import spaces import os os.system("pip install -r requirements.txt") from huggingface_hub import login login(token=os.getenv('HF_AK')) from diffsynth import download_models download_models(["Kolors", "FLUX.1-dev"], downloading_priority=["HuggingFace", "ModelScope"]) def get_file_list(path): file_list = [] for filename in os.listdir(path): file_path = os.path.join(path, filename) if os.path.isdir(file_path): file_list.extend(get_file_list(file_path)) else: file_list.append(file_path) return file_list print(get_file_list("models")) import gradio as gr from diffsynth import ModelManager, SDImagePipeline, SDXLImagePipeline, SD3ImagePipeline, HunyuanDiTImagePipeline, FluxImagePipeline import os, torch from PIL import Image import numpy as np config = { "model_config": { "Stable Diffusion": { "model_folder": "models/stable_diffusion", "pipeline_class": SDImagePipeline, "default_parameters": { "cfg_scale": 7.0, "height": 512, "width": 512, } }, "Stable Diffusion XL": { "model_folder": "models/stable_diffusion_xl", "pipeline_class": SDXLImagePipeline, "default_parameters": { "cfg_scale": 7.0, } }, "Stable Diffusion 3": { "model_folder": "models/stable_diffusion_3", "pipeline_class": SD3ImagePipeline, "default_parameters": { "cfg_scale": 7.0, } }, "Stable Diffusion XL Turbo": { "model_folder": "models/stable_diffusion_xl_turbo", "pipeline_class": SDXLImagePipeline, "default_parameters": { "negative_prompt": "", "cfg_scale": 1.0, "num_inference_steps": 1, "height": 512, "width": 512, } }, "Kolors": { "model_folder": "models/kolors", "pipeline_class": SDXLImagePipeline, "default_parameters": { "cfg_scale": 7.0, } }, "HunyuanDiT": { "model_folder": "models/HunyuanDiT", "pipeline_class": HunyuanDiTImagePipeline, "default_parameters": { "cfg_scale": 7.0, } }, "FLUX": { "model_folder": "models/FLUX", "pipeline_class": FluxImagePipeline, "default_parameters": { "cfg_scale": 1.0, } } }, "max_num_painter_layers": 3, "max_num_model_cache": 2, } def load_model_list(model_type): if model_type is None: return [] folder = config["model_config"][model_type]["model_folder"] file_list = [i for i in os.listdir(folder) if i.endswith(".safetensors")] if model_type in ["HunyuanDiT", "Kolors", "FLUX"]: file_list += [i for i in os.listdir(folder) if os.path.isdir(os.path.join(folder, i))] file_list = sorted(file_list) return file_list def load_model(model_type, model_path): global model_dict model_key = f"{model_type}:{model_path}" if model_key in model_dict: return model_dict[model_key] model_path = os.path.join(config["model_config"][model_type]["model_folder"], model_path) model_manager = ModelManager() if model_type == "HunyuanDiT": model_manager.load_models([ os.path.join(model_path, "clip_text_encoder/pytorch_model.bin"), os.path.join(model_path, "mt5/pytorch_model.bin"), os.path.join(model_path, "model/pytorch_model_ema.pt"), os.path.join(model_path, "sdxl-vae-fp16-fix/diffusion_pytorch_model.bin"), ]) elif model_type == "Kolors": model_manager.load_models([ os.path.join(model_path, "text_encoder"), os.path.join(model_path, "unet/diffusion_pytorch_model.safetensors"), os.path.join(model_path, "vae/diffusion_pytorch_model.safetensors"), ]) elif model_type == "FLUX": model_manager.torch_dtype = torch.bfloat16 file_list = [ os.path.join(model_path, "text_encoder/model.safetensors"), os.path.join(model_path, "text_encoder_2"), ] for file_name in os.listdir(model_path): if file_name.endswith(".safetensors"): file_list.append(os.path.join(model_path, file_name)) model_manager.load_models(file_list) else: model_manager.load_model(model_path) pipe = config["model_config"][model_type]["pipeline_class"].from_model_manager(model_manager) while len(model_dict) + 1 > config["max_num_model_cache"]: key = next(iter(model_dict.keys())) model_manager_to_release, _ = model_dict[key] model_manager_to_release.to("cpu") del model_dict[key] torch.cuda.empty_cache() model_dict[model_key] = model_manager, pipe return model_manager, pipe model_dict = {} with gr.Blocks() as app: gr.Markdown("# DiffSynth-Studio Painter") with gr.Row(): with gr.Column(scale=382, min_width=100): with gr.Accordion(label="Model"): model_type = gr.Dropdown(choices=["Kolors", "FLUX"], label="Model type", value="Kolors") model_path = gr.Dropdown(choices=["Kolors"], interactive=True, label="Model path", value="Kolors") @gr.on(inputs=model_type, outputs=model_path, triggers=model_type.change) def model_type_to_model_path(model_type): return gr.Dropdown(choices=load_model_list(model_type)) with gr.Accordion(label="Prompt"): prompt = gr.Textbox(label="Prompt", lines=3) negative_prompt = gr.Textbox(label="Negative prompt", lines=1) cfg_scale = gr.Slider(minimum=1.0, maximum=10.0, value=7.0, step=0.1, interactive=True, label="Classifier-free guidance scale") embedded_guidance = gr.Slider(minimum=0.0, maximum=10.0, value=0.0, step=0.1, interactive=True, label="Embedded guidance scale (only for FLUX)") with