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| import gradio as gr | |
| import torch | |
| #from torch import autocast // only for GPU | |
| from PIL import Image | |
| import numpy as np | |
| from io import BytesIO | |
| import os | |
| MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD') | |
| #from diffusers import StableDiffusionPipeline | |
| from diffusers import StableDiffusionImg2ImgPipeline | |
| print("hello sylvain") | |
| YOUR_TOKEN=MY_SECRET_TOKEN | |
| device="cpu" | |
| #prompt_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN) | |
| #prompt_pipe.to(device) | |
| img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained( | |
| "runwayml/stable-diffusion-v1-5", | |
| use_auth_token=YOUR_TOKEN, | |
| safety_checker=None, # ← disable safety checker | |
| ) | |
| img_pipe.to(device) | |
| source_img = gr.Image(source="upload", type="filepath", label="init_img | 512*512 px") | |
| gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[1], height="auto") | |
| def resize(value,img): | |
| #baseheight = value | |
| img = Image.open(img) | |
| #hpercent = (baseheight/float(img.size[1])) | |
| #wsize = int((float(img.size[0])*float(hpercent))) | |
| #img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS) | |
| img = img.resize((value,value), Image.Resampling.LANCZOS) | |
| return img | |
| def infer(source_img, prompt, guide, steps, seed, strength): | |
| generator = torch.Generator("cpu").manual_seed(seed) | |
| source_image = Image.open(source_img).convert("RGB") | |
| source_image = source_image.resize((512, 512), Image.Resampling.LANCZOS) | |
| result = img_pipe( | |
| [prompt], | |
| image=source_image, | |
| strength=strength, | |
| guidance_scale=guide, | |
| num_inference_steps=steps, | |
| generator=generator | |
| ) | |
| output_images = result["images"] | |
| output_paths = [] | |
| for idx, img in enumerate(output_images): | |
| filename = f"output_{seed}_{idx}.png" | |
| save_path = os.path.join("outputs", filename) | |
| os.makedirs("outputs", exist_ok=True) | |
| img.save(save_path) | |
| print(f"Saved image to: {save_path}") | |
| output_paths.append(save_path) | |
| # Optional: return paths or Gradio can render them too | |
| return output_images | |
| print("Great sylvain ! Everything is working fine !") | |
| title="Img2Img Stable Diffusion CPU" | |
| description="<p style='text-align: center;'>Img2Img Stable Diffusion example using CPU and HF token. <br />Warning: Slow process... ~5/10 min inference time. <b>NSFW filter enabled. <br /> <img id='visitor-badge' alt='visitor badge' src='https://visitor-badge.glitch.me/badge?page_id=gradio-blocks.stable-diffusion-img2img' style='display: inline-block'/></b></p>" | |
| gr.Interface(fn=infer, inputs=[source_img, | |
| "text", | |
| gr.Slider(2, 15, value = 7, label = 'Guidence Scale'), | |
| gr.Slider(10, 50, value = 25, step = 1, label = 'Number of Iterations'), | |
| gr.Slider(label = "Seed", minimum = 0, maximum = 2147483647, step = 1, randomize = True), | |
| gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .75)], | |
| outputs=gallery,title=title,description=description, allow_flagging="manual", flagging_dir="flagged").queue(max_size=100).launch(enable_queue=True) |