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import torch |
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import numpy as np |
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from tqdm import tqdm |
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@torch.no_grad() |
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def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, progress_tqdm=None): |
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"""DPM-Solver++(2M).""" |
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extra_args = {} if extra_args is None else extra_args |
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s_in = x.new_ones([x.shape[0]]) |
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sigma_fn = lambda t: t.neg().exp() |
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t_fn = lambda sigma: sigma.log().neg() |
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old_denoised = None |
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bar = tqdm if progress_tqdm is None else progress_tqdm |
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for i in bar(range(len(sigmas) - 1)): |
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denoised = model(x, sigmas[i] * s_in, **extra_args) |
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if callback is not None: |
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callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) |
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t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) |
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h = t_next - t |
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if old_denoised is None or sigmas[i + 1] == 0: |
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x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised |
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else: |
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h_last = t - t_fn(sigmas[i - 1]) |
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r = h_last / h |
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denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised |
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x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d |
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old_denoised = denoised |
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return x |
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class KModel: |
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def __init__(self, unet, timesteps=1000, linear_start=0.00085, linear_end=0.012, linear=False): |
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if linear: |
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betas = torch.linspace(linear_start, linear_end, timesteps, dtype=torch.float64) |
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else: |
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betas = torch.linspace(linear_start ** 0.5, linear_end ** 0.5, timesteps, dtype=torch.float64) ** 2 |
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alphas = 1. - betas |
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alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32) |
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self.sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5 |
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self.log_sigmas = self.sigmas.log() |
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self.sigma_data = 1.0 |
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self.unet = unet |
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return |
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@property |
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def sigma_min(self): |
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return self.sigmas[0] |
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@property |
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def sigma_max(self): |
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return self.sigmas[-1] |
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def timestep(self, sigma): |
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log_sigma = sigma.log() |
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dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] |
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return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device) |
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def get_sigmas_karras(self, n, rho=7.): |
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ramp = torch.linspace(0, 1, n) |
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min_inv_rho = self.sigma_min ** (1 / rho) |
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max_inv_rho = self.sigma_max ** (1 / rho) |
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sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho |
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return torch.cat([sigmas, sigmas.new_zeros([1])]) |
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def __call__(self, x, sigma, **extra_args): |
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x_ddim_space = x / (sigma[:, None, None, None] ** 2 + self.sigma_data ** 2) ** 0.5 |
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x_ddim_space = x_ddim_space.to(dtype=self.unet.dtype) |
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t = self.timestep(sigma) |
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cfg_scale = extra_args['cfg_scale'] |
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eps_positive = self.unet(x_ddim_space, t, return_dict=False, **extra_args['positive'])[0] |
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eps_negative = self.unet(x_ddim_space, t, return_dict=False, **extra_args['negative'])[0] |
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noise_pred = eps_negative + cfg_scale * (eps_positive - eps_negative) |
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return x - noise_pred * sigma[:, None, None, None] |
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class KDiffusionSampler: |
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def __init__(self, unet, **kwargs): |
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self.unet = unet |
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self.k_model = KModel(unet=unet, **kwargs) |
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@torch.inference_mode() |
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def __call__( |
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self, |
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initial_latent = None, |
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strength = 1.0, |
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num_inference_steps = 25, |
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guidance_scale = 5.0, |
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batch_size = 1, |
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generator = None, |
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prompt_embeds = None, |
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negative_prompt_embeds = None, |
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cross_attention_kwargs = None, |
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same_noise_in_batch = False, |
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progress_tqdm = None, |
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): |
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device = self.unet.device |
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sigmas = self.k_model.get_sigmas_karras(int(num_inference_steps/strength)) |
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sigmas = sigmas[-(num_inference_steps + 1):].to(device) |
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if same_noise_in_batch: |
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noise = torch.randn(initial_latent.shape, generator=generator, device=device, dtype=self.unet.dtype).repeat(batch_size, 1, 1, 1) |
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initial_latent = initial_latent.repeat(batch_size, 1, 1, 1).to(device=device, dtype=self.unet.dtype) |
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else: |
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initial_latent = initial_latent.repeat(batch_size, 1, 1, 1).to(device=device, dtype=self.unet.dtype) |
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noise = torch.randn(initial_latent.shape, generator=generator, device=device, dtype=self.unet.dtype) |
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latents = initial_latent + noise * sigmas[0].to(initial_latent) |
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latents = latents.to(device) |
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prompt_embeds = prompt_embeds.repeat(batch_size, 1, 1).to(device) |
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negative_prompt_embeds = negative_prompt_embeds.repeat(batch_size, 1, 1).to(device) |
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sampler_kwargs = dict( |
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cfg_scale=guidance_scale, |
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positive=dict( |
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encoder_hidden_states=prompt_embeds, |
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cross_attention_kwargs=cross_attention_kwargs |
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), |
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negative=dict( |
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encoder_hidden_states=negative_prompt_embeds, |
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cross_attention_kwargs=cross_attention_kwargs, |
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) |
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) |
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results = sample_dpmpp_2m(self.k_model, latents, sigmas, extra_args=sampler_kwargs, progress_tqdm=progress_tqdm) |
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return results |
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