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from typing import *
import torch
import numpy as np
from tqdm import tqdm
from easydict import EasyDict as edict
from .base import Sampler
from .classifier_free_guidance_mixin import ClassifierFreeGuidanceSamplerMixin
from .guidance_interval_mixin import GuidanceIntervalSamplerMixin
import trellis.modules.sparse as sp
from trellis.modules.spatial import patchify, unpatchify
class FlowEulerSampler(Sampler):
"""
Generate samples from a flow-matching model using Euler sampling.
Args:
sigma_min: The minimum scale of noise in flow.
"""
def __init__(
self,
sigma_min: float,
):
self.sigma_min = sigma_min
def _eps_to_xstart(self, x_t, t, eps):
assert x_t.shape == eps.shape
return (x_t - (self.sigma_min + (1 - self.sigma_min) * t) * eps) / (1 - t)
def _xstart_to_x_t(self, x_0, t, eps):
assert x_0.shape == eps.shape
return (1-t) * x_0 + (self.sigma_min + (1 - self.sigma_min) * t) * eps
def _xstart_to_x_t(self, x_0, t, eps):
assert x_0.shape == eps.shape
return (1-t) * x_0 + (self.sigma_min + (1 - self.sigma_min) * t) * eps
def _xstart_to_eps(self, x_t, t, x_0):
assert x_t.shape == x_0.shape
return (x_t - (1 - t) * x_0) / (self.sigma_min + (1 - self.sigma_min) * t)
def _v_to_xstart_eps(self, x_t, t, v):
assert x_t.shape == v.shape
eps = (1 - t) * v + x_t
x_0 = (1 - self.sigma_min) * x_t - (self.sigma_min + (1 - self.sigma_min) * t) * v
return x_0, eps
def _xstart_to_v(self, x_0, x_t, t):
assert x_0.shape == x_t.shape
return (x_t - (1 - self.sigma_min) * x_0) / (self.sigma_min + (1 - self.sigma_min) * t)
def _inference_model(self, model, x_t, t, cond=None, **kwargs):
t = torch.tensor([1000 * t] * x_t.shape[0], device=x_t.device, dtype=torch.float32)
return model(x_t.to(torch.float32), t, cond, **kwargs)
def _get_model_prediction(self, model, x_t, t, cond=None, **kwargs):
param = kwargs.pop("parameterization", "v")
if param == "v":
pred_v = self._inference_model(model, x_t, t, cond, **kwargs)
pred_x_0, pred_eps = self._v_to_xstart_eps(x_t=x_t, t=t, v=pred_v)
elif param == "x0":
pred_x_0 = self._inference_model(model, x_t, t, cond, **kwargs)
pred_v = self._xstart_to_v(x_0=pred_x_0, x_t=x_t, t=t)
return pred_x_0, None, pred_v
def _get_model_gt(self, x_0, t, noise):
gt_x_t = self._xstart_to_x_t(x_0, t, noise)
gt_v = self._xstart_to_v(x_0, gt_x_t, t)
return gt_x_t, gt_v
@torch.no_grad()
def sample_once(
self,
model,
x_t,
t: float,
t_prev: float,
cond: Optional[Any] = None,
**kwargs
):
"""
Sample x_{t-1} from the model using Euler method.
Args:
model: The model to sample from.
x_t: The [N x C x ...] tensor of noisy inputs at time t.
t: The current timestep.
t_prev: The previous timestep.
cond: conditional information.
**kwargs: Additional arguments for model inference.
Returns:
a dict containing the following
- 'pred_x_prev': x_{t-1}.
- 'pred_x_0': a prediction of x_0.
"""
pred_x_0, pred_eps, pred_v = self._get_model_prediction(model, x_t, t, cond, **kwargs)
pred_x_prev = x_t - (t - t_prev) * pred_v
return edict({"pred_x_prev": pred_x_prev, "pred_x_0": pred_x_0, "pred_eps": pred_eps})
@torch.no_grad()
def sample_once_featurevolume(
self,
model,
cond_model,
x_t,
t: float,
t_prev: float,
cond: Optional[Any] = None,
**kwargs
):
"""
Sample x_{t-1} from the model using Euler method.
