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1194 lines (1061 loc) · 47.6 KB
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import argparse
import os
from contextlib import nullcontext
from typing import Any
import safetensors.torch
import torch
from accelerate import init_empty_weights
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, Gemma3ForConditionalGeneration, Gemma3Processor
from diffusers import (
AutoencoderKLLTX2Audio,
AutoencoderKLLTX2Video,
FlowMatchEulerDiscreteScheduler,
LTX2LatentUpsamplePipeline,
LTX2Pipeline,
LTX2VideoTransformer3DModel,
)
from diffusers.pipelines.ltx2 import LTX2LatentUpsamplerModel, LTX2TextConnectors, LTX2Vocoder, LTX2VocoderWithBWE
from diffusers.utils.import_utils import is_accelerate_available
CTX = init_empty_weights if is_accelerate_available() else nullcontext
LTX_2_0_TRANSFORMER_KEYS_RENAME_DICT = {
# Input Patchify Projections
"patchify_proj": "proj_in",
"audio_patchify_proj": "audio_proj_in",
# Modulation Parameters
# Handle adaln_single --> time_embed, audioln_single --> audio_time_embed separately as the original keys are
# substrings of the other modulation parameters below
"av_ca_video_scale_shift_adaln_single": "av_cross_attn_video_scale_shift",
"av_ca_a2v_gate_adaln_single": "av_cross_attn_video_a2v_gate",
"av_ca_audio_scale_shift_adaln_single": "av_cross_attn_audio_scale_shift",
"av_ca_v2a_gate_adaln_single": "av_cross_attn_audio_v2a_gate",
# Transformer Blocks
# Per-Block Cross Attention Modulatin Parameters
"scale_shift_table_a2v_ca_video": "video_a2v_cross_attn_scale_shift_table",
"scale_shift_table_a2v_ca_audio": "audio_a2v_cross_attn_scale_shift_table",
# Attention QK Norms
"q_norm": "norm_q",
"k_norm": "norm_k",
}
LTX_2_3_TRANSFORMER_KEYS_RENAME_DICT = {
**LTX_2_0_TRANSFORMER_KEYS_RENAME_DICT,
"audio_prompt_adaln_single": "audio_prompt_adaln",
"prompt_adaln_single": "prompt_adaln",
}
LTX_2_0_VIDEO_VAE_RENAME_DICT = {
# Encoder
"down_blocks.0": "down_blocks.0",
"down_blocks.1": "down_blocks.0.downsamplers.0",
"down_blocks.2": "down_blocks.1",
"down_blocks.3": "down_blocks.1.downsamplers.0",
"down_blocks.4": "down_blocks.2",
"down_blocks.5": "down_blocks.2.downsamplers.0",
"down_blocks.6": "down_blocks.3",
"down_blocks.7": "down_blocks.3.downsamplers.0",
"down_blocks.8": "mid_block",
# Decoder
"up_blocks.0": "mid_block",
"up_blocks.1": "up_blocks.0.upsamplers.0",
"up_blocks.2": "up_blocks.0",
"up_blocks.3": "up_blocks.1.upsamplers.0",
"up_blocks.4": "up_blocks.1",
"up_blocks.5": "up_blocks.2.upsamplers.0",
"up_blocks.6": "up_blocks.2",
"last_time_embedder": "time_embedder",
"last_scale_shift_table": "scale_shift_table",
# Common
# For all 3D ResNets
"res_blocks": "resnets",
"per_channel_statistics.mean-of-means": "latents_mean",
