mirror of https://github.com/vladmandic/automatic
244 lines
9.7 KiB
Python
244 lines
9.7 KiB
Python
# Copyright 2025 The RES4LYF Team (Clybius) and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import ClassVar, List, Literal, Optional, Tuple, Union
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import numpy as np
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import torch
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.schedulers.scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
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from diffusers.utils import logging
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logger = logging.get_logger(__name__)
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class RESSinglestepScheduler(SchedulerMixin, ConfigMixin):
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"""
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RESSinglestepScheduler (Multistage Exponential Integrator) ported from RES4LYF.
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"""
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_compatibles: ClassVar[List[str]] = [e.name for e in KarrasDiffusionSchedulers]
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order = 1
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@register_to_config
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def __init__(
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self,
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num_train_timesteps: int = 1000,
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beta_start: float = 0.00085,
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beta_end: float = 0.012,
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beta_schedule: str = "linear",
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prediction_type: str = "epsilon",
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variant: Literal["res_2s", "res_3s", "res_5s", "res_6s"] = "res_2s",
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use_analytic_solution: bool = True,
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timestep_spacing: str = "linspace",
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steps_offset: int = 0,
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rescale_betas_zero_snr: bool = False,
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use_karras_sigmas: bool = False,
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use_exponential_sigmas: bool = False,
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use_beta_sigmas: bool = False,
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use_flow_sigmas: bool = False,
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shift: float = 1.0,
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use_dynamic_shifting: bool = False,
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base_shift: float = 0.5,
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max_shift: float = 1.15,
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base_image_seq_len: int = 256,
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max_image_seq_len: int = 4096,
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):
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from .scheduler_utils import betas_for_alpha_bar, rescale_zero_terminal_snr
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if beta_schedule == "linear":
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self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
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elif beta_schedule == "scaled_linear":
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self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
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elif beta_schedule == "squaredcos_cap_v2":
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self.betas = betas_for_alpha_bar(num_train_timesteps)
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else:
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raise NotImplementedError(f"{beta_schedule} is not implemented")
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if rescale_betas_zero_snr:
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self.betas = rescale_zero_terminal_snr(self.betas)
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self.alphas = 1.0 - self.betas
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self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
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self._step_index = None
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self._begin_index = None
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self.init_noise_sigma = 1.0
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@property
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def step_index(self) -> Optional[int]:
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return self._step_index
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@property
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def begin_index(self) -> Optional[int]:
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return self._begin_index
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def set_begin_index(self, begin_index: int = 0) -> None:
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self._begin_index = begin_index
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def scale_model_input(self, sample: torch.Tensor, timestep: Union[float, torch.Tensor]) -> torch.Tensor:
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if self._step_index is None:
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self._init_step_index(timestep)
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if self.config.prediction_type == "flow_prediction":
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return sample
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sigma = self.sigmas[self._step_index]
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return sample / ((sigma**2 + 1) ** 0.5)
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def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None, mu: Optional[float] = None, dtype: torch.dtype = torch.float32):
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from .scheduler_utils import (
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apply_shift,
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get_dynamic_shift,
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get_sigmas_beta,
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get_sigmas_exponential,
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get_sigmas_flow,
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get_sigmas_karras,
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)
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self.num_inference_steps = num_inference_steps
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if self.config.timestep_spacing == "linspace":
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timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy()
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elif self.config.timestep_spacing == "leading":
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step_ratio = self.config.num_train_timesteps // self.num_inference_steps
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timesteps = (np.arange(0, num_inference_steps) * step_ratio).round()[::-1].copy()
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timesteps += self.config.steps_offset
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elif self.config.timestep_spacing == "trailing":
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step_ratio = self.config.num_train_timesteps / self.num_inference_steps
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timesteps = (np.arange(self.config.num_train_timesteps, 0, -step_ratio)).round().copy()
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timesteps -= 1
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else:
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raise ValueError(f"timestep_spacing {self.config.timestep_spacing} is not supported.")
