gradio_lineart.py

from share import *
import config

import cv2
import einops
import numpy as np
import torch
import random

from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.lineart import LineartDetector
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler


preprocessor = None

# model_name = 'control_v11p_sd15_lineart'
# model = create_model(f'./models/{model_name}.yaml').cpu()
# model.load_state_dict(load_state_dict('./models/v1-5-pruned.ckpt', location='cuda'), strict=False)
# model.load_state_dict(load_state_dict(f'./models/{model_name}.pth', location='cuda'), strict=False)
# model = model.cuda()
# ddim_sampler = DDIMSampler(model)


def process(model, ddim_sampler, det, input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, detect_resolution, ddim_steps, guess_mode, strength, scale, seed, eta):
    global preprocessor
    if 'Lineart' in det:
        if not isinstance(preprocessor, LineartDetector):
            preprocessor = LineartDetector()

    with torch.no_grad():
        input_image = HWC3(input_image)

        if det == 'None':
            detected_map = input_image.copy()
        else:
            detected_map = preprocessor(resize_image(input_image, detect_resolution), coarse='Coarse' in det)
            detected_map = HWC3(detected_map)

        img = resize_image(input_image, image_resolution)
        H, W, C = img.shape

        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)

        control = 1.0 - 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()

        if seed == -1:
            seed = random.randint(0, 65535)
        seed_everything(seed)

        if config.save_memory:
            model.low_vram_shift(is_diffusing=False)

        cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
        un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
        shape = (4, H // 8, W // 8)

        if config.save_memory:
            model.low_vram_shift(is_diffusing=True)

        model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)
        # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01

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

        if config.save_memory:
            model.low_vram_shift(is_diffusing=False)

        x_samples = 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 [detected_map] + results