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Yatharth Sharma
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zipvoice/models/modules/solver.py
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277
zipvoice/models/modules/solver.py
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#!/usr/bin/env python3
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# Copyright 2024 Xiaomi Corp. (authors: Han Zhu)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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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 Optional, Union
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import torch
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class DiffusionModel(torch.nn.Module):
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"""A wrapper of diffusion models for inference.
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Args:
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model: The diffusion model.
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func_name: The function name to call.
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"""
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def __init__(
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self,
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model: torch.nn.Module,
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func_name: str = "forward_fm_decoder",
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):
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super().__init__()
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self.model = model
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self.func_name = func_name
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self.model_func = getattr(self.model, func_name)
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def forward(
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self,
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t: torch.Tensor,
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x: torch.Tensor,
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text_condition: torch.Tensor,
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speech_condition: torch.Tensor,
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padding_mask: Optional[torch.Tensor] = None,
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guidance_scale: Union[float, torch.Tensor] = 0.0,
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**kwargs
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) -> torch.Tensor:
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"""
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Forward function that Handles the classifier-free guidance.
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Args:
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t: The current timestep, a tensor of a tensor of a single float.
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x: The initial value, with the shape (batch, seq_len, emb_dim).
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text_condition: The text_condition of the diffision model, with
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the shape (batch, seq_len, emb_dim).
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speech_condition: The speech_condition of the diffision model, with the
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shape (batch, seq_len, emb_dim).
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padding_mask: The mask for padding; True means masked position, with the
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shape (batch, seq_len).
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guidance_scale: The scale of classifier-free guidance, a float or a tensor
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of shape (batch, 1, 1).
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Retrun:
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The prediction with the shape (batch, seq_len, emb_dim).
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"""
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if not torch.is_tensor(guidance_scale):
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guidance_scale = torch.tensor(
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guidance_scale, dtype=t.dtype, device=t.device
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)
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if (guidance_scale == 0.0).all():
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return self.model_func(
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t=t,
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xt=x,
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text_condition=text_condition,
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speech_condition=speech_condition,
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padding_mask=padding_mask,
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**kwargs
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)
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else:
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assert t.dim() == 0
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x = torch.cat([x] * 2, dim=0)
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padding_mask = torch.cat([padding_mask] * 2, dim=0)
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text_condition = torch.cat(
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[torch.zeros_like(text_condition), text_condition], dim=0
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)
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if t > 0.5:
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speech_condition = torch.cat(
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[torch.zeros_like(speech_condition), speech_condition], dim=0
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)
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else:
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guidance_scale = guidance_scale * 2
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speech_condition = torch.cat(
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[speech_condition, speech_condition], dim=0
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)
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data_uncond, data_cond = self.model_func(
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t=t,
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xt=x,
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text_condition=text_condition,
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speech_condition=speech_condition,
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padding_mask=padding_mask,
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**kwargs
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).chunk(2, dim=0)
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res = (1 + guidance_scale) * data_cond - guidance_scale * data_uncond
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return res
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class DistillDiffusionModel(DiffusionModel):
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"""A wrapper of distilled diffusion models for inference.
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Args:
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model: The distilled diffusion model.
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func_name: The function name to call.
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"""
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def __init__(
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self,
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model: torch.nn.Module,
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func_name: str = "forward_fm_decoder",
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):
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super().__init__(model=model, func_name=func_name)
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def forward(
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self,
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t: torch.Tensor,
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x: torch.Tensor,
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text_condition: torch.Tensor,
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speech_condition: torch.Tensor,
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padding_mask: Optional[torch.Tensor] = None,
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guidance_scale: Union[float, torch.Tensor] = 0.0,
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**kwargs
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) -> torch.Tensor:
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"""
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Forward function that Handles the classifier-free guidance.
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Args:
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t: The current timestep, a tensor of a single float.
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x: The initial value, with the shape (batch, seq_len, emb_dim).
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text_condition: The text_condition of the diffision model, with
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the shape (batch, seq_len, emb_dim).
