windows系统 Fooocus 图片生成模型 ,4-6GB显存即可玩,27S/p_fooocus模型下载

作者：羊村懒王 | 2024-05-12 11:21:50

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fooocus模型下载

安装步骤

1.下载程序代码框架,大小2GB ,下载 https://github.com/lllyasviel/Fooocus/releases/download/1.0.35/Fooocus_win64_1-1-1035.7z

2.下载模型文件sd_xl_base_1.0_0.9vae.safetensors ,大小6GBhttps://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0_0.9vae.safetensorshttps://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0_0.9vae.safetensors

3下载模型文件 sd_xl_refiner_1.0_0.9vae.safetensors ,大小6GB

https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0_0.9vae.safetensorshttps://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0_0.9vae.safetensors

4.把下载的模型放到model目录

sd_xl_base_1.0_0.9vae.safetensors 放到 “Fooocus\models\checkpoints\sd_xl_base_1.0_0.9 vae.saftensors”。

sd_xl_refiner_1.0_0.9vae.safetensors，放到“Fooocus\models\checkpoints\sd_xl_refiner_1.0,0.9vae.secetensors”。

5. 打开cmd 输入命令安装一个库 ,这步也许可以省去

pip install pygit2

6. 运行代码框架 Fooocus_win64_1-1-1035 文件夹内的 run.bat 就会打开一个网页运行生成了

生成效果

以下都是AI生成的图片,用中文提示词翻译成英文复制然后生成

不检查更新版代码

不在检查更新 ,不在联网

run.bat 修改为


.\python_embeded\python.exe -s Fooocus\webui_new.py
pause

webui_new.py


import os
import sys
 
 
root = os.path.dirname(os.path.abspath(__file__))
sys.path.append(root)
print('root=',root)
os.chdir(root)
 
import platform
import fooocus_version
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
 
 
 
 
from modules.launch_util import is_installed, run, python, \
    run_pip, repo_dir, git_clone, requirements_met, script_path, dir_repos
from modules.model_loader import load_file_from_url
from modules.path import modelfile_path, lorafile_path, vae_approx_path, fooocus_expansion_path, upscale_models_path
 
 
#Comfy_path =r'\Fooocus_win64_1-1-1035\Fooocus\repositories\ComfyUI-from-StabilityAI-Official'
 
comfyui_name = 'ComfyUI-from-StabilityAI-Official'
Comfy_path = os.path.join(root, dir_repos,comfyui_name)
print('Comfy_path =',Comfy_path)
print('dir_repos =',dir_repos)
# 添加到 Python 的模块搜索路径中
sys.path.append(Comfy_path)
 
def clear_comfy_args():
    argv = sys.argv
    sys.argv = [sys.argv[0]]
    from comfy.cli_args import args as comfy_args
    comfy_args.disable_cuda_malloc = True
    sys.argv = argv
clear_comfy_args()
 
 
 
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
import modules.flags as flags
import modules.gradio_hijack as grh
import comfy.model_management as model_management
from modules.sdxl_styles import style_keys, aspect_ratios, fooocus_expansion, default_styles
 
 
 
def generate_clicked(*args):
    execution_start_time = time.perf_counter()
 
    yield gr.update(visible=True, value=modules.html.make_progress_html(1, 'Initializing ...')), \
        gr.update(visible=True, value=None), \
        gr.update(visible=False)
 
    worker.buffer.append(list(args))
    finished = False
 
    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(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(visible=False), \
                    gr.update(visible=False), \
                    gr.update(visible=True, value=product)
                finished = True
 
    execution_time = time.perf_counter() - execution_start_time
    print(f'Total time: {execution_time:.2f} seconds')
    return
 
 
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():
            progress_window = grh.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, elem_classes='type_row', lines=1024)
                with gr.Column(scale=0.15, min_width=0):
                    run_button = gr.Button(label="Generate", value="Generate", elem_classes='type_row', visible=True)
                    stop_button = gr.Button(label="Stop", value="Stop", elem_classes='type_row', visible=False)
 
                    def stop_clicked():
                        model_management.interrupt_current_processing()
                        return gr.update(interactive=False)
 
