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