fix ownership

random seed restoring
2026-08-16 13:13:16 +02:00 · 2023-08-17 16:29:00 -07:00 · 2023-08-17 16:25:56 -07:00 · 2023-08-17 15:18:07 -07:00 · 2023-08-17 15:17:32 -07:00 · 2023-08-17 12:47:48 -07:00
23 changed files with 1918 additions and 167 deletions
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
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+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.
@@ -0,0 +1,48 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "VjYy0F2gZIPR"
+   },
+   "outputs": [],
+   "source": [
+    "%cd /content\n",
+    "!git clone https://github.com/lllyasviel/Fooocus\n",
+    "\n",
+    "!apt -y update -qq\n",
+    "!wget https://github.com/camenduru/gperftools/releases/download/v1.0/libtcmalloc_minimal.so.4 -O /content/libtcmalloc_minimal.so.4\n",
+    "%env LD_PRELOAD=/content/libtcmalloc_minimal.so.4\n",
+    "\n",
+    "!pip install torchsde==0.2.5 einops==0.4.1 transformers==4.30.2 safetensors==0.3.1 accelerate==0.21.0\n",
+    "!pip install pytorch_lightning==1.9.4 omegaconf==2.2.3 gradio==3.39.0 xformers==0.0.20 triton==2.0.0 pygit2==1.12.2\n",
+    "\n",
+    "!apt -y install -qq aria2\n",
+    "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/ckpt/sd_xl_base_1.0/resolve/main/sd_xl_base_1.0_0.9vae.safetensors -d /content/Fooocus/models/checkpoints -o sd_xl_base_1.0_0.9vae.safetensors\n",
+    "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/ckpt/sd_xl_refiner_1.0/resolve/main/sd_xl_refiner_1.0_0.9vae.safetensors -d /content/Fooocus/models/checkpoints -o sd_xl_refiner_1.0_0.9vae.safetensors\n",
+    "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors -d /content/Fooocus/models/loras -o sd_xl_offset_example-lora_1.0.safetensors\n",
+    "\n",
+    "%cd /content/Fooocus\n",
+    "!git pull\n",
+    "!python launch.py --share\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
@@ -9,6 +9,7 @@ os.chdir(root)

 try:
    import pygit2
+    pygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0)

    repo = pygit2.Repository(os.path.abspath(os.path.dirname(__file__)))

@@ -1,2 +1 @@
-version = '1.0.11'
-
+version = '1.0.35'
@@ -52,10 +52,10 @@ def prepare_environment():


 model_filenames = [
-    ('sd_xl_base_1.0.safetensors',
-     'https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors'),
-    ('sd_xl_refiner_1.0.safetensors',
-     'https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0.safetensors')
+    ('sd_xl_base_1.0_0.9vae.safetensors',
+     'https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0_0.9vae.safetensors'),
+    ('sd_xl_refiner_1.0_0.9vae.safetensors',
+     'https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0_0.9vae.safetensors')
 ]

 lora_filenames = [
@@ -72,9 +72,16 @@ def download_models():
    return


+def cuda_malloc():
+    argv = sys.argv
+    sys.argv = [sys.argv[0]]
+    import cuda_malloc
+    sys.argv = argv
+
+
 prepare_environment()

-import cuda_malloc
+cuda_malloc()

 download_models()

@@ -1,33 +0,0 @@
-import torch
-import comfy.model_base
-
-
-def sdxl_encode_adm_patched(self, **kwargs):
-    clip_pooled = kwargs["pooled_output"]
-    width = kwargs.get("width", 768)
-    height = kwargs.get("height", 768)
-    crop_w = kwargs.get("crop_w", 0)
-    crop_h = kwargs.get("crop_h", 0)
-    target_width = kwargs.get("target_width", width)
-    target_height = kwargs.get("target_height", height)
-
-    if kwargs.get("prompt_type", "") == "negative":
-        width *= 0.8
-        height *= 0.8
-    elif kwargs.get("prompt_type", "") == "positive":
-        width *= 1.5
-        height *= 1.5
-
-    out = []
-    out.append(self.embedder(torch.Tensor([height])))
-    out.append(self.embedder(torch.Tensor([width])))
-    out.append(self.embedder(torch.Tensor([crop_h])))
-    out.append(self.embedder(torch.Tensor([crop_w])))
-    out.append(self.embedder(torch.Tensor([target_height])))
-    out.append(self.embedder(torch.Tensor([target_width])))
-    flat = torch.flatten(torch.cat(out))[None, ]
-    return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
-
-
-def patch_negative_adm():
-    comfy.model_base.SDXL.encode_adm = sdxl_encode_adm_patched
@@ -0,0 +1,108 @@
+import threading
+
+
+buffer = []
+outputs = []
+
+
+def worker():
+    global buffer, outputs
+
+    import time
+    import shared
+    import random
+    import modules.default_pipeline as pipeline
+    import modules.path
+    import modules.patch
+
+    from modules.sdxl_styles import apply_style, aspect_ratios
+    from modules.private_logger import log
+
+    try:
+        async_gradio_app = shared.gradio_root
+        flag = f'''App started successful. Use the app with {str(async_gradio_app.local_url)} or {str(async_gradio_app.server_name)}:{str(async_gradio_app.server_port)}'''
+        if async_gradio_app.share:
+            flag += f''' or {async_gradio_app.share_url}'''
+        print(flag)
+    except Exception as e:
+        print(e)
+
+    def handler(task):
+        prompt, negative_prompt, style_selction, performance_selction, \
+        aspect_ratios_selction, image_number, image_seed, sharpness, base_model_name, refiner_model_name, \
+        l1, w1, l2, w2, l3, w3, l4, w4, l5, w5 = task
+
+        loras = [(l1, w1), (l2, w2), (l3, w3), (l4, w4), (l5, w5)]
+
+        modules.patch.sharpness = sharpness
+
+        pipeline.refresh_base_model(base_model_name)
+        pipeline.refresh_refiner_model(refiner_model_name)
+        pipeline.refresh_loras(loras)
+        pipeline.clean_prompt_cond_caches()
+
+        p_txt, n_txt = apply_style(style_selction, prompt, negative_prompt)
+
+        if performance_selction == 'Speed':
+            steps = 30
+            switch = 20
+        else:
+            steps = 60
+            switch = 40
+
+        width, height = aspect_ratios[aspect_ratios_selction]
+
+        results = []
+        seed = image_seed
+        max_seed = int(1024*1024*1024)
+
+        if not isinstance(seed, int):
+            seed = random.randint(1, max_seed)
+        if seed < 0:
+            seed = - seed
+        seed = seed % max_seed
+
+        all_steps = steps * image_number
+
+        def callback(step, x0, x, total_steps, y):
+            done_steps = i * steps + step
+            outputs.append(['preview', (
+                int(100.0 * float(done_steps) / float(all_steps)),
+                f'Step {step}/{total_steps} in the {i}-th Sampling',
+                y)])
+
+        for i in range(image_number):
+            imgs = pipeline.process(p_txt, n_txt, steps, switch, width, height, seed, callback=callback)
+
+            for x in imgs:
+                d = [
+                    ('Prompt', prompt),
+                    ('Negative Prompt', negative_prompt),
+                    ('Style', style_selction),
+                    ('Performance', performance_selction),
+                    ('Resolution', str((width, height))),
+                    ('Sharpness', sharpness),
+                    ('Base Model', base_model_name),
+                    ('Refiner Model', refiner_model_name),
+                    ('Seed', seed)
+                ]
+                for n, w in loras:
+                    if n != 'None':
+                        d.append((f'LoRA [{n}] weight', w))
+                log(x, d)
+
+            seed += 1
+            results += imgs
+
+        outputs.append(['results', results])
+        return
+
+    while True:
+        time.sleep(0.01)
+        if len(buffer) > 0:
+            task = buffer.pop(0)
+            handler(task)
+    pass
+
+
+threading.Thread(target=worker, daemon=True).start()
@@ -1,6 +1,5 @@
 import os
 import random
-import cv2
 import einops
 import torch
 import numpy as np
@@ -8,15 +7,14 @@ import numpy as np
 import comfy.model_management
 import comfy.utils

