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from share import * |
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import einops |
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import numpy as np |
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import torch |
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from PIL import Image |
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import sys |
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from pytorch_lightning import seed_everything |
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from cldm.model import create_model, load_state_dict |
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from ldm.models.diffusion.ddim import DDIMSampler |
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from diffusers.utils import load_image |
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test_prompt = "best quality, extremely detailed, illustration, looking at viewer" |
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test_negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality" |
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@torch.no_grad() |
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def generate(prompt, n_prompt, seed, control, ddim_steps=20, eta=0.0, scale=9.0, H=512, W=512): |
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seed_everything(seed) |
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cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt] * num_samples)]} |
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un_cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]} |
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shape = (4, H // 8, W // 8) |
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latent = torch.randn((1,) + shape, device="cpu", generator=torch.Generator(device="cpu").manual_seed(seed)).cuda() |
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samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples, |
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shape, cond, x_T=latent, |
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verbose=False, eta=eta, |
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unconditional_guidance_scale=scale, |
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unconditional_conditioning=un_cond) |
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x_samples = model.decode_first_stage(samples) |
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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) |
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return Image.fromarray(x_samples[0]) |
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if __name__ == '__main__': |
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model_name = sys.argv[1] |
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control_image_folder = '../huggingface/controlnet_dev/gen_compare/control_images/converted/' |
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output_image_folder = '../huggingface/controlnet_dev/gen_compare/output_images/ref/' |
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num_samples = 1 |
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model = create_model('./models/cldm_v15.yaml').cpu() |
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model.load_state_dict(load_state_dict(f'../huggingface/ControlNet/models/control_sd15_{model_name}.pth', location='cpu')) |
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model = model.cuda() |
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ddim_sampler = DDIMSampler(model) |
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image_types = {'bird', 'human', 'room', 'vermeer'} |
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for image_type in image_types: |
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control_image = Image.open(f'{control_image_folder}control_{image_type}_{model_name}.png') |
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control = np.array(control_image)[:,:,::-1].copy() |
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control = torch.from_numpy(control).float().cuda() / 255.0 |
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control = torch.stack([control for _ in range(num_samples)], dim=0) |
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control = einops.rearrange(control, 'b h w c -> b c h w').clone() |
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for seed in range(4): |
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image = generate(test_prompt, test_negative_prompt, seed=seed, control=control) |
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image.save(f'{output_image_folder}output_{image_type}_{model_name}_{seed}.png') |