gr.Accordion(label="Image"): num_inference_steps = gr.Slider(minimum=1, maximum=100, value=20, step=1, interactive=True, label="Inference steps") height = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Height") width = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Width") with gr.Column(): use_fixed_seed = gr.Checkbox(value=True, interactive=False, label="Use fixed seed") seed = gr.Number(minimum=0, maximum=10**9, value=0, interactive=True, label="Random seed", show_label=False) @gr.on( inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width], outputs=[prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width], triggers=model_path.change ) def model_path_to_default_params(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width): load_model(model_type, model_path) cfg_scale = config["model_config"][model_type]["default_parameters"].get("cfg_scale", cfg_scale) embedded_guidance = config["model_config"][model_type]["default_parameters"].get("embedded_guidance", embedded_guidance) num_inference_steps = config["model_config"][model_type]["default_parameters"].get("num_inference_steps", num_inference_steps) height = config["model_config"][model_type]["default_parameters"].get("height", height) width = config["model_config"][model_type]["default_parameters"].get("width", width) return prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width with gr.Column(scale=618, min_width=100): with gr.Accordion(label="Painter"): enable_local_prompt_list = [] local_prompt_list = [] mask_scale_list = [] canvas_list = [] for painter_layer_id in range(config["max_num_painter_layers"]): with gr.Tab(label=f"Layer {painter_layer_id}"): enable_local_prompt = gr.Checkbox(label="Enable", value=False, key=f"enable_local_prompt_{painter_layer_id}") local_prompt = gr.Textbox(label="Local prompt", key=f"local_prompt_{painter_layer_id}") 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}") canvas = gr.ImageEditor(canvas_size=(512, 1), sources=None, layers=False, interactive=True, image_mode="RGBA", brush=gr.Brush(default_size=100, default_color="#000000", colors=["#000000"]), label="Painter", key=f"canvas_{painter_layer_id}") @gr.on(inputs=[height, width, canvas], outputs=canvas, triggers=[height.change, width.change, canvas.clear, enable_local_prompt.change], show_progress="hidden") def resize_canvas(height, width, canvas): h, w = canvas["background"].shape[:2] if h != height or width != w: return np.ones((height, width, 3), dtype=np.uint8) * 255 else: return canvas enable_local_prompt_list.append(enable_local_prompt) local_prompt_list.append(local_prompt) mask_scale_list.append(mask_scale) canvas_list.append(canvas) with gr.Accordion(label="Results"): run_button = gr.Button(value="Generate", variant="primary") output_image = gr.Image(sources=None, show_label=False, interactive=False, type="pil") output_to_painter_button = gr.Button(value="Set as painter's background") painter_background = gr.State(None) input_background = gr.State(None) @gr.on( inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, seed] + enable_local_prompt_list + local_prompt_list + mask_scale_list + canvas_list, outputs=[output_image], triggers=run_button.click ) @spaces.GPU(duration=60) def generate_image(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, seed, *args, progress=gr.Progress()): _, pipe = load_model(model_type, model_path) input_params = { "prompt": prompt, "negative_prompt": negative_prompt, "cfg_scale": cfg_scale, "num_inference_steps": num_inference_steps, "height": height, "width": width, "progress_bar_cmd": progress.tqdm, } if isinstance(pipe, FluxImagePipeline): input_params["embedded_guidance"] = embedded_guidance enable_local_prompt_list, local_prompt_list, mask_scale_list, canvas_list = ( args[0 * config["max_num_painter_layers"]: 1 * config["max_num_painter_layers"]], args[1 * config["max_num_painter_layers"]: 2 * config["max_num_painter_layers"]], args[2 * config["max_num_painter_layers"]: 3 * config["max_num_painter_layers"]], args[3 * config["max_num_painter_layers"]: 4 * config["max_num_painter_layers"]] ) local_prompts, masks, mask_scales = [], [], [] for enable_local_prompt, local_prompt, mask_scale, canvas in zip( enable_local_prompt_list, local_prompt_list, mask_scale_list, canvas_list ): if enable_local_prompt: local_prompts.append(local_prompt) masks.append(Image.fromarray(canvas["layers"][0][:, :, -1]).convert("RGB")) mask_scales.append(mask_scale) input_params.update({ "local_prompts": local_prompts, "masks": masks, "mask_scales": mask_scales, }) torch.manual_seed(seed) image = pipe(**input_params) return image @gr.on(inputs=[output_image] + canvas_list, outputs=canvas_list, triggers=output_to_painter_button.click) def send_output_to_painter_background(output_image, *canvas_list): for canvas in canvas_list: h, w = canvas["background"].shape[:2] canvas["background"] = output_image.resize((w, h)) return tuple(canvas_list) app.launch()