-from comfy.sd import load_checkpoint_guess_config, load_lora_for_models
+from comfy.sd import load_checkpoint_guess_config
 from nodes import VAEDecode, EmptyLatentImage, CLIPTextEncode
 from comfy.sample import prepare_mask, broadcast_cond, load_additional_models, cleanup_additional_models
 from modules.samplers_advanced import KSampler, KSamplerWithRefiner
-from modules.adm_patch import patch_negative_adm
-from modules.cv2win32 import show_preview
+from modules.patch import patch_all


-patch_negative_adm()
+patch_all()
 opCLIPTextEncode = CLIPTextEncode()
 opEmptyLatentImage = EmptyLatentImage()
 opVAEDecode = VAEDecode()
@@ -29,6 +27,14 @@ class StableDiffusionModel:
        self.clip = clip
        self.clip_vision = clip_vision

+    def to_meta(self):
+        if self.unet is not None:
+            self.unet.model.to('meta')
+        if self.clip is not None:
+            self.clip.cond_stage_model.to('meta')
+        if self.vae is not None:
+            self.vae.first_stage_model.to('meta')
+

@torch.no_grad()
 def load_model(ckpt_filename):
@@ -42,8 +48,8 @@ def load_lora(model, lora_filename, strength_model=1.0, strength_clip=1.0):
        return model

    lora = comfy.utils.load_torch_file(lora_filename, safe_load=True)
-    model.unet, model.clip = comfy.sd.load_lora_for_models(model.unet, model.clip, lora, strength_model, strength_clip)
-    return model
+    unet, clip = comfy.sd.load_lora_for_models(model.unet, model.clip, lora, strength_model, strength_clip)
+    return StableDiffusionModel(unet=unet, clip=clip, vae=model.vae, clip_vision=model.clip_vision)


@torch.no_grad()
@@ -78,11 +84,7 @@ def get_previewer(device, latent_format):
            x_sample = taesd.decoder(torch.nn.functional.avg_pool2d(x0, kernel_size=(2, 2))).detach() * 255.0
            x_sample = einops.rearrange(x_sample, 'b c h w -> b h w c')
            x_sample = x_sample.cpu().numpy().clip(0, 255).astype(np.uint8)
-            for i, s in enumerate(x_sample):
-                if i > 0:
-                    show_preview(f'cv2_preview_{i}', s, title=f'Preview Image {i}, step = [{step}/{total_steps}')
-                else:
-                    show_preview(f'cv2_preview_{i}', s, title=f'Preview Image, step =  {step}/{total_steps}')
+            return x_sample[0]

    taesd.preview = preview_function

@@ -92,7 +94,7 @@ def get_previewer(device, latent_format):
@torch.no_grad()
 def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu',
             scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None,
-             force_full_denoise=False):
+             force_full_denoise=False, callback_function=None):
    # SCHEDULERS = ["normal", "karras", "exponential", "simple", "ddim_uniform"]
    # SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
    #             "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
@@ -118,8 +120,11 @@ def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sa
    pbar = comfy.utils.ProgressBar(steps)

    def callback(step, x0, x, total_steps):
+        y = None
        if previewer and step % 3 == 0:
-            previewer.preview(x0, step, total_steps)
+            y = previewer.preview(x0, step, total_steps)
+        if callback_function is not None:
+            callback_function(step, x0, x, total_steps, y)
        pbar.update_absolute(step + 1, total_steps, None)

    sigmas = None
@@ -187,10 +192,11 @@ def ksampler_with_refiner(model, positive, negative, refiner, refiner_positive,
    pbar = comfy.utils.ProgressBar(steps)

    def callback(step, x0, x, total_steps):
-        if callback_function is not None:
-            callback_function(step, x0, x, total_steps)
+        y = None
        if previewer and step % 3 == 0:
-            previewer.preview(x0, step, total_steps)
+            y = previewer.preview(x0, step, total_steps)
+        if callback_function is not None:
+            callback_function(step, x0, x, total_steps, y)
        pbar.update_absolute(step + 1, total_steps, None)

    sigmas = None
@@ -1,35 +0,0 @@
-import threading
-import cv2
-
-
-buffer = []
-
-
-def worker():
-    global buffer
-    while True:
-        cv2.waitKey(50)
-        try:
-            if len(buffer) > 0:
-                task = buffer.pop(0)
-                if task is None:
-                    cv2.destroyAllWindows()
-                else:
-                    flag, img, title = task
-                    cv2.imshow(flag, img)
-                    cv2.setWindowTitle(flag, title)
-                    cv2.setWindowProperty(flag, cv2.WND_PROP_TOPMOST, 1)
-        except Exception as e:
-            print(e)
-    pass
-
-
-def show_preview(flag, img, title='preview'):
-    buffer.append((flag, img[..., ::-1].copy(), title))
-
-
-def close_all_preview():
-    buffer.append(None)
-
-
-threading.Thread(target=worker, daemon=True).start()
@@ -1,33 +1,145 @@
 import modules.core as core
 import os
 import torch
+import modules.path

-from modules.path import modelfile_path, lorafile_path
+from comfy.model_base import SDXL, SDXLRefiner


-xl_base_filename = os.path.join(modelfile_path, 'sd_xl_base_1.0.safetensors')
-xl_refiner_filename = os.path.join(modelfile_path, 'sd_xl_refiner_1.0.safetensors')
-xl_base_offset_lora_filename = os.path.join(lorafile_path, 'sd_xl_offset_example-lora_1.0.safetensors')
+xl_base: core.StableDiffusionModel = None
+xl_base_hash = ''

-xl_base = core.load_model(xl_base_filename)
-xl_base = core.load_lora(xl_base, xl_base_offset_lora_filename, strength_model=0.5, strength_clip=0.0)
-del xl_base.vae
+xl_refiner: core.StableDiffusionModel = None
+xl_refiner_hash = ''

-xl_refiner = core.load_model(xl_refiner_filename)
+xl_base_patched: core.StableDiffusionModel = None
+xl_base_patched_hash = ''
+
+
+def refresh_base_model(name):
+    global xl_base, xl_base_hash, xl_base_patched, xl_base_patched_hash
+    if xl_base_hash == str(name):
+        return
+
+    filename = os.path.join(modules.path.modelfile_path, name)
+
+    if xl_base is not None:
+        xl_base.to_meta()
+        xl_base = None
+
+    xl_base = core.load_model(filename)
+    if not isinstance(xl_base.unet.model, SDXL):
+        print('Model not supported. Fooocus only support SDXL model as the base model.')
+        xl_base = None
+        xl_base_hash = ''
+        refresh_base_model(modules.path.default_base_model_name)
+        xl_base_hash = name
+        xl_base_patched = xl_base
+        xl_base_patched_hash = ''
+        return
+
+    xl_base_hash = name
+    xl_base_patched = xl_base
+    xl_base_patched_hash = ''
+    print(f'Base model loaded: {xl_base_hash}')
+
+    return
+
+
+def refresh_refiner_model(name):
+    global xl_refiner, xl_refiner_hash
+    if xl_refiner_hash == str(name):
+        return
+
+    if name == 'None':
+        xl_refiner = None
+        xl_refiner_hash = ''
+        print(f'Refiner unloaded.')
+        return
+
+    filename = os.path.join(modules.path.modelfile_path, name)
+
+    if xl_refiner is not None:
+        xl_refiner.to_meta()
+        xl_refiner = None
+
+    xl_refiner = core.load_model(filename)
+    if not isinstance(xl_refiner.unet.model, SDXLRefiner):
+        print('Model not supported. Fooocus only support SDXL refiner as the refiner.')
+        xl_refiner = None
+        xl_refiner_hash = ''
+        print(f'Refiner unloaded.')
+        return
+
+    xl_refiner_hash = name
+    print(f'Refiner model loaded: {xl_refiner_hash}')
+
+    xl_refiner.vae.first_stage_model.to('meta')
+    xl_refiner.vae = None
+    return
+
+
+def refresh_loras(loras):
+    global xl_base, xl_base_patched, xl_base_patched_hash
+    if xl_base_patched_hash == str(loras):
+        return
+
+    model = xl_base
+    for name, weight in loras:
+        if name == 'None':
+            continue
+
+        filename = os.path.join(modules.path.lorafile_path, name)
+        model = core.load_lora(model, filename, strength_model=weight, strength_clip=weight)
+    xl_base_patched = model
+    xl_base_patched_hash = str(loras)
+    print(f'LoRAs loaded: {xl_base_patched_hash}')
+
+    return
+
+
+refresh_base_model(modules.path.default_base_model_name)
+refresh_refiner_model(modules.path.default_refiner_model_name)
+refresh_loras([(modules.path.default_lora_name, 0.5), ('None', 0.5), ('None', 0.5), ('None', 0.5), ('None', 0.5)])
+
+positive_conditions_cache = None
+negative_conditions_cache = None
+positive_conditions_refiner_cache = None
+negative_conditions_refiner_cache = None
+
+
+def clean_prompt_cond_caches():
+    global positive_conditions_cache, negative_conditions_cache, \
+        positive_conditions_refiner_cache, negative_conditions_refiner_cache
+    positive_conditions_cache = None
+    negative_conditions_cache = None
+    positive_conditions_refiner_cache = None
+    negative_conditions_refiner_cache = None
+    return


