Create app.py

app.py

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+import gradio as gr
+import inspect
+import warnings
+import numpy as np
+from typing import List, Optional, Union
+import requests
+from io import BytesIO
+from PIL import Image
+import torch
+from torch import autocast
+from tqdm.auto import tqdm
+from diffusers import StableDiffusionImg2ImgPipeline
+
+from huggingface_hub import notebook_login
+notebook_login()
+
+device = "cuda"
+model_path = "CompVis/stable-diffusion-v1-4"
+access_token = "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"
+
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    model_path,
+    revision="fp16", 
+    torch_dtype=torch.float16,
+    use_auth_token=access_token
+)
+pipe = pipe.to(device)
+
+def predict(img, strength, seed, prompt):
+  seed =  int(seed)
+  img1 = np.asarray(img)
+  img2 = Image.fromarray(img1)
+
+  init_image = img2.resize((768, 512))
+
+  
+  generator = torch.Generator(device=device).manual_seed(seed)
+  with autocast("cuda"):
+    image = pipe(prompt=prompt, init_image=init_image, strength=strength, guidance_scale=5, generator=generator).images[0]
+
+  return image
+
+gr.Interface(
+    predict,
+    title = 'Image to Image using Diffusers',
+    inputs=[
+        gr.Image(),
+        gr.Slider(0, 1, value=0.05, label ="strength (keep it close to 0 to make minimal changes to image (such as 0.1, 0.2, 0.3)"),
+        gr.Number(label = "seed (any number, generally 1024. But it's totally random. Change it and see different outputs)"),
+        gr.Textbox(label="Prompt, empty by default")
+    ],
+    outputs = [
+        gr.Image()
+        ]
+).launch()