improve
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Patrick von Platen
+132
-74
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#!/usr/bin/env python3
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from diffusers import UNetModel, GaussianDiffusion
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from diffusers import UNetModel, GaussianDDPMScheduler
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import torch
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import torch.nn.functional as F
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import numpy as np
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import PIL.Image
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import tqdm
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unet = UNetModel.from_pretrained("fusing/ddpm_dummy")
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diffusion = GaussianDiffusion.from_config("fusing/ddpm_dummy")
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#torch_device = "cuda"
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#
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#unet = UNetModel.from_pretrained("/home/patrick/ddpm-lsun-church")
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#unet.to(torch_device)
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#
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#TIME_STEPS = 10
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#
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#scheduler = GaussianDDPMScheduler.from_config("/home/patrick/ddpm-lsun-church", timesteps=TIME_STEPS)
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#
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#diffusion_config = {
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# "beta_start": 0.0001,
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# "beta_end": 0.02,
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# "num_diffusion_timesteps": TIME_STEPS,
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#}
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#
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# 2. Do one denoising step with model
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batch_size, num_channels, height, width = 1, 3, 32, 32
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dummy_noise = torch.ones((batch_size, num_channels, height, width))
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TIME_STEPS = 10
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#batch_size, num_channels, height, width = 1, 3, 256, 256
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#
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#torch.manual_seed(0)
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#noise_image = torch.randn(batch_size, num_channels, height, width, device="cuda")
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#
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#
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# Helper
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def extract(a, t, x_shape):
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b, *_ = t.shape
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out = a.gather(-1, t)
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return out.reshape(b, *((1,) * (len(x_shape) - 1)))
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#def noise_like(shape, device, repeat=False):
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# def repeat_noise():
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# return torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
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#
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# def noise():
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# return torch.randn(shape, device=device)
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#
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# return repeat_noise() if repeat else noise()
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#
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#
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#betas = np.linspace(diffusion_config["beta_start"], diffusion_config["beta_end"], diffusion_config["num_diffusion_timesteps"], dtype=np.float64)
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#betas = torch.tensor(betas, device=torch_device)
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#alphas = 1.0 - betas
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#
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#alphas_cumprod = torch.cumprod(alphas, axis=0)
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#alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value=1.0)
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#
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#posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)
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#posterior_mean_coef2 = (1.0 - alphas_cumprod_prev) * torch.sqrt(alphas) / (1.0 - alphas_cumprod)
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#
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#posterior_variance = betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod)
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#posterior_log_variance_clipped = torch.log(posterior_variance.clamp(min=1e-20))
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#
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#
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#sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod)
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#sqrt_recipm1_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod - 1)
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#
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#
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#noise_coeff = (1 - alphas) / torch.sqrt(1 - alphas_cumprod)
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#coeff = 1 / torch.sqrt(alphas)
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def noise_like(shape, device, repeat=False):
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def repeat_noise():
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return torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
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def real_fn():
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# Compare the following to Algorithm 2 Sampling of paper: https://arxiv.org/pdf/2006.11239.pdf
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# 1: x_t ~ N(0,1)
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x_t = noise_image
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# 2: for t = T, ...., 1 do
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for i in reversed(range(TIME_STEPS)):
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t = torch.tensor([i]).to(torch_device)
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# 3: z ~ N(0, 1)
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noise = noise_like(x_t.shape, torch_device)
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def noise():
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return torch.randn(shape, device=device)
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# 4: √1αtxt − √1−αt1−α¯tθ(xt, t) + σtz
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# ------------------------- MODEL ------------------------------------#
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with torch.no_grad():
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pred_noise = unet(x_t, t) # pred epsilon_theta
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return repeat_noise() if repeat else noise()
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# pred_x = sqrt_recip_alphas_cumprod[t] * x_t - sqrt_recipm1_alphas_cumprod[t] * pred_noise
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# pred_x.clamp_(-1.0, 1.0)
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# pred mean
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# posterior_mean = posterior_mean_coef1[t] * pred_x + posterior_mean_coef2[t] * x_t
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# --------------------------------------------------------------------#
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posterior_mean = coeff[t] * (x_t - noise_coeff[t] * pred_noise)
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# ------------------------- Variance Scheduler -----------------------#
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# pred variance
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posterior_log_variance = posterior_log_variance_clipped[t]
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b, *_, device = *x_t.shape, x_t.device
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nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x_t.shape) - 1)))
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posterior_variance = nonzero_mask * (0.5 * posterior_log_variance).exp()
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# --------------------------------------------------------------------#
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x_t_1 = (posterior_mean + posterior_variance * noise).to(torch.float32)