"per_channel_statistics.std-of-means": "latents_std",
}
LTX_2_3_VIDEO_VAE_RENAME_DICT = {
**LTX_2_0_VIDEO_VAE_RENAME_DICT,
# Decoder extra blocks
"up_blocks.7": "up_blocks.3.upsamplers.0",
"up_blocks.8": "up_blocks.3",
}
LTX_2_0_AUDIO_VAE_RENAME_DICT = {
"per_channel_statistics.mean-of-means": "latents_mean",
"per_channel_statistics.std-of-means": "latents_std",
}
LTX_2_0_VOCODER_RENAME_DICT = {
"ups": "upsamplers",
"resblocks": "resnets",
"conv_pre": "conv_in",
"conv_post": "conv_out",
}
LTX_2_3_VOCODER_RENAME_DICT = {
# Handle upsamplers ("ups" --> "upsamplers") due to name clash
"resblocks": "resnets",
"conv_pre": "conv_in",
"conv_post": "conv_out",
"act_post": "act_out",
"downsample.lowpass": "downsample",
}
LTX_2_0_CONNECTORS_KEYS_RENAME_DICT = {
"connectors.": "",
"video_embeddings_connector": "video_connector",
"audio_embeddings_connector": "audio_connector",
"transformer_1d_blocks": "transformer_blocks",
"text_embedding_projection.aggregate_embed": "text_proj_in",
# Attention QK Norms
"q_norm": "norm_q",
"k_norm": "norm_k",
}
LTX_2_3_CONNECTORS_KEYS_RENAME_DICT = {
"connectors.": "",
"video_embeddings_connector": "video_connector",
"audio_embeddings_connector": "audio_connector",
"transformer_1d_blocks": "transformer_blocks",
# LTX-2.3 uses per-modality embedding projections
"text_embedding_projection.audio_aggregate_embed": "audio_text_proj_in",
"text_embedding_projection.video_aggregate_embed": "video_text_proj_in",
# Attention QK Norms
"q_norm": "norm_q",
"k_norm": "norm_k",
}
def update_state_dict_inplace(state_dict: dict[str, Any], old_key: str, new_key: str) -> None:
state_dict[new_key] = state_dict.pop(old_key)
def remove_keys_inplace(key: str, state_dict: dict[str, Any]) -> None:
state_dict.pop(key)
def convert_ltx2_transformer_adaln_single(key: str, state_dict: dict[str, Any]) -> None:
# Skip if not a weight, bias
if ".weight" not in key and ".bias" not in key:
return
if key.startswith("adaln_single."):
new_key = key.replace("adaln_single.", "time_embed.")
param = state_dict.pop(key)
state_dict[new_key] = param
if key.startswith("audio_adaln_single."):
new_key = key.replace("audio_adaln_single.", "audio_time_embed.")
param = state_dict.pop(key)
state_dict[new_key] = param
return
def convert_ltx2_audio_vae_per_channel_statistics(key: str, state_dict: dict[str, Any]) -> None:
if key.startswith("per_channel_statistics"):
new_key = ".".join(["decoder", key])
param = state_dict.pop(key)
state_dict[new_key] = param
return
def convert_ltx2_3_vocoder_upsamplers(key: str, state_dict: dict[str, Any]) -> None:
# Skip if not a weight, bias
if ".weight" not in key and ".bias" not in key:
return
if ".ups." in key:
new_key = key.replace(".ups.", ".upsamplers.")
