Spaces:
Running on Zero
Running on Zero
| # Copyright 2026 The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import torch | |
| from ...loaders import LTX2LoraLoaderMixin | |
| from ...utils import logging | |
| from ..modular_pipeline import ModularPipeline | |
| from .utils import LTX2_AUDIO_LATENTS_MEAN, LTX2_AUDIO_LATENTS_STD, LTX2_LATENTS_MEAN, LTX2_LATENTS_STD | |
| logger = logging.get_logger(__name__) | |
| class LTX2ModularPipeline( | |
| ModularPipeline, | |
| LTX2LoraLoaderMixin, | |
| ): | |
| """ | |
| A ModularPipeline for LTX-2 (joint video + audio generation). | |
| """ | |
| default_blocks_name = "LTX2AutoBlocks" | |
| def vae_spatial_compression_ratio(self): | |
| if getattr(self, "vae", None) is not None: | |
| return self.vae.spatial_compression_ratio | |
| return 32 | |
| def vae_temporal_compression_ratio(self): | |
| if getattr(self, "vae", None) is not None: | |
| return self.vae.temporal_compression_ratio | |
| return 8 | |
| # The video latent statistics live on whichever video autoencoder the checkpoint ships: the conv `vae`, or the | |
| # `diffusion_decoder`, which carries the same buffers and is the only one registered when a checkpoint decodes | |
| # with it and does not need the conv encoder. `LTX2_LATENTS_*` are the values of `Lightricks/LTX-2`. | |
| def vae_scaling_factor(self): | |
| if getattr(self, "vae", None) is not None: | |
| return self.vae.config.scaling_factor | |
| if getattr(self, "diffusion_decoder", None) is not None: | |
| return self.diffusion_decoder.config.scaling_factor | |
| return 1.0 | |
| def latents_mean(self): | |
| if getattr(self, "vae", None) is not None: | |
| return self.vae.latents_mean | |
| if getattr(self, "diffusion_decoder", None) is not None: | |
| return self.diffusion_decoder.latents_mean | |
| return torch.tensor(LTX2_LATENTS_MEAN) | |
| def latents_std(self): | |
| if getattr(self, "vae", None) is not None: | |
| return self.vae.latents_std | |
| if getattr(self, "diffusion_decoder", None) is not None: | |
| return self.diffusion_decoder.latents_std | |
| return torch.tensor(LTX2_LATENTS_STD) | |
| def transformer_spatial_patch_size(self): | |
| if getattr(self, "transformer", None) is not None: | |
| return self.transformer.config.patch_size | |
| return 1 | |
| def transformer_temporal_patch_size(self): | |
| if getattr(self, "transformer", None) is not None: | |
| return self.transformer.config.patch_size_t | |
| return 1 | |
| def audio_vae_mel_compression_ratio(self): | |
| if getattr(self, "audio_vae", None) is not None: | |
| return self.audio_vae.mel_compression_ratio | |
| return 4 | |
| def audio_vae_temporal_compression_ratio(self): | |
| if getattr(self, "audio_vae", None) is not None: | |
| return self.audio_vae.temporal_compression_ratio | |
| return 4 | |
| def audio_latents_mean(self): | |
| if getattr(self, "audio_vae", None) is not None: | |
| return self.audio_vae.latents_mean | |
| return torch.tensor(LTX2_AUDIO_LATENTS_MEAN) | |
| def audio_latents_std(self): | |
| if getattr(self, "audio_vae", None) is not None: | |
| return self.audio_vae.latents_std | |
| return torch.tensor(LTX2_AUDIO_LATENTS_STD) | |
| def audio_sampling_rate(self): | |
| if getattr(self, "audio_vae", None) is not None: | |
| return self.audio_vae.config.sample_rate | |
| return 16000 | |
| def audio_hop_length(self): | |
| if getattr(self, "audio_vae", None) is not None: | |
| return self.audio_vae.config.mel_hop_length | |
| return 160 | |
| class LTX25ModularPipeline(LTX2ModularPipeline): | |
| """ | |
| A ModularPipeline for LTX-2.5 (joint video + audio generation). | |
| Identical to [`LTX2ModularPipeline`] except that its default blocks decode with the diffusion video decoder, which | |
| is the native default from LTX-2.5 on. A checkpoint routes here through `modular_model_index.json`. | |
| """ | |
| default_blocks_name = "LTX25AutoBlocks" | |