add lcm
This commit is contained in:
smthemex
@@ -0,0 +1,488 @@
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import math
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import numpy as np
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import torch
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import gc
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from typing import List, Optional, Tuple, Union
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import os
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from functools import lru_cache
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@lru_cache(maxsize=None)
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def CHECK_ENABLE_PROFILING_DEBUG():
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ENABLE_PROFILING_DEBUG = os.getenv("ENABLE_PROFILING_DEBUG", "false").lower() == "true"
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return ENABLE_PROFILING_DEBUG
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@lru_cache(maxsize=None)
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def CHECK_ENABLE_GRAPH_MODE():
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ENABLE_GRAPH_MODE = os.getenv("ENABLE_GRAPH_MODE", "false").lower() == "true"
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return ENABLE_GRAPH_MODE
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@lru_cache(maxsize=None)
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def GET_RUNNING_FLAG():
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RUNNING_FLAG = os.getenv("RUNNING_FLAG", "infer")
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return RUNNING_FLAG
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@lru_cache(maxsize=None)
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def GET_DTYPE():
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RUNNING_FLAG = os.getenv("DTYPE")
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return RUNNING_FLAG
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class BaseScheduler:
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def __init__(self, config):
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self.config = config
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self.step_index = 0
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self.latents = None
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self.infer_steps = config.infer_steps
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self.caching_records = [True] * config.infer_steps
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self.flag_df = False
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self.transformer_infer = None
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def step_pre(self, step_index):
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self.step_index = step_index
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if GET_DTYPE() == "BF16":
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self.latents = self.latents.to(dtype=torch.bfloat16)
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def clear(self):
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pass
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class WanScheduler(BaseScheduler):
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def __init__(self, config):
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super().__init__(config)
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self.device = torch.device("cuda")
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self.infer_steps = self.config.infer_steps
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self.target_video_length = self.config.target_video_length
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self.sample_shift = self.config.sample_shift
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self.shift = 1
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self.num_train_timesteps = 1000
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self.disable_corrector = []
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self.solver_order = 2
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self.noise_pred = None
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self.caching_records_2 = [True] * self.config.infer_steps
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def prepare(self, image_encoder_output=None):
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self.generator = torch.Generator(device=self.device)
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self.generator.manual_seed(self.config.seed)
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self.prepare_latents(self.config.target_shape, dtype=torch.float32)
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if self.config.task in ["t2v"]:
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self.seq_len = math.ceil((self.config.target_shape[2] * self.config.target_shape[3]) / (self.config.patch_size[1] * self.config.patch_size[2]) * self.config.target_shape[1])
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elif self.config.task in ["i2v"]:
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self.seq_len = ((self.config.target_video_length - 1) // self.config.vae_stride[0] + 1) * self.config.lat_h * self.config.lat_w // (self.config.patch_size[1] * self.config.patch_size[2])
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alphas = np.linspace(1, 1 / self.num_train_timesteps, self.num_train_timesteps)[::-1].copy()
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sigmas = 1.0 - alphas
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sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32)
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sigmas = self.shift * sigmas / (1 + (self.shift - 1) * sigmas)
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self.sigmas = sigmas
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self.timesteps = sigmas * self.num_train_timesteps
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self.model_outputs = [None] * self.solver_order
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self.timestep_list = [None] * self.solver_order
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self.last_sample = None
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self.sigmas = self.sigmas.to("cpu")
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self.sigma_min = self.sigmas[-1].item()
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self.sigma_max = self.sigmas[0].item()
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self.set_timesteps(self.infer_steps, device=self.device, shift=self.sample_shift)
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def prepare_latents(self, target_shape, dtype=torch.float32):
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self.latents = torch.randn(
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target_shape[0],
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target_shape[1],
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target_shape[2],
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target_shape[3],
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dtype=dtype,
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device=self.device,
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generator=self.generator,
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)
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def set_timesteps(
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self,
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infer_steps: Union[int, None] = None,
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device: Union[str, torch.device] = None,
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sigmas: Optional[List[float]] = None,
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mu: Optional[Union[float, None]] = None,
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shift: Optional[Union[float, None]] = None,
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):
