app.py

import gradio as gr
import numpy as np
import random
import cv2

import spaces
import torch
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from diffusers.utils import load_image
from PIL import Image

device = "cuda" if torch.cuda.is_available() else "cpu"
sd_model_id = "runwayml/stable-diffusion-v1-5"
controlnet_model_id = "lllyasviel/sd-controlnet-canny"

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

# Load ControlNet model
controlnet = ControlNetModel.from_pretrained(
    controlnet_model_id, 
    torch_dtype=torch_dtype
)

# Load Stable Diffusion with ControlNet
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    sd_model_id, 
    controlnet=controlnet, 
    torch_dtype=torch_dtype, 
    safety_checker=None
)
pipe = pipe.to(device)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

def apply_canny(image, low_threshold, high_threshold):
    """Apply Canny edge detection to the image"""
    # Convert PIL image to numpy array
    image_np = np.array(image)
    
    # Convert to grayscale if the image is colored
    if len(image_np.shape) == 3 and image_np.shape[2] == 3:
        image_gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
    else:
        image_gray = image_np
        
    # Apply Canny edge detection
    edges = cv2.Canny(image_gray, low_threshold, high_threshold)
    
    # Convert back to RGB for the model
    edges_rgb = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
    
    # Convert back to PIL image
    return Image.fromarray(edges_rgb)

@spaces.GPU
def infer(
    prompt,
    input_image,
    negative_prompt,
    seed,
    randomize_seed,
    canny_low_threshold,
    canny_high_threshold,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    if input_image is None:
        return None, seed
    
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)
    
    # Process the image
    if input_image is not None:
        width, height = input_image.size
        
        # Ensure width and height are valid for the model
        if width > MAX_IMAGE_SIZE:
            width = MAX_IMAGE_SIZE
        if height > MAX_IMAGE_SIZE:
            height = MAX_IMAGE_SIZE
            
        # Apply Canny edge detection
        canny_image = apply_canny(input_image, canny_low_threshold, canny_high_threshold)

    image = pipe(
        prompt=prompt,
        image=canny_image,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=generator,
    ).images[0]

    return image, seed, canny_image

css = """
#col-container {
    margin: 0 auto;
    max-width: 840px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # ControlNet Canny - Edge Guided Image Generation")

        with gr.Row():
            with gr.Column(scale=1):
                input_image = gr.Image(
                    label="Input Image",
                    type="pil",
                    height=400
                )
            with gr.Column(scale=1):
                canny_image = gr.Image(
                    label="Canny Edge Detection",
                    height=400
                )
            with gr.Column(scale=1):
                result = gr.Image(
                    label="Result", 
                    height=400
                )

        prompt = gr.Text(
            label="Prompt",
            placeholder="Enter your prompt (e.g., 'a fantasy landscape with mountains')",
        )
        
        run_button = gr.Button("Run", variant="primary")

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
            )
            
            with gr.Row():
                canny_low_threshold = gr.Slider(
                    label="Canny Low Threshold",
                    minimum=1,
                    maximum=255,
                    step=1,
                    value=100,
                )

                canny_high_threshold = gr.Slider(
                    label="Canny High Threshold",
                    minimum=1,
                    maximum=255,
                    step=1,
                    value=200,
                )

            guidance_scale = gr.Slider(
                label="Guidance scale",
                minimum=1.0,
                maximum=20.0,
                step=0.1,
                value=7.5,
            )

            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

            num_inference_steps = gr.Slider(
                label="Number of inference steps",
                minimum=1,
                maximum=100,
                step=1,
                value=30,
            )

    gr.on(
        triggers=[run_button.click],
        fn=infer,
        inputs=[
            prompt,
            input_image,
            negative_prompt,
            seed,
            randomize_seed,
            canny_low_threshold,
            canny_high_threshold,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed, canny_image],
    )

if __name__ == "__main__":
    demo.launch()