Add app.py

app.py

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+# %% [markdown]
+# # 🖼️ Tiny Stable Diffusion (CPU Version)
+# **0.9GB Model | No GPU Required**
+
+# %% [markdown]
+# ## 1. Install Requirements
+!pip install -q torch diffusers transformers pillow huggingface_hub
+import torch
+from diffusers import StableDiffusionPipeline
+from huggingface_hub import snapshot_download
+from PIL import Image
+import gradio as gr
+import os
+
+# Force CPU mode
+torch.backends.quantized.engine = 'qnnpack'  # ARM optimization
+device = torch.device("cpu")
+
+# %% [markdown]
+# ## 2. Download Model (0.9GB)
+model_path = "./tiny_model"
+os.makedirs(model_path, exist_ok=True)
+
+# Download with progress bar
+print("Downloading model... (this may take a few minutes)")
+snapshot_download(
+    repo_id="nota-ai/bk-sdm-tiny",
+    local_dir=model_path,
+    ignore_patterns=["*.bin", "*.fp16*", "*.onnx"],
+    local_dir_use_symlinks=False
+)
+
+# Verify download
+if not os.listdir(model_path):
+    raise ValueError("Model failed to download! Check internet connection")
+else:
+    print("✔ Model downloaded successfully")
+
+# %% [markdown]
+# ## 3. Load Optimized Pipeline
+print("Loading model...")
+pipe = StableDiffusionPipeline.from_pretrained(
+    model_path,
+    torch_dtype=torch.float32,
+    safety_checker=None,
+    requires_safety_checker=False
+).to(device)
+
+# Memory optimizations
+pipe.enable_attention_slicing()
+pipe.unet = torch.compile(pipe.unet)  # Compile for faster inference
+
+# %% [markdown]
+# ## 4. Generation Function
+def generate_image(prompt, steps=15, seed=42):
+    generator = torch.Generator(device).manual_seed(seed)
+    
+    print(f"Generating: {prompt}")
+    image = pipe(
+        prompt,
+        num_inference_steps=steps,
+        guidance_scale=7.0,
+        generator=generator,
+        width=256,
+        height=256
+    ).images[0]
+    
+    return image
+
+# %% [markdown]
+# ## 5. Gradio Interface
+with gr.Blocks(title="Tiny Diffusion (CPU)", css="footer {visibility: hidden}") as demo:
+    gr.Markdown("## 🎨 CPU Image Generator (0.9GB Model)")
+    with gr.Row():
+        prompt = gr.Textbox(label="Prompt", 
+                          value="a cute robot wearing a hat",
+                          placeholder="Describe your image...")
+    with gr.Row():
+        steps = gr.Slider(5, 25, value=15, label="Steps")
+        seed = gr.Number(42, label="Seed")
+    with gr.Row():
+        generate_btn = gr.Button("Generate", variant="primary")
+    with gr.Row():
+        output = gr.Image(label="Output", width=256, height=256)
+    
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[prompt, steps, seed],
+        outputs=output
+    )
+
+# %% [markdown]
+# ## 6. Launch App
+print("Starting interface...")
+demo.launch(
+    server_name="0.0.0.0",
+    server_port=7860,
+    show_error=True
+)