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16e2001 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | from .base_pipeline import BasePipeline
import torch
def FlowMatchSFTLoss(pipe: BasePipeline, **inputs):
if "lora" in inputs:
# Image-to-LoRA models need to load lora here.
pipe.clear_lora(verbose=0)
pipe.load_lora(pipe.dit, state_dict=inputs["lora"], hotload=True, verbose=0)
max_timestep_boundary = int(inputs.get("max_timestep_boundary", 1) * len(pipe.scheduler.timesteps))
min_timestep_boundary = int(inputs.get("min_timestep_boundary", 0) * len(pipe.scheduler.timesteps))
timestep_id = torch.randint(min_timestep_boundary, max_timestep_boundary, (1,))
timestep = pipe.scheduler.timesteps[timestep_id].to(dtype=pipe.torch_dtype, device=pipe.device)
noise = torch.randn_like(inputs["input_latents"]) * inputs.get("noise_scale", 1.0)
inputs["latents"] = pipe.scheduler.add_noise(inputs["input_latents"], noise, timestep)
training_target = pipe.scheduler.training_target(inputs["input_latents"], noise, timestep)
if "first_frame_latents" in inputs:
inputs["latents"][:, :, 0:1] = inputs["first_frame_latents"]
models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
noise_pred = pipe.model_fn(**models, **inputs, timestep=timestep)
if "first_frame_latents" in inputs:
noise_pred = noise_pred[:, :, 1:]
training_target = training_target[:, :, 1:]
loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float())
loss = loss * pipe.scheduler.training_weight(timestep)
return loss
def FlowMatchSFTAudioVideoLoss(pipe: BasePipeline, **inputs):
max_timestep_boundary = int(inputs.get("max_timestep_boundary", 1) * len(pipe.scheduler.timesteps))
min_timestep_boundary = int(inputs.get("min_timestep_boundary", 0) * len(pipe.scheduler.timesteps))
timestep_id = torch.randint(min_timestep_boundary, max_timestep_boundary, (1,))
timestep = pipe.scheduler.timesteps[timestep_id].to(dtype=pipe.torch_dtype, device=pipe.device)
# video
noise = torch.randn_like(inputs["input_latents"])
inputs["video_latents"] = pipe.scheduler.add_noise(inputs["input_latents"], noise, timestep)
training_target = pipe.scheduler.training_target(inputs["input_latents"], noise, timestep)
# audio
if inputs.get("audio_input_latents") is not None:
audio_noise = torch.randn_like(inputs["audio_input_latents"])
inputs["audio_latents"] = pipe.scheduler.add_noise(inputs["audio_input_latents"], audio_noise, timestep)
training_target_audio = pipe.scheduler.training_target(inputs["audio_input_latents"], audio_noise, timestep)
models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
noise_pred, noise_pred_audio = 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)
if inputs.get("audio_input_latents") is not None:
loss_audio = torch.nn.functional.mse_loss(noise_pred_audio.float(), training_target_audio.float())
loss_audio = loss_audio * pipe.scheduler.training_weight(timestep)
loss = loss + loss_audio
return loss
def FlowMatchSFTMiniMaxH3AudioVideoLoss(pipe: BasePipeline, **inputs):
max_timestep_boundary = int(inputs.get("max_timestep_boundary", 1) * len(pipe.scheduler.timesteps))
min_timestep_boundary = int(inputs.get("min_timestep_boundary", 0) * len(pipe.scheduler.timesteps))
timestep_id = torch.randint(min_timestep_boundary, max_timestep_boundary, (1,))
timestep_video = pipe.scheduler.timesteps[timestep_id].to(dtype=pipe.torch_dtype, device=pipe.device)
timestep_audio = pipe.scheduler_audio.timesteps[timestep_id].to(dtype=pipe.torch_dtype, device=pipe.device)
noise = torch.randn_like(inputs["input_latents"])
inputs["video_latents"] = pipe.scheduler.add_noise(inputs["input_latents"], noise, timestep_video)
training_target = pipe.scheduler.training_target(inputs["input_latents"], noise, timestep_video)
audio_noise = torch.randn_like(inputs["audio_input_latents"])
inputs["audio_latents"] = pipe.scheduler_audio.add_noise(inputs["audio_input_latents"], audio_noise, timestep_audio)
training_target_audio = pipe.scheduler_audio.training_target(inputs["audio_input_latents"], audio_noise, timestep_audio)
models = {name: getattr(pipe, name) for name in pipe.in_iteration_models}
noise_pred, noise_pred_audio = pipe.model_fn(
**models, **inputs,
t_video=1.0 - float(timestep_video) / pipe.scheduler.num_train_timesteps,
t_audio=1.0 - float(timestep_audio) / pipe.scheduler_audio.num_train_timesteps,
device=pipe.device,
)
loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float())
loss = loss * pipe.scheduler.training_weight(timestep_video)
loss_audio = torch.nn.functional.mse_loss(noise_pred_audio.float(), training_target_audio.float())
loss_audio = loss_audio * pipe.scheduler_audio.training_weight(timestep_audio)
return loss + loss_audio
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]
denom = sigma_ - sigma
denom = torch.sign(denom) * torch.clamp(denom.abs(), min=1e-6)
target = (latents_ - inputs_shared["latents"]) / denom
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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