app.py

app.py

@@ -0,0 +1,58 @@
+!pip install diffusers==0.3.0 transformers ftfy
+!pip install -qq "ipywidgets>=7,<8"
+! pip install gradio 
+import gradio as gr
+from huggingface_hub import notebook_login
+import inspect
+import warnings
+from typing import List, Optional, Union
+
+import torch
+from torch import autocast
+from tqdm.auto import tqdm
+
+from diffusers import StableDiffusionImg2ImgPipeline
+
+
+device = "cuda"
+model_path = "CompVis/stable-diffusion-v1-4"
+
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    model_path,
+    revision="fp16", 
+    torch_dtype=torch.float16,
+    use_auth_token=True
+)
+pipe = pipe.to(device)
+
+def predict(image_url, strength, seed):
+  seed=  int(seed)
+  
+  response = requests.get(image_url)
+  init_img = Image.open(BytesIO(response.content)).convert("RGB")
+  init_img = init_img.resize((768, 512))
+
+
+  generator = torch.Generator(device=device).manual_seed(seed)
+  with autocast("cuda"):
+    image = pipe(prompt="", init_image=init_img, strength=strength, guidance_scale=5, generator=generator).images[0]
+
+  return image
+  
+  
+  
+  gr.Interface(
+    predict,
+    title = 'Image to Image using Diffusers',
+    inputs=[
+        gr.Textbox(label="image_url"),
+
+        gr.Slider(0, 1, value=0.05, label ="strength"),
+        gr.Number(label = "seed")
+        
+    ],
+    outputs = [
+        gr.Image()
+        ]
+).launch()
+