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import gradio as gr |
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import torch |
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from PIL import Image |
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import numpy as np |
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from io import BytesIO |
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import os |
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MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD') |
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from diffusers import StableDiffusionImg2ImgPipeline |
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print("hello sylvain") |
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YOUR_TOKEN=MY_SECRET_TOKEN |
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device="cpu" |
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img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_auth_token=YOUR_TOKEN) |
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img_pipe.to(device) |
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source_img = gr.Image(source="upload", type="filepath", label="init_img | 512*512 px") |
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gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[1], height="auto") |
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def resize(value,img): |
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img = Image.open(img) |
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img = img.resize((value,value), Image.Resampling.LANCZOS) |
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return img |
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def infer(source_img, prompt, guide, steps, seed, strength): |
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generator = torch.Generator('cpu').manual_seed(seed) |
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source_image = resize(512, source_img) |
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source_image.save('source.png') |
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images_list = img_pipe([prompt] * 1, init_image=source_image, strength=strength, guidance_scale=guide, num_inference_steps=steps) |
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images = [] |
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safe_image = Image.open(r"unsafe.png") |
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for i, image in enumerate(images_list["images"]): |
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if(images_list["nsfw_content_detected"][i]): |
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images.append(safe_image) |
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else: |
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images.append(image) |
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return images |
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print("Great sylvain ! Everything is working fine !") |
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title="Img2Img Stable Diffusion CPU" |
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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>" |
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gr.Interface(fn=infer, inputs=[source_img, |
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"text", |
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gr.Slider(2, 15, value = 7, label = 'Guidence Scale'), |
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gr.Slider(10, 50, value = 25, step = 1, label = 'Number of Iterations'), |
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gr.Slider(label = "Seed", minimum = 0, maximum = 2147483647, step = 1, randomize = True), |
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gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .75)], |
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outputs=gallery,title=title,description=description, allow_flagging="manual", flagging_dir="flagged").queue(max_size=100).launch(enable_queue=True) |
