abspath(f'./presets/{preset}.json')
         try:
             if os.path.exists(preset_path):
                 with open(preset_path, "r", encoding="utf-8") as json_file:
-                config_dict.update(json.load(json_file))
+                    json_content = json.load(json_file)
                     print(f'Loaded preset: {preset_path}')
+                    return json_content
             else:
                 raise FileNotFoundError
         except Exception as e:
             print(f'Load preset [{preset_path}] failed')
             print(e)
+    return {}
 
+available_presets = get_presets()
+preset = args_manager.args.preset
+config_dict.update(try_get_preset_content(preset))
 
 def get_path_output() -> str:
     """
@@ -117,7 +135,7 @@ def get_path_output() -> str:
     global config_dict
     path_output = get_dir_or_set_default('path_outputs', '../outputs/', make_directory=True)
     if args_manager.args.output_path:
-        print(f'[CONFIG] Overriding config value path_outputs with {args_manager.args.output_path}')
+        print(f'Overriding config value path_outputs with {args_manager.args.output_path}')
         config_dict['path_outputs'] = path_output = args_manager.args.output_path
     return path_output
 
@@ -171,13 +189,16 @@ paths_checkpoints = get_dir_or_set_default('path_checkpoints', ['../models/check
 paths_loras = get_dir_or_set_default('path_loras', ['../models/loras/'], True)
 path_embeddings = get_dir_or_set_default('path_embeddings', '../models/embeddings/')
 path_vae_approx = get_dir_or_set_default('path_vae_approx', '../models/vae_approx/')
+path_vae = get_dir_or_set_default('path_vae', '../models/vae/')
 path_upscale_models = get_dir_or_set_default('path_upscale_models', '../models/upscale_models/')
 path_inpaint = get_dir_or_set_default('path_inpaint', '../models/inpaint/')
 path_controlnet = get_dir_or_set_default('path_controlnet', '../models/controlnet/')
 path_clip_vision = get_dir_or_set_default('path_clip_vision', '../models/clip_vision/')
 path_fooocus_expansion = get_dir_or_set_default('path_fooocus_expansion', '../models/prompt_expansion/fooocus_expansion')
+path_wildcards = get_dir_or_set_default('path_wildcards', '../wildcards/')
 path_outputs = get_path_output()
 
+
 def get_config_item_or_set_default(key, default_value, validator, disable_empty_as_none=False):
     global config_dict, visited_keys
 
@@ -206,7 +227,37 @@ def get_config_item_or_set_default(key, default_value, validator, disable_empty_
         return default_value
 
 
-default_base_model_name = get_config_item_or_set_default(
+def init_temp_path(path: str | None, default_path: str) -> str:
+    if args_manager.args.temp_path:
+        path = args_manager.args.temp_path
+
+    if path != '' and path != default_path:
+        try:
+            if not os.path.isabs(path):
+                path = os.path.abspath(path)
+            os.makedirs(path, exist_ok=True)
+            print(f'Using temp path {path}')
+            return path
+        except Exception as e:
+            print(f'Could not create temp path {path}. Reason: {e}')
+            print(f'Using default temp path {default_path} instead.')
+
+    os.makedirs(default_path, exist_ok=True)
+    return default_path
+
+
+default_temp_path = os.path.join(tempfile.gettempdir(), 'fooocus')
+temp_path = init_temp_path(get_config_item_or_set_default(
+    key='temp_path',
+    default_value=default_temp_path,
+    validator=lambda x: isinstance(x, str),
+), default_temp_path)
+temp_path_cleanup_on_launch = get_config_item_or_set_default(
+    key='temp_path_cleanup_on_launch',
+    default_value=True,
+    validator=lambda x: isinstance(x, bool)
+)
+default_base_model_name = default_model = get_config_item_or_set_default(
     key='default_model',
     default_value='model.safetensors',
     validator=lambda x: isinstance(x, str)
@@ -216,7 +267,7 @@ previous_default_models = get_config_item_or_set_default(
     default_value=[],
     validator=lambda x: isinstance(x, list) and all(isinstance(k, str) for k in x)
 )
-default_refiner_model_name = get_config_item_or_set_default(
+default_refiner_model_name = default_refiner = get_config_item_or_set_default(
     key='default_refiner',
     default_value='None',
     validator=lambda x: isinstance(x, str)
@@ -240,28 +291,37 @@ default_loras = get_config_item_or_set_default(
     key='default_loras',
     default_value=[
         [
+            True,
             "None",
             1.0
         ],
         [
+            True,
             "None",
             1.0
         ],
         [
+            True,
             "None",
             1.0
         ],
         [
+            True,
             "None",
             1.0
         ],
         [
+            True,
             "None",
             1.0
         ]
     ],
-    validator=lambda x: isinstance(x, list) and all(len(y) == 2 and isinstance(y[0], str) and isinstance(y[1], numbers.Number) for y in x)
+    validator=lambda x: isinstance(x, list) and all(
+        len(y) == 3 and isinstance(y[0], bool) and isinstance(y[1], str) and isinstance(y[2], numbers.Number)
+        or len(y) == 2 and isinstance(y[0], str) and isinstance(y[1], numbers.Number)
+        for y in x)
 )
+default_loras = [(y[0], y[1], y[2]) if len(y) == 3 else (True, y[0], y[1]) for y in default_loras]
 default_max_lora_number = get_config_item_or_set_default(
     key='default_max_lora_number',
     default_value=len(default_loras) if isinstance(default_loras, list) and len(default_loras) > 0 else 5,
@@ -287,6 +347,11 @@ default_scheduler = get_config_item_or_set_default(
     default_value='karras',
     validator=lambda x: x in modules.flags.scheduler_list
 )
+default_vae = get_config_item_or_set_default(
+    key='default_vae',
+    default_value=modules.flags.default_vae,
+    validator=lambda x: isinstance(x, str)
+)
 default_styles = get_config_item_or_set_default(
     key='default_styles',
     default_value=[
@@ -326,7 +391,7 @@ default_max_image_number = get_config_item_or_set_default(
 default_output_format = get_config_item_or_set_default(
     key='default_output_format',
     default_value='png',
-    validator=lambda x: x in modules.flags.output_formats
+    validator=lambda x: x in OutputFormat.list()
 )
 default_image_number = get_config_item_or_set_default(
     key='default_image_number',
@@ -409,31 +474,33 @@ metadata_created_by = get_config_item_or_set_default(
 
 example_inpaint_prompts = [[x] for x in example_inpaint_prompts]
 
-config_dict["default_loras"] = default_loras = default_loras[:default_max_lora_number] + [['None', 1.0] for _ in range(default_max_lora_number - len(default_loras))]
+config_dict["default_loras"] = default_loras = default_loras[:default_max_lora_number] + [[True, 'None', 1.0] for _ in range(default_max_lora_number - len(default_loras))]
-
-possible_preset_keys = [
-    "default_model",
-    "default_refiner",
-    "default_refiner_switch",
-    "default_loras_min_weight",
-    "default_loras_max_weight",
-    "default_loras",
-    "default_max_lora_number",
-    "default_cfg_scale",
-    "default_sample_sharpness",
-    "default_sampler",
-    "default_scheduler",
-    "default_performance",
-    "default_prompt",
-    "default_prompt_negative",
-    "default_styles",
-    "default_aspect_ratio",
-    "default_save_metadata_to_images",
-    "checkpoint_downloads",
-    "embeddings_downloads",
-    "lora_downloads",
-]
 
+# mapping config to meta parameter 
+possible_preset_keys = {
+    "default_model": "base_model",
+    "default_refiner": "refiner_model",
+    "default_refiner_switch": "refiner_switch",
+    "previous_default_models": "previous_default_models",
+    "default_loras_min_weight": "default_loras_min_weight",
+    "default_loras_max_weight": "default_loras_max_weight",
+    "default_loras": "<processed>",
+    "default_cfg_scale": "guidance_scale",
+    "default_sample_sharpness": "sharpness",
+    "default_sampler": "sampler",
+    "default_scheduler": "scheduler",
+    "default_overwrite_step": "steps",
+    "default_performance": "performance",
+    "default_image_number": "image_number",
+    "default_prompt": "prompt",
+    "default_prompt_negative": "negative_prompt",
+    "default_styles": "styles",
+    "default_aspect_ratio": "resolution",
+    "default_save_metadata_to_images": "default_save_metadata_to_images",
+    "checkpoint_downloads": "checkpoint_downloads",
+    "embeddings_downloads": "embeddings_downloads",
+    "lora_downloads": "lora_downloads"
+}
 
