mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-18 22:08:13 +00:00
316 lines
18 KiB
Python
316 lines
18 KiB
Python
import gradio as gr
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from diffsynth import ModelManager, FluxImagePipeline, download_customized_models
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import os, torch
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from PIL import Image
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import numpy as np
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from PIL import ImageDraw, ImageFont
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import random
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import json
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def save_mask_prompts(masks, mask_prompts, global_prompt, seed=0, random_dir='0000000'):
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save_dir = os.path.join('workdirs/tmp_mask', random_dir)
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os.makedirs(save_dir, exist_ok=True)
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for i, mask in enumerate(masks):
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save_path = os.path.join(save_dir, f'{i}.png')
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mask.save(save_path)
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sample = {
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"global_prompt": global_prompt,
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"mask_prompts": mask_prompts,
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"seed": seed,
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}
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with open(os.path.join(save_dir, f"prompts.json"), 'w') as f:
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json.dump(sample, f, indent=4)
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def visualize_masks(image, masks, mask_prompts, font_size=35, use_random_colors=False):
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# Create a blank image for overlays
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overlay = Image.new('RGBA', image.size, (0, 0, 0, 0))
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colors = [
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(165, 238, 173, 80),
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(76, 102, 221, 80),
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(221, 160, 77, 80),
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(204, 93, 71, 80),
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(145, 187, 149, 80),
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(134, 141, 172, 80),
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(157, 137, 109, 80),
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(153, 104, 95, 80),
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(165, 238, 173, 80),
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(76, 102, 221, 80),
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(221, 160, 77, 80),
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(204, 93, 71, 80),
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(145, 187, 149, 80),
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(134, 141, 172, 80),
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(157, 137, 109, 80),
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(153, 104, 95, 80),
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]
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# Generate random colors for each mask
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if use_random_colors:
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colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))]
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# Font settings
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try:
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font = ImageFont.truetype("arial", font_size) # Adjust as needed
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except IOError:
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font = ImageFont.load_default(font_size)
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# Overlay each mask onto the overlay image
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for mask, mask_prompt, color in zip(masks, mask_prompts, colors):
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# Convert mask to RGBA mode
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mask_rgba = mask.convert('RGBA')
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mask_data = mask_rgba.getdata()
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new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data]
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mask_rgba.putdata(new_data)
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# Draw the mask prompt text on the mask
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draw = ImageDraw.Draw(mask_rgba)
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mask_bbox = mask.getbbox() # Get the bounding box of the mask
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text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position
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draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font)
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# Alpha composite the overlay with this mask
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overlay = Image.alpha_composite(overlay, mask_rgba)
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# Composite the overlay onto the original image
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result = Image.alpha_composite(image.convert('RGBA'), overlay)
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return result
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config = {
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"model_config": {
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"FLUX": {
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"model_folder": "models/FLUX",
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"pipeline_class": FluxImagePipeline,
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"default_parameters": {
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"cfg_scale": 3.0,
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"embedded_guidance": 3.5,
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"num_inference_steps": 50,
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}
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},
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},
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"max_num_painter_layers": 8,
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"max_num_model_cache": 1,
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}
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def load_model_list(model_type):
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if model_type is None:
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return []
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folder = config["model_config"][model_type]["model_folder"]
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file_list = [i for i in os.listdir(folder) if i.endswith(".safetensors")]
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if model_type in ["HunyuanDiT", "Kolors", "FLUX"]:
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file_list += [i for i in os.listdir(folder) if os.path.isdir(os.path.join(folder, i))]
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file_list = sorted(file_list)
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return file_list
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model_dict = {}
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def load_model(model_type, model_path):
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global model_dict
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model_key = f"{model_type}:{model_path}"
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if model_key in model_dict:
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return model_dict[model_key]
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model_path = os.path.join(config["model_config"][model_type]["model_folder"], model_path)
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model_manager = ModelManager()
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if model_type == "FLUX":
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model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"])
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model_manager.load_lora(
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download_customized_models(
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model_id="DiffSynth-Studio/Eligen",
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origin_file_path="model_bf16.safetensors",
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local_dir="models/lora/entity_control",
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),
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lora_alpha=1,
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)
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else:
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model_manager.load_model(model_path)
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pipe = config["model_config"][model_type]["pipeline_class"].from_model_manager(model_manager)
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while len(model_dict) + 1 > config["max_num_model_cache"]:
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key = next(iter(model_dict.keys()))
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model_manager_to_release, _ = model_dict[key]
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model_manager_to_release.to("cpu")
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del model_dict[key]
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torch.cuda.empty_cache()
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model_dict[model_key] = model_manager, pipe
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return model_manager, pipe
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with gr.Blocks() as app:
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gr.Markdown("""
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# 实体级控制文生图模型EliGen
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**UI说明**
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1. **点击Load model读取模型**，然后左侧界面为文生图输入参数；右侧Painter为局部控制区域绘制区域，每个局部控制条件由其Local prompt和绘制的mask组成，支持精准控制文生图和Inpainting两种模式。
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2. **精准控制生图模式：** 输入Globalprompt；激活并绘制一个或多个局部控制条件，点击Generate生成图像; Global Prompt推荐包含每个Local Prompt。
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3. **Inpainting模式：** 你可以上传图像，或者将上一步生成的图像设置为Inpaint Input Image，采用类似的方式输入局部控制条件，进行局部重绘。
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4. 尽情创造！
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""")
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gr.Markdown("""
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# Entity-Level Controlled Text-to-Image Model: EliGen
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**UI Instructions**
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1. **Click "Load model" to load the model.** The left interface is for text-to-image input parameters; the right "Painter" is the area for drawing local control regions. Each local control condition consists of its Local Prompt and the drawn mask, supporting both precise control text-to-image and Inpainting modes.
