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