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()