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| import os | |
| import gradio as gr | |
| import torch | |
| print(f"Is CUDA available: {torch.cuda.is_available()}") | |
| # True | |
| print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}") | |
| from diffusers import StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipeline | |
| os.environ['GRADIO_THEME'] = 'default' | |
| # load the pipeline | |
| device = "cuda" | |
| model_id_img2img = "runwayml/stable-diffusion-v1-5" | |
| img2img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_img2img, torch_dtype=torch.float16) | |
| img2img_pipe = img2img_pipe.to(device) | |
| model_id_inpaint = "runwayml/stable-diffusion-inpainting" | |
| inpaint_pipe = StableDiffusionInpaintPipeline.from_pretrained(model_id_inpaint, torch_dtype=torch.float16) | |
| inpaint_pipe = inpaint_pipe.to(device) | |
| def img2img_diff(prompt, pil_img): | |
| img = pil_img.resize((768, 512)) | |
| return img2img_pipe(prompt=prompt, image=img, strength=0.75, guidance_scale=7.5).images[0] | |
| def imginpaint_diff(prompt, pil_img, mask_pil_img): | |
| return inpaint_pipe(prompt=prompt, image=pil_img, mask_image=mask_pil_img).images[0] | |
| def header_html(title): | |
| return f""" | |
| <div style="text-align: center; max-width: 650px; margin: 0 auto; padding-top: 7px;"> | |
| <div | |
| style=" | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 0.8rem; | |
| font-size: 1.75rem; | |
| " | |
| > | |
| <h1 style="font-weight: 900; margin-bottom: 7px;"> | |
| {title} | |
| </h1> | |
| </div> | |
| </div> | |
| """ | |
| with gr.Blocks() as block: | |
| with gr.Group(): | |
| with gr.Box(): | |
| gr.HTML(header_html("diffusion image to image transform")) | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_img = gr.Image(type='pil', label='draft image') | |
| with gr.Row(): | |
| input_prompt = gr.Text(lable="prompt text") | |
| sumit_button = gr.Button("Generate image").style( | |
| margin=False, | |
| rounded=(False, True, True, False), | |
| full_width=False, | |
| ) | |
| output_img = gr.Image(type="pil") | |
| sumit_button.click(img2img_diff, inputs=[input_prompt, input_img], outputs=[output_img]) | |
| with gr.Box(): | |
| gr.HTML(header_html("diffusion image inpaint")) | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_img = gr.Image(type='pil', label='origin image') | |
| mask_img = gr.Image(type='pil', label='mask image') | |
| with gr.Row(): | |
| input_prompt = gr.Text(lable="prompt text") | |
| sumit_button = gr.Button("Generate image").style( | |
| margin=False, | |
| rounded=(False, True, True, False), | |
| full_width=False, | |
| ) | |
| output_img = gr.Image(type="pil") | |
| sumit_button.click(imginpaint_diff, inputs=[input_prompt, input_img, mask_img], outputs=[output_img]) | |
| block.queue(concurrency_count=40, max_size=20).launch(max_threads=150) |