Update app.py

app.py

@@ -14,9 +14,7 @@ from torchvision import transforms
 from torchvision.transforms import functional as TF
 from tqdm import trange
 from transformers import CLIPProcessor, CLIPModel
-# from vqvae import VQVAE2  # Autoencoder replacement - REMOVED
-# from diffusion_models import Diffusion  # Swapped Diffusion model for DALL·E 2 based model - REMOVED
-from huggingface_hub import hf_hub_url, cached_download
+from huggingface_hub import hf_hub_download # FIXED: Replaced deprecated function
 import gradio as gr  # 🎨 The magic canvas for AI-powered image generation!
 import math
 
@@ -130,8 +128,9 @@ def ddpm_sample(model, x, steps, **kwargs):
 # NOTE: The HuggingFace URLs you provided might be placeholders.
 # Make sure these point to the correct model files.
 try:
-    vqvae_model_path = cached_download(hf_hub_url("dalle-mini/vqgan_imagenet_f16_16384", filename="flax_model.msgpack")) # Using a known public VQGAN
-    diffusion_model_path = cached_download(hf_hub_url("huggingface/dalle-2", filename="diffusion_model.ckpt")) # This URL is likely incorrect
+    # FIXED: Using the new hf_hub_download function with keyword arguments
+    vqvae_model_path = hf_hub_download(repo_id="dalle-mini/vqgan_imagenet_f16_16384", filename="flax_model.msgpack")
+    diffusion_model_path = hf_hub_download(repo_id="huggingface/dalle-2", filename="diffusion_model.ckpt")
 except Exception as e:
     print(f"Could not download models. Please ensure the HuggingFace URLs are correct.")
     print("Using placeholder models which will not produce good images.")
@@ -213,7 +212,7 @@ def generate(n=1, prompts=['a red circle'], images=[], seed=42, steps=15, method
         target_embeds.append(text_embed)
         weights.append(1.0)
 
-    # **FIXED**: Correctly process image prompts from Gradio
+    # Correctly process image prompts from Gradio
     # Assign a default weight for image prompts
     image_prompt_weight = 1.0
     for image_path in images:
@@ -250,7 +249,6 @@ def generate(n=1, prompts=['a red circle'], images=[], seed=42, steps=15, method
         return v
 
     # 🎞️ Run the sampler to generate images
-    # **FIXED**: Call sampling functions directly without the 'sampling.' prefix
     def run(x, steps):
         if method == 'ddpm':
             return ddpm_sample(cfg_model_fn, x, steps)
@@ -310,7 +308,6 @@ iface = gr.Interface(
     fn=gen_ims,
     inputs=[
         gr.Textbox(label="Text prompt"),
-        # **FIXED**: Removed deprecated 'optional=True' argument
         gr.Image(label="Image prompt", type='filepath')
     ],
     outputs=gr.Image(type="pil", label="Generated Image"),