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Automated PR - 2026-01-12

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sync-bot
2026-01-12 14:14:33 +00:00
parent 628956009c
commit e5a15a4777
14 changed files with 66 additions and 31 deletions
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README.md
+1 -1
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@@ -3,7 +3,7 @@
[![Website](https://img.shields.io/badge/Website-LTX-181717?logo=google-chrome)](https://ltx.io)
[![Model](https://img.shields.io/badge/HuggingFace-Model-orange?logo=huggingface)](https://huggingface.co/Lightricks/LTX-2)
[![Demo](https://img.shields.io/badge/Demo-Try%20Now-brightgreen?logo=vercel)](https://app.ltx.studio/ltx-2-playground/i2v)
[![Paper](https://img.shields.io/badge/Paper-PDF-EC1C24?logo=adobeacrobatreader&logoColor=white)](https://videos.ltx.io/LTX-2/grants/LTX_2_Technical_Report_compressed.pdf)
[![Paper](https://img.shields.io/badge/Paper-PDF-EC1C24?logo=adobeacrobatreader&logoColor=white)](https://arxiv.org/abs/2601.03233)
[![Discord](https://img.shields.io/badge/Join-Discord-5865F2?logo=discord)](https://discord.gg/ltxplatform)
**LTX-2** is the first DiT-based audio-video foundation model that contains all core capabilities of modern video generation in one model: synchronized audio and video, high fidelity, multiple performance modes, production-ready outputs, API access, and open access.
packages/ltx-core/src/ltx_core/model/video_vae/resnet.py
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@@ -99,7 +99,7 @@ class ResnetBlock3D(nn.Module):
self.timestep_conditioning = timestep_conditioning
if timestep_conditioning:
self.scale_shift_table = nn.Parameter(torch.randn(4, in_channels) / in_channels**0.5)
self.scale_shift_table = nn.Parameter(torch.zeros(4, in_channels))
def _feed_spatial_noise(
self,
packages/ltx-core/src/ltx_core/model/video_vae/video_vae.py
+11 -10
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@@ -1,5 +1,5 @@
from dataclasses import replace
from typing import Any, Callable, Iterator, List, Optional, Tuple
from typing import Any, Callable, Iterator, List, Tuple
import torch
from einops import rearrange
@@ -521,12 +521,11 @@ class VideoDecoder(nn.Module):
)
self.last_scale_shift_table = nn.Parameter(torch.empty(2, feature_channels))
# def forward(self, sample: torch.Tensor, target_shape) -> torch.Tensor:
def forward(
self,
sample: torch.Tensor,
timestep: Optional[torch.Tensor] = None,
generator: Optional[torch.Generator] = None,
timestep: torch.Tensor | None = None,
generator: torch.Generator | None = None,
) -> torch.Tensor:
r"""
Decode latent representation into video frames.
@@ -651,8 +650,8 @@ class VideoDecoder(nn.Module):
self,
latent: torch.Tensor,
tiling_config: TilingConfig | None = None,
timestep: Optional[torch.Tensor] = None,
generator: Optional[torch.Generator] = None,
timestep: torch.Tensor | None = None,
generator: torch.Generator | None = None,
) -> Iterator[torch.Tensor]:
"""
Decode a latent tensor into video frames using tiled processing.
@@ -769,8 +768,8 @@ class VideoDecoder(nn.Module):
group_tiles: List[Tile],
buffer: torch.Tensor,
latent: torch.Tensor,
timestep: Optional[torch.Tensor],
generator: Optional[torch.Generator],
timestep: torch.Tensor | None,
generator: torch.Generator | None,
) -> torch.Tensor:
"""
Decode and accumulate all tiles of a temporal group into a local buffer.
@@ -815,6 +814,7 @@ def decode_video(
latent: torch.Tensor,
video_decoder: VideoDecoder,
tiling_config: TilingConfig | None = None,
generator: torch.Generator | None = None,
) -> Iterator[torch.Tensor]:
"""
Decode a video latent tensor with the given decoder.
@@ -822,6 +822,7 @@ def decode_video(
latent: Tensor [c, f, h, w]
video_decoder: Decoder module.
tiling_config: Optional tiling settings.
generator: Optional random generator for deterministic decoding.
Yields:
Decoded chunk [f, h, w, c], uint8 in [0, 255].
