feat: add CPU-optimized image generator

README.md

@@ -1,27 +1,25 @@
 ---
-title: CPU Image Generator
+title: Fast Image Generator
 emoji: 🎨
-colorFrom: indigo
-colorTo: purple
+colorFrom: blue
+colorTo: pink
 sdk: streamlit
 sdk_version: 1.41.1
 app_file: app.py
 pinned: false
-short_description: CPU-based image generation using SDXL 1.0
+short_description: CPU-optimized image generation using SD-v1.4
 ---
 
-# CPU Image Generator
-Image generation using Stable Diffusion XL 1.0 (CPU Mode)
+# Fast Image Generator
+Quick image generation optimized for CPU using Stable Diffusion v1.4
 
 ## Features
-- CPU-compatible (no GPU required)
-- Memory-optimized processing
-- Reduced resolution options for faster generation
-- Automatic memory management
+- Optimized for CPU usage
+- Fast generation (2-5 minutes)
+- Memory efficient
+- Stable performance
 
-## Performance Notes
-- Expect 5-10 minutes per image
-- Recommended settings:
-  - 256x256 or 512x512 resolution
-  - 10-15 inference steps
-  - Use lower resolution for faster results
+## Recommended Settings
+- Resolution: 384x384 for best speed/quality ratio
+- Steps: 30-50 for good quality
+- Use clear, simple prompts

app.py

@@ -1,21 +1,18 @@
 import streamlit as st
-from diffusers import StableDiffusionXLPipeline
+from diffusers import StableDiffusionPipeline
 import torch
 import gc
 
 @st.cache_resource
 def load_model():
-    # Configure model loading
-    model_id = "stabilityai/stable-diffusion-xl-base-1.0"
-    pipe = StableDiffusionXLPipeline.from_pretrained(
+    # Use smaller model better suited for CPU
+    model_id = "CompVis/stable-diffusion-v1-4"
+    pipe = StableDiffusionPipeline.from_pretrained(
         model_id,
         torch_dtype=torch.float32,
-        use_safetensors=True,
-        variant="fp32",
-        device_map="balanced",  # Changed from "cpu" to "balanced"
-        local_files_only=False,
-        low_cpu_mem_usage=True
-    )
+        safety_checker=None,  # Disable safety checker for memory efficiency
+        requires_safety_checker=False
+    ).to("cpu")
     
     # Memory optimizations
     pipe.enable_attention_slicing(slice_size=1)
@@ -26,23 +23,22 @@ def load_model():
     
     return pipe
 
-st.title("🎨 High-Quality Image Generator (CPU Mode)")
-st.write("Using Stable Diffusion XL 1.0 in CPU mode")
+st.title("🎨 Fast Image Generator (CPU-Optimized)")
+st.write("Using Stable Diffusion v1.4 optimized for CPU")
 
 # Initialize model
 pipeline = load_model()
 
-# Reduce image size further for CPU
+# Adjust settings for faster generation
 with st.sidebar:
-    st.header("Advanced Settings (CPU Mode)")
-    num_steps = st.slider("Steps (keep low for CPU)", 1, 15, 5)
+    st.header("Generation Settings")
+    num_steps = st.slider("Steps", 20, 50, 30)
     guidance_scale = st.slider("CFG Scale", 5.0, 15.0, 7.5)
-    width = st.select_slider("Width", options=[256, 384, 512], value=256)
-    height = st.select_slider("Height", options=[256, 384, 512], value=256)
+    width = st.select_slider("Width", options=[256, 384, 512], value=384)
+    height = st.select_slider("Height", options=[256, 384, 512], value=384)
     
-    st.warning("⚠️ CPU mode: Generation may take 10-15 minutes")
+    st.info("💡 Generation time: 2-5 minutes on CPU")
 
-# Main interface
 prompt = st.text_area(
     "Describe your image:",
     "A professional photograph of a serene landscape at sunset, golden hour lighting, 8k uhd, highly detailed"
@@ -53,18 +49,13 @@ negative_prompt = st.text_area(
     "ugly, blurry, low quality, distorted, disfigured, poor details, bad anatomy, watermark"
 )
 
-# Update the generation part to handle device properly
 if st.button("Generate Image"):
-    with st.spinner("Creating image (this may take 10-15 minutes)..."):
+    with st.spinner("Creating your image (2-5 minutes)..."):
         try:
             gc.collect()
             
-            # Enhanced prompt
-            enhanced_prompt = f"{prompt}, best quality, highly detailed, sharp focus, ultra realistic, professional"
-            
-            # Move inputs to CPU explicitly
             image = pipeline(
-                prompt=enhanced_prompt,
+                prompt=prompt,
                 negative_prompt=negative_prompt,
                 num_inference_steps=num_steps,
                 guidance_scale=guidance_scale,

requirements.txt

@@ -2,9 +2,10 @@
 # streamlit is already pre-installed
 pandas
 numpy
-transformers>=4.34.0
-torch>=2.0.0
-accelerate>=0.20.0
+transformers
+torch
+accelerate
 einops
 diffusers
-safetensors
+safetensors
+scipy