param = state_dict.pop(key)
state_dict[new_key] = param
return
LTX_2_0_TRANSFORMER_SPECIAL_KEYS_REMAP = {
"video_embeddings_connector": remove_keys_inplace,
"audio_embeddings_connector": remove_keys_inplace,
"adaln_single": convert_ltx2_transformer_adaln_single,
}
LTX_2_0_VAE_SPECIAL_KEYS_REMAP = {
"per_channel_statistics.channel": remove_keys_inplace,
"per_channel_statistics.mean-of-stds": remove_keys_inplace,
}
LTX_2_0_AUDIO_VAE_SPECIAL_KEYS_REMAP = {}
LTX_2_0_VOCODER_SPECIAL_KEYS_REMAP = {}
LTX_2_3_VOCODER_SPECIAL_KEYS_REMAP = {
".ups.": convert_ltx2_3_vocoder_upsamplers,
}
LTX_2_0_CONNECTORS_SPECIAL_KEYS_REMAP = {}
def split_transformer_and_connector_state_dict(state_dict: dict[str, Any]) -> tuple[dict[str, Any], dict[str, Any]]:
connector_prefixes = (
"video_embeddings_connector",
"audio_embeddings_connector",
"transformer_1d_blocks",
"text_embedding_projection",
"connectors.",
"video_connector",
"audio_connector",
"text_proj_in",
)
transformer_state_dict, connector_state_dict = {}, {}
for key, value in state_dict.items():
if key.startswith(connector_prefixes):
connector_state_dict[key] = value
else:
transformer_state_dict[key] = value
return transformer_state_dict, connector_state_dict
def get_ltx2_transformer_config(version: str) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]:
if version == "test":
# Produces a transformer of the same size as used in test_models_transformer_ltx2.py
config = {
"model_id": "diffusers-internal-dev/dummy-ltx2",
"diffusers_config": {
"in_channels": 4,
"out_channels": 4,
"patch_size": 1,
"patch_size_t": 1,
"num_attention_heads": 2,
"attention_head_dim": 8,
"cross_attention_dim": 16,
"vae_scale_factors": (8, 32, 32),
"pos_embed_max_pos": 20,
"base_height": 2048,
"base_width": 2048,
"audio_in_channels": 4,
"audio_out_channels": 4,
"audio_patch_size": 1,
"audio_patch_size_t": 1,
"audio_num_attention_heads": 2,
"audio_attention_head_dim": 4,
"audio_cross_attention_dim": 8,
"audio_scale_factor": 4,
"audio_pos_embed_max_pos": 20,
"audio_sampling_rate": 16000,
"audio_hop_length": 160,
"num_layers": 2,
"activation_fn": "gelu-approximate",
"qk_norm": "rms_norm_across_heads",
"norm_elementwise_affine": False,
"norm_eps": 1e-6,
"caption_channels": 16,
"attention_bias": True,
"attention_out_bias": True,
"rope_theta": 10000.0,
"rope_double_precision": False,
"causal_offset": 1,
"timestep_scale_multiplier": 1000,
"cross_attn_timestep_scale_multiplier": 1,
},
}
rename_dict = LTX_2_0_TRANSFORMER_KEYS_RENAME_DICT
special_keys_remap = LTX_2_0_TRANSFORMER_SPECIAL_KEYS_REMAP
elif version == "2.0":
config = {
"model_id": "Lightricks/LTX-2",
"diffusers_config": {
"in_channels": 128,
"out_channels": 128,
"patch_size": 1,
"patch_size_t": 1,
"num_attention_heads": 32,
"attention_head_dim": 128,
"cross_attention_dim": 4096,
"vae_scale_factors": (8, 32, 32),
"pos_embed_max_pos": 20,
"base_height": 2048,
"base_width": 2048,
"gated_attn": False,
"cross_attn_mod": False,
"audio_in_channels": 128,
"audio_out_channels": 128,
"audio_patch_size": 1,
"audio_patch_size_t": 1,
"audio_num_attention_heads": 32,
"audio_attention_head_dim": 64,