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x_t = x_t_1
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print(x_t.abs().sum())
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# Schedule
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def cosine_beta_schedule(timesteps, s=0.008):
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"""
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cosine schedule
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as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
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"""
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steps = timesteps + 1
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x = torch.linspace(0, timesteps, steps, dtype=torch.float64)
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alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * torch.pi * 0.5) ** 2
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alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
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betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
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return torch.clip(betas, 0, 0.999)
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def post_process_to_image(x_t):
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image = x_t.cpu().permute(0, 2, 3, 1)
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image = (image + 1.0) * 127.5
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image = image.numpy().astype(np.uint8)
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return PIL.Image.fromarray(image[0])
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betas = cosine_beta_schedule(TIME_STEPS)
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alphas = 1.0 - betas
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alphas_cumprod = torch.cumprod(alphas, axis=0)
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alphas_cumprod_prev = F.pad(alphas_cumprod[:-1], (1, 0), value=1.0)
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from pytorch_diffusion import Diffusion
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posterior_mean_coef1 = betas * torch.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)
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posterior_mean_coef2 = (1.0 - alphas_cumprod_prev) * torch.sqrt(alphas) / (1.0 - alphas_cumprod)
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posterior_variance = betas * (1.0 - alphas_cumprod_prev) / (1.0 - alphas_cumprod)
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posterior_log_variance_clipped = torch.log(posterior_variance.clamp(min=1e-20))
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#diffusion = Diffusion.from_pretrained("lsun_church")
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#samples = diffusion.denoise(1)
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#
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#image = post_process_to_image(samples)
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#image.save("check.png")
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#import ipdb; ipdb.set_trace()
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sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod)
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sqrt_recipm1_alphas_cumprod = torch.sqrt(1.0 / alphas_cumprod - 1)
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device = "cuda"
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scheduler = GaussianDDPMScheduler.from_config("/home/patrick/ddpm-lsun-church", timesteps=10)
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import ipdb; ipdb.set_trace()
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model = UNetModel.from_pretrained("/home/patrick/ddpm-lsun-church").to(device)
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torch.manual_seed(0)
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next_image = scheduler.sample_noise((1, model.in_channels, model.resolution, model.resolution), device=device)
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# Compare the following to Algorithm 2 Sampling of paper: https://arxiv.org/pdf/2006.11239.pdf
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# 1: x_t ~ N(0,1)
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x_t = dummy_noise
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# 2: for t = T, ...., 1 do
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for i in reversed(range(TIME_STEPS)):
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t = torch.tensor([i])
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# 3: z ~ N(0, 1)
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noise = noise_like(x_t.shape, "cpu")
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for t in tqdm.tqdm(reversed(range(len(scheduler))), total=len(scheduler)):
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# define coefficients for time step t
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clip_image_coeff = 1 / torch.sqrt(scheduler.get_alpha_prod(t))
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clip_noise_coeff = torch.sqrt(1 / scheduler.get_alpha_prod(t) - 1)
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image_coeff = (1 - scheduler.get_alpha_prod(t - 1)) * torch.sqrt(scheduler.get_alpha(t)) / (1 - scheduler.get_alpha_prod(t))
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clip_coeff = torch.sqrt(scheduler.get_alpha_prod(t - 1)) * scheduler.get_beta(t) / (1 - scheduler.get_alpha_prod(t))
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# 4: √1αtxt − √1−αt1−α¯tθ(xt, t) + σtz
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# ------------------------- MODEL ------------------------------------#
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pred_noise = unet(x_t, t) # pred epsilon_theta
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pred_x = extract(sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - extract(sqrt_recipm1_alphas_cumprod, t, x_t.shape) * pred_noise
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pred_x.clamp_(-1.0, 1.0)
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# pred mean
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posterior_mean = extract(posterior_mean_coef1, t, x_t.shape) * pred_x + extract(posterior_mean_coef2, t, x_t.shape) * x_t
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# --------------------------------------------------------------------#
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# predict noise residual
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with torch.no_grad():
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noise_residual = model(next_image, t)
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# ------------------------- Variance Scheduler -----------------------#
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# pred variance
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posterior_log_variance = extract(posterior_log_variance_clipped, t, x_t.shape)
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b, *_, device = *x_t.shape, x_t.device
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nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x_t.shape) - 1)))
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posterior_variance = nonzero_mask * (0.5 * posterior_log_variance).exp()
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# --------------------------------------------------------------------#
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# compute prev image from noise
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pred_mean = clip_image_coeff * next_image - clip_noise_coeff * noise_residual
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pred_mean = torch.clamp(pred_mean, -1, 1)
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image = clip_coeff * pred_mean + image_coeff * next_image
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x_t_1 = (posterior_mean + posterior_variance * noise).to(torch.float32)
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# sample variance
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variance = scheduler.sample_variance(t, image.shape, device=device)
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# FOR PATRICK TO VERIFY: make sure manual loop is equal to function
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# --------------------------------------------------------------------#
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x_t_12 = diffusion.p_sample(unet, x_t, t, noise=noise)
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assert (x_t_1 - x_t_12).abs().sum().item() < 1e-3
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# --------------------------------------------------------------------#
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# sample previous image
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sampled_image = image + variance
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x_t = x_t_1
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next_image = sampled_image
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image = post_process_to_image(next_image)
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image.save("example_new.png")
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