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import gradio as gr |
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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 StableDiffusionPipeline |
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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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prompt_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN) |
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prompt_pipe.to(device) |
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img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", 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") |
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gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto") |
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def resize(height,img): |
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baseheight = height |
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img = Image.open(img) |
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hpercent = (baseheight/float(img.size[1])) |
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wsize = int((float(img.size[0])*float(hpercent))) |
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img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS) |
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return img |
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def infer(prompt, source_img): |
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if(source_img != None): |
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init_image = resize(512,source_img) |
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init_image.save('source.png') |
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images_list = img_pipe([prompt] * 2, init_image=init_image, strength=0.75) |
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else: |
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images_list = prompt_pipe([prompt] * 2) |
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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["sample"]): |
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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="Stable Diffusion CPU" |
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description="Stable Diffusion example using CPU and HF token. <br />Warning: Slow process... ~5/10 min inference time. <b>NSFW filter enabled.</b>" |
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gr.Interface(fn=infer, inputs=["text", source_img], outputs=gallery,title=title,description=description).launch(enable_queue=True) |