"""
@@ -832,10 +833,10 @@ def decode_video(
return frames
if tiling_config is not None:
for frames in video_decoder.tiled_decode(latent, tiling_config):
for frames in video_decoder.tiled_decode(latent, tiling_config, generator=generator):
yield convert_to_uint8(frames)
else:
decoded_video = video_decoder(latent)
decoded_video = video_decoder(latent, generator=generator)
yield convert_to_uint8(decoded_video)
packages/ltx-core/src/ltx_core/text_encoders/gemma/encoders/base_encoder.py
+14 -12
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@@ -32,7 +32,6 @@ class GemmaTextEncoderModelBase(torch.nn.Module):
dtype: torch.dtype = torch.bfloat16,
) -> None:
super().__init__()
self._gemma_root = None
self.tokenizer = tokenizer
self.model = model
self.processor = img_processor
@@ -73,12 +72,6 @@ class GemmaTextEncoderModelBase(torch.nn.Module):
)
return projected, attention_mask
def _init_image_processor(self) -> None:
img_processor = AutoImageProcessor.from_pretrained(self._gemma_root, local_files_only=True)
if not self.tokenizer:
raise ValueError("Tokenizer is not loaded, cannot load image processor")
self.processor = Gemma3Processor(image_processor=img_processor, tokenizer=self.tokenizer.tokenizer)
def _enhance(
self,
messages: list[dict[str, str]],
@@ -86,8 +79,6 @@ class GemmaTextEncoderModelBase(torch.nn.Module):
max_new_tokens: int = 512,
seed: int = 42,
) -> str:
if self.processor is None:
self._init_image_processor()
text = self.processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = self.processor(
@@ -242,17 +233,23 @@ def _find_matching_dir(root_path: str, pattern: str) -> str:
def module_ops_from_gemma_root(gemma_root: str) -> tuple[ModuleOps, ...]:
gemma_path = _find_matching_dir(gemma_root, "model*.safetensors")
tokenizer_path = _find_matching_dir(gemma_root, "tokenizer.model")
processor_path = _find_matching_dir(gemma_root, "preprocessor_config.json")
def load_gemma(module: GemmaTextEncoderModelBase) -> GemmaTextEncoderModelBase:
module.model = Gemma3ForConditionalGeneration.from_pretrained(
gemma_path, local_files_only=True, torch_dtype=torch.bfloat16
)
module._gemma_root = module._gemma_root or gemma_root
return module
def load_tokenizer(module: GemmaTextEncoderModelBase) -> GemmaTextEncoderModelBase:
module.tokenizer = LTXVGemmaTokenizer(tokenizer_path, 1024)
module._gemma_root = module._gemma_root or gemma_root
return module
def load_processor(module: GemmaTextEncoderModelBase) -> GemmaTextEncoderModelBase:
image_processor = AutoImageProcessor.from_pretrained(processor_path, local_files_only=True)
if not module.tokenizer:
raise ValueError("Tokenizer model operation must be performed before processor model operation")
module.processor = Gemma3Processor(image_processor=image_processor, tokenizer=module.tokenizer.tokenizer)
return module
gemma_load_ops = ModuleOps(
@@ -265,7 +262,12 @@ def module_ops_from_gemma_root(gemma_root: str) -> tuple[ModuleOps, ...]:
matcher=lambda module: isinstance(module, GemmaTextEncoderModelBase) and module.tokenizer is None,
mutator=load_tokenizer,
)
return (gemma_load_ops, tokenizer_load_ops)
processor_load_ops = ModuleOps(
"ProcessorLoad",
matcher=lambda module: isinstance(module, GemmaTextEncoderModelBase) and module.processor is None,
mutator=load_processor,
)
return (gemma_load_ops, tokenizer_load_ops, processor_load_ops)
def encode_text(text_encoder: GemmaTextEncoderModelBase, prompts: list[str]) -> list[tuple[torch.Tensor, torch.Tensor]]:
packages/ltx-pipelines/src/ltx_pipelines/distilled.py
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@@ -185,7 +185,9 @@ class DistilledPipeline:
del video_encoder
cleanup_memory()
decoded_video = vae_decode_video(video_state.latent, self.model_ledger.video_decoder(), tiling_config)
decoded_video = vae_decode_video(
video_state.latent, self.model_ledger.video_decoder(), tiling_config, generator
)
decoded_audio = vae_decode_audio(
audio_state.latent, self.model_ledger.audio_decoder(), self.model_ledger.vocoder()
)
packages/ltx-pipelines/src/ltx_pipelines/ic_lora.py
+3 -1
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@@ -223,7 +223,9 @@ class ICLoraPipeline:
del video_encoder
cleanup_memory()
decoded_video = vae_decode_video(video_state.latent, self.stage_2_model_ledger.video_decoder(), tiling_config)
decoded_video = vae_decode_video(
video_state.latent, self.stage_2_model_ledger.video_decoder(), tiling_config, generator
)
decoded_audio = vae_decode_audio(
audio_state.latent, self.stage_2_model_ledger.audio_decoder(), self.stage_2_model_ledger.vocoder()
)
packages/ltx-pipelines/src/ltx_pipelines/keyframe_interpolation.py
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@@ -223,7 +223,9 @@ class KeyframeInterpolationPipeline:
del video_encoder
cleanup_memory()
decoded_video = vae_decode_video(video_state.latent, self.stage_2_model_ledger.video_decoder(), tiling_config)
