Create tdd_scheduler.py

tdd_scheduler.py

@@ -0,0 +1,515 @@
+from diffusers import TCDScheduler, DPMSolverSinglestepScheduler
+from diffusers.schedulers.scheduling_tcd import *
+from diffusers.schedulers.scheduling_dpmsolver_singlestep import *
+
+class TDDScheduler(DPMSolverSinglestepScheduler):
+ @register_to_config
+ def __init__(
+ self,
+ num_train_timesteps: int = 1000,
+ beta_start: float = 0.0001,
+ beta_end: float = 0.02,
+ beta_schedule: str = "linear",
+ trained_betas: Optional[np.ndarray] = None,
+ solver_order: int = 1,
+ prediction_type: str = "epsilon",
+ thresholding: bool = False,
+ dynamic_thresholding_ratio: float = 0.995,
+ sample_max_value: float = 1.0,
+ algorithm_type: str = "dpmsolver++",
+ solver_type: str = "midpoint",
+ lower_order_final: bool = False,
+ use_karras_sigmas: Optional[bool] = False,
+ final_sigmas_type: Optional[str] = "zero", # "zero", "sigma_min"
+ lambda_min_clipped: float = -float("inf"),
+ variance_type: Optional[str] = None,
+ tdd_train_step: int = 250,
+ special_jump: bool = False,
+ t_l: int = -1
+ ):
+ self.t_l = t_l
+ self.special_jump = special_jump
+ self.tdd_train_step = tdd_train_step
+ if algorithm_type == "dpmsolver":
+ deprecation_message = "algorithm_type `dpmsolver` is deprecated and will be removed in a future version. Choose from `dpmsolver++` or `sde-dpmsolver++` instead"
+ deprecate("algorithm_types=dpmsolver", "1.0.0", deprecation_message)
+
+ if trained_betas is not None:
+ self.betas = torch.tensor(trained_betas, dtype=torch.float32)
+ elif beta_schedule == "linear":
+ self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
+ elif beta_schedule == "scaled_linear":
+ # this schedule is very specific to the latent diffusion model.
+ self.betas = torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
+ elif beta_schedule == "squaredcos_cap_v2":
+ # Glide cosine schedule
+ self.betas = betas_for_alpha_bar(num_train_timesteps)
+ else:
+ raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+
+ self.alphas = 1.0 - self.betas
+ self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
+ # Currently we only support VP-type noise schedule
+ self.alpha_t = torch.sqrt(self.alphas_cumprod)
+ self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)
+ self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)
+ self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5
+
+ # standard deviation of the initial noise distribution
+ self.init_noise_sigma = 1.0
+
+ # settings for DPM-Solver
+ if algorithm_type not in ["dpmsolver", "dpmsolver++"]:
+ if algorithm_type == "deis":
+ self.register_to_config(algorithm_type="dpmsolver++")
+ else:
+ raise NotImplementedError(f"{algorithm_type} does is not implemented for {self.__class__}")
+ if solver_type not in ["midpoint", "heun"]:
+ if solver_type in ["logrho", "bh1", "bh2"]:
+ self.register_to_config(solver_type="midpoint")
+ else:
+ raise NotImplementedError(f"{solver_type} does is not implemented for {self.__class__}")
+
+ if algorithm_type != "dpmsolver++" and final_sigmas_type == "zero":
+ raise ValueError(
+ f"`final_sigmas_type` {final_sigmas_type} is not supported for `algorithm_type` {algorithm_type}. Please chooose `sigma_min` instead."
+ )
+
+ # setable values
+ self.num_inference_steps = None
+ timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=np.float32)[::-1].copy()
+ self.timesteps = torch.from_numpy(timesteps)
+ self.model_outputs = [None] * solver_order
+ self.sample = None
+ self.order_list = self.get_order_list(num_train_timesteps)
+ self._step_index = None
+ self._begin_index = None
+ self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication
+
+ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
+ self.num_inference_steps = num_inference_steps
+ # Clipping the minimum of all lambda(t) for numerical stability.
+ # This is critical for cosine (squaredcos_cap_v2) noise schedule.
