library added

app.py

@@ -1,32 +1,35 @@
 import gradio as gr
 import torch
-from optimum.onnxruntime import ORTStableDiffusionPipeline
+from diffusers import StableDiffusionPipeline
+import os
 
-# Load model once at startup
-pipe = ORTStableDiffusionPipeline.from_pretrained(
-    "optimum/stable-diffusion-v1-5-onnx",
-    provider="CPUExecutionProvider"
+# Use CPU optimized model
+os.environ["HF_HOME"] = "/tmp/hf_cache"
+
+print("Loading model...")
+pipe = StableDiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-2-1-base",
+    torch_dtype=torch.float32,
+    low_cpu_mem_usage=True
 )
+pipe = pipe.to("cpu")
 pipe.set_progress_bar_config(disable=True)
+print("Model loaded!")
 
 def generate(prompt):
-    # Fast generation
     result = pipe(
         prompt=prompt,
-        num_inference_steps=15,  # Keep low for Spaces
-        guidance_scale=7.5,
-        height=512,
-        width=512
+        num_inference_steps=15,
+        guidance_scale=7.5
     )
     return result.images[0]
 
-# Gradio interface
 interface = gr.Interface(
     fn=generate,
-    inputs=gr.Textbox(lines=3, placeholder="Enter your prompt here..."),
+    inputs=gr.Textbox(lines=3, label="Prompt"),
     outputs=gr.Image(type="pil"),
-    title="Fast Stable Diffusion",
-    description="Generate images in under 60 seconds!"
+    title="⚡ Lightweight Stable Diffusion",
+    description="Faster CPU-based image generation"
 )
 
 interface.launch()

requirements.txt

@@ -1,3 +1,6 @@
 optimum[onnxruntime]
 torch
-gradio
+diffusers
+transformers
+gradio
+accelerate