decoded_video = vae_decode_video(
video_state.latent, self.stage_2_model_ledger.video_decoder(), tiling_config, generator
)
decoded_audio = vae_decode_audio(
audio_state.latent, self.stage_2_model_ledger.audio_decoder(), self.stage_2_model_ledger.vocoder()
)
packages/ltx-pipelines/src/ltx_pipelines/ti2vid_one_stage.py
+1 -1
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@@ -147,7 +147,7 @@ class TI2VidOneStagePipeline:
del transformer
cleanup_memory()
decoded_video = vae_decode_video(video_state.latent, self.model_ledger.video_decoder())
decoded_video = vae_decode_video(video_state.latent, self.model_ledger.video_decoder(), generator=generator)
decoded_audio = vae_decode_audio(
audio_state.latent, self.model_ledger.audio_decoder(), self.model_ledger.vocoder()
)
packages/ltx-pipelines/src/ltx_pipelines/ti2vid_two_stages.py
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@@ -225,7 +225,9 @@ class TI2VidTwoStagesPipeline:
del video_encoder
cleanup_memory()
decoded_video = vae_decode_video(video_state.latent, self.stage_2_model_ledger.video_decoder(), tiling_config)
decoded_video = vae_decode_video(
video_state.latent, self.stage_2_model_ledger.video_decoder(), tiling_config, generator
)
decoded_audio = vae_decode_audio(
audio_state.latent, self.stage_2_model_ledger.audio_decoder(), self.stage_2_model_ledger.vocoder()
)
packages/ltx-trainer/configs/ltx2_av_lora.yaml
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@@ -253,6 +253,10 @@ checkpoints:
# Set to -1 to keep all checkpoints
keep_last_n: -1
# Precision to use when saving checkpoint weights
# Options: "bfloat16" (default, smaller files) or "float32" (full precision)
precision: "bfloat16"
# -----------------------------------------------------------------------------
# Flow Matching Configuration
# -----------------------------------------------------------------------------
packages/ltx-trainer/configs/ltx2_v2v_ic_lora.yaml
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@@ -263,6 +263,10 @@ checkpoints:
# Set to -1 to keep all checkpoints
keep_last_n: 3
# Precision to use when saving checkpoint weights
# Options: "bfloat16" (default, smaller files) or "float32" (full precision)
precision: "bfloat16"
# -----------------------------------------------------------------------------
# Flow Matching Configuration
# -----------------------------------------------------------------------------
packages/ltx-trainer/docs/configuration-reference.md
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@@ -292,8 +292,9 @@ Model checkpointing configuration.
```yaml
checkpoints:
interval: 250 # Steps between checkpoint saves (null = disabled)
keep_last_n: 3 # Number of recent checkpoints to retain
interval: 250 # Steps between checkpoint saves (null = disabled)
keep_last_n: 3 # Number of recent checkpoints to retain
precision: bfloat16 # Precision for saved weights (bfloat16 or float32)
```
**Key parameters:**
@@ -302,6 +303,7 @@ checkpoints:
|---------------|------------------------------------------------------------------------|
| `interval` | Steps between intermediate checkpoint saves (set to `null` to disable) |
| `keep_last_n` | Number of most recent checkpoints to keep (-1 = keep all) |
| `precision` | Precision for saved checkpoint weights: `"bfloat16"` (default) or `"float32"` |
### HubConfig
packages/ltx-trainer/src/ltx_trainer/config.py
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@@ -350,6 +350,11 @@ class CheckpointsConfig(ConfigBaseModel):
ge=-1,
)
precision: Literal["bfloat16", "float32"] = Field(
default="bfloat16",
description="Precision to use when saving checkpoint weights. Options: 'bfloat16' or 'float32'.",
)
class HubConfig(ConfigBaseModel):
"""Configuration for Hugging Face Hub integration"""
packages/ltx-trainer/src/ltx_trainer/trainer.py
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@@ -873,6 +873,9 @@ class LtxvTrainer:
save_dir.mkdir(exist_ok=True, parents=True)
# Determine save precision
save_dtype = torch.bfloat16 if self._config.checkpoints.precision == "bfloat16" else torch.float32
# For LoRA: extract only adapter weights; for full: use as-is
if is_lora:
unwrapped = self._accelerator.unwrap_model(self._transformer, keep_torch_compile=False)
@@ -885,9 +888,15 @@ class LtxvTrainer:
# Convert to ComfyUI-compatible format (add "diffusion_model." prefix)
state_dict = {f"diffusion_model.{k}": v for k, v in state_dict.items()}
# Cast to configured precision
state_dict = {k: v.to(save_dtype) if isinstance(v, Tensor) else v for k, v in state_dict.items()}
# Save to disk
save_file(state_dict, saved_weights_path)
else:
# Cast to configured precision
full_state_dict = {k: v.to(save_dtype) if isinstance(v, Tensor) else v for k, v in full_state_dict.items()}
# Save to disk
self._accelerator.save(full_state_dict, saved_weights_path)