 REWRITE_PRESET = False
 
@@ -474,21 +541,34 @@ with open(config_example_path, "w", encoding="utf-8") as json_file:
 
 model_filenames = []
 lora_filenames = []
+vae_filenames = []
+wildcard_filenames = []
+
 sdxl_lcm_lora = 'sdxl_lcm_lora.safetensors'
+sdxl_lightning_lora = 'sdxl_lightning_4step_lora.safetensors'
+loras_metadata_remove = [sdxl_lcm_lora, sdxl_lightning_lora]
 
 
-def get_model_filenames(folder_paths, name_filter=None):
+def get_model_filenames(folder_paths, extensions=None, name_filter=None):
+    if extensions is None:
         extensions = ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch']
     files = []
+
+    if not isinstance(folder_paths, list):
+        folder_paths = [folder_paths]
     for folder in folder_paths:
         files += get_files_from_folder(folder, extensions, name_filter)
+
     return files
 
 
-def update_all_model_names():
+def update_files():
-    global model_filenames, lora_filenames
+    global model_filenames, lora_filenames, vae_filenames, wildcard_filenames, available_presets
     model_filenames = get_model_filenames(paths_checkpoints)
     lora_filenames = get_model_filenames(paths_loras)
+    vae_filenames = get_model_filenames(path_vae)
+    wildcard_filenames = get_files_from_folder(path_wildcards, ['.txt'])
+    available_presets = get_presets()
     return
 
 
@@ -538,6 +618,14 @@ def downloading_sdxl_lcm_lora():
     )
     return sdxl_lcm_lora
 
+def downloading_sdxl_lightning_lora():
+    load_file_from_url(
+        url='https://huggingface.co/ByteDance/SDXL-Lightning/resolve/main/sdxl_lightning_4step_lora.safetensors',
+        model_dir=paths_loras[0],
+        file_name=sdxl_lightning_lora
+    )
+    return sdxl_lightning_lora
+
 
 def downloading_controlnet_canny():
     load_file_from_url(
@@ -604,4 +692,4 @@ def downloading_upscale_model():
     return os.path.join(path_upscale_models, 'fooocus_upscaler_s409985e5.bin')
 
 
-update_all_model_names()
+update_files()

modules/core.py

@@ -35,12 +35,13 @@ opModelSamplingDiscrete = ModelSamplingDiscrete()
 
 
 class StableDiffusionModel:
-    def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None):
+    def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None, vae_filename=None):
         self.unet = unet
         self.vae = vae
         self.clip = clip
         self.clip_vision = clip_vision
         self.filename = filename
+        self.vae_filename = vae_filename
         self.unet_with_lora = unet
         self.clip_with_lora = clip
         self.visited_loras = ''
@@ -73,14 +74,14 @@ class StableDiffusionModel:
 
         loras_to_load = []
 
-        for name, weight in loras:
+        for filename, weight in loras:
-            if name == 'None':
+            if filename == 'None':
                 continue
 
-            if os.path.exists(name):
+            if os.path.exists(filename):
-                lora_filename = name
+                lora_filename = filename
             else:
-                lora_filename = get_file_from_folder_list(name, modules.config.paths_loras)
+                lora_filename = get_file_from_folder_list(filename, modules.config.paths_loras)
 
             if not os.path.exists(lora_filename):
                 print(f'Lora file not found: {lora_filename}')
@@ -142,9 +143,10 @@ def apply_controlnet(positive, negative, control_net, image, strength, start_per
 
 @torch.no_grad()
 @torch.inference_mode()
-def load_model(ckpt_filename):
+def load_model(ckpt_filename, vae_filename=None):
-    unet, clip, vae, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=path_embeddings)
+    unet, clip, vae, vae_filename, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=path_embeddings,
-    return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision, filename=ckpt_filename)
+                                                                vae_filename_param=vae_filename)
+    return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision, filename=ckpt_filename, vae_filename=vae_filename)
 
 
 @torch.no_grad()

modules/default_pipeline.py

@@ -3,6 +3,7 @@ import os
 import torch
 import modules.patch
 import modules.config
+import modules.flags
 import ldm_patched.modules.model_management
 import ldm_patched.modules.latent_formats
 import modules.inpaint_worker
@@ -11,7 +12,7 @@ from extras.expansion import FooocusExpansion
 
 from ldm_patched.modules.model_base import SDXL, SDXLRefiner
 from modules.sample_hijack import clip_separate
-from modules.util import get_file_from_folder_list
+from modules.util import get_file_from_folder_list, get_enabled_loras
 
 
 model_base = core.StableDiffusionModel()
@@ -58,17 +59,21 @@ def assert_model_integrity():
 
 @torch.no_grad()
 @torch.inference_mode()
-def refresh_base_model(name):
+def refresh_base_model(name, vae_name=None):
     global model_base
 
     filename = get_file_from_folder_list(name, modules.config.paths_checkpoints)
 
-    if model_base.filename == filename:
+    vae_filename = None
+    if vae_name is not None and vae_name != modules.flags.default_vae:
+        vae_filename = get_file_from_folder_list(vae_name, modules.config.path_vae)
+
+    if model_base.filename == filename and model_base.vae_filename == vae_filename:
         return
 
-    model_base = core.StableDiffusionModel()
+    model_base = core.load_model(filename, vae_filename)
-    model_base = core.load_model(filename)
     print(f'Base model loaded: {model_base.filename}')
+    print(f'VAE loaded: {model_base.vae_filename}')
     return
 
 
@@ -216,7 +221,7 @@ def prepare_text_encoder(async_call=True):
 @torch.no_grad()
 @torch.inference_mode()
 def refresh_everything(refiner_model_name, base_model_name, loras,
-                       base_model_additional_loras=None, use_synthetic_refiner=False):
+                       base_model_additional_loras=None, use_synthetic_refiner=False, vae_name=None):
     global final_unet, final_clip, final_vae, final_refiner_unet, final_refiner_vae, final_expansion
 
     final_unet = None
@@ -227,11 +232,11 @@ def refresh_everything(refiner_model_name, base_model_name, loras,
 
     if use_synthetic_refiner and refiner_model_name == 'None':
         print('Synthetic Refiner Activated')
-        refresh_base_model(base_model_name)
+        refresh_base_model(base_model_name, vae_name)
         synthesize_refiner_model()
     else:
         refresh_refiner_model(refiner_model_name)
-        refresh_base_model(base_model_name)
+        refresh_base_model(base_model_name, vae_name)
 
     refresh_loras(loras, base_model_additional_loras=base_model_additional_loras)
     assert_model_integrity()
@@ -254,7 +259,8 @@ def refresh_everything(refiner_model_name, base_model_name, loras,
 refresh_everything(
     refiner_model_name=modules.config.default_refiner_model_name,
     base_model_name=modules.config.default_base_model_name,
-    loras=modules.config.default_loras
+    loras=get_enabled_loras(modules.config.default_loras),
+    vae_name=modules.config.default_vae,
 )
 
 

modules/flags.py

@@ -53,6 +53,8 @@ SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys())
 sampler_list = SAMPLER_NAMES
 scheduler_list = SCHEDULER_NAMES
 
+default_vae = 'Default (model)'
+
 refiner_swap_method = 'joint'
 
 cn_ip = "ImagePrompt"
@@ -67,7 +69,7 @@ default_parameters = {
     cn_ip: (0.5, 0.6), cn_ip_face: (0.9, 0.75), cn_canny: (0.5, 1.0), cn_cpds: (0.5, 1.0)
 }  # stop, weight
 
