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refactor scheduler

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Artiprocher
2025-11-30 15:22:39 +08:00
parent 1a6fd69e6b
commit 675ae5e91f
6 changed files with 150 additions and 127 deletions
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257
diffsynth/diffusion/flow_match.py
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@@ -1,129 +1,76 @@
import torch, math
from typing_extensions import Literal
class FlowMatchScheduler():
def __init__(
self,
num_inference_steps=100,
num_train_timesteps=1000,
shift=3.0,
sigma_max=1.0,
sigma_min=0.003/1.002,
inverse_timesteps=False,
extra_one_step=False,
reverse_sigmas=False,
exponential_shift=False,
exponential_shift_mu=None,
shift_terminal=None,
):
self.num_train_timesteps = num_train_timesteps
self.shift = shift
self.sigma_max = sigma_max
self.sigma_min = sigma_min
self.inverse_timesteps = inverse_timesteps
self.extra_one_step = extra_one_step
self.reverse_sigmas = reverse_sigmas
self.exponential_shift = exponential_shift
self.exponential_shift_mu = exponential_shift_mu
self.shift_terminal = shift_terminal
self.set_timesteps(num_inference_steps)
def __init__(self, template: Literal["FLUX.1", "Wan", "Qwen-Image", "FLUX.2", "Z-Image"] = "FLUX.1"):
self.set_timesteps_fn = {
"FLUX.1": FlowMatchScheduler.set_timesteps_flux,
"Wan": FlowMatchScheduler.set_timesteps_wan,
"Qwen-Image": FlowMatchScheduler.set_timesteps_qwen_image,
"FLUX.2": FlowMatchScheduler.set_timesteps_flux2,
"Z-Image": FlowMatchScheduler.set_timesteps_z_image,
}.get(template, FlowMatchScheduler.set_timesteps_flux)
def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, training=False, shift=None, dynamic_shift_len=None, exponential_shift_mu=None):
if shift is not None:
self.shift = shift
sigma_start = self.sigma_min + (self.sigma_max - self.sigma_min) * denoising_strength
if self.extra_one_step:
self.sigmas = torch.linspace(sigma_start, self.sigma_min, num_inference_steps + 1)[:-1]
else:
self.sigmas = torch.linspace(sigma_start, self.sigma_min, num_inference_steps)
if self.inverse_timesteps:
self.sigmas = torch.flip(self.sigmas, dims=[0])
if self.exponential_shift:
if exponential_shift_mu is not None:
mu = exponential_shift_mu
elif dynamic_shift_len is not None:
mu = self.calculate_shift(dynamic_shift_len)
else:
mu = self.exponential_shift_mu
self.sigmas = math.exp(mu) / (math.exp(mu) + (1 / self.sigmas - 1))
else:
self.sigmas = self.shift * self.sigmas / (1 + (self.shift - 1) * self.sigmas)
if self.shift_terminal is not None:
one_minus_z = 1 - self.sigmas
scale_factor = one_minus_z[-1] / (1 - self.shift_terminal)
self.sigmas = 1 - (one_minus_z / scale_factor)
if self.reverse_sigmas:
self.sigmas = 1 - self.sigmas
self.timesteps = self.sigmas * self.num_train_timesteps
if training:
x = self.timesteps
y = torch.exp(-2 * ((x - num_inference_steps / 2) / num_inference_steps) ** 2)
y_shifted = y - y.min()
bsmntw_weighing = y_shifted * (num_inference_steps / y_shifted.sum())
self.linear_timesteps_weights = bsmntw_weighing
self.training = True
else:
self.training = False
def step(self, model_output, timestep, sample, to_final=False, **kwargs):
if isinstance(timestep, torch.Tensor):
timestep = timestep.cpu()
timestep_id = torch.argmin((self.timesteps - timestep).abs())
sigma = self.sigmas[timestep_id]
if to_final or timestep_id + 1 >= len(self.timesteps):
sigma_ = 1 if (self.inverse_timesteps or self.reverse_sigmas) else 0
else:
sigma_ = self.sigmas[timestep_id + 1]
prev_sample = sample + model_output * (sigma_ - sigma)
return prev_sample
@staticmethod
def set_timesteps_flux(num_inference_steps=100, denoising_strength=1.0, shift=None):
sigma_min = 0.003/1.002
sigma_max = 1.0
shift = 3 if shift is None else shift
num_train_timesteps = 1000
sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps)
sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
timesteps = sigmas * num_train_timesteps
return sigmas, timesteps
def return_to_timestep(self, timestep, sample, sample_stablized):
if isinstance(timestep, torch.Tensor):
timestep = timestep.cpu()
timestep_id = torch.argmin((self.timesteps - timestep).abs())
sigma = self.sigmas[timestep_id]
model_output = (sample - sample_stablized) / sigma
return model_output
@staticmethod
def set_timesteps_wan(num_inference_steps=100, denoising_strength=1.0, shift=None):
sigma_min = 0.0
sigma_max = 1.0
shift = 5 if shift is None else shift
num_train_timesteps = 1000
sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
timesteps = sigmas * num_train_timesteps
return sigmas, timesteps
def add_noise(self, original_samples, noise, timestep):
if isinstance(timestep, torch.Tensor):