"audio_cross_attention_dim": 2048,
"audio_scale_factor": 4,
"audio_pos_embed_max_pos": 20,
"audio_sampling_rate": 16000,
"audio_hop_length": 160,
"audio_gated_attn": False,
"audio_cross_attn_mod": False,
"num_layers": 48,
"activation_fn": "gelu-approximate",
"qk_norm": "rms_norm_across_heads",
"norm_elementwise_affine": False,
"norm_eps": 1e-6,
"caption_channels": 3840,
"attention_bias": True,
"attention_out_bias": True,
"rope_theta": 10000.0,
"rope_double_precision": True,
"causal_offset": 1,
"timestep_scale_multiplier": 1000,
"cross_attn_timestep_scale_multiplier": 1000,
"rope_type": "split",
"use_prompt_embeddings": True,
"perturbed_attn": False,
},
}
rename_dict = LTX_2_0_TRANSFORMER_KEYS_RENAME_DICT
special_keys_remap = LTX_2_0_TRANSFORMER_SPECIAL_KEYS_REMAP
elif version == "2.3":
config = {
"model_id": "Lightricks/LTX-2.3",
"diffusers_config": {
"in_channels": 128,
"out_channels": 128,
"patch_size": 1,
"patch_size_t": 1,
"num_attention_heads": 32,
"attention_head_dim": 128,
"cross_attention_dim": 4096,
"vae_scale_factors": (8, 32, 32),
"pos_embed_max_pos": 20,
"base_height": 2048,
"base_width": 2048,
"gated_attn": True,
"cross_attn_mod": True,
"audio_in_channels": 128,
"audio_out_channels": 128,
"audio_patch_size": 1,
"audio_patch_size_t": 1,
"audio_num_attention_heads": 32,
"audio_attention_head_dim": 64,
"audio_cross_attention_dim": 2048,
"audio_scale_factor": 4,
"audio_pos_embed_max_pos": 20,
"audio_sampling_rate": 16000,
"audio_hop_length": 160,
"audio_gated_attn": True,
"audio_cross_attn_mod": True,
"num_layers": 48,
"activation_fn": "gelu-approximate",
"qk_norm": "rms_norm_across_heads",
"norm_elementwise_affine": False,
"norm_eps": 1e-6,
"caption_channels": 3840,
"attention_bias": True,
"attention_out_bias": True,
"rope_theta": 10000.0,
"rope_double_precision": True,
"causal_offset": 1,
"timestep_scale_multiplier": 1000,
"cross_attn_timestep_scale_multiplier": 1000,
"rope_type": "split",
"use_prompt_embeddings": False,
"perturbed_attn": True,
},
}
rename_dict = LTX_2_3_TRANSFORMER_KEYS_RENAME_DICT
special_keys_remap = LTX_2_0_TRANSFORMER_SPECIAL_KEYS_REMAP
return config, rename_dict, special_keys_remap
def get_ltx2_connectors_config(version: str) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]:
if version == "test":
config = {
"model_id": "diffusers-internal-dev/dummy-ltx2",
"diffusers_config": {
"caption_channels": 16,
"text_proj_in_factor": 3,
"video_connector_num_attention_heads": 4,
"video_connector_attention_head_dim": 8,
"video_connector_num_layers": 1,
"video_connector_num_learnable_registers": None,
"audio_connector_num_attention_heads": 4,
"audio_connector_attention_head_dim": 8,
"audio_connector_num_layers": 1,
"audio_connector_num_learnable_registers": None,
"connector_rope_base_seq_len": 32,
"rope_theta": 10000.0,
"rope_double_precision": False,
"causal_temporal_positioning": False,
},
}
elif version == "2.0":
config = {
"model_id": "Lightricks/LTX-2",
"diffusers_config": {
"caption_channels": 3840,
"text_proj_in_factor": 49,
"video_connector_num_attention_heads": 30,
"video_connector_attention_head_dim": 128,
"video_connector_num_layers": 2,