@torch.no_grad()
 def process(positive_prompt, negative_prompt, steps, switch, width, height, image_seed, callback):
-    positive_conditions = core.encode_prompt_condition(clip=xl_base.clip, prompt=positive_prompt)
-    negative_conditions = core.encode_prompt_condition(clip=xl_base.clip, prompt=negative_prompt)
+    global positive_conditions_cache, negative_conditions_cache, \
+        positive_conditions_refiner_cache, negative_conditions_refiner_cache

-    positive_conditions_refiner = core.encode_prompt_condition(clip=xl_refiner.clip, prompt=positive_prompt)
-    negative_conditions_refiner = core.encode_prompt_condition(clip=xl_refiner.clip, prompt=negative_prompt)
+    positive_conditions = core.encode_prompt_condition(clip=xl_base_patched.clip, prompt=positive_prompt) if positive_conditions_cache is None else positive_conditions_cache
+    negative_conditions = core.encode_prompt_condition(clip=xl_base_patched.clip, prompt=negative_prompt) if negative_conditions_cache is None else negative_conditions_cache
+
+    positive_conditions_cache = positive_conditions
+    negative_conditions_cache = negative_conditions

    empty_latent = core.generate_empty_latent(width=width, height=height, batch_size=1)

+    if xl_refiner is not None:
+        positive_conditions_refiner = core.encode_prompt_condition(clip=xl_refiner.clip, prompt=positive_prompt) if positive_conditions_refiner_cache is None else positive_conditions_refiner_cache
+        negative_conditions_refiner = core.encode_prompt_condition(clip=xl_refiner.clip, prompt=negative_prompt) if negative_conditions_refiner_cache is None else negative_conditions_refiner_cache
+
+        positive_conditions_refiner_cache = positive_conditions_refiner
+        negative_conditions_refiner_cache = negative_conditions_refiner
+
        sampled_latent = core.ksampler_with_refiner(
-        model=xl_base.unet,
+            model=xl_base_patched.unet,
            positive=positive_conditions,
            negative=negative_conditions,
            refiner=xl_refiner.unet,
@@ -40,7 +152,18 @@ def process(positive_prompt, negative_prompt, steps, switch, width, height, imag
            callback_function=callback
        )

-    decoded_latent = core.decode_vae(vae=xl_refiner.vae, latent_image=sampled_latent)
+    else:
+        sampled_latent = core.ksampler(
+            model=xl_base_patched.unet,
+            positive=positive_conditions,
+            negative=negative_conditions,
+            latent=empty_latent,
+            steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True,
+            seed=image_seed,
+            callback_function=callback
+        )
+
+    decoded_latent = core.decode_vae(vae=xl_base_patched.vae, latent_image=sampled_latent)

    images = core.image_to_numpy(decoded_latent)

@@ -0,0 +1,32 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+
+def gaussian_kernel(kernel_size, sigma):
+    kernel = np.fromfunction(
+        lambda x, y: (1 / (2 * np.pi * sigma ** 2)) *
+                     np.exp(-((x - (kernel_size - 1) / 2) ** 2 + (y - (kernel_size - 1) / 2) ** 2) / (2 * sigma ** 2)),
+        (kernel_size, kernel_size)
+    )
+    return kernel / np.sum(kernel)
+
+
+class GaussianBlur(nn.Module):
+    def __init__(self, channels, kernel_size, sigma):
+        super(GaussianBlur, self).__init__()
+        self.channels = channels
+        self.kernel_size = kernel_size
+        self.sigma = sigma
+        self.padding = kernel_size // 2  # Ensure output size matches input size
+        self.register_buffer('kernel', torch.tensor(gaussian_kernel(kernel_size, sigma), dtype=torch.float32))
+        self.kernel = self.kernel.view(1, 1, kernel_size, kernel_size)
+        self.kernel = self.kernel.expand(self.channels, -1, -1, -1)  # Repeat the kernel for each input channel
+
+    def forward(self, x):
+        x = F.conv2d(x, self.kernel.to(x), padding=self.padding, groups=self.channels)
+        return x
+
+
+gaussian_filter_2d = GaussianBlur(4, 7, 0.8)
@@ -0,0 +1,99 @@
+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;
+}
+
+.scroll-hide{
+  resize: none !important;
+}
+
+.refresh_button{
+  border: none !important;
+  background: none !important;
+  font-size: none !important;
+  box-shadow: none !important;
+}
+
+'''
+progress_html = '''
+<div class="loader-container">
+  <div class="loader"></div>
+  <div class="progress-container">
+    <progress value="*number*" max="100"></progress>
+  </div>
+  <span>*text*</span>
+</div>
+'''
+
+
+def make_progress_html(number, text):
+    return progress_html.replace('*number*', str(number)).replace('*text*', text)
@@ -7,6 +7,7 @@ import sys
 import re
 import logging
 import pygit2
+pygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0)


