| |
|
|
| from share import * |
|
|
| import einops |
| import numpy as np |
| import torch |
| from PIL import Image |
| import sys |
|
|
| from pytorch_lightning import seed_everything |
| from cldm.model import create_model, load_state_dict |
| from ldm.models.diffusion.ddim import DDIMSampler |
| from diffusers.utils import load_image |
|
|
| test_prompt = "best quality, extremely detailed" |
| test_negative_prompt = "lowres, bad anatomy, worst quality, low quality" |
|
|
| @torch.no_grad() |
| def generate(prompt, n_prompt, seed, control, ddim_steps=20, eta=0.0, scale=9.0, H=512, W=512): |
| seed_everything(seed) |
|
|
| cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt] * num_samples)]} |
| un_cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]} |
| shape = (4, H // 8, W // 8) |
|
|
| latent = torch.randn((1,) + shape, device="cpu", generator=torch.Generator(device="cpu").manual_seed(seed)).cuda() |
| samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples, |
| shape, cond, x_T=latent, |
| verbose=False, eta=eta, |
| unconditional_guidance_scale=scale, |
| unconditional_conditioning=un_cond) |
| 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) |
| |
| return Image.fromarray(x_samples[0]) |
|
|
| if __name__ == '__main__': |
| model_name = sys.argv[1] |
| control_image_folder = '../huggingface/controlnet_dev/gen_compare/control_images/converted/' |
| output_image_folder = '../huggingface/controlnet_dev/gen_compare/output_images/ref/' |
|
|
| num_samples = 1 |
| model = create_model('./models/cldm_v15.yaml').cpu() |
| model.load_state_dict(load_state_dict(f'../huggingface/ControlNet/models/control_sd15_{model_name}.pth', location='cpu')) |
| model = model.cuda() |
| ddim_sampler = DDIMSampler(model) |
|
|
| image_types = {'bird', 'human', 'room', 'vermeer'} |
|
|
| for image_type in image_types: |
| control_image = Image.open(f'{control_image_folder}control_{image_type}_{model_name}.png') |
| control = np.array(control_image)[:,:,::-1].copy() |
| control = torch.from_numpy(control).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() |
|
|
| for seed in range(4): |
| image = generate(test_prompt, test_negative_prompt, seed=seed, control=control) |
| image.save(f'{output_image_folder}output_{image_type}_{model_name}_{seed}.png') |
