| from typing import * |
| import copy |
| import torch |
| import torch.nn.functional as F |
| from torch.utils.data import DataLoader |
| import numpy as np |
| from easydict import EasyDict as edict |
|
|
| from ..basic import BasicTrainer |
| from ...pipelines import samplers |
| from ...utils.general_utils import dict_reduce |
| from .mixins.classifier_free_guidance import ClassifierFreeGuidanceMixin |
| from .mixins.text_conditioned import TextConditionedMixin |
| from .mixins.image_conditioned import ImageConditionedMixin |
|
|
|
|
| class FlowMatchingTrainer(BasicTrainer): |
| """ |
| Trainer for diffusion model with flow matching objective. |
| |
| Args: |
| models (dict[str, nn.Module]): Models to train. |
| dataset (torch.utils.data.Dataset): Dataset. |
| output_dir (str): Output directory. |
| load_dir (str): Load directory. |
| step (int): Step to load. |
| batch_size (int): Batch size. |
| batch_size_per_gpu (int): Batch size per GPU. If specified, batch_size will be ignored. |
| batch_split (int): Split batch with gradient accumulation. |
| max_steps (int): Max steps. |
| optimizer (dict): Optimizer config. |
| lr_scheduler (dict): Learning rate scheduler config. |
| elastic (dict): Elastic memory management config. |
| grad_clip (float or dict): Gradient clip config. |
| ema_rate (float or list): Exponential moving average rates. |
| fp16_mode (str): FP16 mode. |
| - None: No FP16. |
| - 'inflat_all': Hold a inflated fp32 master param for all params. |
| - 'amp': Automatic mixed precision. |
| fp16_scale_growth (float): Scale growth for FP16 gradient backpropagation. |
| finetune_ckpt (dict): Finetune checkpoint. |
| log_param_stats (bool): Log parameter stats. |
| i_print (int): Print interval. |
| i_log (int): Log interval. |
| i_sample (int): Sample interval. |
| i_save (int): Save interval. |
| i_ddpcheck (int): DDP check interval. |
| |
| t_schedule (dict): Time schedule for flow matching. |
| sigma_min (float): Minimum noise level. |
| """ |
| def __init__( |
| self, |
| *args, |
| t_schedule: dict = { |
| 'name': 'logitNormal', |
| 'args': { |
| 'mean': 0.0, |
| 'std': 1.0, |
| } |
| }, |
| sigma_min: float = 1e-5, |
| **kwargs |
| ): |
| super().__init__(*args, **kwargs) |
| self.t_schedule = t_schedule |
| self.sigma_min = sigma_min |
|
|
| def diffuse(self, x_0: torch.Tensor, t: torch.Tensor, noise: Optional[torch.Tensor] = None) -> torch.Tensor: |
| """ |
| Diffuse the data for a given number of diffusion steps. |
| In other words, sample from q(x_t | x_0). |
| |
| Args: |
| x_0: The [N x C x ...] tensor of noiseless inputs. |
| t: The [N] tensor of diffusion steps [0-1]. |
| noise: If specified, use this noise instead of generating new noise. |
| |
| Returns: |
| x_t, the noisy version of x_0 under timestep t. |
| """ |
| if noise is None: |
| noise = torch.randn_like(x_0) |
| assert noise.shape == x_0.shape, "noise must have same shape as x_0" |
|
|
| t = t.view(-1, *[1 for _ in range(len(x_0.shape) - 1)]) |
| x_t = (1 - t) * x_0 + (self.sigma_min + (1 - self.sigma_min) * t) * noise |
|
|
| return x_t |
|
|
| def reverse_diffuse(self, x_t: torch.Tensor, t: torch.Tensor, noise: torch.Tensor) -> torch.Tensor: |
| """ |
| Get original image from noisy version under timestep t. |
| """ |
| assert noise.shape == x_t.shape, "noise must have same shape as x_t" |
| t = t.view(-1, *[1 for _ in range(len(x_t.shape) - 1)]) |
| x_0 = (x_t - (self.sigma_min + (1 - self.sigma_min) * t) * noise) / (1 - t) |
| return x_0 |
|
|
| def get_v(self, x_0: torch.Tensor, noise: torch.Tensor, t: torch.Tensor) -> torch.Tensor: |
| """ |
| Compute the velocity of the diffusion process at time t. |
| """ |
| return (1 - self.sigma_min) * noise - x_0 |
|
|
| def get_cond(self, cond, **kwargs): |
| """ |
| Get the conditioning data. |
| """ |
| return cond |
| |
| def get_inference_cond(self, cond, **kwargs): |
| """ |
| Get the conditioning data for inference. |
| """ |
| return {'cond': cond, **kwargs} |
|
|
| def get_sampler(self, **kwargs) -> samplers.FlowEulerSampler: |
| """ |
| Get the sampler for the diffusion process. |
| """ |