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sigmas = np.linspace(self.sigma_max, self.sigma_min, infer_steps + 1).copy()[:-1]
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if shift is None:
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shift = self.shift
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sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
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sigma_last = 0
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timesteps = sigmas * self.num_train_timesteps
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sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
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self.sigmas = torch.from_numpy(sigmas)
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self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.int64)
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assert len(self.timesteps) == self.infer_steps
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self.model_outputs = [
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None,
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] * self.solver_order
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self.lower_order_nums = 0
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self.last_sample = None
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self._begin_index = None
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self.sigmas = self.sigmas.to("cpu")
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def _sigma_to_alpha_sigma_t(self, sigma):
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return 1 - sigma, sigma
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def convert_model_output(
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self,
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model_output: torch.Tensor,
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*args,
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sample: torch.Tensor = None,
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**kwargs,
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) -> torch.Tensor:
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timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None)
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if sample is None:
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if len(args) > 1:
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sample = args[1]
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else:
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raise ValueError("missing `sample` as a required keyward argument")
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sigma = self.sigmas[self.step_index]
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alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
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sigma_t = self.sigmas[self.step_index]
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x0_pred = sample - sigma_t * model_output
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return x0_pred
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def reset(self):
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self.model_outputs = [None] * self.solver_order
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self.timestep_list = [None] * self.solver_order
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self.last_sample = None
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self.noise_pred = None
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self.this_order = None
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self.lower_order_nums = 0
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self.prepare_latents(self.config.target_shape, dtype=torch.float32)
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gc.collect()
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torch.cuda.empty_cache()
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def multistep_uni_p_bh_update(
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self,
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model_output: torch.Tensor,
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*args,
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sample: torch.Tensor = None,
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order: int = None,
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**kwargs,
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) -> torch.Tensor:
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prev_timestep = args[0] if len(args) > 0 else kwargs.pop("prev_timestep", None)
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if sample is None:
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if len(args) > 1:
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sample = args[1]
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else:
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raise ValueError(" missing `sample` as a required keyward argument")
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if order is None:
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if len(args) > 2:
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order = args[2]
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else:
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raise ValueError(" missing `order` as a required keyward argument")
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model_output_list = self.model_outputs
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s0 = self.timestep_list[-1]
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m0 = model_output_list[-1]
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x = sample
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sigma_t, sigma_s0 = (
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self.sigmas[self.step_index + 1],
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self.sigmas[self.step_index],
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)
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alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
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alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
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lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
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lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
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h = lambda_t - lambda_s0
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device = sample.device
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rks = []
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D1s = []
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for i in range(1, order):
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si = self.step_index - i
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mi = model_output_list[-(i + 1)]
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alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
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lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
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rk = (lambda_si - lambda_s0) / h
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rks.append(rk)
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D1s.append((mi - m0) / rk)
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rks.append(1.0)
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rks = torch.tensor(rks, device=device)
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R = []
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b = []
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hh = -h
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h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1
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h_phi_k = h_phi_1 / hh - 1
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factorial_i = 1
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B_h = torch.expm1(hh)