-output_formats = ['png', 'jpg', 'webp']
+output_formats = ['png', 'jpeg', 'webp']
 
 inpaint_engine_versions = ['None', 'v1', 'v2.5', 'v2.6']
 inpaint_option_default = 'Inpaint or Outpaint (default)'
@@ -89,37 +91,51 @@ metadata_scheme = [
     (f'{MetadataScheme.A1111.value} (plain text)', MetadataScheme.A1111.value),
 ]
 
-lora_count = 5
-
 controlnet_image_count = 4
 
 
+class OutputFormat(Enum):
+    PNG = 'png'
+    JPEG = 'jpeg'
+    WEBP = 'webp'
+
+    @classmethod
+    def list(cls) -> list:
+        return list(map(lambda c: c.value, cls))
+
+
 class Steps(IntEnum):
     QUALITY = 60
     SPEED = 30
     EXTREME_SPEED = 8
+    LIGHTNING = 4
 
 
 class StepsUOV(IntEnum):
     QUALITY = 36
     SPEED = 18
     EXTREME_SPEED = 8
+    LIGHTNING = 4
 
 
 class Performance(Enum):
     QUALITY = 'Quality'
     SPEED = 'Speed'
     EXTREME_SPEED = 'Extreme Speed'
+    LIGHTNING = 'Lightning'
 
     @classmethod
     def list(cls) -> list:
         return list(map(lambda c: c.value, cls))
 
+    @classmethod
+    def has_restricted_features(cls, x) -> bool:
+        if isinstance(x, Performance):
+            x = x.value
+        return x in [cls.EXTREME_SPEED.value, cls.LIGHTNING.value]
+
     def steps(self) -> int | None:
         return Steps[self.name].value if Steps[self.name] else None
 
     def steps_uov(self) -> int | None:
         return StepsUOV[self.name].value if Steps[self.name] else None
-
-
-performance_selections = Performance.list()

modules/gradio_hijack.py

@@ -17,7 +17,7 @@ from gradio_client.documentation import document, set_documentation_group
 from gradio_client.serializing import ImgSerializable
 from PIL import Image as _Image  # using _ to minimize namespace pollution
 
-from gradio import processing_utils, utils
+from gradio import processing_utils, utils, Error
 from gradio.components.base import IOComponent, _Keywords, Block
 from gradio.deprecation import warn_style_method_deprecation
 from gradio.events import (
@@ -275,7 +275,10 @@ class Image(
                 x, mask = x["image"], x["mask"]
 
         assert isinstance(x, str)
+        try:
             im = processing_utils.decode_base64_to_image(x)
+        except PIL.UnidentifiedImageError:
+            raise Error("Unsupported image type in input")
         with warnings.catch_warnings():
             warnings.simplefilter("ignore")
             im = im.convert(self.image_mode)

modules/html.py

@@ -1,136 +1,3 @@
-css = '''
-.loader-container {
-  display: flex; /* Use flex to align items horizontally */
-  align-items: center; /* Center items vertically within the container */
-  white-space: nowrap; /* Prevent line breaks within the container */
-}
-
-.loader {
-  border: 8px solid #f3f3f3; /* Light grey */
-  border-top: 8px solid #3498db; /* Blue */
-  border-radius: 50%;
-  width: 30px;
-  height: 30px;
-  animation: spin 2s linear infinite;
-}
-
-@keyframes spin {
-  0% { transform: rotate(0deg); }
-  100% { transform: rotate(360deg); }
-}
-
-/* Style the progress bar */
-progress {
-  appearance: none; /* Remove default styling */
-  height: 20px; /* Set the height of the progress bar */
-  border-radius: 5px; /* Round the corners of the progress bar */
-  background-color: #f3f3f3; /* Light grey background */
-  width: 100%;
-}
-
-/* Style the progress bar container */
-.progress-container {
-  margin-left: 20px;
-  margin-right: 20px;
-  flex-grow: 1; /* Allow the progress container to take up remaining space */
-}
-
-/* Set the color of the progress bar fill */
-progress::-webkit-progress-value {
-  background-color: #3498db; /* Blue color for the fill */
-}
-
-progress::-moz-progress-bar {
-  background-color: #3498db; /* Blue color for the fill in Firefox */
-}
-
-/* Style the text on the progress bar */
-progress::after {
-  content: attr(value '%'); /* Display the progress value followed by '%' */
-  position: absolute;
-  top: 50%;
-  left: 50%;
-  transform: translate(-50%, -50%);
-  color: white; /* Set text color */
-  font-size: 14px; /* Set font size */
-}
-
-/* Style other texts */
-.loader-container > span {
-  margin-left: 5px; /* Add spacing between the progress bar and the text */
-}
-
-.progress-bar > .generating {
-  display: none !important;
-}
-
-.progress-bar{
-  height: 30px !important;
-}
-
-.type_row{
-  height: 80px !important;
-}
-
-.type_row_half{
-  height: 32px !important;
-}
-
-.scroll-hide{
-  resize: none !important;
-}
-
-.refresh_button{
-  border: none !important;
-  background: none !important;
-  font-size: none !important;
-  box-shadow: none !important;
-}
-
-.advanced_check_row{
-  width: 250px !important;
-}
-
-.min_check{
-  min-width: min(1px, 100%) !important;
-}
-
-.resizable_area {
-  resize: vertical;
-  overflow: auto !important;
-}
-
-.aspect_ratios label {
-    width: 140px !important;
-}
-
-.aspect_ratios label span {
-    white-space: nowrap !important;
-}
-
-.aspect_ratios label input {
-    margin-left: -5px !important;
-}
-
-.lora_enable label {
-  height: 100%;
-}
-
-.lora_enable label input {
-  margin: auto;
-}
-
-.lora_enable label span {
-  display: none;
-}
-
-@-moz-document url-prefix() {
-  .lora_weight input[type=number] {
-    width: 80px;
-  }
-}
-
-'''
 progress_html = '''
 <div class="loader-container">
   <div class="loader"></div>

modules/launch_util.py

@@ -1,6 +1,7 @@
 import os
 import importlib
 import importlib.util
+import shutil
 import subprocess
 import sys
 import re
@@ -9,9 +10,6 @@ import importlib.metadata
 import packaging.version
 from packaging.requirements import Requirement
 
-
-
-
 logging.getLogger("torch.distributed.nn").setLevel(logging.ERROR)  # sshh...
 logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
 
@@ -101,3 +99,19 @@ def requirements_met(requirements_file):
 
     return True
 
+
+def delete_folder_content(folder, prefix=None):
+    result = True
+
+    for filename in os.listdir(folder):
+        file_path = os.path.join(folder, filename)
+        try:
+            if os.path.isfile(file_path) or os.path.islink(file_path):
+                os.unlink(file_path)
+            elif os.path.isdir(file_path):
+                shutil.rmtree(file_path)
+        except Exception as e:
+            print(f'{prefix}Failed to delete {file_path}. Reason: {e}')
+            result = False
+
+    return result

modules/meta_parser.py

@@ -1,5 +1,4 @@
 import json
-import os
 import re
 from abc import ABC, abstractmethod
 from pathlib import Path
@@ -12,7 +11,7 @@ import modules.config
 import modules.sdxl_styles
 from modules.flags import MetadataScheme, Performance, Steps
 from modules.flags import SAMPLERS, CIVITAI_NO_KARRAS
-from modules.util import quote, unquote, extract_styles_from_prompt, is_json, get_file_from_folder_list, calculate_sha256
+from modules.util import quote, unquote, extract_styles_from_prompt, is_json, get_file_from_folder_list, sha256
 
 re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
@@ -27,8 +26,9 @@ def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
         loaded_parameter_dict = json.loads(raw_metadata)
     assert isinstance(loaded_parameter_dict, dict)
 
-    results = [len(loaded_parameter_dict) > 0, 1]
+    results = [len(loaded_parameter_dict) > 0]
 