"video_connector_num_learnable_registers": 128,
"video_gated_attn": False,
"audio_connector_num_attention_heads": 30,
"audio_connector_attention_head_dim": 128,
"audio_connector_num_layers": 2,
"audio_connector_num_learnable_registers": 128,
"audio_gated_attn": False,
"connector_rope_base_seq_len": 4096,
"rope_theta": 10000.0,
"rope_double_precision": True,
"causal_temporal_positioning": False,
"rope_type": "split",
"per_modality_projections": False,
"proj_bias": False,
},
}
rename_dict = LTX_2_0_CONNECTORS_KEYS_RENAME_DICT
special_keys_remap = LTX_2_0_CONNECTORS_SPECIAL_KEYS_REMAP
elif version == "2.3":
config = {
"model_id": "Lightricks/LTX-2.3",
"diffusers_config": {
"caption_channels": 3840,
"text_proj_in_factor": 49,
"video_connector_num_attention_heads": 32,
"video_connector_attention_head_dim": 128,
"video_connector_num_layers": 8,
"video_connector_num_learnable_registers": 128,
"video_gated_attn": True,
"audio_connector_num_attention_heads": 32,
"audio_connector_attention_head_dim": 64,
"audio_connector_num_layers": 8,
"audio_connector_num_learnable_registers": 128,
"audio_gated_attn": True,
"connector_rope_base_seq_len": 4096,
"rope_theta": 10000.0,
"rope_double_precision": True,
"causal_temporal_positioning": False,
"rope_type": "split",
"per_modality_projections": True,
"video_hidden_dim": 4096,
"audio_hidden_dim": 2048,
"proj_bias": True,
},
}
rename_dict = LTX_2_3_CONNECTORS_KEYS_RENAME_DICT
special_keys_remap = LTX_2_0_CONNECTORS_SPECIAL_KEYS_REMAP
return config, rename_dict, special_keys_remap
def convert_ltx2_transformer(original_state_dict: dict[str, Any], version: str) -> dict[str, Any]:
config, rename_dict, special_keys_remap = get_ltx2_transformer_config(version)
diffusers_config = config["diffusers_config"]
transformer_state_dict, _ = split_transformer_and_connector_state_dict(original_state_dict)
with init_empty_weights():
transformer = LTX2VideoTransformer3DModel.from_config(diffusers_config)
# Handle official code --> diffusers key remapping via the remap dict
for key in list(transformer_state_dict.keys()):
new_key = key[:]
for replace_key, rename_key in rename_dict.items():
new_key = new_key.replace(replace_key, rename_key)
update_state_dict_inplace(transformer_state_dict, key, new_key)
# Handle any special logic which can't be expressed by a simple 1:1 remapping with the handlers in
# special_keys_remap
for key in list(transformer_state_dict.keys()):
for special_key, handler_fn_inplace in special_keys_remap.items():
if special_key not in key:
continue
handler_fn_inplace(key, transformer_state_dict)
transformer.load_state_dict(transformer_state_dict, strict=True, assign=True)
return transformer
def convert_ltx2_connectors(original_state_dict: dict[str, Any], version: str) -> LTX2TextConnectors:
config, rename_dict, special_keys_remap = get_ltx2_connectors_config(version)
diffusers_config = config["diffusers_config"]
_, connector_state_dict = split_transformer_and_connector_state_dict(original_state_dict)
if len(connector_state_dict) == 0:
raise ValueError("No connector weights found in the provided state dict.")