+ #original_steps = self.config.original_inference_steps
+ if True:
+ original_steps=self.tdd_train_step
+ k = 1000 / original_steps
+ tcd_origin_timesteps = np.asarray(list(range(1, int(original_steps) + 1))) * k - 1
+ else:
+ tcd_origin_timesteps = np.asarray(list(range(0, int(self.config.num_train_timesteps))))
+ # TCD Inference Steps Schedule
+ tcd_origin_timesteps = tcd_origin_timesteps[::-1].copy()
+ # Select (approximately) evenly spaced indices from tcd_origin_timesteps.
+ inference_indices = np.linspace(0, len(tcd_origin_timesteps), num=num_inference_steps, endpoint=False)
+ inference_indices = np.floor(inference_indices).astype(np.int64)
+ timesteps = tcd_origin_timesteps[inference_indices]
+ if self.special_jump:
+ if self.tdd_train_step == 50:
+ #timesteps = np.array([999., 879., 759., 499., 259.])
+ print(timesteps)
+ elif self.tdd_train_step == 250:
+ if num_inference_steps == 5:
+ timesteps = np.array([999., 875., 751., 499., 251.])
+ elif num_inference_steps == 6:
+ timesteps = np.array([999., 875., 751., 627., 499., 251.])
+ elif num_inference_steps == 7:
+ timesteps = np.array([999., 875., 751., 627., 499., 375., 251.])
+
+ sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
+ if self.config.use_karras_sigmas:
+ log_sigmas = np.log(sigmas)
+ sigmas = np.flip(sigmas).copy()
+ sigmas = self._convert_to_karras(in_sigmas=sigmas, num_inference_steps=num_inference_steps)
+ timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sigmas]).round()
+ else:
+ sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
+
+ if self.config.final_sigmas_type == "sigma_min":
+ sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5
+ elif self.config.final_sigmas_type == "zero":
+ sigma_last = 0
+ else:
+ raise ValueError(
+ f" `final_sigmas_type` must be one of `sigma_min` or `zero`, but got {self.config.final_sigmas_type}"
+ )
+ sigmas = np.concatenate([sigmas, [sigma_last]]).astype(np.float32)
+
+ self.sigmas = torch.from_numpy(sigmas).to(device=device)
+
+ self.timesteps = torch.from_numpy(timesteps).to(device=device, dtype=torch.int64)
+ self.model_outputs = [None] * self.config.solver_order
+ self.sample = None
+
+ if not self.config.lower_order_final and num_inference_steps % self.config.solver_order != 0:
+ logger.warning(
+ "Changing scheduler {self.config} to have `lower_order_final` set to True to handle uneven amount of inference steps. Please make sure to always use an even number of `num_inference steps when using `lower_order_final=False`."
+ )
+ self.register_to_config(lower_order_final=True)
+
+ if not self.config.lower_order_final and self.config.final_sigmas_type == "zero":
+ logger.warning(
+ " `last_sigmas_type='zero'` is not supported for `lower_order_final=False`. Changing scheduler {self.config} to have `lower_order_final` set to True."
+ )
+ self.register_to_config(lower_order_final=True)
+
+ self.order_list = self.get_order_list(num_inference_steps)
+
+ # add an index counter for schedulers that allow duplicated timesteps
+ self._step_index = None
+ self._begin_index = None
+ self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication
+
+ def set_timesteps_s(self, eta: float = 0.0):
+ # Clipping the minimum of all lambda(t) for numerical stability.
+ # This is critical for cosine (squaredcos_cap_v2) noise schedule.
+ num_inference_steps = self.num_inference_steps
+ device = self.timesteps.device
+ if True:
+ original_steps=self.tdd_train_step
+ k = 1000 / original_steps
+ tcd_origin_timesteps = np.asarray(list(range(1, int(original_steps) + 1))) * k - 1
+ else:
+ tcd_origin_timesteps = np.asarray(list(range(0, int(self.config.num_train_timesteps))))
+ # TCD Inference Steps Schedule
+ tcd_origin_timesteps = tcd_origin_timesteps[::-1].copy()
+ # Select (approximately) evenly spaced indices from tcd_origin_timesteps.
+ inference_indices = np.linspace(0, len(tcd_origin_timesteps), num=num_inference_steps, endpoint=False)
+ inference_indices = np.floor(inference_indices).astype(np.int64)
+ timesteps = tcd_origin_timesteps[inference_indices]
+ if self.special_jump:
+ if self.tdd_train_step == 50:
+ timesteps = np.array([999., 879., 759., 499., 259.])