Args:
model: The model to sample from.
x_t: The [N x C x ...] tensor of noisy inputs at time t.
t: The current timestep.
t_prev: The previous timestep.
cond: conditional information.
**kwargs: Additional arguments for model inference.
Returns:
a dict containing the following
- 'pred_x_prev': x_{t-1}.
- 'pred_x_0': a prediction of x_0.
"""
if isinstance(cond, sp.SparseTensor):
t_tmp = torch.tensor([1000 * t] * x_t.shape[0], device=x_t.device, dtype=x_t.dtype)
t_embed = model.t_embedder(t_tmp).to(x_t.dtype)
for block in cond_model:
cond = block(cond, t_embed)
if model.pe_mode == "ape":
cond = cond + model.pos_embedder(cond.coords[:, 1:]).to(x_t.dtype)
if 'neg_cond' in kwargs.keys():
neg_cond = kwargs['neg_cond']
for block in cond_model:
neg_cond = block(neg_cond, t_embed)
if model.pe_mode == "ape":
neg_cond = neg_cond + model.pos_embedder(neg_cond.coords[:, 1:]).to(x_t.dtype)
kwargs['neg_cond'] = neg_cond
else:
for block in cond_model:
cond = block(cond)
cond = patchify(cond, model.patch_size)
cond = cond.view(*cond.shape[:2], -1).permute(0, 2, 1).contiguous()
cond = cond + model.pos_emb[None].type(model.dtype)
if 'neg_cond' in kwargs.keys():
neg_cond = kwargs['neg_cond']
for block in cond_model:
neg_cond = block(neg_cond)
neg_cond = patchify(neg_cond, model.patch_size)
neg_cond = neg_cond.view(*neg_cond.shape[:2], -1).permute(0, 2, 1).contiguous()
neg_cond = neg_cond + model.pos_emb[None].type(model.dtype)
kwargs['neg_cond'] = neg_cond
pred_x_0, pred_eps, pred_v = self._get_model_prediction(model, x_t, t, cond, **kwargs)
pred_x_prev = x_t - (t - t_prev) * pred_v
return edict({"pred_x_prev": pred_x_prev, "pred_x_0": pred_x_0, "pred_eps": pred_eps})
@torch.no_grad()
def sample_featurevolume(
self,
model,
cond_model,
noise,
cond: Optional[Any] = None,
steps: int = 50,
rescale_t: float = 1.0,
verbose: bool = True,
**kwargs
):
"""
Generate samples from the model using Euler method.
Args:
model: The model to sample from.
noise: The initial noise tensor.
cond: conditional information.
steps: The number of steps to sample.
rescale_t: The rescale factor for t.
verbose: If True, show a progress bar.
**kwargs: Additional arguments for model_inference.
Returns:
a dict containing the following
- 'samples': the model samples.
- 'pred_x_t': a list of prediction of x_t.
- 'pred_x_0': a list of prediction of x_0.
"""
sample = noise
t_seq = np.linspace(1, 0, steps + 1)
t_seq = rescale_t * t_seq / (1 + (rescale_t - 1) * t_seq)
t_pairs = list((t_seq[i], t_seq[i + 1]) for i in range(steps))
ret = edict({"samples": None, "pred_x_t": [], "pred_x_0": []})
for t, t_prev in tqdm(t_pairs, desc="Sampling", disable=not verbose):
out = self.sample_once_featurevolume(model, cond_model, sample, t, t_prev, cond, **kwargs)
sample = out.pred_x_prev
ret.pred_x_t.append(out.pred_x_prev)
ret.pred_x_0.append(out.pred_x_0)
ret.samples = sample
return ret
@torch.no_grad()
def sample(
self,
model,
noise,
cond: Optional[Any] = None,
steps: int = 50,
rescale_t: float = 1.0,
verbose: bool = True,
**kwargs
):
"""
Generate samples from the model using Euler method.
