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https://github.com/hpcaitech/Open-Sora.git
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84 lines
2.9 KiB
Python
84 lines
2.9 KiB
Python
from functools import partial
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import torch
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from tqdm import tqdm
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from opensora.registry import SCHEDULERS
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from .rectified_flow import RFlowScheduler, timestep_transform
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@SCHEDULERS.register_module("rflow")
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class RFLOW:
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def __init__(
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self,
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num_sampling_steps=10,
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num_timesteps=1000,
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cfg_scale=4.0,
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use_discrete_timesteps=False,
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use_timestep_transform=False,
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**kwargs,
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):
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self.num_sampling_steps = num_sampling_steps
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self.num_timesteps = num_timesteps
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self.cfg_scale = cfg_scale
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self.use_discrete_timesteps = use_discrete_timesteps
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self.use_timestep_transform = use_timestep_transform
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self.scheduler = RFlowScheduler(
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num_timesteps=num_timesteps,
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num_sampling_steps=num_sampling_steps,
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use_discrete_timesteps=use_discrete_timesteps,
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use_timestep_transform=use_timestep_transform,
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**kwargs,
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)
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def sample(
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self,
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model,
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text_encoder,
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z,
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prompts,
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device,
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additional_args=None,
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mask=None,
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guidance_scale=None,
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progress=True,
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):
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assert mask is None, "mask is not supported in rectified flow inference yet"
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# if no specific guidance scale is provided, use the default scale when initializing the scheduler
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if guidance_scale is None:
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guidance_scale = self.cfg_scale
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n = len(prompts)
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model_args = text_encoder.encode(prompts)
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y_null = text_encoder.null(n)
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model_args["y"] = torch.cat([model_args["y"], y_null], 0)
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if additional_args is not None:
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model_args.update(additional_args)
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timesteps = [(1.0 - i / self.num_sampling_steps) * self.num_timesteps for i in range(self.num_sampling_steps)]
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# convert float timesteps to most close int timesteps
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if self.use_discrete_timesteps:
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timesteps = [int(round(t)) for t in timesteps]
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if self.use_timestep_transform:
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timesteps = [timestep_transform(t, additional_args, num_timesteps=self.num_timesteps) for t in timesteps]
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progress_wrap = tqdm if progress else (lambda x: x)
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for i, t in progress_wrap(enumerate(timesteps)):
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z_in = torch.cat([z, z], 0)
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pred = model(z_in, torch.tensor([t] * z_in.shape[0], device=device), **model_args).chunk(2, dim=1)[0]
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pred_cond, pred_uncond = pred.chunk(2, dim=0)
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v_pred = pred_uncond + guidance_scale * (pred_cond - pred_uncond)
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dt = timesteps[i] - timesteps[i + 1] if i < len(timesteps) - 1 else timesteps[i]
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dt = dt / self.num_timesteps
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z = z + v_pred * dt
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return z
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def training_losses(self, model, x_start, model_kwargs=None, noise=None, mask=None, weights=None):
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return self.scheduler.training_losses(model, x_start, model_kwargs, noise, mask, weights)
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