app.py

# %% [markdown]
# # 🖼️ Tiny Stable Diffusion (CPU Version)
# **0.9GB Model | No GPU Required**

# %% [markdown]
# ## 1. Install Requirements
pip install torch
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
)