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for i in range(1, order + 1):
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R.append(torch.pow(rks, i - 1))
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b.append(h_phi_k * factorial_i / B_h)
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factorial_i *= i + 1
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h_phi_k = h_phi_k / hh - 1 / factorial_i
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R = torch.stack(R)
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b = torch.tensor(b, device=device)
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if len(D1s) > 0:
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D1s = torch.stack(D1s, dim=1) # (B, K)
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# for order 2, we use a simplified version
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if order == 2:
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rhos_p = torch.tensor([0.5], dtype=x.dtype, device=device)
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else:
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rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]).to(device).to(x.dtype)
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else:
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D1s = None
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x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
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if D1s is not None:
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pred_res = torch.einsum("k,bkc...->bc...", rhos_p, D1s)
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else:
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pred_res = 0
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x_t = x_t_ - alpha_t * B_h * pred_res
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x_t = x_t.to(x.dtype)
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return x_t
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def multistep_uni_c_bh_update(
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self,
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this_model_output: torch.Tensor,
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*args,
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last_sample: torch.Tensor = None,
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this_sample: torch.Tensor = None,
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order: int = None,
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**kwargs,
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) -> torch.Tensor:
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this_timestep = args[0] if len(args) > 0 else kwargs.pop("this_timestep", None)
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if last_sample is None:
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if len(args) > 1:
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last_sample = args[1]
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else:
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raise ValueError(" missing`last_sample` as a required keyward argument")
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if this_sample is None:
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if len(args) > 2:
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this_sample = args[2]
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else:
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raise ValueError(" missing`this_sample` as a required keyward argument")
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if order is None:
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if len(args) > 3:
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order = args[3]
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else:
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raise ValueError(" missing`order` as a required keyward argument")
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model_output_list = self.model_outputs
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m0 = model_output_list[-1]
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x = last_sample
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x_t = this_sample
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model_t = this_model_output
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sigma_t, sigma_s0 = (
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self.sigmas[self.step_index],
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self.sigmas[self.step_index - 1],
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)
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alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
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alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
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lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
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lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
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h = lambda_t - lambda_s0
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device = this_sample.device
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rks = []
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D1s = []
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for i in range(1, order):
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si = self.step_index - (i + 1)
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mi = model_output_list[-(i + 1)]
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alpha_si, sigma_si = self._sigma_to_alpha_sigma_t(self.sigmas[si])
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lambda_si = torch.log(alpha_si) - torch.log(sigma_si)
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rk = (lambda_si - lambda_s0) / h
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rks.append(rk)
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D1s.append((mi - m0) / rk)
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rks.append(1.0)
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rks = torch.tensor(rks, device=device)
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R = []
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b = []
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hh = -h
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h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1
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h_phi_k = h_phi_1 / hh - 1
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factorial_i = 1
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B_h = torch.expm1(hh)
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for i in range(1, order + 1):
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R.append(torch.pow(rks, i - 1))
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b.append(h_phi_k * factorial_i / B_h)
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factorial_i *= i + 1
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h_phi_k = h_phi_k / hh - 1 / factorial_i
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R = torch.stack(R)
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b = torch.tensor(b, device=device)
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if len(D1s) > 0:
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D1s = torch.stack(D1s, dim=1)
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else:
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D1s = None
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# for order 1, we use a simplified version
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if order == 1:
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rhos_c = torch.tensor([0.5], dtype=x.dtype, device=device)
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else:
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rhos_c = torch.linalg.solve(R, b).to(device).to(x.dtype)