+    get_image_number('image_number', 'Image Number', loaded_parameter_dict, results)
     get_str('prompt', 'Prompt', loaded_parameter_dict, results)
     get_str('negative_prompt', 'Negative Prompt', loaded_parameter_dict, results)
     get_list('styles', 'Styles', loaded_parameter_dict, results)
@@ -46,6 +46,7 @@ def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool):
     get_float('refiner_switch', 'Refiner Switch', loaded_parameter_dict, results)
     get_str('sampler', 'Sampler', loaded_parameter_dict, results)
     get_str('scheduler', 'Scheduler', loaded_parameter_dict, results)
+    get_str('vae', 'VAE', loaded_parameter_dict, results)
     get_seed('seed', 'Seed', loaded_parameter_dict, results)
 
     if is_generating:
@@ -92,13 +93,25 @@ def get_float(key: str, fallback: str | None, source_dict: dict, results: list,
         results.append(gr.update())
 
 
+def get_image_number(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
+    try:
+        h = source_dict.get(key, source_dict.get(fallback, default))
+        assert h is not None
+        h = int(h)
+        h = min(h, modules.config.default_max_image_number)
+        results.append(h)
+    except:
+        results.append(1)
+
+
 def get_steps(key: str, fallback: str | None, source_dict: dict, results: list, default=None):
     try:
         h = source_dict.get(key, source_dict.get(fallback, default))
         assert h is not None
         h = int(h)
         # if not in steps or in steps and performance is not the same
-        if h not in iter(Steps) or Steps(h).name.casefold() != source_dict.get('performance', '').replace(' ', '_').casefold():
+        if h not in iter(Steps) or Steps(h).name.casefold() != source_dict.get('performance', '').replace(' ',
+                                                                                                          '_').casefold():
             results.append(h)
             return
         results.append(-1)
@@ -117,8 +130,8 @@ def get_resolution(key: str, fallback: str | None, source_dict: dict, results: l
             results.append(-1)
         else:
             results.append(gr.update())
-            results.append(width)
+            results.append(int(width))
-            results.append(height)
+            results.append(int(height))
     except:
         results.append(gr.update())
         results.append(gr.update())
@@ -169,11 +182,20 @@ def get_freeu(key: str, fallback: str | None, source_dict: dict, results: list,
 
 def get_lora(key: str, fallback: str | None, source_dict: dict, results: list):
     try:
-        n, w = source_dict.get(key, source_dict.get(fallback)).split(' : ')
+        split_data = source_dict.get(key, source_dict.get(fallback)).split(' : ')
-        w = float(w)
+        enabled = True
-        results.append(True)
+        name = split_data[0]
-        results.append(n)
+        weight = split_data[1]
-        results.append(w)
+
+        if len(split_data) == 3:
+            enabled = split_data[0] == 'True'
+            name = split_data[1]
+            weight = split_data[2]
+
+        weight = float(weight)
+        results.append(enabled)
+        results.append(name)
+        results.append(weight)
     except:
         results.append(True)
         results.append('None')
@@ -183,7 +205,8 @@ def get_lora(key: str, fallback: str | None, source_dict: dict, results: list):
 def get_sha256(filepath):
     global hash_cache
     if filepath not in hash_cache:
-        hash_cache[filepath] = calculate_sha256(filepath)
+        # is_safetensors = os.path.splitext(filepath)[1].lower() == '.safetensors'
+        hash_cache[filepath] = sha256(filepath)
 
     return hash_cache[filepath]
 
@@ -231,6 +254,7 @@ class MetadataParser(ABC):
         self.refiner_model_name: str = ''
         self.refiner_model_hash: str = ''
         self.loras: list = []
+        self.vae_name: str = ''
 
     @abstractmethod
     def get_scheme(self) -> MetadataScheme:
@@ -245,7 +269,7 @@ class MetadataParser(ABC):
         raise NotImplementedError
 
     def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name,
-                 refiner_model_name, loras):
+                 refiner_model_name, loras, vae_name):
         self.raw_prompt = raw_prompt
         self.full_prompt = full_prompt
         self.raw_negative_prompt = raw_negative_prompt
@@ -267,6 +291,13 @@ class MetadataParser(ABC):
                 lora_path = get_file_from_folder_list(lora_name, modules.config.paths_loras)
                 lora_hash = get_sha256(lora_path)
                 self.loras.append((Path(lora_name).stem, lora_weight, lora_hash))
+        self.vae_name = Path(vae_name).stem
+
+    @staticmethod
+    def remove_special_loras(lora_filenames):
+        for lora_to_remove in modules.config.loras_metadata_remove:
+            if lora_to_remove in lora_filenames:
+                lora_filenames.remove(lora_to_remove)
 
 
 class A1111MetadataParser(MetadataParser):
@@ -282,6 +313,7 @@ class A1111MetadataParser(MetadataParser):
         'steps': 'Steps',
         'sampler': 'Sampler',
         'scheduler': 'Scheduler',
+        'vae': 'VAE',
         'guidance_scale': 'CFG scale',
         'seed': 'Seed',
         'resolution': 'Size',
@@ -369,20 +401,26 @@ class A1111MetadataParser(MetadataParser):
                     data['sampler'] = k
                     break
 
-        for key in ['base_model', 'refiner_model']:
+        for key in ['base_model', 'refiner_model', 'vae']:
             if key in data:
-                for filename in modules.config.model_filenames:
+                if key == 'vae':
-                    path = Path(filename)
+                    self.add_extension_to_filename(data, modules.config.vae_filenames, 'vae')
-                    if data[key] == path.stem:
+                else:
-                        data[key] = filename
+                    self.add_extension_to_filename(data, modules.config.model_filenames, key)
-                        break
 
-        if 'lora_hashes' in data:
+        lora_data = ''
+        if 'lora_weights' in data and data['lora_weights'] != '':
+            lora_data = data['lora_weights']
+        elif 'lora_hashes' in data and data['lora_hashes'] != '' and data['lora_hashes'].split(', ')[0].count(':') == 2:
+            lora_data = data['lora_hashes']
+
+        if lora_data != '':
             lora_filenames = modules.config.lora_filenames.copy()
-            if modules.config.sdxl_lcm_lora in lora_filenames:
+            self.remove_special_loras(lora_filenames)
-                lora_filenames.remove(modules.config.sdxl_lcm_lora)
+            for li, lora in enumerate(lora_data.split(', ')):
-            for li, lora in enumerate(data['lora_hashes'].split(', ')):
+                lora_split = lora.split(': ')
-                lora_name, lora_hash, lora_weight = lora.split(': ')
+                lora_name = lora_split[0]
+                lora_weight = lora_split[2] if len(lora_split) == 3 else lora_split[1]
                 for filename in lora_filenames:
                     path = Path(filename)
                     if lora_name == path.stem:
@@ -398,6 +436,7 @@ class A1111MetadataParser(MetadataParser):
 
         sampler = data['sampler']
         scheduler = data['scheduler']
+
         if sampler in SAMPLERS and SAMPLERS[sampler] != '':
             sampler = SAMPLERS[sampler]
             if sampler not in CIVITAI_NO_KARRAS and scheduler == 'karras':
@@ -416,6 +455,7 @@ class A1111MetadataParser(MetadataParser):
 
             self.fooocus_to_a1111['performance']: data['performance'],
             self.fooocus_to_a1111['scheduler']: scheduler,
+            self.fooocus_to_a1111['vae']: Path(data['vae']).stem,
             # workaround for multiline prompts
             self.fooocus_to_a1111['raw_prompt']: self.raw_prompt,
             self.fooocus_to_a1111['raw_negative_prompt']: self.raw_negative_prompt,
@@ -431,16 +471,19 @@ class A1111MetadataParser(MetadataParser):
             if key in data:
                 generation_params[self.fooocus_to_a1111[key]] = data[key]
 
