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Configuration error
Configuration error
| 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) | |