app.py

import cv2
import gradio as gr
import numpy as np
import torch

from diffusers import StableDiffusionControlNetPipeline, StableDiffusionLatentUpscalePipeline, ControlNetModel, AutoencoderKL
from diffusers import UniPCMultistepScheduler
from PIL import Image

from lpw import _encode_prompt

controlnet_ColorCanny = ControlNetModel.from_pretrained("ghoskno/Color-Canny-Controlnet-model", torch_dtype=torch.float16)

vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)

pipe = StableDiffusionControlNetPipeline.from_pretrained("Lykon/DreamShaper", vae=vae, controlnet=controlnet_ColorCanny, torch_dtype=torch.float16)

pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_attention_slicing()

# Generator seed
generator = torch.manual_seed(0)

def HWC3(x):
    assert x.dtype == np.uint8
    if x.ndim == 2:
        x = x[:, :, None]
    assert x.ndim == 3
    H, W, C = x.shape
    assert C == 1 or C == 3 or C == 4
    if C == 3:
        return x
    if C == 1:
        return np.concatenate([x, x, x], axis=2)
    if C == 4:
        color = x[:, :, 0:3].astype(np.float32)
        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
        y = color * alpha + 255.0 * (1.0 - alpha)
        y = y.clip(0, 255).astype(np.uint8)
        return y

def resize_image(input_image, resolution, max_edge=False, edge_limit=False):
    H, W, C = input_image.shape

    H = float(H)
    W = float(W)
    if max_edge:
        k = float(resolution) / max(H, W)
    else:
        k = float(resolution) / min(H, W)
    H *= k
    W *= k

    H, W = int(H), int(W)

    img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
    if not edge_limit:
        return img
    pH = int(np.round(H / 64.0)) * 64
    pW = int(np.round(W / 64.0)) * 64
    pimg = np.zeros((pH, pW, 3), dtype=img.dtype)

    oH, oW = (pH-H)//2, (pW-W)//2
    pimg[oH:oH+H, oW:oW+W] = img
    return pimg

def get_canny_filter(image, format='pil', low_threshold=100, high_threshold=200):
    
    if not isinstance(image, np.ndarray):
        image = np.array(image) 
        
    image = cv2.Canny(image, low_threshold, high_threshold)
    image = image[:, :, None]
    image = np.concatenate([image, image, image], axis=2)
    if format == 'pil':
        image = Image.fromarray(image)
    return image

def get_color_filter(cond_image, mask_size=64):
    H, W = cond_image.shape[:2]
    cond_image = cv2.resize(cond_image, (W // mask_size, H // mask_size), interpolation=cv2.INTER_CUBIC)
    color = cv2.resize(cond_image, (W, H), interpolation=cv2.INTER_NEAREST)
    return color

def get_colorcanny(image, mask_size):
    
    if not isinstance(image, np.ndarray):
        image = np.array(image) 
     
    canny_img = get_canny_filter(image, format='np')

    color_img = get_color_filter(image, int(mask_size))

    color_img[np.where(canny_img > 128)] = 255
    color_img = Image.fromarray(color_img)
    return color_img
    
def process(input_image, prompt, n_prompt, strength=1.0, color_mask_size=96, size=512, scale=6.0, ddim_steps=20):
    prompt_embeds, negative_prompt_embeds = _encode_prompt(pipe, prompt, pipe.device, 1, True, n_prompt, 3)
    input_image = resize_image(input_image, size, max_edge=True, edge_limit=True)

    cond_img = get_colorcanny(input_image, color_mask_size)
    output = pipe(
        prompt_embeds=prompt_embeds, 
        negative_prompt_embeds=negative_prompt_embeds,
        image=cond_img,
        generator=generator,
        num_images_per_prompt=1,
        num_inference_steps=ddim_steps,
        guidance_scale=scale,
        controlnet_conditioning_scale=float(strength)
    )
    return [output.images[0], cond_img]


block = gr.Blocks().queue()

with block:
    gr.Markdown("""
    # 🧨 Color-Canny-ControlNet

    This is an extended model of ControlNet that not only utilizes the Canny edge of images but also incorporates the color features. 
    
    We trained this model on the cleaned laion-art dataset that contains 2.6 million images with 2 epochs, using the Canny edge and color mosaic of the images as input. The processed dataset and pretrained model can be found in [ghoskno/laion-art-en-colorcanny](https://huggingface.co/datasets/ghoskno/laion-art-en-colorcanny) and [ghoskno/Color-Canny-Controlnet-model](https://huggingface.co/ghoskno/Color-Canny-Controlnet-model).
    
    This allows generated images to maintain the same color composition as the original images. If you are looking to control both the contours and colors of the original image while using ControlNet to generate images, then this is the best option for you! You can try out this model or test the examples provided below 🤗.
    """)
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(source='upload', type="numpy")
            prompt = gr.Textbox(label="Prompt", value='')
            n_prompt = gr.Textbox(label="Negative Prompt", value='')
            run_button = gr.Button(label="Run")
            with gr.Accordion('Advanced', open=False):
                strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
                color_mask_size = gr.Slider(label="Color Mask Size", minimum=32, maximum=256, value=96, step=16)
                size = gr.Slider(label="Size", minimum=256, maximum=768, value=512, step=128)
                scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=6.0, step=0.1)
                ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=20, step=1)
            
        with gr.Column():
            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
    ips = [input_image, prompt, n_prompt, strength, color_mask_size, size, scale, ddim_steps]
    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])

    gr.Examples(
        examples=[
            ["./asserts/1.png", "a concept art of by Makoto Shinkai, a girl is standing in the middle of the sea", "text, bad anatomy, blurry, (low quality, blurry)"],
            ["./asserts/2.png", "a concept illustration with saturated vivid watercolors by Erin Hanson and Moebius stylized graphic scene", "text, bad anatomy, blurry, (low quality, blurry)"],
            ["./asserts/3.png", "sky city on the sea, with waves churning and wind power plants on the island", "text, bad anatomy, blurry, (low quality, blurry)"],
        ],
        inputs=[
            input_image, prompt, n_prompt
        ],
        outputs=result_gallery,
        fn=process,
        cache_examples=True,
    )
block.launch(debug = True, server_name='0.0.0.0')