Args:
model: The model to sample from.
noise: The initial noise tensor.
cond: conditional information.
steps: The number of steps to sample.
rescale_t: The rescale factor for t.
verbose: If True, show a progress bar.
**kwargs: Additional arguments for model_inference.
Returns:
a dict containing the following
- 'samples': the model samples.
- 'pred_x_t': a list of prediction of x_t.
- 'pred_x_0': a list of prediction of x_0.
"""
sample = noise
t_seq = np.linspace(1, 0, steps + 1)
t_seq = rescale_t * t_seq / (1 + (rescale_t - 1) * t_seq)
t_pairs = list((t_seq[i], t_seq[i + 1]) for i in range(steps))
ret = edict({"samples": None, "pred_x_t": [], "pred_x_0": []})
for t, t_prev in tqdm(t_pairs, desc="Sampling", disable=not verbose):
out = self.sample_once(model, sample, t, t_prev, cond, **kwargs)
sample = out.pred_x_prev
ret.pred_x_t.append(out.pred_x_prev)
ret.pred_x_0.append(out.pred_x_0)
ret.samples = sample
return ret
class FlowMatchingSampler(FlowEulerSampler):
"""
Implementation of Flow Matching using Euler sampling.
Inherits from FlowEulerSampler and modifies key methods for flow matching.
"""
def __init__(self, sigma_min: float = 0.0):
super().__init__(sigma_min=sigma_min)
def _compute_velocity(self, x_t: torch.Tensor, x_0: torch.Tensor, t: float) -> torch.Tensor:
return ((1 - self.sigma_min) * x_t - x_0 ) / (self.sigma_min + (1 - self.sigma_min) * t)
def _get_model_gt(self, x_1: torch.Tensor, t: float, x_0: torch.Tensor = None):
# TODO: Implement this method
pass
# """
# Get ground truth for training.
# Args:
# x_1: Target endpoint
# t: Time point
# noise: Initial noise to use as x_0
# """
# x_t = (1 - t) * x_0 + t * x_1
# v = self._compute_velocity(x_t, x_0, t)
# eps = x_t + (1 - t) * v # Convert velocity to noise
# return x_t, eps, v
def _v_to_xstart_eps(self, x_t: torch.Tensor, t: float, v: torch.Tensor):
"""Convert velocity to x_0 and noise predictions"""
eps = x_t + (1 - t) * v
x_0 = self._eps_to_xstart(x_t, t, eps)
return x_0, eps
@torch.no_grad()
def sample(
self,
model,
x_1: torch.Tensor,
cond: Optional[Any] = None,
steps: int = 50,
rescale_t: float = 1.0,
verbose: bool = True,
**kwargs
) -> Dict[str, torch.Tensor]:
"""
Generate samples by following the flow from noise to x_1.
Args:
model: The model to sample from
x_1: Target endpoint
cond: Conditional information
steps: Number of sampling steps
rescale_t: Time rescaling factor
verbose: Whether to show progress bar
**kwargs: Additional model arguments
Returns:
Dictionary containing sampling trajectory and predictions
"""
# Initialize with noise as x_0
noise = torch.randn_like(x_1)
current_x = noise
t_seq = np.linspace(1, 0, steps + 1)
t_seq = rescale_t * t_seq / (1 + (rescale_t - 1) * t_seq)
t_pairs = list(zip(t_seq[:-1], t_seq[1:]))
ret = edict({
"samples": None,
"pred_x_t": [],
"pred_x_0": []
})
for t, t_prev in tqdm(t_pairs, desc="Sampling", disable=not verbose):
out = self.sample_once(model, current_x, t, t_prev, cond, **kwargs)
current_x = out.pred_x_prev
ret.pred_x_t.append(out.pred_x_prev)
ret.pred_x_0.append(out.pred_x_0)
ret.samples = current_x
return ret
def sample_once(
self,
model,
x_t: torch.Tensor,
t: float,
t_prev: float,
cond: Optional[Any] = None,
**kwargs
) -> Dict:
"""
Sample x_{t-1} from the model using Euler method.