with init_empty_weights():
connectors = LTX2TextConnectors.from_config(diffusers_config)
for key in list(connector_state_dict.keys()):
new_key = key[:]
for replace_key, rename_key in rename_dict.items():
new_key = new_key.replace(replace_key, rename_key)
update_state_dict_inplace(connector_state_dict, key, new_key)
for key in list(connector_state_dict.keys()):
for special_key, handler_fn_inplace in special_keys_remap.items():
if special_key not in key:
continue
handler_fn_inplace(key, connector_state_dict)
connectors.load_state_dict(connector_state_dict, strict=True, assign=True)
return connectors
def get_ltx2_video_vae_config(
version: str, timestep_conditioning: bool = False
) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]:
if version == "test":
config = {
"model_id": "diffusers-internal-dev/dummy-ltx2",
"diffusers_config": {
"in_channels": 3,
"out_channels": 3,
"latent_channels": 128,
"block_out_channels": (256, 512, 1024, 2048),
"down_block_types": (
"LTX2VideoDownBlock3D",
"LTX2VideoDownBlock3D",
"LTX2VideoDownBlock3D",
"LTX2VideoDownBlock3D",
),
"decoder_block_out_channels": (256, 512, 1024),
"layers_per_block": (4, 6, 6, 2, 2),
"decoder_layers_per_block": (5, 5, 5, 5),
"spatio_temporal_scaling": (True, True, True, True),
"decoder_spatio_temporal_scaling": (True, True, True),
"decoder_inject_noise": (False, False, False, False),
"downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"),
"upsample_residual": (True, True, True),
"upsample_factor": (2, 2, 2),
"timestep_conditioning": timestep_conditioning,
"patch_size": 4,
"patch_size_t": 1,
"resnet_norm_eps": 1e-6,
"encoder_causal": True,
"decoder_causal": False,
"encoder_spatial_padding_mode": "zeros",
"decoder_spatial_padding_mode": "reflect",
"spatial_compression_ratio": 32,
"temporal_compression_ratio": 8,
},
}
rename_dict = LTX_2_0_VIDEO_VAE_RENAME_DICT
special_keys_remap = LTX_2_0_VAE_SPECIAL_KEYS_REMAP
elif version == "2.0":
config = {
"model_id": "Lightricks/LTX-2",
"diffusers_config": {
"in_channels": 3,
"out_channels": 3,
"latent_channels": 128,
"block_out_channels": (256, 512, 1024, 2048),
"down_block_types": (
"LTX2VideoDownBlock3D",
"LTX2VideoDownBlock3D",
"LTX2VideoDownBlock3D",
"LTX2VideoDownBlock3D",
),
"decoder_block_out_channels": (256, 512, 1024),
"layers_per_block": (4, 6, 6, 2, 2),
"decoder_layers_per_block": (5, 5, 5, 5),
"spatio_temporal_scaling": (True, True, True, True),
"decoder_spatio_temporal_scaling": (True, True, True),
"decoder_inject_noise": (False, False, False, False),
"downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"),
"upsample_type": ("spatiotemporal", "spatiotemporal", "spatiotemporal"),
"upsample_residual": (True, True, True),
"upsample_factor": (2, 2, 2),
"timestep_conditioning": timestep_conditioning,
"patch_size": 4,
"patch_size_t": 1,
"resnet_norm_eps": 1e-6,
"encoder_causal": True,
"decoder_causal": False,
"encoder_spatial_padding_mode": "zeros",
"decoder_spatial_padding_mode": "reflect",
"spatial_compression_ratio": 32,
"temporal_compression_ratio": 8,
},
}
rename_dict = LTX_2_0_VIDEO_VAE_RENAME_DICT
special_keys_remap = LTX_2_0_VAE_SPECIAL_KEYS_REMAP
elif version == "2.3":
config = {
"model_id": "Lightricks/LTX-2.3",
"diffusers_config": {
"in_channels": 3,
"out_channels": 3,
"latent_channels": 128,
"block_out_channels": (256, 512, 1024, 1024),
"down_block_types": (
"LTX2VideoDownBlock3D",
"LTX2VideoDownBlock3D",
"LTX2VideoDownBlock3D",
"LTX2VideoDownBlock3D",
),
"decoder_block_out_channels": (256, 512, 512, 1024),
"layers_per_block": (4, 6, 4, 2, 2),
"decoder_layers_per_block": (4, 6, 4, 2, 2),
