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# MIT License
# Copyright (c) Microsoft
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# Copyright (c) [2025] [Microsoft]
# SPDX-License-Identifier: MIT
from typing import *
import torch
import numpy as np
from tqdm import tqdm
from .base import Sampler
from .classifier_free_guidance_mixin import ClassifierFreeGuidanceSamplerMixin
from .guidance_interval_mixin import GuidanceIntervalSamplerMixin
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_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 _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, t, cond, **kwargs)
def _get_model_prediction(self, model, x_t, t, cond=None, **kwargs):
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)
return pred_x_0, pred_eps, pred_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 {"pred_x_prev": pred_x_prev, "pred_x_0": pred_x_0}
@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 = {"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 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)
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