timestep = timestep.cpu()
timestep_id = torch.argmin((self.timesteps - timestep).abs())
sigma = self.sigmas[timestep_id]
sample = (1 - sigma) * original_samples + sigma * noise
return sample
def training_target(self, sample, noise, timestep):
target = noise - sample
return target
def training_weight(self, timestep):
timestep_id = torch.argmin((self.timesteps - timestep.to(self.timesteps.device)).abs())
weights = self.linear_timesteps_weights[timestep_id]
return weights
def calculate_shift(
self,
image_seq_len,
base_seq_len: int = 256,
max_seq_len: int = 8192,
base_shift: float = 0.5,
max_shift: float = 0.9,
):
@staticmethod
def _calculate_shift_qwen_image(image_seq_len, base_seq_len=256, max_seq_len=8192, base_shift=0.5, max_shift=0.9):
m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
b = base_shift - m * base_seq_len
mu = image_seq_len * m + b
return mu
def compute_empirical_mu(self, image_seq_len: int, num_steps: int) -> float:
@staticmethod
def set_timesteps_qwen_image(num_inference_steps=100, denoising_strength=1.0, exponential_shift_mu=None, dynamic_shift_len=None):
sigma_min = 0.0
sigma_max = 1.0
num_train_timesteps = 1000
shift_terminal = 0.02
# Sigmas
sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
# Mu
if exponential_shift_mu is not None:
mu = exponential_shift_mu
elif dynamic_shift_len is not None:
mu = FlowMatchScheduler._calculate_shift_qwen_image(dynamic_shift_len)
else:
mu = 0.8
sigmas = math.exp(mu) / (math.exp(mu) + (1 / sigmas - 1))
# Shift terminal
one_minus_z = 1 - sigmas
scale_factor = one_minus_z[-1] / (1 - shift_terminal)
sigmas = 1 - (one_minus_z / scale_factor)
# Timesteps
timesteps = sigmas * num_train_timesteps
return sigmas, timesteps
@staticmethod
def compute_empirical_mu(image_seq_len, num_steps):
a1, b1 = 8.73809524e-05, 1.89833333
a2, b2 = 0.00016927, 0.45666666
@@ -138,4 +85,84 @@ class FlowMatchScheduler():
b = m_200 - 200.0 * a
mu = a * num_steps + b
return float(mu)
return float(mu)
@staticmethod
def set_timesteps_flux2(num_inference_steps=100, denoising_strength=1.0, dynamic_shift_len=None):
sigma_min = 1 / num_inference_steps
sigma_max = 1.0
num_train_timesteps = 1000
sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps)
mu = FlowMatchScheduler.compute_empirical_mu(dynamic_shift_len, num_inference_steps)
sigmas = math.exp(mu) / (math.exp(mu) + (1 / sigmas - 1))
timesteps = sigmas * num_train_timesteps
return sigmas, timesteps
@staticmethod
def set_timesteps_z_image(num_inference_steps=100, denoising_strength=1.0, shift=None):
sigma_min = 0.0
sigma_max = 1.0
shift = 3 if shift is None else shift
num_train_timesteps = 1000
sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
timesteps = sigmas * num_train_timesteps
return sigmas, timesteps
def set_training_weight(self, num_inference_steps):
x = self.timesteps
y = torch.exp(-2 * ((x - num_inference_steps / 2) / num_inference_steps) ** 2)
y_shifted = y - y.min()
bsmntw_weighing = y_shifted * (num_inference_steps / y_shifted.sum())
self.linear_timesteps_weights = bsmntw_weighing
def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, training=False, **kwargs):
self.sigmas, self.timesteps = self.set_timesteps_fn(
num_inference_steps=num_inference_steps,
denoising_strength=denoising_strength,
**kwargs,
)
if training:
self.set_training_weight(num_inference_steps)
self.training = True
else:
self.training = False
def step(self, model_output, timestep, sample, to_final=False, **kwargs):
if isinstance(timestep, torch.Tensor):
timestep = timestep.cpu()
timestep_id = torch.argmin((self.timesteps - timestep).abs())
sigma = self.sigmas[timestep_id]
if to_final or timestep_id + 1 >= len(self.timesteps):
sigma_ = 0
else:
sigma_ = self.sigmas[timestep_id + 1]
prev_sample = sample + model_output * (sigma_ - sigma)
return prev_sample
def return_to_timestep(self, timestep, sample, sample_stablized):
if isinstance(timestep, torch.Tensor):
timestep = timestep.cpu()
timestep_id = torch.argmin((self.timesteps - timestep).abs())
sigma = self.sigmas[timestep_id]
model_output = (sample - sample_stablized) / sigma
return model_output
def add_noise(self, original_samples, noise, timestep):
if isinstance(timestep, torch.Tensor):
timestep = timestep.cpu()
timestep_id = torch.argmin((self.timesteps - timestep).abs())
sigma = self.sigmas[timestep_id]
sample = (1 - sigma) * original_samples + sigma * noise
return sample
def training_target(self, sample, noise, timestep):
target = noise - sample
return target
def training_weight(self, timestep):
timestep_id = torch.argmin((self.timesteps - timestep.to(self.timesteps.device)).abs())
weights = self.linear_timesteps_weights[timestep_id]
return weights

