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on
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Running
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Zero
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import spaces
import gradio as gr
from tryon_inference import run_inference
import os
import numpy as np
from PIL import Image
import tempfile
import torch
from diffusers import FluxTransformer2DModel, FluxFillPipeline
import subprocess
subprocess.run("rm -rf /data-nvme/zerogpu-offload/*", env={}, shell=True)
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Start loading LoRA weights")
state_dict, network_alphas = FluxFillPipeline.lora_state_dict(
pretrained_model_name_or_path_or_dict="xiaozaa/catvton-flux-lora-alpha", ## The tryon Lora weights
weight_name="pytorch_lora_weights.safetensors",
return_alphas=True
)
is_correct_format = all("lora" in key or "dora_scale" in key for key in state_dict.keys())
if not is_correct_format:
raise ValueError("Invalid LoRA checkpoint.")
print('Loading diffusion model ...')
pipe = FluxFillPipeline.from_pretrained(
"black-forest-labs/FLUX.1-Fill-dev",
torch_dtype=torch.bfloat16
).to(device)
FluxFillPipeline.load_lora_into_transformer(
state_dict=state_dict,
network_alphas=network_alphas,
transformer=pipe.transformer,
)
print('Loading Finished!')
@spaces.GPU
def gradio_inference(
image_data,
garment,
num_steps=50,
guidance_scale=30.0,
seed=-1,
width=768,
height=1024
):
"""Wrapper function for Gradio interface"""
# Use temporary directory
with tempfile.TemporaryDirectory() as tmp_dir:
# Save inputs to temp directory
temp_image = os.path.join(tmp_dir, "image.png")
temp_mask = os.path.join(tmp_dir, "mask.png")
temp_garment = os.path.join(tmp_dir, "garment.png")
# Extract image and mask from ImageEditor data
image = image_data["background"]
mask = image_data["layers"][0] # First layer contains the mask
# Convert to numpy array and process mask
mask_array = np.array(mask)
is_black = np.all(mask_array < 10, axis=2)
mask = Image.fromarray(((~is_black) * 255).astype(np.uint8))
# Save files to temp directory
image.save(temp_image)
mask.save(temp_mask)
garment.save(temp_garment)
try:
# Run inference
_, tryon_result = run_inference(
pipe=pipe,
image_path=temp_image,
mask_path=temp_mask,
garment_path=temp_garment,
num_steps=num_steps,
guidance_scale=guidance_scale,
seed=seed,
size=(width, height)
)
return tryon_result
except Exception as e:
raise gr.Error(f"Error during inference: {str(e)}")
with gr.Blocks() as demo:
gr.Markdown("""
# CATVTON FLUX Virtual Try-On Demo (by using LoRA weights)
Upload a model image, draw a mask, and a garment image to generate virtual try-on results.
[](https://huggingface.co/xiaozaa/catvton-flux-alpha)
[](https://github.com/nftblackmagic/catvton-flux)
""")
# gr.Video("example/github.mp4", label="Demo Video: How to use the tool")
with gr.Column():
with gr.Row():
with gr.Column():
image_input = gr.ImageMask(
label="Model Image (Click 'Edit' and draw mask over the clothing area)",
type="pil",
height=600,
width=300
)
gr.Examples(
examples=[
["./example/person/00008_00.jpg"],
["./example/person/00055_00.jpg"],
["./example/person/00057_00.jpg"],
["./example/person/00067_00.jpg"],
["./example/person/00069_00.jpg"],
],
inputs=[image_input],
label="Person Images",
)
with gr.Column():
garment_input = gr.Image(label="Garment Image", type="pil", height=600, width=300)
gr.Examples(
examples=[
["./example/garment/04564_00.jpg"],
["./example/garment/00055_00.jpg"],
["./example/garment/00396_00.jpg"],
["./example/garment/00067_00.jpg"],
["./example/garment/00069_00.jpg"],
],
inputs=[garment_input],
label="Garment Images",
)
with gr.Column():
tryon_output = gr.Image(label="Try-On Result", height=600, width=300)
with gr.Row():
num_steps = gr.Slider(
minimum=1,
maximum=100,
value=30,
step=1,
label="Number of Steps"
)
guidance_scale = gr.Slider(
minimum=1.0,
maximum=50.0,
value=30.0,
step=0.5,
label="Guidance Scale"
)
seed = gr.Slider(
minimum=-1,
maximum=2147483647,
step=1,
value=-1,
label="Seed (-1 for random)"
)
width = gr.Slider(
minimum=256,
maximum=1024,
step=64,
value=768,
label="Width"
)
height = gr.Slider(
minimum=256,
maximum=1024,
step=64,
value=1024,
label="Height"
)
submit_btn = gr.Button("Generate Try-On", variant="primary")
with gr.Row():
gr.Markdown("""
### Notes:
- The model is trained on VITON-HD dataset. It focuses on the woman upper body try-on generation.
- The mask should indicate the region where the garment will be placed.
- The garment image should be on a clean background.
- The model is not perfect. It may generate some artifacts.
- The model is slow. Please be patient.
- The model is just for research purpose.
""")
submit_btn.click(
fn=gradio_inference,
inputs=[
image_input,
garment_input,
num_steps,
guidance_scale,
seed,
width,
height
],
outputs=[tryon_output],
api_name="try-on"
)
demo.launch()
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