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c335050 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 | import json
import math
import os
from typing import Optional
import wandb
from src.model_training.transformers_compat import patch_transformers_hybrid_cache
patch_transformers_hybrid_cache()
from diffsynth.trainers.utils import ModelLogger as BaseModelLogger
class ModelLogger(BaseModelLogger):
"""Compatibility wrapper for legacy training scripts."""
def __init__(
self,
output_path,
remove_prefix_in_ckpt=None,
state_dict_converter=lambda x: x,
wandb_run_name=None,
ckpt_interval=None,
resume_step_count=0,
save_full_model=False,
context_drop_prob: float = 0.0,
enable_video_sampling=False,
sampling_interval_steps: int = 0,
sampling_two_chunk_memory: bool = False,
sampling_action_path: Optional[str] = None,
sampling_two_chunk_action_path: Optional[str] = None,
sampling_negative_prompt: str = "oversaturated colors, overexposed, static, blurry details",
sampling_height: int = 352,
sampling_width: int = 640,
sampling_num_frames: int = 81,
sampling_num_inference_steps: int = 50,
context_memory_frames: int = 1,
context_source: str = "replay",
context_per_frame_vae: bool = False,
sampling_sigma_shift: float = 5.0,
):
super().__init__(output_path, remove_prefix_in_ckpt=remove_prefix_in_ckpt, state_dict_converter=state_dict_converter)
self.wandb_run_name = wandb_run_name
self.ckpt_interval = int(ckpt_interval) if ckpt_interval else None
self.step_count = int(resume_step_count)
self.save_full_model = bool(save_full_model)
self.total_steps = None
self.context_drop_prob = float(context_drop_prob)
self.enable_video_sampling = bool(enable_video_sampling)
self.sampling_interval_steps = int(sampling_interval_steps)
self.sampling_two_chunk_memory = bool(sampling_two_chunk_memory)
self.sampling_action_path = sampling_action_path
self.sampling_two_chunk_action_path = sampling_two_chunk_action_path
self.sampling_negative_prompt = sampling_negative_prompt
self.sampling_height = int(sampling_height)
self.sampling_width = int(sampling_width)
self.sampling_num_frames = int(sampling_num_frames)
self.sampling_num_inference_steps = int(sampling_num_inference_steps)
self.context_memory_frames = int(context_memory_frames)
self.context_source = context_source.strip().lower()
self.context_per_frame_vae = bool(context_per_frame_vae)
# Noise-schedule shift used by the periodic sampling monitor. Must match
# the TRAINING shift (set in train.py from --timestep_shift), else the
# monitor samples from a noise schedule the model never trained on ->
# structure roughly OK but colors blown out / distorted.
self.sampling_sigma_shift = float(sampling_sigma_shift)
self.wandb_logger = None
if self.wandb_run_name:
self.wandb_logger = wandb.init(project="wan-cam", name=self.wandb_run_name, reinit=True)
def _save_step_or_epoch_ckpt(self, accelerator, model, path: str):
state_dict = None
unwrapped = accelerator.unwrap_model(model)
if self.save_full_model:
# Save full DiT (including action/camera/memory modules), not whole pipeline.
state_dict = accelerator.get_state_dict(unwrapped.pipe.dit)
for module_name in (
"spatial_memory_module",
"spatial_memory_readout_module",
"geometry_spatial_memory_module",
"geometry_spatial_memory_readout_module",
):
module = getattr(unwrapped, module_name, None)
if module is not None:
state_dict.update(
{
f"{module_name}.{name}": param
for name, param in accelerator.get_state_dict(module).items()
}
)
if state_dict is None:
full_state = accelerator.get_state_dict(model)
state_dict = unwrapped.export_trainable_state_dict(full_state, remove_prefix=self.remove_prefix_in_ckpt)
state_dict = self.state_dict_converter(state_dict)
os.makedirs(self.output_path, exist_ok=True)
accelerator.save(state_dict, path, safe_serialization=True)
def _maybe_sample_paper_process(self, accelerator=None, model=None, current_batch=None):
if not (
self.enable_video_sampling
and self.sampling_two_chunk_memory
and self.sampling_interval_steps > 0
and self.step_count % self.sampling_interval_steps == 0
and accelerator is not None
and model is not None
and current_batch is not None
):
return
from diffsynth import save_video
from src.model_training.multichunk_sample_utils import (
run_two_chunk_memory_monitor,
sync_pipe_memory_from_training_module,
)
sample = current_batch[0] if isinstance(current_batch, list) else current_batch
first_frame = sample["video"][0]
unwrapped = accelerator.unwrap_model(model)
pipe = unwrapped.pipe
sync_pipe_memory_from_training_module(pipe, unwrapped)
