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https://github.com/modelscope/DiffSynth-Studio.git
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Merge branch 'main' into ltx-2
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@@ -4,7 +4,7 @@ from typing_extensions import Literal
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class FlowMatchScheduler():
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def __init__(self, template: Literal["FLUX.1", "Wan", "Qwen-Image", "FLUX.2", "Z-Image", "LTX-2"] = "FLUX.1"):
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def __init__(self, template: Literal["FLUX.1", "Wan", "Qwen-Image", "FLUX.2", "Z-Image", "LTX-2", "Qwen-Image-Lightning"] = "FLUX.1"):
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self.set_timesteps_fn = {
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"FLUX.1": FlowMatchScheduler.set_timesteps_flux,
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"Wan": FlowMatchScheduler.set_timesteps_wan,
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@@ -12,6 +12,7 @@ class FlowMatchScheduler():
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"FLUX.2": FlowMatchScheduler.set_timesteps_flux2,
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"Z-Image": FlowMatchScheduler.set_timesteps_z_image,
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"LTX-2": FlowMatchScheduler.set_timesteps_ltx2,
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"Qwen-Image-Lightning": FlowMatchScheduler.set_timesteps_qwen_image_lightning,
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}.get(template, FlowMatchScheduler.set_timesteps_flux)
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self.num_train_timesteps = 1000
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@@ -71,6 +72,28 @@ class FlowMatchScheduler():
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timesteps = sigmas * num_train_timesteps
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return sigmas, timesteps
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@staticmethod
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def set_timesteps_qwen_image_lightning(num_inference_steps=100, denoising_strength=1.0, exponential_shift_mu=None, dynamic_shift_len=None):
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sigma_min = 0.0
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sigma_max = 1.0
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num_train_timesteps = 1000
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base_shift = math.log(3)
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max_shift = math.log(3)
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# Sigmas
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sigma_start = sigma_min + (sigma_max - sigma_min) * denoising_strength
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sigmas = torch.linspace(sigma_start, sigma_min, num_inference_steps + 1)[:-1]
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# Mu
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if exponential_shift_mu is not None:
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mu = exponential_shift_mu
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elif dynamic_shift_len is not None:
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mu = FlowMatchScheduler._calculate_shift_qwen_image(dynamic_shift_len, base_shift=base_shift, max_shift=max_shift)
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else:
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mu = 0.8
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sigmas = math.exp(mu) / (math.exp(mu) + (1 / sigmas - 1))
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# Timesteps
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timesteps = sigmas * num_train_timesteps
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return sigmas, timesteps
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@staticmethod
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def compute_empirical_mu(image_seq_len, num_steps):
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a1, b1 = 8.73809524e-05, 1.89833333
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