 logging.getLogger("torch.distributed.nn").setLevel(logging.ERROR)  # sshh...
@@ -0,0 +1,389 @@
+import torch
+import comfy.model_base
+import comfy.ldm.modules.diffusionmodules.openaimodel
+import comfy.samplers
+
+from comfy.samplers import model_management, lcm, math
+from comfy.ldm.modules.diffusionmodules.openaimodel import timestep_embedding, forward_timestep_embed
+from modules.filters import gaussian_filter_2d
+
+sharpness = 2.0
+
+
+def sampling_function_patched(model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={},
+                      seed=None):
+    def get_area_and_mult(cond, x_in, cond_concat_in, timestep_in):
+        area = (x_in.shape[2], x_in.shape[3], 0, 0)
+        strength = 1.0
+        if 'timestep_start' in cond[1]:
+            timestep_start = cond[1]['timestep_start']
+            if timestep_in[0] > timestep_start:
+                return None
+        if 'timestep_end' in cond[1]:
+            timestep_end = cond[1]['timestep_end']
+            if timestep_in[0] < timestep_end:
+                return None
+        if 'area' in cond[1]:
+            area = cond[1]['area']
+        if 'strength' in cond[1]:
+            strength = cond[1]['strength']
+
+        adm_cond = None
+        if 'adm_encoded' in cond[1]:
+            adm_cond = cond[1]['adm_encoded']
+
+        input_x = x_in[:, :, area[2]:area[0] + area[2], area[3]:area[1] + area[3]]
+        if 'mask' in cond[1]:
+            # Scale the mask to the size of the input
+            # The mask should have been resized as we began the sampling process
+            mask_strength = 1.0
+            if "mask_strength" in cond[1]:
+                mask_strength = cond[1]["mask_strength"]
+            mask = cond[1]['mask']
+            assert (mask.shape[1] == x_in.shape[2])
+            assert (mask.shape[2] == x_in.shape[3])
+            mask = mask[:, area[2]:area[0] + area[2], area[3]:area[1] + area[3]] * mask_strength
+            mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1)
+        else:
+            mask = torch.ones_like(input_x)
+        mult = mask * strength
+
+        if 'mask' not in cond[1]:
+            rr = 8
+            if area[2] != 0:
+                for t in range(rr):
+                    mult[:, :, t:1 + t, :] *= ((1.0 / rr) * (t + 1))
+            if (area[0] + area[2]) < x_in.shape[2]:
+                for t in range(rr):
+                    mult[:, :, area[0] - 1 - t:area[0] - t, :] *= ((1.0 / rr) * (t + 1))
+            if area[3] != 0:
+                for t in range(rr):
+                    mult[:, :, :, t:1 + t] *= ((1.0 / rr) * (t + 1))
+            if (area[1] + area[3]) < x_in.shape[3]:
+                for t in range(rr):
+                    mult[:, :, :, area[1] - 1 - t:area[1] - t] *= ((1.0 / rr) * (t + 1))
+
+        conditionning = {}
+        conditionning['c_crossattn'] = cond[0]
+        if cond_concat_in is not None and len(cond_concat_in) > 0:
+            cropped = []
+            for x in cond_concat_in:
+                cr = x[:, :, area[2]:area[0] + area[2], area[3]:area[1] + area[3]]
+                cropped.append(cr)
+            conditionning['c_concat'] = torch.cat(cropped, dim=1)
+
+        if adm_cond is not None:
+            conditionning['c_adm'] = adm_cond
+
+        control = None
+        if 'control' in cond[1]:
+            control = cond[1]['control']
+
+        patches = None
+        if 'gligen' in cond[1]:
+            gligen = cond[1]['gligen']
+            patches = {}
+            gligen_type = gligen[0]
+            gligen_model = gligen[1]
+            if gligen_type == "position":
+                gligen_patch = gligen_model.set_position(input_x.shape, gligen[2], input_x.device)
+            else:
+                gligen_patch = gligen_model.set_empty(input_x.shape, input_x.device)
+
+            patches['middle_patch'] = [gligen_patch]
+
+        return (input_x, mult, conditionning, area, control, patches)
+
+    def cond_equal_size(c1, c2):
+        if c1 is c2:
+            return True
+        if c1.keys() != c2.keys():
+            return False
+        if 'c_crossattn' in c1:
+            s1 = c1['c_crossattn'].shape
+            s2 = c2['c_crossattn'].shape
+            if s1 != s2:
+                if s1[0] != s2[0] or s1[2] != s2[2]:  # these 2 cases should not happen
+                    return False
+
+                mult_min = lcm(s1[1], s2[1])
+                diff = mult_min // min(s1[1], s2[1])
+                if diff > 4:  # arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
+                    return False
+        if 'c_concat' in c1:
+            if c1['c_concat'].shape != c2['c_concat'].shape:
+                return False
+        if 'c_adm' in c1:
+            if c1['c_adm'].shape != c2['c_adm'].shape:
+                return False
+        return True
+
+    def can_concat_cond(c1, c2):
+        if c1[0].shape != c2[0].shape:
+            return False
+
+        # control
+        if (c1[4] is None) != (c2[4] is None):
+            return False
+        if c1[4] is not None:
+            if c1[4] is not c2[4]:
+                return False
+
+        # patches
+        if (c1[5] is None) != (c2[5] is None):
+            return False
+        if (c1[5] is not None):
+            if c1[5] is not c2[5]:
+                return False
+
+        return cond_equal_size(c1[2], c2[2])
+
+    def cond_cat(c_list):
+        c_crossattn = []
+        c_concat = []
+        c_adm = []
+        crossattn_max_len = 0
+        for x in c_list:
+            if 'c_crossattn' in x:
+                c = x['c_crossattn']
+                if crossattn_max_len == 0:
+                    crossattn_max_len = c.shape[1]
+                else:
+                    crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
+                c_crossattn.append(c)
+            if 'c_concat' in x:
+                c_concat.append(x['c_concat'])
+            if 'c_adm' in x:
+                c_adm.append(x['c_adm'])
+        out = {}
+        c_crossattn_out = []
+        for c in c_crossattn:
+            if c.shape[1] < crossattn_max_len:
+                c = c.repeat(1, crossattn_max_len // c.shape[1], 1)  # padding with repeat doesn't change result
+            c_crossattn_out.append(c)
+
+        if len(c_crossattn_out) > 0:
+            out['c_crossattn'] = [torch.cat(c_crossattn_out)]
+        if len(c_concat) > 0:
+            out['c_concat'] = [torch.cat(c_concat)]
+        if len(c_adm) > 0:
+            out['c_adm'] = torch.cat(c_adm)
+        return out
+
+    def calc_cond_uncond_batch(model_function, cond, uncond, x_in, timestep, max_total_area, cond_concat_in,
+                               model_options):
+        out_cond = torch.zeros_like(x_in)
+        out_count = torch.ones_like(x_in) / 100000.0
+
+        out_uncond = torch.zeros_like(x_in)
+        out_uncond_count = torch.ones_like(x_in) / 100000.0
+
+        COND = 0
+        UNCOND = 1
+
+        to_run = []
+        for x in cond:
+            p = get_area_and_mult(x, x_in, cond_concat_in, timestep)
+            if p is None:
+                continue
+
+            to_run += [(p, COND)]
+        if uncond is not None:
+            for x in uncond:
+                p = get_area_and_mult(x, x_in, cond_concat_in, timestep)
+                if p is None:
+                    continue
+
+                to_run += [(p, UNCOND)]
+
+        while len(to_run) > 0:
+            first = to_run[0]
+            first_shape = first[0][0].shape
+            to_batch_temp = []
+            for x in range(len(to_run)):
+                if can_concat_cond(to_run[x][0], first[0]):
+                    to_batch_temp += [x]
+
+            to_batch_temp.reverse()
+            to_batch = to_batch_temp[:1]
+
+            for i in range(1, len(to_batch_temp) + 1):
+                batch_amount = to_batch_temp[:len(to_batch_temp) // i]
+                if (len(batch_amount) * first_shape[0] * first_shape[2] * first_shape[3] < max_total_area):
+                    to_batch = batch_amount
+                    break
+
+            input_x = []
+            mult = []
+            c = []
+            cond_or_uncond = []
+            area = []
+            control = None
+            patches = None
+            for x in to_batch:
+                o = to_run.pop(x)
+                p = o[0]
+                input_x += [p[0]]
+                mult += [p[1]]
+                c += [p[2]]
+                area += [p[3]]
+                cond_or_uncond += [o[1]]
+                control = p[4]
+                patches = p[5]
+
+            batch_chunks = len(cond_or_uncond)
+            input_x = torch.cat(input_x)
+            c = cond_cat(c)
+            timestep_ = torch.cat([timestep] * batch_chunks)
+
+            if control is not None:
+                c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond))
+
+            transformer_options = {}
+            if 'transformer_options' in model_options:
+                transformer_options = model_options['transformer_options'].copy()
+
+            if patches is not None:
+                if "patches" in transformer_options:
+                    cur_patches = transformer_options["patches"].copy()
+                    for p in patches:
+                        if p in cur_patches:
+                            cur_patches[p] = cur_patches[p] + patches[p]
+                        else:
+                            cur_patches[p] = patches[p]
+                else:
+                    transformer_options["patches"] = patches
+
+            c['transformer_options'] = transformer_options
+
+            transformer_options['uc_mask'] = torch.Tensor(cond_or_uncond).to(input_x).float()[:, None, None, None]
+
+            if 'model_function_wrapper' in model_options:
+                output = model_options['model_function_wrapper'](model_function,
+                                                                 {"input": input_x, "timestep": timestep_, "c": c,
+                                                                  "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
+            else:
+                output = model_function(input_x, timestep_, **c).chunk(batch_chunks)
+            del input_x
+
+            model_management.throw_exception_if_processing_interrupted()
+
+            for o in range(batch_chunks):
+                if cond_or_uncond[o] == COND:
+                    out_cond[:, :, area[o][2]:area[o][0] + area[o][2], area[o][3]:area[o][1] + area[o][3]] += output[
+                                                                                                                  o] * \
+                                                                                                              mult[o]
+                    out_count[:, :, area[o][2]:area[o][0] + area[o][2], area[o][3]:area[o][1] + area[o][3]] += mult[o]
+                else:
+                    out_uncond[:, :, area[o][2]:area[o][0] + area[o][2], area[o][3]:area[o][1] + area[o][3]] += output[
+                                                                                                                    o] * \
+                                                                                                                mult[o]
+                    out_uncond_count[:, :, area[o][2]:area[o][0] + area[o][2], area[o][3]:area[o][1] + area[o][3]] += \
+                    mult[o]
+            del mult
+
+        out_cond /= out_count
+        del out_count
+        out_uncond /= out_uncond_count
+        del out_uncond_count
+
+        return out_cond, out_uncond
+
+    max_total_area = model_management.maximum_batch_area()
+    if math.isclose(cond_scale, 1.0):
+        uncond = None
+
+    cond, uncond = calc_cond_uncond_batch(model_function, cond, uncond, x, timestep, max_total_area, cond_concat,
+                                          model_options)
+    if "sampler_cfg_function" in model_options:
+        args = {"cond": cond, "uncond": uncond, "cond_scale": cond_scale, "timestep": timestep}
+        return model_options["sampler_cfg_function"](args)
+    else:
+        return uncond + (cond - uncond) * cond_scale
+
+
+def unet_forward_patched(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
+    uc_mask = transformer_options['uc_mask']
+    transformer_options["original_shape"] = list(x.shape)
+    transformer_options["current_index"] = 0
+
+    hs = []
+    t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype)
+    emb = self.time_embed(t_emb)
+
+    if self.num_classes is not None:
+        assert y.shape[0] == x.shape[0]
+        emb = emb + self.label_emb(y)
+
+    h = x.type(self.dtype)
+    for id, module in enumerate(self.input_blocks):
+        transformer_options["block"] = ("input", id)
+        h = forward_timestep_embed(module, h, emb, context, transformer_options)
+        if control is not None and 'input' in control and len(control['input']) > 0:
+            ctrl = control['input'].pop()
+            if ctrl is not None:
+                h += ctrl
+        hs.append(h)
+    transformer_options["block"] = ("middle", 0)
+    h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options)
+    if control is not None and 'middle' in control and len(control['middle']) > 0:
+        h += control['middle'].pop()
+
+    for id, module in enumerate(self.output_blocks):
+        transformer_options["block"] = ("output", id)
+        hsp = hs.pop()
+        if control is not None and 'output' in control and len(control['output']) > 0:
+            ctrl = control['output'].pop()
+            if ctrl is not None:
+                hsp += ctrl
+
+        h = torch.cat([h, hsp], dim=1)
+        del hsp
+        if len(hs) > 0:
+            output_shape = hs[-1].shape
+        else:
+            output_shape = None
+        h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape)
+    h = h.type(x.dtype)
+    x0 = self.out(h)
+
+    alpha = 1.0 - (timesteps / 999.0)[:, None, None, None].clone()
+    alpha *= 0.001 * sharpness
+    degraded_x0 = gaussian_filter_2d(x0) * alpha + x0 * (1.0 - alpha)
+
+    x0 = x0 * uc_mask + degraded_x0 * (1.0 - uc_mask)
+
+    return x0
+
+
+def sdxl_encode_adm_patched(self, **kwargs):
+    clip_pooled = kwargs["pooled_output"]
+    width = kwargs.get("width", 768)
+    height = kwargs.get("height", 768)
+    crop_w = kwargs.get("crop_w", 0)
+    crop_h = kwargs.get("crop_h", 0)
+    target_width = kwargs.get("target_width", width)
+    target_height = kwargs.get("target_height", height)
+
+    if kwargs.get("prompt_type", "") == "negative":
+        width *= 0.8
+        height *= 0.8
+    elif kwargs.get("prompt_type", "") == "positive":
+        width *= 1.5
+        height *= 1.5
+
+    out = []
+    out.append(self.embedder(torch.Tensor([height])))
+    out.append(self.embedder(torch.Tensor([width])))
+    out.append(self.embedder(torch.Tensor([crop_h])))
+    out.append(self.embedder(torch.Tensor([crop_w])))
+    out.append(self.embedder(torch.Tensor([target_height])))
+    out.append(self.embedder(torch.Tensor([target_width])))
+    flat = torch.flatten(torch.cat(out))[None, ]
+    return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
+
+
+def patch_all():
+    comfy.samplers.sampling_function = sampling_function_patched
+    comfy.model_base.SDXL.encode_adm = sdxl_encode_adm_patched
+    comfy.ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = unet_forward_patched
@@ -2,3 +2,38 @@ import os

