app.py

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


from pytorch_lightning import seed_everything
from util import resize_image, HWC3, apply_canny
from ldm.models.diffusion.ddim import DDIMSampler


from cldm.model import create_model, load_state_dict

from huggingface_hub import hf_hub_url, cached_download

REPO_ID = "lllyasviel/ControlNet"
canny_checkpoint = "models/control_sd15_canny.pth"
scribble_checkpoint = "models/control_sd15_scribble.pth"

canny_model = create_model('./models/cldm_v15.yaml')
canny_model.load_state_dict(load_state_dict(cached_download(
    hf_hub_url(REPO_ID, canny_checkpoint)
), location='cpu'))
canny_model = canny_model.cuda()
ddim_sampler = DDIMSampler(canny_model)


scribble_model = create_model('./models/cldm_v15.yaml')
scribble_model.load_state_dict(load_state_dict(cached_download(
    hf_hub_url(REPO_ID, scribble_checkpoint)
), location='cpu'))
scribble_model = canny_model.cuda()
ddim_sampler_scribble = DDIMSampler(scribble_model)

def process(input_image, prompt, input_control, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold):
    # TODO: Add other control tasks
    if input_control == "Scribble":
        return process_scribble(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta)
    return process_canny(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold)
    
def process_canny(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold):
    with torch.no_grad():
        img = resize_image(HWC3(input_image), image_resolution)
        H, W, C = img.shape

        detected_map = apply_canny(img, low_threshold, high_threshold)
        detected_map = HWC3(detected_map)

        control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
        control = torch.stack([control for _ in range(num_samples)], dim=0)
        control = einops.rearrange(control, 'b h w c -> b c h w').clone()

        seed_everything(seed)

        cond = {"c_concat": [control], "c_crossattn": [canny_model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
        un_cond = {"c_concat": [control], "c_crossattn": [canny_model.get_learned_conditioning([n_prompt] * num_samples)]}
        shape = (4, H // 8, W // 8)

        samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
                                                     shape, cond, verbose=False, eta=eta,
                                                     unconditional_guidance_scale=scale,
                                                     unconditional_conditioning=un_cond)
        x_samples = canny_model.decode_first_stage(samples)
        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)

        results = [x_samples[i] for i in range(num_samples)]
    return [255 - detected_map] + results

def process_scribble(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta):
    with torch.no_grad():
        img = resize_image(HWC3(input_image), image_resolution)
        H, W, C = img.shape

        detected_map = np.zeros_like(img, dtype=np.uint8)
        detected_map[np.min(img, axis=2) < 127] = 255

        control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
        control = torch.stack([control for _ in range(num_samples)], dim=0)
        control = einops.rearrange(control, 'b h w c -> b c h w').clone()

        seed_everything(seed)

        cond = {"c_concat": [control], "c_crossattn": [scribble_model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
        un_cond = {"c_concat": [control], "c_crossattn": [scribble_model.get_learned_conditioning([n_prompt] * num_samples)]}
        shape = (4, H // 8, W // 8)


        samples, intermediates = ddim_sampler_scribble.sample(ddim_steps, num_samples,
                                                     shape, cond, verbose=False, eta=eta,
                                                     unconditional_guidance_scale=scale,
                                                     unconditional_conditioning=un_cond)

        x_samples = scribble_model.decode_first_stage(samples)
        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)

        results = [x_samples[i] for i in range(num_samples)]
    return [255 - detected_map] + results
    
def create_canvas(w, h):
    new_control_options = ["Interactive Scribble"]
    return np.zeros(shape=(h, w, 3), dtype=np.uint8) + 255

    
block = gr.Blocks().queue()
control_task_list = [
    "Canny Edge Map",
    "Scribble"
]
with block:
    gr.Markdown("## Adding Conditional Control to Text-to-Image Diffusion Models")
    gr.HTML('''
     <p style="margin-bottom: 10px; font-size: 94%">
                This is an unofficial demo for ControlNet, which is a neural network structure to control diffusion models by adding extra conditions such as canny edge detection. The demo is based on the <a href="https://github.com/lllyasviel/ControlNet" style="text-decoration: underline;" target="_blank"> Github </a> implementation. 
              </p>
              ''')
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(source='upload', type="numpy")
            input_control = gr.Dropdown(control_task_list, value="Scribble", label="Control Task")
            prompt = gr.Textbox(label="Prompt")
            run_button = gr.Button(label="Run")
            
            with gr.Accordion("Advanced options", open=False):
                num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
                image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=256)
                low_threshold = gr.Slider(label="Canny low threshold", minimum=1, maximum=255, value=100, step=1)
                high_threshold = gr.Slider(label="Canny high threshold", minimum=1, maximum=255, value=200, step=1)
                ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
                scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
                seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True)
                eta = gr.Number(label="eta (DDIM)", value=0.0)
                a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed')
                n_prompt = gr.Textbox(label="Negative Prompt",
                                      value='longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality')
        with gr.Column():
            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
    ips = [input_image, prompt, input_control, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold]
    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
    examples_list = [
        [
            "bird.png", 
            "bird",
            "Canny Edge Map",
            "best quality, extremely detailed",
            'longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality',
             1, 
            512,
            20, 
            9.0, 
            123490213,
            0.0, 
            100, 
            200
            
        ],
                [
            "turtle.png", 
            "turtle",
            "Scribble",
            "best quality, extremely detailed",
            'longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality',
             1, 
            512,
            20, 
            9.0, 
            123490213,
            0.0, 
            100, 
            200
            
        ]
    ]
    examples = gr.Examples(examples=examples_list,inputs = [input_image, prompt, input_control, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta, low_threshold, high_threshold], outputs = [result_gallery], cache_examples = True, fn = process)
    examples.dataset.headers = [""]    

block.launch(debug = True)