                    stop_button.click(stop_clicked, outputs=stop_button, queue=False)
            with gr.Row(elem_classes='advanced_check_row'):
                input_image_checkbox = gr.Checkbox(label='Input Image', value=False, container=False, elem_classes='min_check')
                advanced_checkbox = gr.Checkbox(label='Advanced', value=False, container=False, elem_classes='min_check')
            with gr.Row(visible=False) as image_input_panel:
                with gr.Tabs():
                    with gr.TabItem(label='Upscale or Variation') as uov_tab:
                        with gr.Row():
                            with gr.Column():
                                uov_input_image = grh.Image(label='Drag above image to here', source='upload', type='numpy')
                            with gr.Column():
                                uov_method = gr.Radio(label='Upscale or Variation:', choices=flags.uov_list, value=flags.disabled)
                                gr.HTML('<a href="https://github.com/lllyasviel/Fooocus/discussions/390">\U0001F4D4 Document</a>')
                    with gr.TabItem(label='Inpaint or Outpaint (beta)') as inpaint_tab:
                        inpaint_input_image = grh.Image(label='Drag above image to here', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF")
                        gr.HTML('Outpaint Expansion (<a href="https://github.com/lllyasviel/Fooocus/discussions/414">\U0001F4D4 Document</a>):')
                        outpaint_selections = gr.CheckboxGroup(choices=['Left', 'Right', 'Top', 'Bottom'], value=[], label='Outpaint', show_label=False, container=False)
                        gr.HTML('* \"Inpaint or Outpaint\" is powered by the sampler \"DPMPP Fooocus Seamless 2M SDE Karras Inpaint Sampler\" (beta)')
 
            input_image_checkbox.change(lambda x: gr.update(visible=x), inputs=input_image_checkbox, outputs=image_input_panel, queue=False,
                                        _js="(x) => {if(x){setTimeout(() => window.scrollTo({ top: window.scrollY + 500, behavior: 'smooth' }), 50);}else{setTimeout(() => window.scrollTo({ top: 0, behavior: 'smooth' }), 50);} return x}")
 
            current_tab = gr.Textbox(value='uov', visible=False)
 
            default_image = None
 
            def update_default_image(x):
                global default_image
                if isinstance(x, dict):
                    default_image = x['image']
                else:
                    default_image = x
                return
 
            def clear_default_image():
                global default_image
                default_image = None
                return
 
            uov_input_image.upload(update_default_image, inputs=uov_input_image, queue=False)
            inpaint_input_image.upload(update_default_image, inputs=inpaint_input_image, queue=False)
 
            uov_input_image.clear(clear_default_image, queue=False)
            inpaint_input_image.clear(clear_default_image, queue=False)
 
            uov_tab.select(lambda: ['uov', default_image], outputs=[current_tab, uov_input_image], queue=False)
            inpaint_tab.select(lambda: ['inpaint', default_image], outputs=[current_tab, inpaint_input_image], queue=False)
 
        with gr.Column(scale=0.5, visible=False) as right_col:
            with gr.Tab(label='Setting'):
                performance_selction = gr.Radio(label='Performance', choices=['Speed', 'Quality'], value='Speed')
                aspect_ratios_selction = gr.Radio(label='Aspect Ratios', choices=list(aspect_ratios.keys()),
                                                  value='1152×896', info='width × height')
                image_number = gr.Slider(label='Image Number', minimum=1, maximum=32, step=1, value=2)
                negative_prompt = gr.Textbox(label='Negative Prompt', show_label=True, placeholder="Type prompt here.",
                                             info='Describing objects that you do not want to see.')
                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], queue=False)
 
            with gr.Tab(label='Style'):
                style_selections = gr.CheckboxGroup(show_label=False, container=False,
                                                    choices=[fooocus_expansion] + style_keys,
                                                    value=[fooocus_expansion] + default_styles,
                                                    label='Image Style')
            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=30.0, 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, queue=False)
 
        advanced_checkbox.change(lambda x: gr.update(visible=x), advanced_checkbox, right_col, queue=False)
        ctrls = [
            prompt, negative_prompt, style_selections,
            performance_selction, aspect_ratios_selction, image_number, image_seed, sharpness
        ]
        ctrls += [base_model, refiner_model] + lora_ctrls
        ctrls += [input_image_checkbox, current_tab]
        ctrls += [uov_method, uov_input_image]
        ctrls += [outpaint_selections, inpaint_input_image]
 
        run_button.click(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=False), []), outputs=[stop_button, run_button, gallery])\
            .then(fn=refresh_seed, inputs=[seed_random, image_seed], outputs=image_seed)\
            .then(fn=generate_clicked, inputs=ctrls, outputs=[progress_html, progress_window, gallery])\
            .then(lambda: (gr.update(visible=True), gr.update(visible=False)), outputs=[run_button, stop_button])
 
 
parser = argparse.ArgumentParser()
parser.add_argument("--port", type=int, default=None, help="Set the listen port.")
parser.add_argument("--share", action='store_true', default=False,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)

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