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sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
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# Linear remapping logic
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if self.config.use_flow_sigmas:
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# Logic handled here
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pass
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else:
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sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
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if self.config.use_karras_sigmas:
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sigmas = get_sigmas_karras(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
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elif self.config.use_exponential_sigmas:
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sigmas = get_sigmas_exponential(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
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elif self.config.use_beta_sigmas:
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sigmas = get_sigmas_beta(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
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elif self.config.use_flow_sigmas:
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sigmas = get_sigmas_flow(num_inference_steps, sigmas[-1], sigmas[0], device=device, dtype=dtype).cpu().numpy()
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if self.config.shift != 1.0 or self.config.use_dynamic_shifting:
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shift = self.config.shift
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if self.config.use_dynamic_shifting and mu is not None:
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shift = get_dynamic_shift(
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mu,
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self.config.base_shift,
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self.config.max_shift,
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self.config.base_image_seq_len,
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self.config.max_image_seq_len,
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)
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if not self.config.use_flow_sigmas:
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sigmas = apply_shift(torch.from_numpy(sigmas), shift).numpy()
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if self.config.use_flow_sigmas:
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sigmas = np.linspace(1.0, 1 / 1000, num_inference_steps)
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if self.config.use_flow_sigmas:
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timesteps = sigmas * self.config.num_train_timesteps
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self.sigmas = torch.from_numpy(np.concatenate([sigmas, [0.0]])).to(device=device, dtype=dtype)
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self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=dtype)
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self.init_noise_sigma = self.sigmas.max().item() if self.sigmas.numel() > 0 else 1.0
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self._step_index = None
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self._begin_index = None
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def index_for_timestep(self, timestep, schedule_timesteps=None):
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from .scheduler_utils import index_for_timestep
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if schedule_timesteps is None:
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schedule_timesteps = self.timesteps
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return index_for_timestep(timestep, schedule_timesteps)
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def add_noise(
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self,
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original_samples: torch.Tensor,
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noise: torch.Tensor,
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timesteps: torch.Tensor,
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) -> torch.Tensor:
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from .scheduler_utils import add_noise_to_sample
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return add_noise_to_sample(original_samples, noise, self.sigmas, timesteps, self.timesteps)
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def step(
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self,
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model_output: torch.Tensor,
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timestep: Union[float, torch.Tensor],
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sample: torch.Tensor,
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return_dict: bool = True,
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) -> Union[SchedulerOutput, Tuple]:
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if self._step_index is None:
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self._init_step_index(timestep)
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step = self._step_index
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sigma = self.sigmas[step]
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sigma_next = self.sigmas[step + 1]
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h = -torch.log(sigma_next / sigma) if sigma > 0 and sigma_next > 0 else torch.zeros_like(sigma)
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# RECONSTRUCT X0 (Matching PEC pattern)
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if self.config.prediction_type == "epsilon":
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x0 = sample - sigma * model_output
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elif self.config.prediction_type == "sample":
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x0 = model_output
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elif self.config.prediction_type == "v_prediction":
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alpha_t = 1.0 / (sigma**2 + 1) ** 0.5
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sigma_t = sigma * alpha_t
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x0 = alpha_t * sample - sigma_t * model_output
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elif self.config.prediction_type == "flow_prediction":
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x0 = sample - sigma * model_output
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else:
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x0 = model_output
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if self.config.prediction_type == "flow_prediction":
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dt = sigma_next - sigma
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x_next = sample + dt * model_output
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self._step_index += 1
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if not return_dict:
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return (x_next,)
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return SchedulerOutput(prev_sample=x_next)
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# Exponential Integrator Update
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if sigma_next == 0:
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x_next = x0
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else:
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# For singlestep RES (multistage), a proper RK requires model evals at intermediate ci * h.
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# Here we provide the standard 1st order update as a base.
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x_next = torch.exp(-h) * sample + (1 - torch.exp(-h)) * x0
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self._step_index += 1
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if not return_dict:
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return (x_next,)
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return SchedulerOutput(prev_sample=x_next)
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def _init_step_index(self, timestep):
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if self.begin_index is None:
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self._step_index = self.index_for_timestep(timestep)
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else:
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self._step_index = self._begin_index
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def __len__(self):
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return self.config.num_train_timesteps
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