+        if len(self.loras) > 0:
             lora_hashes = []
+            lora_weights = []
             for index, (lora_name, lora_weight, lora_hash) in enumerate(self.loras):
                 # workaround for Fooocus not knowing LoRA name in LoRA metadata
-            lora_hashes.append(f'{lora_name}: {lora_hash}: {lora_weight}')
+                lora_hashes.append(f'{lora_name}: {lora_hash}')
+                lora_weights.append(f'{lora_name}: {lora_weight}')
             lora_hashes_string = ', '.join(lora_hashes)
+            lora_weights_string = ', '.join(lora_weights)
+            generation_params[self.fooocus_to_a1111['lora_hashes']] = lora_hashes_string
+            generation_params[self.fooocus_to_a1111['lora_weights']] = lora_weights_string
 
-        generation_params |= {
+        generation_params[self.fooocus_to_a1111['version']] = data['version']
-            self.fooocus_to_a1111['lora_hashes']: lora_hashes_string,
-            self.fooocus_to_a1111['version']: data['version']
-        }
 
         if modules.config.metadata_created_by != '':
             generation_params[self.fooocus_to_a1111['created_by']] = modules.config.metadata_created_by
@@ -453,6 +496,14 @@ class A1111MetadataParser(MetadataParser):
         negative_prompt_text = f"\nNegative prompt: {negative_prompt_resolved}" if negative_prompt_resolved else ""
         return f"{positive_prompt_resolved}{negative_prompt_text}\n{generation_params_text}".strip()
 
+    @staticmethod
+    def add_extension_to_filename(data, filenames, key):
+        for filename in filenames:
+            path = Path(filename)
+            if data[key] == path.stem:
+                data[key] = filename
+                break
+
 
 class FooocusMetadataParser(MetadataParser):
     def get_scheme(self) -> MetadataScheme:
@@ -461,9 +512,8 @@ class FooocusMetadataParser(MetadataParser):
     def parse_json(self, metadata: dict) -> dict:
         model_filenames = modules.config.model_filenames.copy()
         lora_filenames = modules.config.lora_filenames.copy()
-        if modules.config.sdxl_lcm_lora in lora_filenames:
+        vae_filenames = modules.config.vae_filenames.copy()
-            lora_filenames.remove(modules.config.sdxl_lcm_lora)
+        self.remove_special_loras(lora_filenames)
-
         for key, value in metadata.items():
             if value in ['', 'None']:
                 continue
@@ -471,6 +521,8 @@ class FooocusMetadataParser(MetadataParser):
                 metadata[key] = self.replace_value_with_filename(key, value, model_filenames)
             elif key.startswith('lora_combined_'):
                 metadata[key] = self.replace_value_with_filename(key, value, lora_filenames)
+            elif key == 'vae':
+                metadata[key] = self.replace_value_with_filename(key, value, vae_filenames)
             else:
                 continue
 
@@ -497,6 +549,7 @@ class FooocusMetadataParser(MetadataParser):
             res['refiner_model'] = self.refiner_model_name
             res['refiner_model_hash'] = self.refiner_model_hash
 
+        res['vae'] = self.vae_name
         res['loras'] = self.loras
 
         if modules.config.metadata_created_by != '':

modules/private_logger.py

@@ -6,8 +6,9 @@ import urllib.parse
 
 from PIL import Image
 from PIL.PngImagePlugin import PngInfo
-from modules.util import generate_temp_filename
+from modules.flags import OutputFormat
 from modules.meta_parser import MetadataParser, get_exif
+from modules.util import generate_temp_filename
 
 log_cache = {}
 
@@ -21,7 +22,7 @@ def get_current_html_path(output_format=None):
 
 
 def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None) -> str:
-    path_outputs = args_manager.args.temp_path if args_manager.args.disable_image_log else modules.config.path_outputs
+    path_outputs = modules.config.temp_path if args_manager.args.disable_image_log else modules.config.path_outputs
     output_format = output_format if output_format else modules.config.default_output_format
     date_string, local_temp_filename, only_name = generate_temp_filename(folder=path_outputs, extension=output_format)
     os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True)
@@ -29,7 +30,7 @@ def log(img, metadata, metadata_parser: MetadataParser | None = None, output_for
     parsed_parameters = metadata_parser.parse_string(metadata.copy()) if metadata_parser is not None else ''
     image = Image.fromarray(img)
 
-    if output_format == 'png':
+    if output_format == OutputFormat.PNG.value:
         if parsed_parameters != '':
             pnginfo = PngInfo()
             pnginfo.add_text('parameters', parsed_parameters)
@@ -37,9 +38,9 @@ def log(img, metadata, metadata_parser: MetadataParser | None = None, output_for
         else:
             pnginfo = None
         image.save(local_temp_filename, pnginfo=pnginfo)
-    elif output_format == 'jpg':
+    elif output_format == OutputFormat.JPEG.value:
         image.save(local_temp_filename, quality=95, optimize=True, progressive=True, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif())
-    elif output_format == 'webp':
+    elif output_format == OutputFormat.WEBP.value:
         image.save(local_temp_filename, quality=95, lossless=False, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif())
     else:
         image.save(local_temp_filename)

modules/sdxl_styles.py

@@ -2,13 +2,12 @@ import os
 import re
 import json
 import math
+import modules.config
 
 from modules.util import get_files_from_folder
 
-
 # cannot use modules.config - validators causing circular imports
 styles_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../sdxl_styles/'))
-wildcards_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../wildcards/'))
 wildcards_max_bfs_depth = 64
 
 
@@ -60,7 +59,7 @@ def apply_style(style, positive):
     return p.replace('{prompt}', positive).splitlines(), n.splitlines()
 
 
-def apply_wildcards(wildcard_text, rng, directory=wildcards_path):
+def apply_wildcards(wildcard_text, rng, i, read_wildcards_in_order):
     for _ in range(wildcards_max_bfs_depth):
         placeholders = re.findall(r'__([\w-]+)__', wildcard_text)
         if len(placeholders) == 0:
@@ -69,9 +68,13 @@ def apply_wildcards(wildcard_text, rng, directory=wildcards_path):
         print(f'[Wildcards] processing: {wildcard_text}')
         for placeholder in placeholders:
             try:
-                words = open(os.path.join(directory, f'{placeholder}.txt'), encoding='utf-8').read().splitlines()
+                matches = [x for x in modules.config.wildcard_filenames if os.path.splitext(os.path.basename(x))[0] == placeholder]
+                words = open(os.path.join(modules.config.path_wildcards, matches[0]), encoding='utf-8').read().splitlines()
                 words = [x for x in words if x != '']
                 assert len(words) > 0
+                if read_wildcards_in_order:
+                    wildcard_text = wildcard_text.replace(f'__{placeholder}__', words[i % len(words)], 1)
+                else:
                     wildcard_text = wildcard_text.replace(f'__{placeholder}__', rng.choice(words), 1)
             except:
                 print(f'[Wildcards] Warning: {placeholder}.txt missing or empty. '
@@ -82,8 +85,9 @@ def apply_wildcards(wildcard_text, rng, directory=wildcards_path):
     print(f'[Wildcards] BFS stack overflow. Current text: {wildcard_text}')
     return wildcard_text
 
+
 def get_words(arrays, totalMult, index):
-    if(len(arrays) == 1):
+    if len(arrays) == 1:
         return [arrays[0].split(',')[index]]
     else:
         words = arrays[0].split(',')

modules/util.py

@@ -7,9 +7,9 @@ import math
 import os
 import cv2
 import json
+import hashlib
 
 from PIL import Image
-from hashlib import sha256
 
 import modules.sdxl_styles
 
@@ -163,7 +163,7 @@ def generate_temp_filename(folder='./outputs/', extension='png'):
     return date_string, os.path.abspath(result), filename
 
 
-def get_files_from_folder(folder_path, exensions=None, name_filter=None):
+def get_files_from_folder(folder_path, extensions=None, name_filter=None):
     if not os.path.isdir(folder_path):
         raise ValueError("Folder path is not a valid directory.")
 