+ elif self.tdd_train_step == 250:
+ if num_inference_steps == 5:
+ timesteps = np.array([999., 875., 751., 499., 251.])
+ elif num_inference_steps == 6:
+ timesteps = np.array([999., 875., 751., 627., 499., 251.])
+ elif num_inference_steps == 7:
+ timesteps = np.array([999., 875., 751., 627., 499., 375., 251.])
+
+ timesteps_s = np.floor((1 - eta) * timesteps).astype(np.int64)
+
+ sigmas_s = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
+ if self.config.use_karras_sigmas:
+ print("have not write")
+ pass
+ else:
+ sigmas_s = np.interp(timesteps_s, np.arange(0, len(sigmas_s)), sigmas_s)
+ 
+ if self.config.final_sigmas_type == "sigma_min":
+ sigma_last = ((1 - self.alphas_cumprod[0]) / self.alphas_cumprod[0]) ** 0.5
+ elif self.config.final_sigmas_type == "zero":
+ sigma_last = 0
+ else:
+ raise ValueError(
+ f" `final_sigmas_type` must be one of `sigma_min` or `zero`, but got {self.config.final_sigmas_type}"
+ )
+ 
+ sigmas_s = np.concatenate([sigmas_s, [sigma_last]]).astype(np.float32)
+ self.sigmas_s = torch.from_numpy(sigmas_s).to(device=device)
+ self.timesteps_s = torch.from_numpy(timesteps_s).to(device=device, dtype=torch.int64)
+
+ def step(
+ self,
+ model_output: torch.FloatTensor,
+ timestep: int,
+ sample: torch.FloatTensor,
+ eta: float,
+ generator: Optional[torch.Generator] = None,
+ return_dict: bool = True,
+ ) -> Union[SchedulerOutput, Tuple]:
+ if self.num_inference_steps is None:
+ raise ValueError(
+ "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
+ )
+
+ if self.step_index is None:
+ self._init_step_index(timestep)
+
+ if self.step_index == 0:
+ self.set_timesteps_s(eta)
+
+ model_output = self.convert_model_output(model_output, sample=sample)
+ for i in range(self.config.solver_order - 1):
+ self.model_outputs[i] = self.model_outputs[i + 1]
+ self.model_outputs[-1] = model_output
+
+ order = self.order_list[self.step_index]
+
+ # For img2img denoising might start with order>1 which is not possible
+ # In this case make sure that the first two steps are both order=1
+ while self.model_outputs[-order] is None:
+ order -= 1
+
+ # For single-step solvers, we use the initial value at each time with order = 1.
+ if order == 1:
+ self.sample = sample
+
+ prev_sample = self.singlestep_dpm_solver_update(self.model_outputs, sample=self.sample, order=order)
+
+ if eta > 0:
+ if self.step_index != self.num_inference_steps - 1:
+
+ alpha_prod_s = self.alphas_cumprod[self.timesteps_s[self.step_index + 1]]
+ alpha_prod_t_prev = self.alphas_cumprod[self.timesteps[self.step_index + 1]]
+
+ noise = randn_tensor(
+ model_output.shape, generator=generator, device=model_output.device, dtype=prev_sample.dtype
+ )
+ prev_sample = (alpha_prod_t_prev / alpha_prod_s).sqrt() * prev_sample + (
+ 1 - alpha_prod_t_prev / alpha_prod_s
+ ).sqrt() * noise
+
+ # upon completion increase step index by one
+ self._step_index += 1
+
+ if not return_dict:
+ return (prev_sample,)
+
+ return SchedulerOutput(prev_sample=prev_sample)
+
+ def dpm_solver_first_order_update(
+ self,
+ model_output: torch.FloatTensor,
+ *args,
+ sample: torch.FloatTensor = None,
+ **kwargs,
+ ) -> torch.FloatTensor:
+ timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None)
+ prev_timestep = args[1] if len(args) > 1 else kwargs.pop("prev_timestep", None)
+ if sample is None:
+ if len(args) > 2:
+ sample = args[2]
+ else:
+ raise ValueError(" missing `sample` as a required keyward argument")
+ if timestep is not None:
+ deprecate(
+ "timesteps",
+ "1.0.0",
+ "Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
+ )
+
+ if prev_timestep is not None:
+ deprecate(
+ "prev_timestep",
+ "1.0.0",
+ "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