| return samplers.FlowEulerSampler(self.sigma_min) |
| |
| def vis_cond(self, **kwargs): |
| """ |
| Visualize the conditioning data. |
| """ |
| return {} |
|
|
| def sample_t(self, batch_size: int) -> torch.Tensor: |
| """ |
| Sample timesteps. |
| """ |
| if self.t_schedule['name'] == 'uniform': |
| t = torch.rand(batch_size) |
| elif self.t_schedule['name'] == 'logitNormal': |
| mean = self.t_schedule['args']['mean'] |
| std = self.t_schedule['args']['std'] |
| t = torch.sigmoid(torch.randn(batch_size) * std + mean) |
| else: |
| raise ValueError(f"Unknown t_schedule: {self.t_schedule['name']}") |
| return t |
|
|
| def training_losses( |
| self, |
| x_0: torch.Tensor, |
| cond=None, |
| **kwargs |
| ) -> Tuple[Dict, Dict]: |
| """ |
| Compute training losses for a single timestep. |
| |
| Args: |
| x_0: The [N x C x ...] tensor of noiseless inputs. |
| cond: The [N x ...] tensor of additional conditions. |
| kwargs: Additional arguments to pass to the backbone. |
| |
| Returns: |
| a dict with the key "loss" containing a tensor of shape [N]. |
| may also contain other keys for different terms. |
| """ |
| noise = torch.randn_like(x_0) |
| t = self.sample_t(x_0.shape[0]).to(x_0.device).float() |
| x_t = self.diffuse(x_0, t, noise=noise) |
| cond = self.get_cond(cond, **kwargs) |
| |
| pred = self.training_models['denoiser'](x_t, t * 1000, cond, **kwargs) |
| assert pred.shape == noise.shape == x_0.shape |
| target = self.get_v(x_0, noise, t) |
| terms = edict() |
| terms["mse"] = F.mse_loss(pred, target) |
| terms["loss"] = terms["mse"] |
|
|
| |
| mse_per_instance = np.array([ |
| F.mse_loss(pred[i], target[i]).item() |
| for i in range(x_0.shape[0]) |
| ]) |
| time_bin = np.digitize(t.cpu().numpy(), np.linspace(0, 1, 11)) - 1 |
| for i in range(10): |
| if (time_bin == i).sum() != 0: |
| terms[f"bin_{i}"] = {"mse": mse_per_instance[time_bin == i].mean()} |
|
|
| return terms, {} |
| |
| @torch.no_grad() |
| def run_snapshot( |
| self, |
| num_samples: int, |
| batch_size: int, |
| verbose: bool = False, |
| ) -> Dict: |
| dataloader = DataLoader( |
| copy.deepcopy(self.dataset), |
| batch_size=batch_size, |
| shuffle=True, |
| num_workers=0, |
| collate_fn=self.dataset.collate_fn if hasattr(self.dataset, 'collate_fn') else None, |
| ) |
|
|
| |
| sampler = self.get_sampler() |
| sample_gt = [] |
| sample = [] |
| cond_vis = [] |
| for i in range(0, num_samples, batch_size): |
| batch = min(batch_size, num_samples - i) |
| data = next(iter(dataloader)) |
| data = {k: v[:batch].cuda() if isinstance(v, torch.Tensor) else v[:batch] for k, v in data.items()} |
| noise = torch.randn_like(data['x_0']) |
| sample_gt.append(data['x_0']) |
| cond_vis.append(self.vis_cond(**data)) |
| del data['x_0'] |
| args = self.get_inference_cond(**data) |
| res = sampler.sample( |
| self.models['denoiser'], |
| noise=noise, |
| **args, |
| steps=50, cfg_strength=3.0, verbose=verbose, |
| ) |
| sample.append(res.samples) |
|
|
| sample_gt = torch.cat(sample_gt, dim=0) |
| sample = torch.cat(sample, dim=0) |
| sample_dict = { |
| 'sample_gt': {'value': sample_gt, 'type': 'sample'}, |
| 'sample': {'value': sample, 'type': 'sample'}, |
| } |
| sample_dict.update(dict_reduce(cond_vis, None, { |
| 'value': lambda x: torch.cat(x, dim=0), |
| 'type': lambda x: x[0], |
| })) |
| |
| return sample_dict |
|
|
| |
| class FlowMatchingCFGTrainer(ClassifierFreeGuidanceMixin, FlowMatchingTrainer): |
| """ |
| Trainer for diffusion model with flow matching objective and classifier-free guidance. |
| |
| Args: |
| models (dict[str, nn.Module]): Models to train. |
| dataset (torch.utils.data.Dataset): Dataset. |
| output_dir (str): Output directory. |
| load_dir (str): Load directory. |
| step (int): Step to load. |
| batch_size (int): Batch size. |
| batch_size_per_gpu (int): Batch size per GPU. If specified, batch_size will be ignored. |
| batch_split (int): Split batch with gradient accumulation. |
| max_steps (int): Max steps. |
| optimizer (dict): Optimizer config. |
| lr_scheduler (dict): Learning rate scheduler config. |
| elastic (dict): Elastic memory management config. |
| grad_clip (float or dict): Gradient clip config. |
