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merge into: nodes/aigc-apps-EasyAnimate:fix_get_meta_file_and_autocast
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nodes/aigc-apps-EasyAnimate:main nodes/aigc-apps-EasyAnimate:readme_update nodes/aigc-apps-EasyAnimate:camera_txts nodes/aigc-apps-EasyAnimate:fix_get_meta_file_and_autocast nodes/aigc-apps-EasyAnimate:mask_v2v nodes/aigc-apps-EasyAnimate:v5.1_7b nodes/aigc-apps-EasyAnimate:teacache nodes/aigc-apps-EasyAnimate:v5.1 nodes/aigc-apps-EasyAnimate:parallel_inference nodes/aigc-apps-EasyAnimate:v2
nodes/aigc-apps-EasyAnimate:v1
..
pull from: nodes/aigc-apps-EasyAnimate:camera_txts
Branches Tags
nodes/aigc-apps-EasyAnimate:main nodes/aigc-apps-EasyAnimate:readme_update nodes/aigc-apps-EasyAnimate:camera_txts nodes/aigc-apps-EasyAnimate:fix_get_meta_file_and_autocast nodes/aigc-apps-EasyAnimate:mask_v2v nodes/aigc-apps-EasyAnimate:v5.1_7b nodes/aigc-apps-EasyAnimate:teacache nodes/aigc-apps-EasyAnimate:v5.1 nodes/aigc-apps-EasyAnimate:parallel_inference nodes/aigc-apps-EasyAnimate:v2
nodes/aigc-apps-EasyAnimate:v1
3 Commits
fix_get_meta_file_and_autocast .. camera_txts
Author SHA1 Message Date
bubbliiiing dfc123f9be Update camera txts 2025-02-21 15:02:49 +00:00
Bubbliiiing b75dcb1cac Support num_decoded_latents > 1 now (#204) 2025-02-21 16:31:30 +08:00
hkz b808f604c9 Fix get_meta_file.py and autocast (#203) 2025-02-19 13:44:08 +08:00
6 changed files with 230 additions and 2 deletions
Expand all files Collapse all files
asset/Pan_Down.txt
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asset/Pan_Left.txt
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0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.285714286 0.000000000 0.000000000 1.000000000 0.000000000
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.316326531 0.000000000 0.000000000 1.000000000 0.000000000
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.346938776 0.000000000 0.000000000 1.000000000 0.000000000
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.377551020 0.000000000 0.000000000 1.000000000 0.000000000
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.408163265 0.000000000 0.000000000 1.000000000 0.000000000
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.438775510 0.000000000 0.000000000 1.000000000 0.000000000
0 0.532139961 0.946026558 0.500000000 0.500000000 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000 1.000000000 0.000000000 1.469387755 0.000000000 0.000000000 1.000000000 0.000000000
easyanimate/models/autoencoder_magvit.py
Regular → Executable
+20
View File
@@ -185,6 +185,8 @@ class AutoencoderKLMagvit(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
self.slice_compression_vae = slice_compression_vae
self.cache_compression_vae = cache_compression_vae
self.cache_mag_vae = cache_mag_vae
self.cache_compression_vae_copy = cache_compression_vae
self.cache_mag_vae_copy = cache_mag_vae
self.mini_batch_encoder = mini_batch_encoder
self.mini_batch_decoder = mini_batch_decoder
self.use_slicing = False
@@ -197,6 +199,24 @@ class AutoencoderKLMagvit(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
self.tile_latent_min_size = int(self.tile_sample_min_size / (2 ** (len(ch_mult) - 1)))
self.scaling_factor = scaling_factor
def disable_cache_in_vae(self):
self.cache_compression_vae = False
self.encoder.cache_compression_vae = False
self.decoder.cache_compression_vae = False
self.cache_mag_vae = False
self.encoder.cache_mag_vae = False
self.decoder.cache_mag_vae = False
def enable_cache_in_vae(self):
self.cache_compression_vae = self.cache_compression_vae_copy
self.encoder.cache_compression_vae = self.cache_compression_vae_copy
self.decoder.cache_compression_vae = self.cache_compression_vae_copy
self.cache_mag_vae = self.cache_mag_vae_copy
self.encoder.cache_mag_vae = self.cache_mag_vae_copy
self.decoder.cache_mag_vae = self.cache_mag_vae_copy
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (omnigen_Mag_Encoder, omnigen_Mag_Decoder)):
module.gradient_checkpointing = value
scripts/train_reward_lora.py
Regular → Executable
+10 -2
View File
@@ -1148,8 +1148,10 @@ def main():
if args.vae_gradient_checkpointing:
# Since 3D casual VAE need a cache to decode all latents autoregressively, .Thus, gradient checkpointing can only be
# enabled when decoding the first batch (i.e. the first three) of latents, in which case the cache is not being used.
if args.num_decoded_latents > 3:
raise ValueError("The vae_gradient_checkpointing is not supported for num_decoded_latents > 3.")
# num_decoded_latents > 3 is support in EasyAnimate now.
# if args.num_decoded_latents > 3:
# raise ValueError("The vae_gradient_checkpointing is not supported for num_decoded_latents > 3.")
vae.enable_gradient_checkpointing()
# Enable TF32 for faster training on Ampere GPUs,
@@ -1418,6 +1420,8 @@ def main():
if hasattr(noise_scheduler, "init_noise_sigma"):
latents = latents * noise_scheduler.init_noise_sigma
if hasattr(vae, "enable_cache_in_vae"):
vae.enable_cache_in_vae()
# Prepare inpaint latents if it needs.
# Use zero latents if we want to t2v.
@@ -1553,6 +1557,8 @@ def main():
# compute the previous noisy sample x_t -> x_t-1
latents = noise_scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
if hasattr(vae, "disable_cache_in_vae"):
vae.disable_cache_in_vae()
# decode latents (tensor)
# latents = latents.permute(0, 2, 1, 3, 4) # [B, C, T, H, W]
# Since the casual VAE decoding consumes a large amount of VRAM, and we need to keep the decoding
@@ -1652,6 +1658,8 @@ def main():
args.validation_prompts = validation_prompts[:args.validation_batch_size]
validation_prompts_idx = [(i, p) for i, p in enumerate(args.validation_prompts)]
if hasattr(vae, "enable_cache_in_vae"):
vae.enable_cache_in_vae()
accelerator.wait_for_everyone()
with accelerator.split_between_processes(validation_prompts_idx) as splitted_prompts_idx:
validation_loss, validation_reward = log_validation(