Update app.py

app.py

@@ -2,44 +2,40 @@ import gradio as gr
 import torch
 from diffusers import StableDiffusionPipeline
 
-# Load the model (CPU only)
-model_id = "runwayml/stable-diffusion-v1-5"
-pipe = StableDiffusionPipeline.from_pretrained(model_id)
-pipe = pipe.to("cpu")
+# Load smaller, faster model
+model_id = "stabilityai/stable-diffusion-1-4"
+pipe = StableDiffusionPipeline.from_pretrained(model_id).to("cpu")
 
-def generate_image(prompt, negative_prompt, steps, guidance_scale, seed):
-    generator = torch.Generator(device='cpu').manual_seed(seed)
+# Inference function with portrait image size
+def generate(prompt, negative, steps, scale, seed):
+    generator = torch.Generator("cpu").manual_seed(seed)
     image = pipe(
         prompt=prompt,
-        negative_prompt=negative_prompt,
+        negative_prompt=negative,
+        height=768,
+        width=512,
         num_inference_steps=steps,
-        guidance_scale=guidance_scale,
-        generator=generator
+        guidance_scale=scale,
+        generator=generator,
     ).images[0]
     return image
 
-# Gradio UI
+# Build Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("# 🎨 Stable Diffusion v1.5 (CPU Inference)")
-
-    with gr.Row():
-        prompt = gr.Textbox(label="Prompt", placeholder="A fantasy landscape with waterfalls")
-        negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Blurry, low-resolution")
-
+    gr.Markdown("### 🎨 Stable Diffusion 1.4 (CPU Optimized Portrait Generator)")
+    
     with gr.Row():
-        steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, value=20, step=1)
-        guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=20, value=7.5, step=0.1)
-        seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True)
-
+        prompt = gr.Textbox(label="Prompt", placeholder="e.g. A fantasy castle on a cliff")
+        negative = gr.Textbox(label="Negative Prompt", placeholder="e.g. low quality, blurry")
+    
     with gr.Row():
-        generate_btn = gr.Button("Generate Image")
+        steps = gr.Slider(10, 50, value=20, label="Steps")
+        scale = gr.Slider(1, 20, value=7.5, step=0.1, label="Guidance Scale")
+        seed = gr.Slider(0, 100000, step=1, value=42, label="Seed", randomize=True)
 
-    output_image = gr.Image(label="Generated Image", type="pil")
+    run_btn = gr.Button("🎨 Generate Portrait")
+    output = gr.Image(label="Result", type="pil")
 
-    generate_btn.click(
-        fn=generate_image,
-        inputs=[prompt, negative_prompt, steps, guidance_scale, seed],
-        outputs=output_image
-    )
+    run_btn.click(fn=generate, inputs=[prompt, negative, steps, scale, seed], outputs=output)
 
 demo.launch(show_api=True)