+ )
+ sigma_t, sigma_s = self.sigmas_s[self.step_index + 1], self.sigmas[self.step_index]
+ alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
+ alpha_s, sigma_s = self._sigma_to_alpha_sigma_t(sigma_s)
+ lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
+ lambda_s = torch.log(alpha_s) - torch.log(sigma_s)
+ h = lambda_t - lambda_s
+ if self.config.algorithm_type == "dpmsolver++":
+ x_t = (sigma_t / sigma_s) * sample - (alpha_t * (torch.exp(-h) - 1.0)) * model_output
+ elif self.config.algorithm_type == "dpmsolver":
+ x_t = (alpha_t / alpha_s) * sample - (sigma_t * (torch.exp(h) - 1.0)) * model_output
+ return x_t
+
+ def singlestep_dpm_solver_second_order_update(
+ self,
+ model_output_list: List[torch.FloatTensor],
+ *args,
+ sample: torch.FloatTensor = None,
+ **kwargs,
+ ) -> torch.FloatTensor:
+ timestep_list = args[0] if len(args) > 0 else kwargs.pop("timestep_list", None)
+ prev_timestep = args[1] if len(args) > 1 else kwargs.pop("prev_timestep", None)
+ if sample is None:
+ if len(args) > 2:
+ sample = args[2]
+ else:
+ raise ValueError(" missing `sample` as a required keyward argument")
+ if timestep_list is not None:
+ deprecate(
+ "timestep_list",
+ "1.0.0",
+ "Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
+ )
+
+ if prev_timestep is not None:
+ deprecate(
+ "prev_timestep",
+ "1.0.0",
+ "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
+ )
+ sigma_t, sigma_s0, sigma_s1 = (
+ self.sigmas_s[self.step_index + 1],
+ self.sigmas[self.step_index],
+ self.sigmas[self.step_index - 1],
+ )
+
+ alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma_t)
+ alpha_s0, sigma_s0 = self._sigma_to_alpha_sigma_t(sigma_s0)
+ alpha_s1, sigma_s1 = self._sigma_to_alpha_sigma_t(sigma_s1)
+
+ lambda_t = torch.log(alpha_t) - torch.log(sigma_t)
+ lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0)
+ lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1)
+
+ m0, m1 = model_output_list[-1], model_output_list[-2]
+
+ h, h_0 = lambda_t - lambda_s1, lambda_s0 - lambda_s1
+ r0 = h_0 / h
+ D0, D1 = m1, (1.0 / r0) * (m0 - m1)
+ if self.config.algorithm_type == "dpmsolver++":
+ # See https://arxiv.org/abs/2211.01095 for detailed derivations
+ if self.config.solver_type == "midpoint":
+ x_t = (
+ (sigma_t / sigma_s1) * sample
+ - (alpha_t * (torch.exp(-h) - 1.0)) * D0
+ - 0.5 * (alpha_t * (torch.exp(-h) - 1.0)) * D1
+ )
+ elif self.config.solver_type == "heun":
+ x_t = (
+ (sigma_t / sigma_s1) * sample
+ - (alpha_t * (torch.exp(-h) - 1.0)) * D0
+ + (alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0)) * D1
+ )
+ elif self.config.algorithm_type == "dpmsolver":
+ # See https://arxiv.org/abs/2206.00927 for detailed derivations
+ if self.config.solver_type == "midpoint":
+ x_t = (
+ (alpha_t / alpha_s1) * sample
+ - (sigma_t * (torch.exp(h) - 1.0)) * D0
+ - 0.5 * (sigma_t * (torch.exp(h) - 1.0)) * D1
+ )
+ elif self.config.solver_type == "heun":
+ x_t = (
+ (alpha_t / alpha_s1) * sample
+ - (sigma_t * (torch.exp(h) - 1.0)) * D0
+ - (sigma_t * ((torch.exp(h) - 1.0) / h - 1.0)) * D1
+ )
+ return x_t
+
+ def singlestep_dpm_solver_update(
+ self,
+ model_output_list: List[torch.FloatTensor],
+ *args,
+ sample: torch.FloatTensor = None,
+ order: int = None,
+ **kwargs,
+ ) -> torch.FloatTensor:
+ timestep_list = args[0] if len(args) > 0 else kwargs.pop("timestep_list", None)
+ prev_timestep = args[1] if len(args) > 1 else kwargs.pop("prev_timestep", None)
+ if sample is None:
+ if len(args) > 2:
+ sample = args[2]
+ else:
+ raise ValueError(" missing`sample` as a required keyward argument")
+ if order is None:
+ if len(args) > 3:
+ order = args[3]
+ else:
+ raise ValueError(" missing `order` as a required keyward argument")
+ if timestep_list is not None:
+ deprecate(
+ "timestep_list",
+ "1.0.0",
+ "Passing `timestep_list` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
+ )
+
+ if prev_timestep is not None:
+ deprecate(
+ "prev_timestep",
+ "1.0.0",
+ "Passing `prev_timestep` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
+ )
+
+ if order == 1:
+ return self.dpm_solver_first_order_update(model_output_list[-1], sample=sample)
+ elif order == 2:
+ return self.singlestep_dpm_solver_second_order_update(model_output_list, sample=sample)
+ else:
+ raise ValueError(f"Order must be 1, 2, got {order}")
+
+ def convert_model_output(
+ self,
+ model_output: torch.FloatTensor,
+ *args,
+ sample: torch.FloatTensor = None,
+ **kwargs,
+ ) -> torch.FloatTensor:
+ """
+ Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is
+ designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an
+ integral of the data prediction model.
+
+ <Tip>
+
+ The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise
+ prediction and data prediction models.
+
+ </Tip>
+
+ Args:
+ model_output (`torch.FloatTensor`):
+ The direct output from the learned diffusion model.
+ sample (`torch.FloatTensor`):
+ A current instance of a sample created by the diffusion process.
+
+ Returns:
+ `torch.FloatTensor`:
+ The converted model output.
+ """
+ timestep = args[0] if len(args) > 0 else kwargs.pop("timestep", None)
+ if sample is None:
+ if len(args) > 1:
+ sample = args[1]
+ else:
+ raise ValueError("missing `sample` as a required keyward argument")
+ if timestep is not None:
+ deprecate(
+ "timesteps",
+ "1.0.0",
+ "Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
+ )
+ # DPM-Solver++ needs to solve an integral of the data prediction model.
+ if self.config.algorithm_type == "dpmsolver++":
+ if self.config.prediction_type == "epsilon":
+ # DPM-Solver and DPM-Solver++ only need the "mean" output.
+ if self.config.variance_type in ["learned_range"]:
+ model_output = model_output[:, :3]
+ sigma = self.sigmas[self.step_index]
+ alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
+ x0_pred = (sample - sigma_t * model_output) / alpha_t
+ elif self.config.prediction_type == "sample":
+ x0_pred = model_output
+ elif self.config.prediction_type == "v_prediction":
+ sigma = self.sigmas[self.step_index]
+ alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
+ x0_pred = alpha_t * sample - sigma_t * model_output
+ else:
+ raise ValueError(
+ f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
+ " `v_prediction` for the DPMSolverSinglestepScheduler."
+ )
+
+ if self.step_index <= self.t_l:
+ if self.config.thresholding:
+ x0_pred = self._threshold_sample(x0_pred)
+
+ return x0_pred
+ # DPM-Solver needs to solve an integral of the noise prediction model.
+ elif self.config.algorithm_type == "dpmsolver":
+ if self.config.prediction_type == "epsilon":
+ # DPM-Solver and DPM-Solver++ only need the "mean" output.
+ if self.config.variance_type in ["learned_range"]:
+ model_output = model_output[:, :3]
+ return model_output
+ elif self.config.prediction_type == "sample":
+ sigma = self.sigmas[self.step_index]
+ alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
+ epsilon = (sample - alpha_t * model_output) / sigma_t
+ return epsilon
+ elif self.config.prediction_type == "v_prediction":
+ sigma = self.sigmas[self.step_index]
+ alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
+ epsilon = alpha_t * model_output + sigma_t * sample
+ return epsilon
+ else:
+ raise ValueError(
+ f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
+ " `v_prediction` for the DPMSolverSinglestepScheduler."
+ )