"spatio_temporal_scaling": (True, True, True, True),
"decoder_spatio_temporal_scaling": (True, True, True, True),
"decoder_inject_noise": (False, False, False, False, False),
"downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"),
"upsample_type": ("spatiotemporal", "spatiotemporal", "temporal", "spatial"),
"upsample_residual": (False, False, False, False),
"upsample_factor": (2, 2, 1, 2),
"timestep_conditioning": timestep_conditioning,
"patch_size": 4,
"patch_size_t": 1,
"resnet_norm_eps": 1e-6,
"encoder_causal": True,
"decoder_causal": False,
"encoder_spatial_padding_mode": "zeros",
"decoder_spatial_padding_mode": "zeros",
"spatial_compression_ratio": 32,
"temporal_compression_ratio": 8,
},
}
rename_dict = LTX_2_3_VIDEO_VAE_RENAME_DICT
special_keys_remap = LTX_2_0_VAE_SPECIAL_KEYS_REMAP
return config, rename_dict, special_keys_remap
def convert_ltx2_video_vae(
original_state_dict: dict[str, Any], version: str, timestep_conditioning: bool
) -> dict[str, Any]:
config, rename_dict, special_keys_remap = get_ltx2_video_vae_config(version, timestep_conditioning)
diffusers_config = config["diffusers_config"]
with init_empty_weights():
vae = AutoencoderKLLTX2Video.from_config(diffusers_config)
# Handle official code --> diffusers key remapping via the remap dict
for key in list(original_state_dict.keys()):
new_key = key[:]
for replace_key, rename_key in rename_dict.items():
new_key = new_key.replace(replace_key, rename_key)
update_state_dict_inplace(original_state_dict, key, new_key)
# Handle any special logic which can't be expressed by a simple 1:1 remapping with the handlers in
# special_keys_remap
for key in list(original_state_dict.keys()):
for special_key, handler_fn_inplace in special_keys_remap.items():
if special_key not in key:
continue
handler_fn_inplace(key, original_state_dict)
vae.load_state_dict(original_state_dict, strict=True, assign=True)
return vae
def get_ltx2_audio_vae_config(version: str) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]:
if version == "2.0":
config = {
"model_id": "Lightricks/LTX-2",
"diffusers_config": {
"base_channels": 128,
"output_channels": 2,
"ch_mult": (1, 2, 4),
"num_res_blocks": 2,
"attn_resolutions": None,
"in_channels": 2,
"resolution": 256,
"latent_channels": 8,
"norm_type": "pixel",
"causality_axis": "height",
"dropout": 0.0,
"mid_block_add_attention": False,
"sample_rate": 16000,
"mel_hop_length": 160,
"is_causal": True,
"mel_bins": 64,
"double_z": True,
},
}
rename_dict = LTX_2_0_AUDIO_VAE_RENAME_DICT
special_keys_remap = LTX_2_0_AUDIO_VAE_SPECIAL_KEYS_REMAP
elif version == "2.3":
config = {
"model_id": "Lightricks/LTX-2.3",
"diffusers_config": {
"base_channels": 128,
"output_channels": 2,
"ch_mult": (1, 2, 4),
"num_res_blocks": 2,
"attn_resolutions": None,
"in_channels": 2,
"resolution": 256,
"latent_channels": 8,
"norm_type": "pixel",
"causality_axis": "height",
"dropout": 0.0,
"mid_block_add_attention": False,
"sample_rate": 16000,
"mel_hop_length": 160,
"is_causal": True,
"mel_bins": 64,
"double_z": True,
}, # Same config as LTX-2.0
}
rename_dict = LTX_2_0_AUDIO_VAE_RENAME_DICT
special_keys_remap = LTX_2_0_AUDIO_VAE_SPECIAL_KEYS_REMAP
return config, rename_dict, special_keys_remap
def convert_ltx2_audio_vae(original_state_dict: dict[str, Any], version: str) -> dict[str, Any]:
config, rename_dict, special_keys_remap = get_ltx2_audio_vae_config(version)
diffusers_config = config["diffusers_config"]
with init_empty_weights():
vae = AutoencoderKLLTX2Audio.from_config(diffusers_config)