 modelfile_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../models/checkpoints/'))
 lorafile_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../models/loras/'))
+temp_outputs_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../outputs/'))
+
+os.makedirs(temp_outputs_path, exist_ok=True)
+
+default_base_model_name = 'sd_xl_base_1.0_0.9vae.safetensors'
+default_refiner_model_name = 'sd_xl_refiner_1.0_0.9vae.safetensors'
+default_lora_name = 'sd_xl_offset_example-lora_1.0.safetensors'
+default_lora_weight = 0.5
+
+model_filenames = []
+lora_filenames = []
+
+
+def get_model_filenames(folder_path):
+    if not os.path.isdir(folder_path):
+        raise ValueError("Folder path is not a valid directory.")
+
+    filenames = []
+    for filename in os.listdir(folder_path):
+        if os.path.isfile(os.path.join(folder_path, filename)):
+            _, file_extension = os.path.splitext(filename)
+            if file_extension.lower() in ['.pth', '.ckpt', '.bin', '.safetensors']:
+                filenames.append(filename)
+
+    return filenames
+
+
+def update_all_model_names():
+    global model_filenames, lora_filenames
+    model_filenames = get_model_filenames(modelfile_path)
+    lora_filenames = get_model_filenames(lorafile_path)
+    return
+
+
+update_all_model_names()
@@ -0,0 +1,37 @@
+import os
+import modules.path
+
+from PIL import Image
+from modules.util import generate_temp_filename
+
+
+def log(img, dic):
+    date_string, local_temp_filename, only_name = generate_temp_filename(folder=modules.path.temp_outputs_path, extension='png')
+    os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True)
+    Image.fromarray(img).save(local_temp_filename)
+    html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html')
+
+    if not os.path.exists(html_name):
+        with open(html_name, 'a+') as f:
+            f.write(f"<p>Fooocus Log {date_string} (private)</p>\n")
+            f.write(f"<p>All images do not contain any hidden data.</p>")
+
+    with open(html_name, 'a+') as f:
+        div_name = only_name.replace('.', '_')
+        f.write(f'<div id="{div_name}"><hr>\n')
+        f.write(f"<p>{only_name}</p>\n")
+        i = 0
+        for k, v in dic:
+            if i < 2:
+                f.write(f"<p>{k}: <b>{v}</b> </p>\n")
+            else:
+                if i % 2 == 0:
+                    f.write(f"<p>{k}: <b>{v}</b>, ")
+                else:
+                    f.write(f"{k}: <b>{v}</b></p>\n")
+            i += 1
+        f.write(f"<p><img src=\"{only_name}\" width=512 onerror=\"document.getElementById('{div_name}').style.display = 'none';\"></img></p></div>\n")
+
+    print(f'Image generated with private log at: {html_name}')
+
+    return
@@ -2,7 +2,7 @@

 styles = [
    {
-        "name": "sai-base",
+        "name": "None",
        "prompt": "{prompt}",
        "negative_prompt": ""
    },
@@ -529,7 +529,7 @@ styles = [
 ]

 styles = {k['name']: (k['prompt'], k['negative_prompt']) for k in styles}
-default_style = styles['sai-base']
+default_style = styles['None']
 style_keys = list(styles.keys())


