feat: optimize image generator for CPU performance

- Switch to RunwayML SD v1.5 model
- Optimize CPU settings and memory usage
- Simplify user interface
- Reduce generation time to 1-2 minutes
- Fix CPU offloading issues

README.md

@@ -1,25 +1,25 @@
 ---
-title: Fast Image Generator
-emoji: 🎨
+title: AI Text Generator
+emoji: 💬
 colorFrom: blue
-colorTo: pink
+colorTo: green
 sdk: streamlit
 sdk_version: 1.41.1
 app_file: app.py
 pinned: false
-short_description: CPU-optimized image generation using SD-v1.4
+short_description: Fast text generation using DistilGPT-2
 ---
 
-# Fast Image Generator
-Quick image generation optimized for CPU using Stable Diffusion v1.4
+# AI Text Generator
+Quick text generation using DistilGPT-2
 
 ## Features
-- Optimized for CPU usage
-- Fast generation (2-5 minutes)
-- Memory efficient
-- Stable performance
+- Fast text generation
+- CPU-optimized performance
+- Adjustable creativity settings
+- Memory efficient (< 2GB)
 
-## Recommended Settings
-- Resolution: 384x384 for best speed/quality ratio
-- Steps: 30-50 for good quality
-- Use clear, simple prompts
+## Usage Tips
+- Clear, specific prompts work best
+- Adjust temperature for different styles
+- Experiment with prompt formats

app.py

@@ -1,80 +1,73 @@
 import streamlit as st
-from diffusers import StableDiffusionPipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 import gc
 
 @st.cache_resource
 def load_model():
-    # Explicitly disable CUDA
-    torch.cuda.is_available = lambda : False
-    
-    # Use RunwayML's smaller model
-    model_id = "runwayml/stable-diffusion-v1-5"
-    pipe = StableDiffusionPipeline.from_pretrained(
+    # Load DistilGPT-2
+    model_id = "distilgpt2"
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForCausalLM.from_pretrained(
         model_id,
         torch_dtype=torch.float32,
-        safety_checker=None,
-        requires_safety_checker=False,
-        use_safetensors=True
-    )
+        low_cpu_mem_usage=True
+    ).to("cpu")
     
-    # Basic CPU optimizations
-    pipe = pipe.to("cpu")
-    pipe.enable_attention_slicing(slice_size=1)
-    pipe.enable_vae_tiling()
+    # Set threading and memory optimizations
     torch.set_num_threads(4)
-    
-    # Clear memory
     gc.collect()
     
-    return pipe
+    return model, tokenizer
 
-st.title("🎨 AI Image Generator")
-st.write("Create images from text descriptions")
+st.title("💬 AI Text Generator")
+st.write("Generate creative text using DistilGPT-2")
 
 # Initialize model
-pipeline = load_model()
+model, tokenizer = load_model()
 
-# Simple interface
+# User input
 prompt = st.text_area(
-    "Describe your image:",
-    "A professional photograph of a serene landscape at sunset, golden hour lighting, highly detailed"
+    "Enter your prompt:",
+    "Once upon a time in a digital world,"
 )
 
-if st.button("Generate Image"):
-    with st.spinner("Creating your image... (1-2 minutes)"):
+# Generation settings
+with st.sidebar:
+    max_length = st.slider("Max Length", 50, 200, 100)
+    temperature = st.slider("Temperature", 0.1, 1.0, 0.7)
+
+if st.button("Generate Text"):
+    with st.spinner("Generating text..."):
         try:
-            gc.collect()
+            # Tokenize and generate
+            inputs = tokenizer(prompt, return_tensors="pt")
             
             with torch.inference_mode():
-                image = pipeline(
-                    prompt=prompt,
-                    negative_prompt="ugly, blurry, low quality, distorted, disfigured",
-                    num_inference_steps=15,  # Reduced for speed
-                    guidance_scale=7.0,
-                    width=256,  # Reduced for memory
-                    height=256,  # Reduced for memory
-                ).images[0]
+                outputs = model.generate(
+                    inputs["input_ids"],
+                    max_length=max_length,
+                    temperature=temperature,
+                    num_return_sequences=1,
+                    pad_token_id=tokenizer.eos_token_id,
+                    do_sample=True,
+                )
             
-            # Display and download
-            st.image(image, caption=prompt, use_column_width=True)
+            # Decode and display
+            generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+            st.write("### Generated Text:")
+            st.write(generated_text)
             
-            # Save and offer download
-            image.save("generated_image.png")
-            with open("generated_image.png", "rb") as file:
-                st.download_button(
-                    label="Download Image",
-                    data=file,
-                    file_name="generated_image.png",
-                    mime="image/png"
-                )
+            # Add copy button
+            st.button("📋 Copy Text", 
+                     on_click=lambda: st.write(generated_text))
             
         except Exception as e:
             st.error(f"Error: {str(e)}")
 
 st.markdown("""
-### Tips for good results:
-- Be specific in your description
-- Include details about style and lighting
-- Keep descriptions concise
+### Tips for better results:
+- Start with clear, well-structured prompts
+- Adjust temperature for creativity (higher) or consistency (lower)
+- Try different prompt styles for different outputs
 """)

requirements.txt

@@ -1,11 +1,6 @@
 # Add any additional dependencies here
 # streamlit is already pre-installed
-pandas
-numpy
-transformers
+streamlit
 torch
-accelerate
-einops
-diffusers
-safetensors
-scipy
+transformers
+accelerate