add cs180 encoder

app.py

@@ -0,0 +1,154 @@
+import gradio as gr
+import spaces
+import torch
+from diffusers import DiffusionPipeline
+from transformers import T5EncoderModel
+import tempfile
+
+# Global variable to store the text pipeline
+text_pipe = None
+
+def load_model():
+    """Load the T5 text encoder model"""
+    global text_pipe
+    if text_pipe is None:
+        print("Loading T5 text encoder...")
+        
+        # Get token from environment
+        import os
+        token = os.getenv("HF_TOKEN")
+        
+        text_encoder = T5EncoderModel.from_pretrained(
+            "DeepFloyd/IF-I-L-v1.0",
+            subfolder="text_encoder",
+            load_in_8bit=True,
+            variant="8bit",
+            device_map="auto",
+            token=token  # Add this line
+        )
+        text_pipe = DiffusionPipeline.from_pretrained(
+            "DeepFloyd/IF-I-L-v1.0",
+            text_encoder=text_encoder,
+            unet=None,
+            token=token  # Add this line
+        )
+        print("Model loaded successfully!")
+    return text_pipe
+
+@spaces.GPU
+def generate_embeddings(prompts_text):
+    """
+    Generate embeddings from text prompts
+    Args:
+        prompts_text: String with one prompt per line
+    Returns:
+        Path to the saved .pth file and a status message
+    """
+    try:
+        # Load model if not already loaded
+        pipe = load_model()
+        
+        # Parse prompts (one per line)
+        prompts = [p.strip() for p in prompts_text.strip().split('\n') if p.strip()]
+        
+        if not prompts:
+            return None, "Error: Please enter at least one prompt"
+        
+        # Add empty string for CFG (Classifier Free Guidance)
+        if '' not in prompts:
+            prompts.append('')
+        
+        # Generate embeddings
+        print(f"Generating embeddings for {len(prompts)} prompts...")
+        prompt_embeds_list = []
+        for prompt in prompts:
+            embeds = pipe.encode_prompt(prompt)
+            prompt_embeds_list.append(embeds)
+        
+        # Extract positive prompt embeddings
+        prompt_embeds, negative_prompt_embeds = zip(*prompt_embeds_list)
+        
+        # Create dictionary
+        prompt_embeds_dict = dict(zip(prompts, prompt_embeds))
+        
+        # Save to temporary file
+        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.pth')
+        torch.save(prompt_embeds_dict, temp_file.name)
+        temp_file.close()
+        
+        status_msg = f"✅ Successfully generated embeddings for {len(prompts)} prompts!\n"
+        status_msg += "Each embedding has shape: [1, 77, 4096]\n"
+        status_msg += "Prompts processed:\n" + "\n".join([f"  - '{p}'" for p in prompts])
+        
+        return temp_file.name, status_msg
+        
+    except Exception as e:
+        return None, f"❌ Error: {str(e)}"
+
+# Create Gradio interface
+with gr.Blocks(title="T5 Text Encoder - Embeddings Generator") as demo:
+    gr.Markdown("""
+    # 🔤 CS180 HW5: T5 Text Encoder Embeddings Generator
+    
+    This space uses the **DeepFloyd IF** T5 text encoder to generate embeddings from your text prompts.
+    
+    ### How to use:
+    1. Enter your prompts in the text box (one prompt per line)
+    2. Click "Generate Embeddings"
+    3. Download the generated `.pth` file containing the embeddings
+    
+    ### About the embeddings:
+    - Each embedding has shape: `[1, 77, 4096]`
+    - `77` = max sequence length
+    - `4096` = embedding dimension of the T5 encoder
+    - An empty prompt (`''`) is automatically added for Classifier Free Guidance (CFG)
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            prompts_input = gr.Textbox(
+                label="Enter Prompts (one per line)",
+                placeholder="an oil painting of a snowy mountain village\na photo of the amalfi coast\na photo of a man\n...",
+                lines=15,
+                value="""an oil painting of a snowy mountain village
+a photo of the amalfi coast
+a photo of a man
+a photo of a hipster barista
+a photo of a dog
+an oil painting of people around a campfire
+an oil painting of an old man
+a lithograph of waterfalls
+a lithograph of a skull
+a man wearing a hat
+a high quality photo
+a rocket ship
+a pencil"""
+            )
+            
+            generate_btn = gr.Button("🚀 Generate Embeddings", variant="primary", size="lg")
+        
+        with gr.Column():
+            status_output = gr.Textbox(
+                label="Status",
+                lines=10,
+                interactive=False
+            )
+            file_output = gr.File(
+                label="Download Embeddings (.pth file)"
+            )
+    
+    generate_btn.click(
+        fn=generate_embeddings,
+        inputs=[prompts_input],
+        outputs=[file_output, status_output]
+    )
+    
+    gr.Markdown("""
+    ### 📝 Note:
+    - The first run may take a while as the model needs to download (~8GB)
+    - Subsequent runs will be faster
+    - The generated `.pth` file can be loaded in PyTorch using: `torch.load('prompt_embeds_dict.pth')`
+    """)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+torch
+diffusers
+transformers
+accelerate
+bitsandbytes
+sentencepiece
+protobuf
+bitsandbytes