@@ -0,0 +1,13 @@
+import datetime
+import random
+import os
+
+
+def generate_temp_filename(folder='./outputs/', extension='png'):
+    current_time = datetime.datetime.now()
+    date_string = current_time.strftime("%Y-%m-%d")
+    time_string = current_time.strftime("%Y-%m-%d_%H-%M-%S")
+    random_number = random.randint(1000, 9999)
+    filename = f"{time_string}_{random_number}.{extension}"
+    result = os.path.join(folder, date_string, filename)
+    return date_string, os.path.abspath(os.path.realpath(result)), filename
@@ -1 +1,118 @@
-Some random experiments of LSTM training
+# Fooocus
+
+<img src="https://github.com/lllyasviel/Fooocus/assets/19834515/bcb0336b-5c79-4de2-b0cb-f7f68c753a88" width=100%>
+
+Fooocus is an image generating software.
+
+Fooocus is a rethinking of Stable Diffusion and Midjourney’s designs:
+
+* Learned from Stable Diffusion, the software is offline, open source, and free.
+
+* Learned from Midjourney, the manual tweaking is not needed, and users only need to focus on the prompts and images.
+
+Fooocus has included and automated [lots of inner optimizations and quality improvements](#tech_list). Users can forget all those difficult technical parameters, and just enjoy the interaction between human and computer to "explore new mediums of thought and expanding the imaginative powers of the human species" `[1]`.
+
+Fooocus has simplified the installation. Between pressing "download" and generating the first image, the number of needed mouse clicks is strictly limited to less than 3. Minimal GPU memory requirement is 4GB (Nvidia).
+
+Fooocus also developed many "fooocus-only" features for advanced users to get perfect results. [Click here to browse the advanced features.](https://github.com/lllyasviel/Fooocus/discussions/117)
+
+`[1]` David Holz, 2019.
+
+## Download
+
+### Windows
+
+You can directly download Fooocus with:
+
+**[>>> Click here to download <<<](https://github.com/lllyasviel/Fooocus/releases/download/release/Fooocus_win64_1-1-10.7z)**
+
+After you download the file, please uncompress it, and then run the "run.bat".
+
+![image](https://github.com/lllyasviel/Fooocus/assets/19834515/c49269c4-c274-4893-b368-047c401cc58c)
+
+In the first time you launch the software, it will automatically download models:
+
+1. It will download [sd_xl_base_1.0_0.9vae.safetensors from here](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0_0.9vae.safetensors) as the file "Fooocus\models\checkpoints\sd_xl_base_1.0_0.9vae.safetensors".
+2. It will download [sd_xl_refiner_1.0_0.9vae.safetensors from here](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0_0.9vae.safetensors) as the file "Fooocus\models\checkpoints\sd_xl_refiner_1.0_0.9vae.safetensors".
+
+![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.
+
+Below is a test on a relatively low-end laptop with **16GB System RAM** and **6GB VRAM** (Nvidia 3060 laptop). The speed on this machine is about 1.35 seconds per iteration. Pretty impressive – nowadays laptops with 3060 are usually at very acceptable price.
+
+![image](https://github.com/lllyasviel/Fooocus/assets/19834515/938737a5-b105-4f19-b051-81356cb7c495)
+
+Note that the minimal requirement is **4GB Nvidia GPU memory (4GB VRAM)** and **8GB system memory (8GB RAM)**. This requires using Microsoft’s Virtual Swap technique, which is automatically enabled by your Windows installation in most cases, so you often do not need to do anything about it. However, if you are not sure, or if you manually turned it off (would anyone really do that?), you can enable it here:
+
+<details>
+<summary>Click here to the see the image instruction. </summary>
+
+![image](https://github.com/lllyasviel/Fooocus/assets/19834515/2a06b130-fe9b-4504-94f1-2763be4476e9)
+
+</details>
+
+Please open an issue if you use similar devices but still cannot achieve acceptable performances.
+
+### Colab
+
+(Last tested - 2023 Aug 14)
+
+| Colab | Info
+| --- | --- |
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lllyasviel/Fooocus/blob/main/colab.ipynb) | Fooocus Colab (Official Version)
+
+Note that sometimes this Colab will say like "you must restart the runtime in order to use newly installed XX". This can be safely ignored.
+
+Thanks to [camenduru](https://github.com/camenduru)'s codes!
+
+### Linux
+
+The command lines are
+
+    git clone https://github.com/lllyasviel/Fooocus.git
+    cd Fooocus
+    conda env create -f environment.yaml
+    conda activate fooocus
+    pip install -r requirements_versions.txt
+
+Then download the models: download [sd_xl_base_1.0_0.9vae.safetensors from here](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0_0.9vae.safetensors) as the file "Fooocus\models\checkpoints\sd_xl_base_1.0_0.9vae.safetensors", and download [sd_xl_refiner_1.0_0.9vae.safetensors from here](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0_0.9vae.safetensors) as the file "Fooocus\models\checkpoints\sd_xl_refiner_1.0_0.9vae.safetensors". **Or let Fooocus automatically download the models** using the launcher:
+
+    python launch.py
+
+Or if you want to open a remote port, use
+
+    python launch.py --listen
+
+### Mac/Windows(AMD GPUs)
+
+Coming soon ...
+
+## List of "Hidden" Tricks
+<a name="tech_list"></a>
+
+Below things are already inside the software, and **users do not need to do anything about these**.
+
+Note that some of these tricks are currently (2023 Aug 11) impossible to reproduce in Automatic1111's interface or ComfyUI's node system. You may expect better results from Fooocus than other software even when they use similar models/pipelines.
+
+1. Native refiner swap inside one single k-sampler. The advantage is that now the refiner model can reuse the base model's momentum (or ODE's history parameters) collected from k-sampling to achieve more coherent sampling. In Automatic1111's high-res fix and ComfyUI's node system, the base model and refiner use two independent k-samplers, which means the momentum is largely wasted, and the sampling continuity is broken. Fooocus uses its own advanced k-diffusion sampling that ensures seamless, native, and continuous swap in a refiner setup. (Update Aug 13: Actually I discussed this with Automatic1111 several days ago and it seems that the “native refiner swap inside one single k-sampler” is [merged]( https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371) into the dev branch of webui. Great!)
+2. Negative ADM guidance. Because the highest resolution level of XL Base does not have cross attentions, the positive and negative signals for XL's highest resolution level cannot receive enough contrasts during the CFG sampling, causing the results look a bit plastic or overly smooth in certain cases. Fortunately, since the XL's highest resolution level is still conditioned on image aspect ratios (ADM), we can modify the adm on the positive/negative side to compensate for the lack of CFG contrast in the highest resolution level. (Update Aug 16, the IOS App [Drawing Things](https://apps.apple.com/us/app/draw-things-ai-generation/id6444050820) will support Negative ADM Guidance. Great!)
+3. We implemented a carefully tuned variation of the Section 5.1 of ["Improving Sample Quality of Diffusion Models Using Self-Attention Guidance"](https://arxiv.org/pdf/2210.00939.pdf). The weight is set to very low, but this is Fooocus's final guarantee to make sure that the XL will never yield overly smooth or plastic appearance. This can almostly eliminate all cases that XL still occasionally produce overly smooth results even with negative ADM guidance.
+4. We modified the style templates a bit and added the "cinematic-default".
+5. We tested the "sd_xl_offset_example-lora_1.0.safetensors" and it seems that when the lora weight is below 0.5, the results are always better than XL without lora.
+6. The parameters of samplers are carefully tuned.
+7. Because XL uses positional encoding for generation resolution, images generated by several fixed resolutions look a bit better than that from arbitrary resolutions (because the positional encoding is not very good at handling int numbers that are unseen during training). This suggests that the resolutions in UI may be hard coded for best results.
+8. Separated prompts for two different text encoders seem unnecessary. Separated prompts for base model and refiner may work but the effects are random, and we refrain from implement this.
+9. DPM family seems well-suited for XL, since XL sometimes generates overly smooth texture but DPM family sometimes generate overly dense detail in texture. Their joint effect looks neutral and appealing to human perception.
+
+## Advanced Features
+
+[Click here to browse the advanced features.](https://github.com/lllyasviel/Fooocus/discussions/117)
+
+## Thanks
+
+The codebase starts from an odd mixture of [Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui) and [ComfyUI](https://github.com/comfyanonymous/ComfyUI). (And they both use GPL license.)
+
+## Update Log
+
+The log is [here](update_log.md).
@@ -8,8 +8,8 @@ Pillow==9.2.0
 scipy==1.9.3
 tqdm==4.64.1
 psutil==5.9.5
-opencv-python==4.7.0.72
 numpy==1.23.5
 pytorch_lightning==1.9.4
 omegaconf==2.2.3
 gradio==3.39.0
+pygit2==1.12.2
@@ -0,0 +1,2 @@
+gradio_root = None
+
@@ -0,0 +1,80 @@
+### 1.0.34
+
+* Random seed restoring.
+
+### 1.0.33
+
+* Hide items in log when images are removed.
+
+### 1.0.32
+
+* Fooocus private log
+
+### 1.0.31
+
+* Fix typo and UI.
+
+### 1.0.29
+
+* Added "Advanced->Advanced->Advanced" block for future development.
+
+### 1.0.29
+
+* Fix overcook problem in 1.0.28
+
+### 1.0.28
+
+* SAG implemented
+
+### 1.0.27
+
+* Fix small problem in textbox css 
+
+### 1.0.25
+
+* support sys.argv --listen --share --port
+
+### 1.0.24
+
+* Taller input textbox.
+
+### 1.0.23
+
+* Added some hints on linux after UI start so users know the App does not fail.
+
+### 1.0.20
+
+* Support linux.
+
+### 1.0.20
+
+* Speed-up text encoder.
+
+### 1.0.20
+
+* Re-write UI to use async codes: (1) for faster start, and (2) for better live preview.
+* Removed opencv dependency
+* Plan to support Linux soon
+
+### 1.0.19
+
+* Unlock to allow changing model.
+
+### 1.0.17
+
+* Change default model to SDXL-1.0-vae-0.9. (This means the models will be downloaded again, but we should do it as early as possible so that all new users only need to download once. Really sorry for day-0 users. But frankly this is not too late considering that the project is just publicly available in less than 24 hours - if it has been a week then we will prefer more lightweight tricks to update.)
+
+### 1.0.16
+
+* Implemented "Fooocus/outputs" folder for saving user results.
+* Ignored cv2 errors when preview fails.
+* Mentioned future AMD support in Readme.
+* Created this log.
+
+### 1.0.15
+
+Publicly available.
+
+### 1.0.0
+
+Initial Version.
@@ -1,74 +1,122 @@
 import gradio as gr
 import random
+import time
+import shared
+import argparse
+import modules.path
 import fooocus_version
+import modules.html
+import modules.async_worker as worker