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2. **Precise Control Image Generation Mode:** Enter the Global Prompt; activate and draw one or more local control conditions, then click "Generate" to create the image. It is recommended that the Global Prompt includes all Local Prompts.
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3. **Inpainting Mode:** You can upload an image or set the image generated in the previous step as the "Inpaint Input Image." Use a similar method to input local control conditions for local redrawing.
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4. Enjoy!
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""")
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with gr.Row():
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random_mask_dir = gr.State('')
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with gr.Column(scale=382, min_width=100):
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model_type = gr.State('FLUX')
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model_path = gr.State('FLUX.1-dev')
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with gr.Accordion(label="Model"):
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load_model_button = gr.Button(value="Load model")
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with gr.Accordion(label="Global prompt"):
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prompt = gr.Textbox(label="Prompt", lines=3)
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negative_prompt = gr.Textbox(label="Negative prompt", value="worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,", lines=1)
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cfg_scale = gr.Slider(minimum=1.0, maximum=10.0, value=7.0, step=0.1, interactive=True, label="Classifier-free guidance scale")
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embedded_guidance = gr.Slider(minimum=0.0, maximum=10.0, value=0.0, step=0.1, interactive=True, label="Embedded guidance scale")
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with gr.Accordion(label="Inference Options"):
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num_inference_steps = gr.Slider(minimum=1, maximum=100, value=20, step=1, interactive=True, label="Inference steps")
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height = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Height")
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width = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Width")
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return_with_mask = gr.Checkbox(value=True, interactive=True, label="show result with mask painting")
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with gr.Column():
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use_fixed_seed = gr.Checkbox(value=True, interactive=False, label="Use fixed seed")
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seed = gr.Number(minimum=0, maximum=10**9, value=0, interactive=True, label="Random seed", show_label=False)
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with gr.Accordion(label="Inpaint Input Image (Testing)"):
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input_image = gr.Image(sources=None, show_label=False, interactive=True, type="pil")
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background_weight = gr.Slider(minimum=0.0, maximum=1000., value=0., step=1, interactive=False, label="background_weight")
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with gr.Column():
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reset_input_button = gr.Button(value="Reset Inpaint Input")
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send_input_to_painter = gr.Button(value="Set as painter's background")
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@gr.on(inputs=[input_image], outputs=[input_image], triggers=reset_input_button.click)
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def reset_input_image(input_image):
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return None
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@gr.on(
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inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask],
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outputs=[prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, load_model_button, random_mask_dir],
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triggers=load_model_button.click
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)
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def model_path_to_default_params(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask):
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load_model(model_type, model_path)
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cfg_scale = config["model_config"][model_type]["default_parameters"].get("cfg_scale", cfg_scale)
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embedded_guidance = config["model_config"][model_type]["default_parameters"].get("embedded_guidance", embedded_guidance)
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num_inference_steps = config["model_config"][model_type]["default_parameters"].get("num_inference_steps", num_inference_steps)
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height = config["model_config"][model_type]["default_parameters"].get("height", height)
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width = config["model_config"][model_type]["default_parameters"].get("width", width)
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return_with_mask = config["model_config"][model_type]["default_parameters"].get("return_with_mask", return_with_mask)
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return prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, gr.update(value="Loaded FLUX"), gr.State(f'{random.randint(0, 1000000):08d}')
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with gr.Column(scale=618, min_width=100):
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with gr.Accordion(label="Painter"):
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enable_local_prompt_list = []
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local_prompt_list = []
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mask_scale_list = []
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canvas_list = []
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for painter_layer_id in range(config["max_num_painter_layers"]):
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with gr.Tab(label=f"Layer {painter_layer_id}"):
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enable_local_prompt = gr.Checkbox(label="Enable", value=False, key=f"enable_local_prompt_{painter_layer_id}")
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local_prompt = gr.Textbox(label="Local prompt", key=f"local_prompt_{painter_layer_id}")
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mask_scale = gr.Slider(minimum=0.0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Mask scale", key=f"mask_scale_{painter_layer_id}")
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canvas = gr.ImageEditor(canvas_size=(512, 1), sources=None, layers=False, interactive=True, image_mode="RGBA",
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brush=gr.Brush(default_size=30, default_color="#000000", colors=["#000000"]),
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label="Painter", key=f"canvas_{painter_layer_id}")
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@gr.on(inputs=[height, width, canvas], outputs=canvas, triggers=[height.change, width.change, canvas.clear, enable_local_prompt.change], show_progress="hidden")
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def resize_canvas(height, width, canvas):
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h, w = canvas["background"].shape[:2]
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if h != height or width != w:
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return np.ones((height, width, 3), dtype=np.uint8) * 255
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else:
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return canvas
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enable_local_prompt_list.append(enable_local_prompt)
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local_prompt_list.append(local_prompt)
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mask_scale_list.append(mask_scale)
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canvas_list.append(canvas)