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speech_condition: The speech_condition of the diffision model, with the
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shape (batch, seq_len, emb_dim).
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padding_mask: The mask for padding; True means masked position, with the
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shape (batch, seq_len).
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guidance_scale: The scale of classifier-free guidance, a float or a tensor
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of shape (batch, 1, 1).
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Retrun:
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The prediction with the shape (batch, seq_len, emb_dim).
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"""
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if not torch.is_tensor(guidance_scale):
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guidance_scale = torch.tensor(
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guidance_scale, dtype=t.dtype, device=t.device
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)
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return self.model_func(
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t=t,
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xt=x,
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text_condition=text_condition,
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speech_condition=speech_condition,
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padding_mask=padding_mask,
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guidance_scale=guidance_scale,
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**kwargs
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)
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class EulerSolver:
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def __init__(
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self,
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model: torch.nn.Module,
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func_name: str = "forward_fm_decoder",
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):
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"""Construct a Euler Solver
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Args:
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model: The diffusion model.
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func_name: The function name to call.
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"""
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self.model = DiffusionModel(model, func_name=func_name)
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def sample(
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self,
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x: torch.Tensor,
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text_condition: torch.Tensor,
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speech_condition: torch.Tensor,
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padding_mask: torch.Tensor,
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num_step: int = 10,
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guidance_scale: Union[float, torch.Tensor] = 0.0,
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t_start: float = 0.0,
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t_end: float = 1.0,
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t_shift: float = 1.0,
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**kwargs
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) -> torch.Tensor:
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device = x.device
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assert isinstance(t_start, float) and isinstance(t_end, float)
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# Generate the schedule of timesteps
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timesteps = get_time_steps(
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t_start=t_start,
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t_end=t_end,
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num_step=num_step,
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t_shift=t_shift,
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device=device,
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)
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for step in range(num_step):
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t_cur = timesteps[step]
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t_next = timesteps[step + 1]
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# Predict velocity (v)
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v = self.model(
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t=t_cur,
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x=x,
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text_condition=text_condition,
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speech_condition=speech_condition,
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padding_mask=padding_mask,
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guidance_scale=guidance_scale,
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**kwargs
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)
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# 1. Predict the clean 'data' (x_1) and 'noise' (x_0)
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# Flow matching formulation: x_t = (1 - t) * x_0 + t * x_1
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# Therefore: v = x_1 - x_0
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x_1_pred = x + (1.0 - t_cur) * v
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x_0_pred = x - t_cur * v
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if step < num_step - 1:
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# 2. Probability Flow ODE update (Anchor-based)
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# This 'anchors' the next point along the predicted line,
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# making it more robust than simple Euler integration.
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x = (1.0 - t_next) * x_0_pred + t_next * x_1_pred
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else:
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# Final step: Snap directly to the predicted clean data
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x = x_1_pred
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return x
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class DistillEulerSolver(EulerSolver):
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def __init__(
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self,
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model: torch.nn.Module,
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func_name: str = "forward_fm_decoder",
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):
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"""Construct a Euler Solver for distilled diffusion models.
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Args:
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model: The diffusion model.
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"""
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self.model = DistillDiffusionModel(model, func_name=func_name)
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def get_time_steps(
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t_start: float = 0.0,
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t_end: float = 1.0,
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num_step: int = 10,
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t_shift: float = 1.0,
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device: torch.device = torch.device("cpu"),
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) -> torch.Tensor:
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"""Compute the intermediate time steps for sampling.
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Args:
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t_start: The starting time of the sampling (default is 0).
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t_end: The starting time of the sampling (default is 1).
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num_step: The number of sampling.
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t_shift: shift the t toward smaller numbers so that the sampling
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will emphasize low SNR region. Should be in the range of (0, 1].
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The shifting will be more significant when the number is smaller.
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device: A torch device.
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Returns:
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The time step with the shape (num_step + 1,).
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"""
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timesteps = torch.linspace(t_start, t_end, num_step + 1).to(device)
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timesteps = t_shift * timesteps / (1 + (t_shift - 1) * timesteps)
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return timesteps
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