| ema_rate (float or list): Exponential moving average rates. |
| fp16_mode (str): FP16 mode. |
| - None: No FP16. |
| - 'inflat_all': Hold a inflated fp32 master param for all params. |
| - 'amp': Automatic mixed precision. |
| fp16_scale_growth (float): Scale growth for FP16 gradient backpropagation. |
| finetune_ckpt (dict): Finetune checkpoint. |
| log_param_stats (bool): Log parameter stats. |
| i_print (int): Print interval. |
| i_log (int): Log interval. |
| i_sample (int): Sample interval. |
| i_save (int): Save interval. |
| i_ddpcheck (int): DDP check interval. |
| |
| t_schedule (dict): Time schedule for flow matching. |
| sigma_min (float): Minimum noise level. |
| p_uncond (float): Probability of dropping conditions. |
| """ |
| pass |
|
|
|
|
| class TextConditionedFlowMatchingCFGTrainer(TextConditionedMixin, FlowMatchingCFGTrainer): |
| """ |
| Trainer for text-conditioned diffusion model with flow matching objective and classifier-free guidance. |
| |
| Args: |
| models (dict[str, nn.Module]): Models to train. |
| dataset (torch.utils.data.Dataset): Dataset. |
| output_dir (str): Output directory. |
| load_dir (str): Load directory. |
| step (int): Step to load. |
| batch_size (int): Batch size. |
| batch_size_per_gpu (int): Batch size per GPU. If specified, batch_size will be ignored. |
| batch_split (int): Split batch with gradient accumulation. |
| max_steps (int): Max steps. |
| optimizer (dict): Optimizer config. |
| lr_scheduler (dict): Learning rate scheduler config. |
| elastic (dict): Elastic memory management config. |
| grad_clip (float or dict): Gradient clip config. |
| ema_rate (float or list): Exponential moving average rates. |
| fp16_mode (str): FP16 mode. |
| - None: No FP16. |
| - 'inflat_all': Hold a inflated fp32 master param for all params. |
| - 'amp': Automatic mixed precision. |
| fp16_scale_growth (float): Scale growth for FP16 gradient backpropagation. |
| finetune_ckpt (dict): Finetune checkpoint. |
| log_param_stats (bool): Log parameter stats. |
| i_print (int): Print interval. |
| i_log (int): Log interval. |
| i_sample (int): Sample interval. |
| i_save (int): Save interval. |
| i_ddpcheck (int): DDP check interval. |
| |
| t_schedule (dict): Time schedule for flow matching. |
| sigma_min (float): Minimum noise level. |
| p_uncond (float): Probability of dropping conditions. |
| text_cond_model(str): Text conditioning model. |
| """ |
| pass |
|
|
|
|
| class ImageConditionedFlowMatchingCFGTrainer(ImageConditionedMixin, FlowMatchingCFGTrainer): |
| """ |
| Trainer for image-conditioned diffusion model with flow matching objective and classifier-free guidance. |
| |
| Args: |
| models (dict[str, nn.Module]): Models to train. |
| dataset (torch.utils.data.Dataset): Dataset. |
| output_dir (str): Output directory. |
| load_dir (str): Load directory. |
| step (int): Step to load. |
| batch_size (int): Batch size. |
| batch_size_per_gpu (int): Batch size per GPU. If specified, batch_size will be ignored. |
| batch_split (int): Split batch with gradient accumulation. |
| max_steps (int): Max steps. |
| optimizer (dict): Optimizer config. |
| lr_scheduler (dict): Learning rate scheduler config. |
| elastic (dict): Elastic memory management config. |
| grad_clip (float or dict): Gradient clip config. |
| ema_rate (float or list): Exponential moving average rates. |
| fp16_mode (str): FP16 mode. |
| - None: No FP16. |
| - 'inflat_all': Hold a inflated fp32 master param for all params. |
| - 'amp': Automatic mixed precision. |
| fp16_scale_growth (float): Scale growth for FP16 gradient backpropagation. |
| finetune_ckpt (dict): Finetune checkpoint. |
| log_param_stats (bool): Log parameter stats. |
| i_print (int): Print interval. |
| i_log (int): Log interval. |
| i_sample (int): Sample interval. |
| i_save (int): Save interval. |
| i_ddpcheck (int): DDP check interval. |
| |
| t_schedule (dict): Time schedule for flow matching. |
| sigma_min (float): Minimum noise level. |
| p_uncond (float): Probability of dropping conditions. |
| image_cond_model (str): Image conditioning model. |
| """ |
| pass |
|
|