action0 = self.sampling_two_chunk_action_path or self.sampling_action_path
action1 = self.sampling_action_path
# Sampling runs the inference pipe.__call__, which (a) overwrites the
# training scheduler with an inference schedule and (b) spikes GPU
# memory on the main rank. Wrap it so a sampling failure can never abort
# training, and always restore training scheduler state + free cache.
try:
frames0, frames1, meta = run_two_chunk_memory_monitor(
pipe,
prompt=sample.get("prompt") or sample.get("description") or "A scene.",
negative_prompt=self.sampling_negative_prompt,
action_path=self.sampling_action_path,
chunk0_action_path=action0,
chunk1_action_path=action1,
first_frame_pil=first_frame,
context_memory_frames=self.context_memory_frames,
chunk_frames=self.sampling_num_frames,
h=self.sampling_height,
w=self.sampling_width,
seed=42 + self.step_count + accelerator.process_index,
sigma_shift=self.sampling_sigma_shift,
num_inference_steps=self.sampling_num_inference_steps,
cfg_scale=5.0,
inference_noise_level=0.0,
omit_context_actions=False,
context_source=self.context_source,
context_position=os.environ.get("CONTEXT_POSITION", "suffix"),
context_per_frame_vae=self.context_per_frame_vae,
device=pipe.device,
log_prefix=f"[paper-sampling][step={self.step_count}]",
)
out_dir = os.path.join(self.output_path, "paper_process_sampling")
os.makedirs(out_dir, exist_ok=True)
tag = f"step_{self.step_count:07d}_rank{accelerator.process_index}"
save_video(list(frames0) + list(frames1), os.path.join(out_dir, f"{tag}_pred.mp4"), fps=15, quality=5)
with open(os.path.join(out_dir, f"{tag}_meta.json"), "w", encoding="utf-8") as f:
json.dump(meta, f, ensure_ascii=False, indent=2)
except Exception as e:
# Sampling is a monitoring side-effect; never let it kill training.
import traceback
print(f"[paper-sampling][step={self.step_count}] FAILED (training continues): {e}")
traceback.print_exc()
finally:
# Restore training scheduler + free the sampling step's GPU cache on
# the main rank so the next training step (target VAE encode) is clean.
_train_module = getattr(unwrapped, "restore_after_sampling", None)
if callable(_train_module):
_train_module()
else:
import gc
gc.collect()
if __import__("torch").cuda.is_available():
__import__("torch").cuda.empty_cache()
def on_step_end(self, loss, accelerator=None, model=None, current_batch=None,
aux_loss=None):
self.step_count += 1
if self.wandb_logger is not None:
if accelerator is None or accelerator.is_main_process:
loss_v = float(loss.detach().float().item())
metrics = {"train/loss": loss_v, "step": self.step_count}
# CGLA SSE load-balancing aux loss (summed per-block in the
# training loop; passed in so dummy/skip steps don't log stale
# values). Logged only when CGLA is active (cgla_aux_loss_weight>0).
if aux_loss is not None and not math.isnan(aux_loss):
metrics["train/aux"] = float(aux_loss)
self.wandb_logger.log(metrics)
if accelerator is not None and accelerator.is_main_process:
self._maybe_sample_paper_process(accelerator, model, current_batch)
if accelerator is not None and self.enable_video_sampling and self.sampling_two_chunk_memory and self.sampling_interval_steps > 0:
accelerator.wait_for_everyone()
if (
self.ckpt_interval
and accelerator is not None
and model is not None
and (self.step_count % self.ckpt_interval == 0)
):
accelerator.wait_for_everyone()
if accelerator.is_main_process:
path = os.path.join(self.output_path, f"Step-{self.step_count}.safetensors")
self._save_step_or_epoch_ckpt(accelerator, model, path)
def on_epoch_end(self, accelerator, model, epoch_id):
accelerator.wait_for_everyone()
if accelerator.is_main_process:
path = os.path.join(self.output_path, f"epoch-{epoch_id}.safetensors")
self._save_step_or_epoch_ckpt(accelerator, model, path)
def finish(self):
if self.wandb_logger is not None:
wandb.finish()
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