@@ -175,23 +175,51 @@ def get_files_from_folder(folder_path, exensions=None, name_filter=None):
             relative_path = ""
         for filename in sorted(files, key=lambda s: s.casefold()):
             _, file_extension = os.path.splitext(filename)
-            if (exensions is None or file_extension.lower() in exensions) and (name_filter is None or name_filter in _):
+            if (extensions is None or file_extension.lower() in extensions) and (name_filter is None or name_filter in _):
                 path = os.path.join(relative_path, filename)
                 filenames.append(path)
 
     return filenames
 
 
-def calculate_sha256(filename, length=HASH_SHA256_LENGTH) -> str:
+def sha256(filename, use_addnet_hash=False, length=HASH_SHA256_LENGTH):
-    hash_sha256 = sha256()
+    print(f"Calculating sha256 for {filename}: ", end='')
+    if use_addnet_hash:
+        with open(filename, "rb") as file:
+            sha256_value = addnet_hash_safetensors(file)
+    else:
+        sha256_value = calculate_sha256(filename)
+    print(f"{sha256_value}")
+
+    return sha256_value[:length] if length is not None else sha256_value
+
+
+def addnet_hash_safetensors(b):
+    """kohya-ss hash for safetensors from https://github.com/kohya-ss/sd-scripts/blob/main/library/train_util.py"""
+    hash_sha256 = hashlib.sha256()
+    blksize = 1024 * 1024
+
+    b.seek(0)
+    header = b.read(8)
+    n = int.from_bytes(header, "little")
+
+    offset = n + 8
+    b.seek(offset)
+    for chunk in iter(lambda: b.read(blksize), b""):
+        hash_sha256.update(chunk)
+
+    return hash_sha256.hexdigest()
+
+
+def calculate_sha256(filename) -> str:
+    hash_sha256 = hashlib.sha256()
     blksize = 1024 * 1024
 
     with open(filename, "rb") as f:
         for chunk in iter(lambda: f.read(blksize), b""):
             hash_sha256.update(chunk)
 
-    res = hash_sha256.hexdigest()
+    return hash_sha256.hexdigest()
-    return res[:length] if length else res
 
 
 def quote(text):
@@ -343,6 +371,9 @@ def is_json(data: str) -> bool:
 
 
 def get_file_from_folder_list(name, folders):
+    if not isinstance(folders, list):
+        folders = [folders]
+
     for folder in folders:
         filename = os.path.abspath(os.path.realpath(os.path.join(folder, name)))
         if os.path.isfile(filename):
@@ -360,3 +391,7 @@ def makedirs_with_log(path):
         os.makedirs(path, exist_ok=True)
     except OSError as error:
         print(f'Directory {path} could not be created, reason: {error}')
+
+
+def get_enabled_loras(loras: list) -> list:
+    return [[lora[1], lora[2]] for lora in loras if lora[0]]

presets/.gitignore

@@ -0,0 +1,6 @@
+*.json
+!anime.json
+!default.json
+!lcm.json
+!realistic.json
+!sai.json

presets/anime.json

@@ -4,22 +4,27 @@
     "default_refiner_switch": 0.5,
     "default_loras": [
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ]
@@ -29,11 +34,11 @@
     "default_sampler": "dpmpp_2m_sde_gpu",
     "default_scheduler": "karras",
     "default_performance": "Speed",
-    "default_prompt": "1girl, ",
+    "default_prompt": "",
     "default_prompt_negative": "",
     "default_styles": [
         "Fooocus V2",
-        "Fooocus Negative",
+        "Fooocus Semi Realistic",
         "Fooocus Masterpiece"
     ],
     "default_aspect_ratio": "896*1152",

presets/default.json

@@ -4,22 +4,27 @@
     "default_refiner_switch": 0.5,
     "default_loras": [
         [
+            true,
             "sd_xl_offset_example-lora_1.0.safetensors",
             0.1
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ]

presets/lcm.json

@@ -4,22 +4,27 @@
     "default_refiner_switch": 0.5,
     "default_loras": [
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ]

presets/lightning.json

@@ -0,0 +1,57 @@
+{
+    "default_model": "juggernautXL_v8Rundiffusion.safetensors",
+    "default_refiner": "None",
+    "default_refiner_switch": 0.5,
+    "default_loras": [
+        [
+            true,
+            "None",
+            1.0
+        ],
+        [
+            true,
+            "None",
+            1.0
+        ],
+        [
+            true,
+            "None",
+            1.0
+        ],
+        [
+            true,
+            "None",
+            1.0
+        ],
+        [
+            true,
+            "None",
+            1.0
+        ]
+    ],
+    "default_cfg_scale": 4.0,
+    "default_sample_sharpness": 2.0,
+    "default_sampler": "dpmpp_2m_sde_gpu",
+    "default_scheduler": "karras",
+    "default_performance": "Lightning",
+    "default_prompt": "",
+    "default_prompt_negative": "",
+    "default_styles": [
+        "Fooocus V2",
+        "Fooocus Enhance",
+        "Fooocus Sharp"
+    ],
+    "default_aspect_ratio": "1152*896",
+    "checkpoint_downloads": {
+        "juggernautXL_v8Rundiffusion.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/juggernautXL_v8Rundiffusion.safetensors"
+    },
+    "embeddings_downloads": {},
+    "lora_downloads": {},
+    "previous_default_models": [
+        "juggernautXL_version8Rundiffusion.safetensors",
+        "juggernautXL_version7Rundiffusion.safetensors",
+        "juggernautXL_v7Rundiffusion.safetensors",
+        "juggernautXL_version6Rundiffusion.safetensors",
+        "juggernautXL_v6Rundiffusion.safetensors"
+    ]
+}

presets/realistic.json

@@ -1,25 +1,30 @@
 {
     "default_model": "realisticStockPhoto_v20.safetensors",
-    "default_refiner": "",
+    "default_refiner": "None",
     "default_refiner_switch": 0.5,
     "default_loras": [
         [
+            true,
             "SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors",
             0.25
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ]

presets/sai.json

@@ -4,22 +4,27 @@
     "default_refiner_switch": 0.75,
     "default_loras": [
         [
+            true,
             "sd_xl_offset_example-lora_1.0.safetensors",
             0.5
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ],
         [
+            true,
             "None",
             1.0
         ]

readme.md

@@ -84,6 +84,10 @@ The first time you launch the software, it will automatically download models:
 
 After Fooocus 2.1.60, you will also have `run_anime.bat` and `run_realistic.bat`. They are different model presets (and require different models, but they will be automatically downloaded). [Check here for more details](https://github.com/lllyasviel/Fooocus/discussions/679).
 
+After Fooocus 2.3.0 you can also switch presets directly in the browser. Keep in mind to add these arguments if you want to change the default behavior:
+* Use `--disable-preset-selection` to disable preset selection in the browser.
+* Use `--always-download-new-model` to download missing models on preset switch. Default is fallback to `previous_default_models` defined in the corresponding preset, also see terminal output.
+
 ![image](https://github.com/lllyasviel/Fooocus/assets/19834515/d386f817-4bd7-490c-ad89-c1e228c23447)
 
 If you already have these files, you can copy them to the above locations to speed up installation.
@@ -115,17 +119,21 @@ See also the common problems and troubleshoots [here](troubleshoot.md).
 
 ### Colab
 
-(Last tested - 2023 Dec 12)
+(Last tested - 2024 Mar 18 by [mashb1t](https://github.com/mashb1t))
 
 | Colab | Info
 | --- | --- |
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lllyasviel/Fooocus/blob/main/fooocus_colab.ipynb) | Fooocus Official
 
-In Colab, you can modify the last line to `!python entry_with_update.py --share` or `!python entry_with_update.py --preset anime --share` or `!python entry_with_update.py --preset realistic --share` for Fooocus Default/Anime/Realistic Edition.
+In Colab, you can modify the last line to `!python entry_with_update.py --share --always-high-vram` or `!python entry_with_update.py --share --always-high-vram --preset anime` or `!python entry_with_update.py --share --always-high-vram --preset realistic` for Fooocus Default/Anime/Realistic Edition.
+
+You can also change the preset in the UI. Please be aware that this may lead to timeouts after 60 seconds. If this is the case, please wait until the download has finished, change the preset to initial and back to the one you've selected or reload the page.
 