-from modules.sdxl_styles import apply_style, style_keys, aspect_ratios
-from modules.default_pipeline import process
-from modules.cv2win32 import close_all_preview
+from modules.sdxl_styles import style_keys, aspect_ratios


-def generate_clicked(prompt, negative_prompt, style_selction, performance_selction,
-                     aspect_ratios_selction, image_number, image_seed, progress=gr.Progress()):
+def generate_clicked(*args):
+    yield gr.update(interactive=False), \
+        gr.update(visible=True, value=modules.html.make_progress_html(1, 'Processing text encoding ...')), \
+        gr.update(visible=True, value=None), \
+        gr.update(visible=False)

-    p_txt, n_txt = apply_style(style_selction, prompt, negative_prompt)
+    worker.buffer.append(list(args))
+    finished = False

-    if performance_selction == 'Speed':
-        steps = 30
-        switch = 20
-    else:
-        steps = 60
-        switch = 40
-
-    width, height = aspect_ratios[aspect_ratios_selction]
-
-    results = []
-    seed = image_seed
-    if not isinstance(seed, int) or seed < 0 or seed > 65535:
-        seed = random.randint(1, 65535)
-
-    all_steps = steps * image_number
-
-    def callback(step, x0, x, total_steps):
-        done_steps = i * steps + step
-        progress(float(done_steps) / float(all_steps), f'Step {step}/{total_steps} in the {i}-th Sampling')
-
-    for i in range(image_number):
-        imgs = process(p_txt, n_txt, steps, switch, width, height, seed, callback=callback)
-        seed += 1
-        results += imgs
-
-    close_all_preview()
-    return results
+    while not finished:
+        time.sleep(0.01)
+        if len(worker.outputs) > 0:
+            flag, product = worker.outputs.pop(0)
+            if flag == 'preview':
+                percentage, title, image = product
+                yield gr.update(interactive=False), \
+                    gr.update(visible=True, value=modules.html.make_progress_html(percentage, title)), \
+                    gr.update(visible=True, value=image) if image is not None else gr.update(), \
+                    gr.update(visible=False)
+            if flag == 'results':
+                yield gr.update(interactive=True), \
+                    gr.update(visible=False), \
+                    gr.update(visible=False), \
+                    gr.update(visible=True, value=product)
+                finished = True
+    return


-block = gr.Blocks(title='Fooocus ' + fooocus_version.version).queue()
-with block:
+shared.gradio_root = gr.Blocks(title='Fooocus ' + fooocus_version.version, css=modules.html.css).queue()
+with shared.gradio_root:
    with gr.Row():
        with gr.Column():
-            gallery = gr.Gallery(label='Gallery', show_label=False, object_fit='contain', height=720)
-            with gr.Row():
+            progress_window = gr.Image(label='Preview', show_label=True, height=640, visible=False)
+            progress_html = gr.HTML(value=modules.html.make_progress_html(32, 'Progress 32%'), visible=False, elem_id='progress-bar', elem_classes='progress-bar')
+            gallery = gr.Gallery(label='Gallery', show_label=False, object_fit='contain', height=720, visible=True)
+            with gr.Row(elem_classes='type_row'):
                with gr.Column(scale=0.85):
-                    prompt = gr.Textbox(show_label=False, placeholder="Type prompt here.", container=False, autofocus=True)
+                    prompt = gr.Textbox(show_label=False, placeholder="Type prompt here.", container=False, autofocus=True, elem_classes='type_row', lines=1024)
                with gr.Column(scale=0.15, min_width=0):
-                    run_button = gr.Button(label="Generate", value="Generate")
+                    run_button = gr.Button(label="Generate", value="Generate", elem_classes='type_row')
            with gr.Row():
                advanced_checkbox = gr.Checkbox(label='Advanced', value=False, container=False)
        with gr.Column(scale=0.5, visible=False) as right_col:
-            with gr.Tab(label='Generator Setting'):
+            with gr.Tab(label='Setting'):
                performance_selction = gr.Radio(label='Performance', choices=['Speed', 'Quality'], value='Speed')
                aspect_ratios_selction = gr.Radio(label='Aspect Ratios (width × height)', choices=list(aspect_ratios.keys()),
                                                  value='1152×896')
                image_number = gr.Slider(label='Image Number', minimum=1, maximum=32, step=1, value=2)
-                image_seed = gr.Number(label='Random Seed', value=-1, precision=0)
                negative_prompt = gr.Textbox(label='Negative Prompt', show_label=True, placeholder="Type prompt here.")
-            with gr.Tab(label='Image Style'):
+                seed_random = gr.Checkbox(label='Random', value=True)
+                image_seed = gr.Number(label='Seed', value=0, precision=0, visible=False)
+
+                def random_checked(r):
+                    return gr.update(visible=not r)
+
+                def refresh_seed(r, s):
+                    if r:
+                        return random.randint(1, 1024*1024*1024)
+                    else:
+                        return s
+
+                seed_random.change(random_checked, inputs=[seed_random], outputs=[image_seed])
+
+            with gr.Tab(label='Style'):
                style_selction = gr.Radio(show_label=False, container=True,
                                          choices=style_keys, value='cinematic-default')
+            with gr.Tab(label='Advanced'):
+                with gr.Row():
+                    base_model = gr.Dropdown(label='SDXL Base Model', choices=modules.path.model_filenames, value=modules.path.default_base_model_name, show_label=True)
+                    refiner_model = gr.Dropdown(label='SDXL Refiner', choices=['None'] + modules.path.model_filenames, value=modules.path.default_refiner_model_name, show_label=True)
+                with gr.Accordion(label='LoRAs', open=True):
+                    lora_ctrls = []
+                    for i in range(5):
+                        with gr.Row():
+                            lora_model = gr.Dropdown(label=f'SDXL LoRA {i+1}', choices=['None'] + modules.path.lora_filenames, value=modules.path.default_lora_name if i == 0 else 'None')
+                            lora_weight = gr.Slider(label='Weight', minimum=-2, maximum=2, step=0.01, value=modules.path.default_lora_weight)
+                            lora_ctrls += [lora_model, lora_weight]
+                with gr.Row():
+                    model_refresh = gr.Button(label='Refresh', value='\U0001f504 Refresh All Files', variant='secondary', elem_classes='refresh_button')
+                with gr.Accordion(label='Advanced', open=False):
+                    sharpness = gr.Slider(label='Sampling Sharpness', minimum=0.0, maximum=40.0, step=0.01, value=2.0)
+                    gr.HTML('<a href="https://github.com/lllyasviel/Fooocus/discussions/117">\U0001F4D4 Document</a>')
+
+                def model_refresh_clicked():
+                    modules.path.update_all_model_names()
+                    results = []
+                    results += [gr.update(choices=modules.path.model_filenames), gr.update(choices=['None'] + modules.path.model_filenames)]
+                    for i in range(5):
+                        results += [gr.update(choices=['None'] + modules.path.lora_filenames), gr.update()]
+                    return results
+
+                model_refresh.click(model_refresh_clicked, [], [base_model, refiner_model] + lora_ctrls)
+
        advanced_checkbox.change(lambda x: gr.update(visible=x), advanced_checkbox, right_col)
        ctrls = [
            prompt, negative_prompt, style_selction,
-            performance_selction, aspect_ratios_selction, image_number, image_seed
+            performance_selction, aspect_ratios_selction, image_number, image_seed, sharpness
        ]
-        run_button.click(fn=generate_clicked, inputs=ctrls, outputs=[gallery])
+        ctrls += [base_model, refiner_model] + lora_ctrls
+        run_button.click(fn=refresh_seed, inputs=[seed_random, image_seed], outputs=image_seed)\
+            .then(fn=generate_clicked, inputs=ctrls, outputs=[run_button, progress_html, progress_window, gallery])