Args:
model: The model to sample from
x_t: Current state
t: Current time
t_prev: Next time step
cond: Conditional information
**kwargs: Additional model arguments
Returns:
Dictionary containing predictions
"""
pred_x_0, pred_eps, pred_v = self._get_model_prediction(model, x_t, t, cond, **kwargs)
pred_x_prev = x_t + (t_prev - t) * pred_v
return edict({
"pred_x_prev": pred_x_prev,
"pred_x_0": pred_x_0,
"pred_eps": pred_eps
})
class FlowEulerCfgSampler(ClassifierFreeGuidanceSamplerMixin, FlowEulerSampler):
"""
Generate samples from a flow-matching model using Euler sampling with classifier-free guidance.
"""
@torch.no_grad()
def sample(
self,
model,
noise,
cond,
neg_cond,
steps: int = 50,
rescale_t: float = 1.0,
cfg_strength: float = 3.0,
verbose: bool = True,
**kwargs
):
"""
Generate samples from the model using Euler method.
Args:
model: The model to sample from.
noise: The initial noise tensor.
cond: conditional information.
neg_cond: negative conditional information.
steps: The number of steps to sample.
rescale_t: The rescale factor for t.
cfg_strength: The strength of classifier-free guidance.
verbose: If True, show a progress bar.
**kwargs: Additional arguments for model_inference.
Returns:
a dict containing the following
- 'samples': the model samples.
- 'pred_x_t': a list of prediction of x_t.
- 'pred_x_0': a list of prediction of x_0.
"""
return super().sample(model, noise, cond, steps, rescale_t, verbose, neg_cond=neg_cond, cfg_strength=cfg_strength, **kwargs)
class FlowEulerGuidanceIntervalSampler(GuidanceIntervalSamplerMixin, FlowEulerSampler):
"""
Generate samples from a flow-matching model using Euler sampling with classifier-free guidance and interval.
"""
@torch.no_grad()
def sample(
self,
model,
noise,
cond,
neg_cond,
steps: int = 50,
rescale_t: float = 1.0,
cfg_strength: float = 3.0,
cfg_interval: Tuple[float, float] = (0.0, 1.0),
verbose: bool = True,
**kwargs
):
"""
Generate samples from the model using Euler method.
Args:
model: The model to sample from.
noise: The initial noise tensor.
cond: conditional information.
neg_cond: negative conditional information.
steps: The number of steps to sample.
rescale_t: The rescale factor for t.
cfg_strength: The strength of classifier-free guidance.
cfg_interval: The interval for classifier-free guidance.
verbose: If True, show a progress bar.
**kwargs: Additional arguments for model_inference.
Returns:
a dict containing the following
- 'samples': the model samples.
- 'pred_x_t': a list of prediction of x_t.
- 'pred_x_0': a list of prediction of x_0.
"""
return super().sample(model, noise, cond, steps, rescale_t, verbose, neg_cond=neg_cond, cfg_strength=cfg_strength, cfg_interval=cfg_interval, **kwargs)
@torch.no_grad()
def sample_featurevolume(
self,
model,
cond_model,
noise,
cond,
neg_cond,
steps: int = 50,
rescale_t: float = 1.0,
cfg_strength: float = 3.0,
cfg_interval: Tuple[float, float] = (0.0, 1.0),
verbose: bool = True,
**kwargs
):
"""
Generate samples from the model using Euler method.
Args:
model: The model to sample from.
noise: The initial noise tensor.
cond: conditional information.
neg_cond: negative conditional information.
steps: The number of steps to sample.
rescale_t: The rescale factor for t.
cfg_strength: The strength of classifier-free guidance.
cfg_interval: The interval for classifier-free guidance.
verbose: If True, show a progress bar.
**kwargs: Additional arguments for model_inference.
Returns:
a dict containing the following
- 'samples': the model samples.
- 'pred_x_t': a list of prediction of x_t.
- 'pred_x_0': a list of prediction of x_0.
"""
return super().sample_featurevolume(model, cond_model, noise, cond, steps, rescale_t, verbose, neg_cond=neg_cond, cfg_strength=cfg_strength, cfg_interval=cfg_interval, **kwargs)
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