10
diffsynth/pipelines/flux2_image.py
View File

@@ -23,7 +23,7 @@ class Flux2ImagePipeline(BasePipeline):
device=device, torch_dtype=torch_dtype,
height_division_factor=16, width_division_factor=16,
)
self.scheduler = FlowMatchScheduler()
self.scheduler = FlowMatchScheduler("FLUX.2")
self.text_encoder: Flux2TextEncoder = None
self.dit: Flux2DiT = None
self.vae: Flux2VAE = None
@@ -86,6 +86,8 @@ class Flux2ImagePipeline(BasePipeline):
# Progress bar
progress_bar_cmd = tqdm,
):
self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, dynamic_shift_len=height//16*width//16)
# Parameters
inputs_posi = {
"prompt": prompt,
@@ -103,12 +105,6 @@ class Flux2ImagePipeline(BasePipeline):
for unit in self.units:
inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
# using dynamic shift Scheduler
self.scheduler.exponential_shift = True
self.scheduler.sigma_min = 1 / num_inference_steps
mu = self.scheduler.compute_empirical_mu(inputs_shared["latents"].shape[1], num_inference_steps)
self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, exponential_shift_mu=mu)
# Denoise
self.load_models_to_device(self.in_iteration_models)
models = {name: getattr(self, name) for name in self.in_iteration_models}

2
diffsynth/pipelines/flux_image.py
View File

@@ -60,7 +60,7 @@ class FluxImagePipeline(BasePipeline):
device=device, torch_dtype=torch_dtype,
height_division_factor=16, width_division_factor=16,
)
self.scheduler = FlowMatchScheduler()
self.scheduler = FlowMatchScheduler("FLUX.1")
self.tokenizer_1: CLIPTokenizer = None
self.tokenizer_2: T5TokenizerFast = None
self.text_encoder_1: FluxTextEncoderClip = None

2
diffsynth/pipelines/qwen_image.py
View File

@@ -24,7 +24,7 @@ class QwenImagePipeline(BasePipeline):
)
from transformers import Qwen2Tokenizer, Qwen2VLProcessor
self.scheduler = FlowMatchScheduler(sigma_min=0, sigma_max=1, extra_one_step=True, exponential_shift=True, exponential_shift_mu=0.8, shift_terminal=0.02)
self.scheduler = FlowMatchScheduler("Qwen-Image")
self.text_encoder: QwenImageTextEncoder = None
self.dit: QwenImageDiT = None
self.vae: QwenImageVAE = None

4
diffsynth/pipelines/wan_video.py
View File

@@ -35,7 +35,7 @@ class WanVideoPipeline(BasePipeline):
device=device, torch_dtype=torch_dtype,
height_division_factor=16, width_division_factor=16, time_division_factor=4, time_division_remainder=1
)
self.scheduler = FlowMatchScheduler(shift=5, sigma_min=0.0, extra_one_step=True)
self.scheduler = FlowMatchScheduler("Wan")
self.tokenizer: HuggingfaceTokenizer = None
self.audio_processor: Wav2Vec2Processor = None
self.text_encoder: WanTextEncoder = None
@@ -283,7 +283,7 @@ class WanVideoPipeline(BasePipeline):
models = {name: getattr(self, name) for name in self.in_iteration_models}
for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
# Switch DiT if necessary
if timestep.item() < switch_DiT_boundary * self.scheduler.num_train_timesteps and self.dit2 is not None and not models["dit"] is self.dit2:
if timestep.item() < switch_DiT_boundary * 1000 and self.dit2 is not None and not models["dit"] is self.dit2:
self.load_models_to_device(self.in_iteration_models_2)
models["dit"] = self.dit2
models["vace"] = self.vace2

2
diffsynth/pipelines/z_image.py
View File

@@ -23,7 +23,7 @@ class ZImagePipeline(BasePipeline):
device=device, torch_dtype=torch_dtype,
height_division_factor=16, width_division_factor=16,
)
self.scheduler = FlowMatchScheduler()
self.scheduler = FlowMatchScheduler("Z-Image")
self.text_encoder: ZImageTextEncoder = None
self.dit: ZImageDiT = None
self.vae_encoder: FluxVAEEncoder = None