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x_t_ = sigma_t / sigma_s0 * x - alpha_t * h_phi_1 * m0
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if D1s is not None:
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corr_res = torch.einsum("k,bkc...->bc...", rhos_c[:-1], D1s)
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else:
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corr_res = 0
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D1_t = model_t - m0
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x_t = x_t_ - alpha_t * B_h * (corr_res + rhos_c[-1] * D1_t)
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x_t = x_t.to(x.dtype)
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return x_t
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def step_post(self):
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model_output = self.noise_pred.to(torch.float32)
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timestep = self.timesteps[self.step_index]
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sample = self.latents.to(torch.float32)
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use_corrector = self.step_index > 0 and self.step_index - 1 not in self.disable_corrector and self.last_sample is not None
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model_output_convert = self.convert_model_output(model_output, sample=sample)
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if use_corrector:
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sample = self.multistep_uni_c_bh_update(
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this_model_output=model_output_convert,
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last_sample=self.last_sample,
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this_sample=sample,
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order=self.this_order,
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)
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for i in range(self.solver_order - 1):
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self.model_outputs[i] = self.model_outputs[i + 1]
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self.timestep_list[i] = self.timestep_list[i + 1]
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self.model_outputs[-1] = model_output_convert
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self.timestep_list[-1] = timestep
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this_order = min(self.solver_order, len(self.timesteps) - self.step_index)
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self.this_order = min(this_order, self.lower_order_nums + 1) # warmup for multistep
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assert self.this_order > 0
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self.last_sample = sample
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prev_sample = self.multistep_uni_p_bh_update(
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model_output=model_output,
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sample=sample,
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order=self.this_order,
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)
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if self.lower_order_nums < self.solver_order:
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self.lower_order_nums += 1
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self.latents = prev_sample
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class WanStepDistillScheduler(WanScheduler):
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def __init__(self, config):
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super().__init__(config)
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self.denoising_step_list = config.denoising_step_list
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self.infer_steps = len(self.denoising_step_list)
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self.sample_shift = self.config.sample_shift
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self.order = 1
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self.num_train_timesteps = 1000
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self.sigma_max = 1.0
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self.sigma_min = 0.0
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def prepare(self, ):
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self.generator = torch.Generator(device=self.device)
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self.generator.manual_seed(self.config.seed)
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self.prepare_latents(self.config.target_shape, dtype=torch.float32)
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if self.config.task in ["t2v"]:
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self.seq_len = math.ceil((self.config.target_shape[2] * self.config.target_shape[3]) / (self.config.patch_size[1] * self.config.patch_size[2]) * self.config.target_shape[1])
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elif self.config.task in ["i2v"]:
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self.seq_len = self.config.lat_h * self.config.lat_w // (self.config.patch_size[1] * self.config.patch_size[2]) * self.config.target_shape[1]
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self.set_denoising_timesteps(device=self.device)
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def set_denoising_timesteps(self, device: Union[str, torch.device] = None):
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sigma_start = self.sigma_min + (self.sigma_max - self.sigma_min)
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self.sigmas = torch.linspace(sigma_start, self.sigma_min, self.num_train_timesteps + 1)[:-1]
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self.sigmas = self.sample_shift * self.sigmas / (1 + (self.sample_shift - 1) * self.sigmas)
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self.timesteps = self.sigmas * self.num_train_timesteps
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self.denoising_step_index = [self.num_train_timesteps - x for x in self.denoising_step_list]
|
||||
self.timesteps = self.timesteps[self.denoising_step_index].to(device)
|
||||
self.sigmas = self.sigmas[self.denoising_step_index].to("cpu")
|
||||
|
||||
def reset(self):
|
||||
self.prepare_latents(self.config.target_shape, dtype=torch.float32)
|
||||
|
||||
def add_noise(self, original_samples, noise, sigma):
|
||||
sample = (1 - sigma) * original_samples + sigma * noise
|
||||
return sample.type_as(noise)
|
||||
|
||||
def step_post(self):
|
||||
flow_pred = self.noise_pred.to(torch.float32)
|
||||
sigma = self.sigmas[self.step_index].item()
|
||||
noisy_image_or_video = self.latents.to(torch.float32) - sigma * flow_pred
|
||||
if self.step_index < self.infer_steps - 1:
|
||||
sigma = self.sigmas[self.step_index + 1].item()
|
||||
noisy_image_or_video = self.add_noise(noisy_image_or_video, torch.randn_like(noisy_image_or_video), self.sigmas[self.step_index + 1].item())
|
||||
self.latents = noisy_image_or_video.to(self.latents.dtype)
|
||||
|
||||
def step(self, model_output, timestep, sample, generator=None, return_dict=True):
|
||||
"""
|
||||
使用模型输出预测数据并执行去噪步骤。
|
||||
|
||||
Args:
|
||||
model_output (`torch.Tensor`): 直接输出来自模型的预测。
|
||||
timestep (`int`): 当前的离散时间步。
|
||||
sample (`torch.Tensor`): 在时间步t处的输入样本。
|
||||
generator (`torch.Generator`, optional): 用于采样的随机数生成器。
|
||||
return_dict (`bool`, optional): 是否返回字典格式的结果。
|
||||
|
||||
Returns:
|
||||
`torch.Tensor` 或 `Dict[str, torch.Tensor]`: 更新后的样本。
|
||||
"""
|
||||
# 设置当前步骤索引
|
||||
step_index = torch.where(self.timesteps == timestep)[0].item()
|
||||
|
||||
# 保存必要的属性以供step_post使用
|
||||
self.noise_pred = model_output
|
||||
self.latents = sample
|
||||
self.step_index = step_index
|
||||
|
||||
# 执行后处理步骤
|
||||
self.step_post()
|
||||
|
||||
# 返回更新后的样本
|
||||
if return_dict:
|
||||
return {"prev_sample": self.latents}
|
||||
else:
|
||||
return (self.latents,)
|
||||
Reference in New Issue
Block a user