add files from colab

app.py

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+from diffusers import StableDiffusionPipeline
+import torch
+import requests
+import base64
+import gradio as gr
+import os
+from gradio.components import Textbox
+
+pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
+pipe = pipe.to("cuda")
+
+device = "cuda"
+generator = torch.Generator(device=device)
+seed = generator.seed()
+
+def improve_image(img):
+  url = "https://hf.space/embed/abidlabs/GFPGAN/+/api/predict"
+  img_to_str = gr.processing_utils.encode_pil_to_base64(img)
+  inputs = {'data': [img_to_str, 2]}
+  resp = requests.post(url, json = inputs)
+  resp_img = gr.processing_utils.decode_base64_to_image(resp.json()['data'][0])
+  return resp_img
+
+
+HF_TOKEN = os.getenv('HF_TOKEN')
+hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "movie-img-generator")
+
+def generate(celebrity, setting):
+  prompt = "A movie poster with title and caption of {} in {}".format(celebrity, setting)
+  image = pipe(prompt, 
+               latents=torch.randn((1, 4, 64, 64), generator=torch.manual_seed(12)),
+               guidance_scale=7).images[0]
+
+  enhanced_img = improve_image(image)
+  return enhanced_img
+
+demo = gr.Interface(
+  title="🎬 Movie Poster Image Generator",
+  description="Generate an image of a celebritiy in a cinematic setting by entering in any celebrity name and selecting a TV show/movie to see what image is generated!🍿",
+  inputs=[gr.Textbox(lines=1, label="Enter a celebrity name:"), 
+          gr.Dropdown(["Stranger Things", 
+                       "Mean Girls", 
+                       "Titanic", 
+                       "The Big Bang Theory", 
+                       "Game of Thrones"], label="Select a setting:"),],
+  outputs="image",
+  fn=generate,
+  allow_flagging="manual",
+  flagging_options=["Wrong Celebrity", "Wrong Setting", "Image Not Clear"],
+  flagging_callback=hf_writer
+)  
+demo.launch(debug=True)

requirements.txt

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+torch 
+gradio
+diffusers