 Note that this Colab will disable refiner by default because Colab free's resources are relatively limited (and some "big" features like image prompt may cause free-tier Colab to disconnect). We make sure that basic text-to-image is always working on free-tier Colab.
 
-Thanks to [camenduru](https://github.com/camenduru)!
+Using `--always-high-vram` shifts resource allocation from RAM to VRAM and achieves the overall best balance between performance, flexibility and stability on the default T4 instance. Please find more information [here](https://github.com/lllyasviel/Fooocus/pull/1710#issuecomment-1989185346).
+
+Thanks to [camenduru](https://github.com/camenduru) for the template!
 
 ### Linux (Using Anaconda)
 

sdxl_styles/sdxl_styles_fooocus.json

@@ -3,6 +3,10 @@
         "name": "Fooocus Enhance",
         "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)"
     },
+    {
+        "name": "Fooocus Semi Realistic",
+        "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)"
+    },
     {
         "name": "Fooocus Sharp",
         "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous, film grain, grainy",

update_log.md

@@ -1,3 +1,21 @@
+# [2.3.1](https://github.com/lllyasviel/Fooocus/releases/tag/2.3.1)
+
+* Remove positive prompt from anime prefix to not reset prompt after switching presets
+* Fix image number being reset to 1 when switching preset, now doesn't reset anymore
+* Fix outpainting dimension calculation when extending left/right
+* Fix LoRA compatibility for LoRAs in a1111 metadata scheme
+
+# [2.3.0](https://github.com/lllyasviel/Fooocus/releases/tag/2.3.0)
+
+* Add performance "lightning" (based on [SDXL-Lightning 4 step LoRA](https://huggingface.co/ByteDance/SDXL-Lightning/blob/main/sdxl_lightning_4step_lora.safetensors))
+* Add preset selection to UI, disable with argument `--disable-preset-selection`. Use `--always-download-new-model` to download missing models on preset switch.
+* Improve face swap consistency by switching later in the process to (synthetic) refiner
+* Add temp path cleanup on startup
+* Add support for wildcard subdirectories
+* Add scrollable 2 column layout for styles for better structure
+* Improve Colab resource needs for T4 instances (default), positively tested with all image prompt features
+* Improve anime preset, now uses style `Fooocus Semi Realistic` instead of `Fooocus Negative` (less wet look images)
+
 # [2.2.1](https://github.com/lllyasviel/Fooocus/releases/tag/2.2.1)
 
 * Fix some small bugs (e.g. image grid, upscale fast 2x, LoRA weight width in Firefox)

webui.py

@@ -15,6 +15,7 @@ import modules.style_sorter as style_sorter
 import modules.meta_parser
 import args_manager
 import copy
+import launch
 
 from modules.sdxl_styles import legal_style_names
 from modules.private_logger import get_current_html_path
@@ -28,12 +29,16 @@ def get_task(*args):
 
     return worker.AsyncTask(args=args)
 
-def generate_clicked(task):
+def generate_clicked(task: worker.AsyncTask):
     import ldm_patched.modules.model_management as model_management
 
     with model_management.interrupt_processing_mutex:
         model_management.interrupt_processing = False
     # outputs=[progress_html, progress_window, progress_gallery, gallery]
+
+    if len(task.args) == 0:
+        return
+
     execution_start_time = time.perf_counter()
     finished = False
 
@@ -76,6 +81,7 @@ def generate_clicked(task):
                 # delete Fooocus temp images, only keep gradio temp images
                 if args_manager.args.disable_image_log:
                     for filepath in product:
+                        if isinstance(filepath, str) and os.path.exists(filepath):
                             os.remove(filepath)
 
     execution_time = time.perf_counter() - execution_start_time
@@ -90,9 +96,7 @@ title = f'Fooocus {fooocus_version.version}'
 if isinstance(args_manager.args.preset, str):
     title += ' ' + args_manager.args.preset
 
-shared.gradio_root = gr.Blocks(
+shared.gradio_root = gr.Blocks(title=title).queue()
-    title=title,
-    css=modules.html.css).queue()
 
 with shared.gradio_root:
     currentTask = gr.State(worker.AsyncTask(args=[]))
@@ -253,8 +257,13 @@ with shared.gradio_root:
 
         with gr.Column(scale=1, visible=modules.config.default_advanced_checkbox) as advanced_column:
             with gr.Tab(label='Setting'):
+                if not args_manager.args.disable_preset_selection:
+                    preset_selection = gr.Radio(label='Preset',
+                                                choices=modules.config.available_presets,
+                                                value=args_manager.args.preset if args_manager.args.preset else "initial",
+                                                interactive=True)
                 performance_selection = gr.Radio(label='Performance',
-                                                 choices=modules.flags.performance_selections,
+                                                 choices=flags.Performance.list(),
                                                  value=modules.config.default_performance)
                 aspect_ratios_selection = gr.Radio(label='Aspect Ratios', choices=modules.config.available_aspect_ratios,
                                                    value=modules.config.default_aspect_ratio, info='width × height',
@@ -262,7 +271,7 @@ with shared.gradio_root:
                 image_number = gr.Slider(label='Image Number', minimum=1, maximum=modules.config.default_max_image_number, step=1, value=modules.config.default_image_number)
 
                 output_format = gr.Radio(label='Output Format',
-                                            choices=modules.flags.output_formats,
+                                            choices=flags.OutputFormat.list(),
                                             value=modules.config.default_output_format)
 
                 negative_prompt = gr.Textbox(label='Negative Prompt', show_label=True, placeholder="Type prompt here.",
@@ -299,7 +308,7 @@ with shared.gradio_root:
                 history_link = gr.HTML()
                 shared.gradio_root.load(update_history_link, outputs=history_link, queue=False, show_progress=False)
 
-            with gr.Tab(label='Style'):
+            with gr.Tab(label='Style', elem_classes=['style_selections_tab']):
                 style_sorter.try_load_sorted_styles(
                     style_names=legal_style_names,
                     default_selected=modules.config.default_styles)
@@ -352,20 +361,20 @@ with shared.gradio_root:
                 with gr.Group():
                     lora_ctrls = []
 
-                    for i, (n, v) in enumerate(modules.config.default_loras):
+                    for i, (enabled, filename, weight) in enumerate(modules.config.default_loras):
                         with gr.Row():
-                            lora_enabled = gr.Checkbox(label='Enable', value=True,
+                            lora_enabled = gr.Checkbox(label='Enable', value=enabled,
                                                        elem_classes=['lora_enable', 'min_check'], scale=1)
                             lora_model = gr.Dropdown(label=f'LoRA {i + 1}',
-                                                     choices=['None'] + modules.config.lora_filenames, value=n,
+                                                     choices=['None'] + modules.config.lora_filenames, value=filename,
                                                      elem_classes='lora_model', scale=5)
                             lora_weight = gr.Slider(label='Weight', minimum=modules.config.default_loras_min_weight,
-                                                    maximum=modules.config.default_loras_max_weight, step=0.01, value=v,
+                                                    maximum=modules.config.default_loras_max_weight, step=0.01, value=weight,
                                                     elem_classes='lora_weight', scale=5)
                             lora_ctrls += [lora_enabled, lora_model, lora_weight]
 
                 with gr.Row():
-                    model_refresh = gr.Button(label='Refresh', value='\U0001f504 Refresh All Files', variant='secondary', elem_classes='refresh_button')
+                    refresh_files = gr.Button(label='Refresh', value='\U0001f504 Refresh All Files', variant='secondary', elem_classes='refresh_button')
             with gr.Tab(label='Advanced'):
                 guidance_scale = gr.Slider(label='Guidance Scale', minimum=1.0, maximum=30.0, step=0.01,
                                            value=modules.config.default_cfg_scale,
@@ -397,6 +406,8 @@ with shared.gradio_root:
                                                    value=modules.config.default_sampler)
                         scheduler_name = gr.Dropdown(label='Scheduler', choices=flags.scheduler_list,
                                                      value=modules.config.default_scheduler)
+                        vae_name = gr.Dropdown(label='VAE', choices=[modules.flags.default_vae] + modules.config.vae_filenames,
+                                                     value=modules.config.default_vae, show_label=True)
 