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with gr.Accordion(label="Results"):
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run_button = gr.Button(value="Generate", variant="primary")
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output_image = gr.Image(sources=None, show_label=False, interactive=False, type="pil")
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with gr.Row():
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with gr.Column():
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output_to_painter_button = gr.Button(value="Set as painter's background")
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with gr.Column():
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output_to_input_button = gr.Button(value="Set as input image")
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real_output = gr.State(None)
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mask_out = gr.State(None)
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@gr.on(
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inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir] + enable_local_prompt_list + local_prompt_list + mask_scale_list + canvas_list,
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outputs=[output_image, real_output, mask_out],
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triggers=run_button.click
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)
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def generate_image(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir, *args, progress=gr.Progress()):
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_, pipe = load_model(model_type, model_path)
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input_params = {
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"prompt": prompt,
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"negative_prompt": negative_prompt,
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"cfg_scale": cfg_scale,
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"num_inference_steps": num_inference_steps,
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"height": height,
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"width": width,
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"progress_bar_cmd": progress.tqdm,
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}
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if isinstance(pipe, FluxImagePipeline):
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input_params["embedded_guidance"] = embedded_guidance
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if input_image is not None:
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input_params["input_image"] = input_image.resize((width, height)).convert("RGB")
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input_params["enable_eligen_inpaint"] = True
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enable_local_prompt_list, local_prompt_list, mask_scale_list, canvas_list = (
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args[0 * config["max_num_painter_layers"]: 1 * config["max_num_painter_layers"]],
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args[1 * config["max_num_painter_layers"]: 2 * config["max_num_painter_layers"]],
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args[2 * config["max_num_painter_layers"]: 3 * config["max_num_painter_layers"]],
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args[3 * config["max_num_painter_layers"]: 4 * config["max_num_painter_layers"]]
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)
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local_prompts, masks, mask_scales = [], [], []
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for enable_local_prompt, local_prompt, mask_scale, canvas in zip(
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enable_local_prompt_list, local_prompt_list, mask_scale_list, canvas_list
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):
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if enable_local_prompt:
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local_prompts.append(local_prompt)
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masks.append(Image.fromarray(canvas["layers"][0][:, :, -1]).convert("RGB"))
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mask_scales.append(mask_scale)
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entity_masks = None if len(masks) == 0 else masks
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entity_prompts = None if len(local_prompts) == 0 else local_prompts
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input_params.update({
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"eligen_entity_prompts": entity_prompts,
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"eligen_entity_masks": entity_masks,
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})
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torch.manual_seed(seed)
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image = pipe(**input_params)
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# visualize masks
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masks = [mask.resize(image.size) for mask in masks]
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image_with_mask = visualize_masks(image, masks, local_prompts)
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# save_mask_prompts(masks, local_prompts, prompt, seed, random_mask_dir.value)
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real_output = gr.State(image)
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mask_out = gr.State(image_with_mask)
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if return_with_mask:
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return image_with_mask, real_output, mask_out
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return image, real_output, mask_out
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@gr.on(inputs=[input_image] + canvas_list, outputs=canvas_list, triggers=send_input_to_painter.click)
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def send_input_to_painter_background(input_image, *canvas_list):
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if input_image is None:
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return tuple(canvas_list)
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for canvas in canvas_list:
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h, w = canvas["background"].shape[:2]
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canvas["background"] = input_image.resize((w, h))
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return tuple(canvas_list)
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@gr.on(inputs=[real_output] + canvas_list, outputs=canvas_list, triggers=output_to_painter_button.click)
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def send_output_to_painter_background(real_output, *canvas_list):
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if real_output is None:
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return tuple(canvas_list)
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for canvas in canvas_list:
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h, w = canvas["background"].shape[:2]
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canvas["background"] = real_output.value.resize((w, h))
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return tuple(canvas_list)
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@gr.on(inputs=[return_with_mask, real_output, mask_out], outputs=[output_image], triggers=[return_with_mask.change], show_progress="hidden")
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def show_output(return_with_mask, real_output, mask_out):
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if return_with_mask:
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return mask_out.value
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else:
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return real_output.value
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@gr.on(inputs=[real_output], outputs=[input_image], triggers=output_to_input_button.click)
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def send_output_to_pipe_input(real_output):
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return real_output.value
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app.launch()
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