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app.py

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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("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN)
+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=[2], 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(prompt, source_img): 
+         
+    source_image = resize(512, source_img)
+    source_image.save('source.png')
+    images_list = img_pipe([prompt] * 2, init_image=source_image, strength=0.75)
+    images = []
+    safe_image = Image.open(r"unsafe.png")
+    for i, image in enumerate(images_list["sample"]):
+        if(images_list["nsfw_content_detected"][i]):
+            images.append(safe_image)
+        else:
+            images.append(image)    
+    return images
+
+print("Great sylvain ! Everything is working fine !")
+
+title="Img2Img Stable Diffusion CPU"
+description="Img2Img Stable Diffusion example using CPU and HF token. <br />Warning: Slow process... ~5/10 min inference time. <b>NSFW filter enabled.</b>" 
+
+gr.Interface(fn=infer, inputs=["text", source_img], outputs=gallery,title=title,description=description).queue(max_size=100).launch(enable_queue=True)