                         generate_image_grid = gr.Checkbox(label='Generate Image Grid for Each Batch',
                                                           info='(Experimental) This may cause performance problems on some computers and certain internet conditions.',
@@ -427,12 +438,13 @@ with shared.gradio_root:
                         disable_preview = gr.Checkbox(label='Disable Preview', value=False,
                                                       info='Disable preview during generation.')
                         disable_intermediate_results = gr.Checkbox(label='Disable Intermediate Results', 
-                                                      value=modules.config.default_performance == 'Extreme Speed',
+                                                      value=modules.config.default_performance == flags.Performance.EXTREME_SPEED.value,
-                                                      interactive=modules.config.default_performance != 'Extreme Speed',
+                                                      interactive=modules.config.default_performance != flags.Performance.EXTREME_SPEED.value,
                                                       info='Disable intermediate results during generation, only show final gallery.')
                         disable_seed_increment = gr.Checkbox(label='Disable seed increment',
                                                              info='Disable automatic seed increment when image number is > 1.',
                                                              value=False)
+                        read_wildcards_in_order = gr.Checkbox(label="Read wildcards in order", value=False)
 
                         if not args_manager.args.disable_metadata:
                             save_metadata_to_images = gr.Checkbox(label='Save Metadata to Images', value=modules.config.default_save_metadata_to_images,
@@ -511,24 +523,61 @@ with shared.gradio_root:
                 def dev_mode_checked(r):
                     return gr.update(visible=r)
 
-
                 dev_mode.change(dev_mode_checked, inputs=[dev_mode], outputs=[dev_tools],
                                 queue=False, show_progress=False)
 
-                def model_refresh_clicked():
+                def refresh_files_clicked():
-                    modules.config.update_all_model_names()
+                    modules.config.update_files()
                     results = [gr.update(choices=modules.config.model_filenames)]
                     results += [gr.update(choices=['None'] + modules.config.model_filenames)]
+                    results += [gr.update(choices=['None'] + modules.config.vae_filenames)]
+                    if not args_manager.args.disable_preset_selection:
+                        results += [gr.update(choices=modules.config.available_presets)]
                     for i in range(modules.config.default_max_lora_number):
-                        results += [gr.update(interactive=True), gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]
+                        results += [gr.update(interactive=True),
+                                    gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]
                     return results
 
-                model_refresh.click(model_refresh_clicked, [], [base_model, refiner_model] + lora_ctrls,
+                refresh_files_output = [base_model, refiner_model, vae_name]
+                if not args_manager.args.disable_preset_selection:
+                    refresh_files_output += [preset_selection]
+                refresh_files.click(refresh_files_clicked, [], refresh_files_output + lora_ctrls,
                                     queue=False, show_progress=False)
 
-        performance_selection.change(lambda x: [gr.update(interactive=x != 'Extreme Speed')] * 11 +
+        state_is_generating = gr.State(False)
-                                               [gr.update(visible=x != 'Extreme Speed')] * 1 +
+
-                                               [gr.update(interactive=x != 'Extreme Speed', value=x == 'Extreme Speed', )] * 1,
+        load_data_outputs = [advanced_checkbox, image_number, prompt, negative_prompt, style_selections,
+                             performance_selection, overwrite_step, overwrite_switch, aspect_ratios_selection,
+                             overwrite_width, overwrite_height, guidance_scale, sharpness, adm_scaler_positive,
+                             adm_scaler_negative, adm_scaler_end, refiner_swap_method, adaptive_cfg, base_model,
+                             refiner_model, refiner_switch, sampler_name, scheduler_name, vae_name, seed_random,
+                             image_seed, generate_button, load_parameter_button] + freeu_ctrls + lora_ctrls
+
+        if not args_manager.args.disable_preset_selection:
+            def preset_selection_change(preset, is_generating):
+                preset_content = modules.config.try_get_preset_content(preset) if preset != 'initial' else {}
+                preset_prepared = modules.meta_parser.parse_meta_from_preset(preset_content)
+
+                default_model = preset_prepared.get('base_model')
+                previous_default_models = preset_prepared.get('previous_default_models', [])
+                checkpoint_downloads = preset_prepared.get('checkpoint_downloads', {})
+                embeddings_downloads = preset_prepared.get('embeddings_downloads', {})
+                lora_downloads = preset_prepared.get('lora_downloads', {})
+
+                preset_prepared['base_model'], preset_prepared['lora_downloads'] = launch.download_models(
+                    default_model, previous_default_models, checkpoint_downloads, embeddings_downloads, lora_downloads)
+
+                if 'prompt' in preset_prepared and preset_prepared.get('prompt') == '':
+                    del preset_prepared['prompt']
+
+                return modules.meta_parser.load_parameter_button_click(json.dumps(preset_prepared), is_generating)
+
+            preset_selection.change(preset_selection_change, inputs=[preset_selection, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=True) \
+                .then(fn=style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) \
+
+        performance_selection.change(lambda x: [gr.update(interactive=not flags.Performance.has_restricted_features(x))] * 11 +
+                                               [gr.update(visible=not flags.Performance.has_restricted_features(x))] * 1 +
+                                               [gr.update(interactive=not flags.Performance.has_restricted_features(x), value=flags.Performance.has_restricted_features(x))] * 1,
                                      inputs=performance_selection,
                                      outputs=[
                                          guidance_scale, sharpness, adm_scaler_end, adm_scaler_positive,
@@ -578,7 +627,8 @@ with shared.gradio_root:
         ctrls = [currentTask, generate_image_grid]
         ctrls += [
             prompt, negative_prompt, style_selections,
-            performance_selection, aspect_ratios_selection, image_number, output_format, image_seed, sharpness, guidance_scale
+            performance_selection, aspect_ratios_selection, image_number, output_format, image_seed,
+            read_wildcards_in_order, sharpness, guidance_scale
         ]
 
         ctrls += [base_model, refiner_model, refiner_switch] + lora_ctrls
@@ -587,7 +637,7 @@ with shared.gradio_root:
         ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt, inpaint_mask_image]
         ctrls += [disable_preview, disable_intermediate_results, disable_seed_increment]
         ctrls += [adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg]
-        ctrls += [sampler_name, scheduler_name]
+        ctrls += [sampler_name, scheduler_name, vae_name]
         ctrls += [overwrite_step, overwrite_switch, overwrite_width, overwrite_height, overwrite_vary_strength]
         ctrls += [overwrite_upscale_strength, mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint]
         ctrls += [debugging_cn_preprocessor, skipping_cn_preprocessor, canny_low_threshold, canny_high_threshold]
@@ -600,8 +650,6 @@ with shared.gradio_root:
 
         ctrls += ip_ctrls
 
-        state_is_generating = gr.State(False)
-
         def parse_meta(raw_prompt_txt, is_generating):
             loaded_json = None
             if is_json(raw_prompt_txt):
@@ -617,13 +665,6 @@ with shared.gradio_root:
 
         prompt.input(parse_meta, inputs=[prompt, state_is_generating], outputs=[prompt, generate_button, load_parameter_button], queue=False, show_progress=False)
 
-        load_data_outputs = [advanced_checkbox, image_number, prompt, negative_prompt, style_selections,
-                             performance_selection, overwrite_step, overwrite_switch, aspect_ratios_selection,
-                             overwrite_width, overwrite_height, guidance_scale, sharpness, adm_scaler_positive,
-                             adm_scaler_negative, adm_scaler_end, refiner_swap_method, adaptive_cfg, base_model,
-                             refiner_model, refiner_switch, sampler_name, scheduler_name, seed_random, image_seed,
-                             generate_button, load_parameter_button] + freeu_ctrls + lora_ctrls
-
         load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=False)
 
         def trigger_metadata_import(filepath, state_is_generating):
@@ -637,7 +678,6 @@ with shared.gradio_root:
 
             return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating)
 
-
         metadata_import_button.click(trigger_metadata_import, inputs=[metadata_input_image, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=True) \
             .then(style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False)