Create new file

app.py

@@ -0,0 +1,52 @@
+from diffusers import StableDiffusionPipeline
+import requests
+import os
+import gradio as gr
+import torch
+
+SEED = 42
+AUTH_TOKEN = os.getenv("auth_token")
+HF_TOKEN = os.getenv('HF_TOKEN')
+DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=AUTH_TOKEN)
+pip = pipe.to(DEVICE)
+hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "celebrity-set-dataset")
+
+# Ensure consistently generated images
+generator = torch.Generator(device=DEVICE).manual_seed(SEED)
+latent = torch.randn(
+    (1, 4, 64, 64),
+    generator = generator,
+    device = DEVICE
+)
+
+def generate(celebrity, setting):
+  prompt = "A movie poster with {} in {}.".format(celebrity, setting)
+  return improve_image(pipe(prompt, latents=latent).images[0], 2)
+
+# Use the GANS model of Abubakar
+def improve_image(img, rescaling_factor = 1):
+  return gr.processing_utils.decode_base64_to_image(
+      requests.post(
+          url = 'https://hf.space/embed/abidlabs/GFPGAN/+/api/predict', 
+          json = { 
+              "data": [
+                  gr.processing_utils.encode_pil_to_base64(img), 
+                  rescaling_factor
+                  ]}
+                  ).json()['data'][0])
+                  
+gr.Interface(
+    inputs = [
+        gr.Textbox(label = 'Celebrity'), 
+        gr.Dropdown(
+            choices = ['Star Trek', 'Star Wars', 'The Wire', 'Breaking Bad', 'a rainforest', 'a skyscraper.'], 
+            label = 'Movie / Show / Setting')
+        ],
+    fn = generate,
+    outputs = "image"
+    allow_flagging = "manual",
+    flagging_options = ["Looks good", "Looks bad"],
+    flagging_callback = hf_writer
+).launch()