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31488f2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | from .base_pipeline_L2P import BasePipeline
import torch
def FlowMatchSFTLoss(pipe: BasePipeline, **inputs):
if "num_inference_steps" in inputs.keys():
pipe.scheduler.set_timesteps(inputs["num_inference_steps"])
timestep_id = torch.randint(0, len(pipe.scheduler.timesteps), (1,))
# print("num_inference_steps:", inputs["num_inference_steps"])
else:
max_timestep_boundary = int(inputs.get("max_timestep_boundary", 0.8) * len(pipe.scheduler.timesteps))
min_timestep_boundary = int(inputs.get("min_timestep_boundary", 0) * len(pipe.scheduler.timesteps))
timestep_id = torch.randint(-100, max_timestep_boundary, (1,)).clamp(min_timestep_boundary, max_timestep_boundary)
timestep = pipe.scheduler.timesteps[timestep_id].to(dtype=pipe.torch_dtype, device=pipe.device)
# print(timestep)
if "seed" in inputs.keys():
seed=inputs["seed"]
generator = torch.Generator(inputs["input_latents"].device).manual_seed(seed)
else:
generator = None
noise = torch.randn(inputs["input_latents"].shape, generator=generator, device=inputs["input_latents"].device, dtype=inputs["input_latents"].dtype)
inputs["latents"] = pipe.scheduler.add_noise(inputs["input_latents"], noise, timestep)
training_target = pipe.scheduler.training_target(inputs["input_latents"], noise, timestep)
models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
noise_pred = pipe.model_fn(**models, **inputs, timestep=timestep)
loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float())
# loss = loss * pipe.scheduler.training_weight(timestep)
return loss
def DirectDistillLoss(pipe: BasePipeline, **inputs):
pipe.scheduler.set_timesteps(inputs["num_inference_steps"])
pipe.scheduler.training = True
models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
for progress_id, timestep in enumerate(pipe.scheduler.timesteps):
timestep = timestep.unsqueeze(0).to(dtype=pipe.torch_dtype, device=pipe.device)
noise_pred = pipe.model_fn(**models, **inputs, timestep=timestep, progress_id=progress_id)
inputs["latents"] = pipe.step(pipe.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs)
loss = torch.nn.functional.mse_loss(inputs["latents"].float(), inputs["input_latents"].float())
return loss
class TrajectoryImitationLoss(torch.nn.Module):
def __init__(self):
super().__init__()
self.initialized = False
def initialize(self, device):
import lpips # TODO: remove it
self.loss_fn = lpips.LPIPS(net='alex').to(device)
self.initialized = True
def fetch_trajectory(self, pipe: BasePipeline, timesteps_student, inputs_shared, inputs_posi, inputs_nega, num_inference_steps, cfg_scale):
trajectory = [inputs_shared["latents"].clone()]
pipe.scheduler.set_timesteps(num_inference_steps, target_timesteps=timesteps_student)
models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
for progress_id, timestep in enumerate(pipe.scheduler.timesteps):
timestep = timestep.unsqueeze(0).to(dtype=pipe.torch_dtype, device=pipe.device)
noise_pred = pipe.cfg_guided_model_fn(
pipe.model_fn, cfg_scale,
inputs_shared, inputs_posi, inputs_nega,
**models, timestep=timestep, progress_id=progress_id
)
inputs_shared["latents"] = pipe.step(pipe.scheduler, progress_id=progress_id, noise_pred=noise_pred.detach(), **inputs_shared)
trajectory.append(inputs_shared["latents"].clone())
return pipe.scheduler.timesteps, trajectory
def align_trajectory(self, pipe: BasePipeline, timesteps_teacher, trajectory_teacher, inputs_shared, inputs_posi, inputs_nega, num_inference_steps, cfg_scale):
loss = 0
pipe.scheduler.set_timesteps(num_inference_steps, training=True)
models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
for progress_id, timestep in enumerate(pipe.scheduler.timesteps):
timestep = timestep.unsqueeze(0).to(dtype=pipe.torch_dtype, device=pipe.device)
progress_id_teacher = torch.argmin((timesteps_teacher - timestep).abs())
inputs_shared["latents"] = trajectory_teacher[progress_id_teacher]
noise_pred = pipe.cfg_guided_model_fn(
pipe.model_fn, cfg_scale,
inputs_shared, inputs_posi, inputs_nega,
**models, timestep=timestep, progress_id=progress_id
)
sigma = pipe.scheduler.sigmas[progress_id]
sigma_ = 0 if progress_id + 1 >= len(pipe.scheduler.timesteps) else pipe.scheduler.sigmas[progress_id + 1]
if progress_id + 1 >= len(pipe.scheduler.timesteps):
latents_ = trajectory_teacher[-1]
else:
progress_id_teacher = torch.argmin((timesteps_teacher - pipe.scheduler.timesteps[progress_id + 1]).abs())
latents_ = trajectory_teacher[progress_id_teacher]
target = (latents_ - inputs_shared["latents"]) / (sigma_ - sigma)
loss = loss + torch.nn.functional.mse_loss(noise_pred.float(), target.float()) * pipe.scheduler.training_weight(timestep)
return loss
def compute_regularization(self, pipe: BasePipeline, trajectory_teacher, inputs_shared, inputs_posi, inputs_nega, num_inference_steps, cfg_scale):
inputs_shared["latents"] = trajectory_teacher[0]
pipe.scheduler.set_timesteps(num_inference_steps)
models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
for progress_id, timestep in enumerate(pipe.scheduler.timesteps):
timestep = timestep.unsqueeze(0).to(dtype=pipe.torch_dtype, device=pipe.device)
noise_pred = pipe.cfg_guided_model_fn(
pipe.model_fn, cfg_scale,
inputs_shared, inputs_posi, inputs_nega,
**models, timestep=timestep, progress_id=progress_id
)
inputs_shared["latents"] = pipe.step(pipe.scheduler, progress_id=progress_id, noise_pred=noise_pred.detach(), **inputs_shared)
image_pred = pipe.vae_decoder(inputs_shared["latents"])
image_real = pipe.vae_decoder(trajectory_teacher[-1])
loss = self.loss_fn(image_pred.float(), image_real.float())
return loss
def forward(self, pipe: BasePipeline, inputs_shared, inputs_posi, inputs_nega):
if not self.initialized:
self.initialize(pipe.device)
with torch.no_grad():
pipe.scheduler.set_timesteps(8)
timesteps_teacher, trajectory_teacher = self.fetch_trajectory(inputs_shared["teacher"], pipe.scheduler.timesteps, inputs_shared, inputs_posi, inputs_nega, 50, 2)
timesteps_teacher = timesteps_teacher.to(dtype=pipe.torch_dtype, device=pipe.device)
loss_1 = self.align_trajectory(pipe, timesteps_teacher, trajectory_teacher, inputs_shared, inputs_posi, inputs_nega, 8, 1)
loss_2 = self.compute_regularization(pipe, trajectory_teacher, inputs_shared, inputs_posi, inputs_nega, 8, 1)
loss = loss_1 + loss_2
return loss
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