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gradio_sd_inpainting.py
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import pdb
import torch
from diffusers import UniPCMultistepScheduler, AutoencoderKL
from diffusers.pipelines import StableDiffusionInpaintPipeline
import gradio as gr
import argparse
from garment_adapter.garment_diffusion import ClothAdapter
from pipelines.OmsDiffusionInpaintPipeline import OmsDiffusionInpaintPipeline
parser = argparse.ArgumentParser(description='oms diffusion')
parser.add_argument('--model_path', type=str, required=True)
parser.add_argument('--pipe_path', type=str, default="runwayml/stable-diffusion-inpainting")
args = parser.parse_args()
device = "cuda"
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse").to(dtype=torch.float16)
pipe = OmsDiffusionInpaintPipeline.from_pretrained(args.pipe_path, vae=vae, torch_dtype=torch.float16)
pipe.safety_checker = None
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
full_net = ClothAdapter(pipe, args.model_path, device, False)
def process(person_image, person_mask, cloth_image, cloth_mask_image, num_samples, width, height, sample_steps, cloth_guidance_scale, seed):
# person_image = person_image_mask['background'].convert("RGB")
# person_mask = person_image_mask['layers'][0].split()[-1]
images, cloth_mask_image = full_net.generate_inpainting(cloth_image, cloth_mask_image, num_samples, seed, cloth_guidance_scale, sample_steps, height, width, image=person_image, mask_image=person_mask)
return images, cloth_mask_image
block = gr.Blocks().queue()
with block:
with gr.Row():
gr.Markdown("##You can enlarge image resolution to get better face, but the cloth maybe lose control, we will release high-resolution checkpoint soon##")
with gr.Row():
with gr.Column():
cloth_image = gr.Image(label="cloth Image", type="pil")
cloth_mask_image = gr.Image(label="cloth mask Image, if not support, will be produced by inner segment algorithm", type="pil")
run_button = gr.Button(value="Run")
with gr.Accordion("Advanced options", open=False):
num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
height = gr.Slider(label="Height", minimum=256, maximum=1024, value=1024, step=64)
width = gr.Slider(label="Width", minimum=192, maximum=768, value=768, step=64)
sample_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
cloth_guidance_scale = gr.Slider(label="Cloth guidance Scale", minimum=1, maximum=10., value=2.5, step=0.1)
seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, value=1234)
with gr.Column():
person_image = gr.Image(label="person Image", type="pil")
person_mask = gr.Image(label="person mask", type="pil")
# person_image_mask = gr.ImageMask(label="person Image", type="pil")
with gr.Column():
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery")
cloth_seg_image = gr.Image(label="cloth mask", type="pil", width=192, height=256)
ips = [person_image, person_mask, cloth_image, cloth_mask_image, num_samples, width, height, sample_steps, cloth_guidance_scale, seed]
run_button.click(fn=process, inputs=ips, outputs=[result_gallery, cloth_seg_image])
block.launch(server_name="0.0.0.0", server_port=7860)