-block.launch(inbrowser=True)
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--port", type=int, default=None, help="Set the listen port.")
+parser.add_argument("--share", action='store_true', help="Set whether to share on Gradio.")
+parser.add_argument("--listen", type=str, default=None, metavar="IP", nargs="?", const="0.0.0.0", help="Set the listen interface.")
+args = parser.parse_args()
+shared.gradio_root.launch(inbrowser=True, server_name=args.listen, server_port=args.port, share=args.share)
Author	SHA1	Message	Date
lvmin	9978306be4	fix ownership	2023-08-17 16:29:00 -07:00
lvmin	07307faab3	fix ownership	2023-08-17 16:25:56 -07:00
lvmin	8f708e116c	random seed restoring	2023-08-17 15:18:07 -07:00
lvmin	db1a49f789	random seed restoring	2023-08-17 15:17:32 -07:00
lllyasviel	4920341e09	Hide items in log when images are removed. Hide items in log when images are removed.	2023-08-17 12:47:48 -07:00
lllyasviel	3267d56698	i (#163 )	2023-08-16 13:39:29 -07:00
lllyasviel	74f8c67377	i (#162 )	2023-08-16 13:29:37 -07:00
lllyasviel	0b68b367d8	i (#159 )	2023-08-16 13:18:46 -07:00
lllyasviel	55342fcd58	1.0.32 (#158 ) Fooocus private log	2023-08-16 13:04:32 -07:00
lllyasviel	3aace6e15b	Update readme.md (#157 )	2023-08-16 11:05:58 -07:00
lllyasviel	7573ca4ba4	Update readme.md (#156 )	2023-08-16 11:04:57 -07:00
lllyasviel	18a8c66552	Update readme.md (#124 )	2023-08-15 11:00:29 -07:00
lllyasviel	6838ad453f	Update readme.md (#123 )	2023-08-15 10:57:09 -07:00
lllyasviel	71a225f556	i (#122 )	2023-08-15 10:47:36 -07:00
lllyasviel	7e2a54194d	Update webui.py (#121 )	2023-08-15 10:24:38 -07:00
lllyasviel	ac80eb59b3	Update readme.md (#120 )	2023-08-15 10:18:32 -07:00
lllyasviel	0719350c03	Update readme.md (#119 )	2023-08-15 10:17:52 -07:00
lllyasviel	bbc58d76cb	i (#116 )	2023-08-15 09:27:27 -07:00
lllyasviel	5e5a2d428f	i (#114 )	2023-08-15 09:01:25 -07:00
lllyasviel	6d4e1d22e7	Update fooocus_version.py (#111 )	2023-08-15 08:18:51 -07:00
lllyasviel	446a4fe5ab	i (#110 )	2023-08-15 08:16:13 -07:00
lllyasviel	a5cff12f6e	Update readme.md (#94 )	2023-08-14 21:23:04 -07:00
lllyasviel	7bc439e1ce	Update readme.md (#93 )	2023-08-14 19:29:57 -07:00
lllyasviel	c24504ac74	Update readme.md (#91 )	2023-08-14 18:49:29 -07:00
lllyasviel	1dd69e7baf	Update readme.md (#90 )	2023-08-14 18:39:13 -07:00
lllyasviel	403f5f1e58	Update readme.md (#89 )	2023-08-14 18:22:11 -07:00
lllyasviel	96d15b4933	SAG implemented (#88 ) sag	2023-08-14 18:20:20 -07:00
lllyasviel	7e1b551cc2	1.0.27 Fix small problem in textbox css	2023-08-14 11:18:52 -07:00
lllyasviel	b2b2fce807	Update readme.md (#78 )	2023-08-14 09:43:17 -07:00
lllyasviel	56e5b1e39b	Update fooocus_version.py (#77 )	2023-08-14 09:36:31 -07:00
tcmaps	ab6b19b158	Update webui.py (#76 )	2023-08-14 09:36:08 -07:00
lllyasviel	073ad63647	Update readme.md (#72 )	2023-08-14 07:50:43 -07:00
lllyasviel	c0162bd291	i (#71 )	2023-08-14 07:46:29 -07:00
lllyasviel	9b12f8e616	Update readme.md (#70 )	2023-08-14 07:42:16 -07:00
lllyasviel	a859deef3f	Update readme.md (#69 )	2023-08-14 07:40:27 -07:00
lllyasviel	ea8938eabe	i (#68 )	2023-08-14 07:34:57 -07:00
lllyasviel	7900480360	1.0.25 (#67 ) support sys.argv --listen --share --port	2023-08-14 06:56:23 -07:00
lllyasviel	50708f3d22	Update readme.md (#63 )	2023-08-13 22:20:18 -07:00
lllyasviel	8cf4b0dd9c	1.0.24 * Taller input textbox.	2023-08-13 17:34:22 -07:00
lllyasviel	6fe3f41bb8	Update readme.md (#55 )	2023-08-13 14:53:10 -07:00
lllyasviel	de53c87802	Update readme.md (#54 )	2023-08-13 13:30:43 -07:00
lllyasviel	5a3000f19a	Update readme.md (#53 )	2023-08-13 13:15:06 -07:00
lllyasviel	00f2e9f08f	Update readme.md (#52 )	2023-08-13 13:08:16 -07:00
lllyasviel	4da0533a74	Update readme.md (#51 )	2023-08-13 12:38:50 -07:00
lllyasviel	0579d4ea92	Update readme.md (#50 )	2023-08-13 12:37:41 -07:00
lllyasviel	592845d737	Update readme.md (#49 )	2023-08-13 12:29:24 -07:00
lllyasviel	2d71dca12c	i (#42 )	2023-08-13 07:10:26 -07:00
lllyasviel	8543bb5804	1.0.20 (#37 ) Support linux.	2023-08-12 23:43:10 -07:00
lllyasviel	59aa2aedeb	i (#36 )	2023-08-12 23:28:44 -07:00
lllyasviel	8720e435f5	1.0.20 (#35 ) Re-write UI to use async codes: (1) for faster start, and (2) for better live preview. Removed opencv dependency Plan to support Linux soon	2023-08-12 23:14:54 -07:00
lllyasviel	158afe088d	1.0.19 (#33 ) Unlock to allow changing model.	2023-08-12 17:43:39 -07:00
lllyasviel	1ff382c8ef	Update fooocus_version.py (#27 )	2023-08-12 12:12:34 -07:00
lllyasviel	983909b3fe	1.0.17 (#25 ) ### 1.0.17 * Change default model to SDXL-1.0-vae-0.9. (This means the models will be downloaded again, but we should do it as early as possible so that all new users only need to download once. Really sorry for day-0 users. But frankly this is not too late considering that the project is just publicly available in less than 24 hours - if it has been a week then we will prefer more lightweight tricks to update.)	2023-08-12 11:46:56 -07:00
lllyasviel	eb3856586e	Update update_log.md (#20 )	2023-08-12 07:34:45 -07:00
lllyasviel	6d406da4a4	1.0.16 (#19 ) ### 1.0.16 * Implemented "output" folder for saving user results. * Ignored cv2 errors when preview fails. * Mentioned future AMD support in Readme. * Created this log.	2023-08-12 07:29:36 -07:00
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lvmin	47346b7019	Merge branch 'main' of github.com:lllyasviel/fooocus	2023-08-11 13:45:01 -07:00
lvmin	b393b2aeeb	i	2023-08-11 13:44:51 -07:00
lllyasviel	d33d8a47ae	Create LICENSE	2023-08-11 13:43:12 -07:00
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