# Handle official code --> diffusers key remapping via the remap dict
for key in list(original_state_dict.keys()):
new_key = key[:]
for replace_key, rename_key in rename_dict.items():
new_key = new_key.replace(replace_key, rename_key)
update_state_dict_inplace(original_state_dict, key, new_key)
# Handle any special logic which can't be expressed by a simple 1:1 remapping with the handlers in
# special_keys_remap
for key in list(original_state_dict.keys()):
for special_key, handler_fn_inplace in special_keys_remap.items():
if special_key not in key:
continue
handler_fn_inplace(key, original_state_dict)
vae.load_state_dict(original_state_dict, strict=True, assign=True)
return vae
def get_ltx2_vocoder_config(version: str) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]:
if version == "2.0":
config = {
"model_id": "Lightricks/LTX-2",
"diffusers_config": {
"in_channels": 128,
"hidden_channels": 1024,
"out_channels": 2,
"upsample_kernel_sizes": [16, 15, 8, 4, 4],
"upsample_factors": [6, 5, 2, 2, 2],
"resnet_kernel_sizes": [3, 7, 11],
"resnet_dilations": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
"act_fn": "leaky_relu",
"leaky_relu_negative_slope": 0.1,
"antialias": False,
"final_act_fn": "tanh",
"final_bias": True,
"output_sampling_rate": 24000,
},
}
rename_dict = LTX_2_0_VOCODER_RENAME_DICT
special_keys_remap = LTX_2_0_VOCODER_SPECIAL_KEYS_REMAP
elif version == "2.3":
config = {
"model_id": "Lightricks/LTX-2.3",
"diffusers_config": {
"in_channels": 128,
"hidden_channels": 1536,
"out_channels": 2,
"upsample_kernel_sizes": [11, 4, 4, 4, 4, 4],
"upsample_factors": [5, 2, 2, 2, 2, 2],
"resnet_kernel_sizes": [3, 7, 11],
"resnet_dilations": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
"act_fn": "snakebeta",
"leaky_relu_negative_slope": 0.1,
"antialias": True,
"antialias_ratio": 2,
"antialias_kernel_size": 12,
"final_act_fn": None,
"final_bias": False,
"bwe_in_channels": 128,
"bwe_hidden_channels": 512,
"bwe_out_channels": 2,
"bwe_upsample_kernel_sizes": [12, 11, 4, 4, 4],
"bwe_upsample_factors": [6, 5, 2, 2, 2],
"bwe_resnet_kernel_sizes": [3, 7, 11],
"bwe_resnet_dilations": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
"bwe_act_fn": "snakebeta",
"bwe_leaky_relu_negative_slope": 0.1,
"bwe_antialias": True,
"bwe_antialias_ratio": 2,
"bwe_antialias_kernel_size": 12,
"bwe_final_act_fn": None,
"bwe_final_bias": False,
"filter_length": 512,
"hop_length": 80,
"window_length": 512,
"num_mel_channels": 64,
"input_sampling_rate": 16000,
"output_sampling_rate": 48000,
},
}
rename_dict = LTX_2_3_VOCODER_RENAME_DICT
special_keys_remap = LTX_2_3_VOCODER_SPECIAL_KEYS_REMAP
return config, rename_dict, special_keys_remap
def convert_ltx2_vocoder(original_state_dict: dict[str, Any], version: str) -> dict[str, Any]:
config, rename_dict, special_keys_remap = get_ltx2_vocoder_config(version)
diffusers_config = config["diffusers_config"]
if version == "2.3":
vocoder_cls = LTX2VocoderWithBWE
else:
vocoder_cls = LTX2Vocoder
with init_empty_weights():
vocoder = vocoder_cls.from_config(diffusers_config)
# Handle official code --> diffusers key remapping via the remap dict
for key in list(original_state_dict.keys()):
new_key = key[:]
for replace_key, rename_key in rename_dict.items():
new_key = new_key.replace(replace_key, rename_key)
update_state_dict_inplace(original_state_dict, key, new_key)
# Handle any special logic which can't be expressed by a simple 1:1 remapping with the handlers in
# special_keys_remap
for key in list(original_state_dict.keys()):
for special_key, handler_fn_inplace in special_keys_remap.items():
if special_key not in key:
continue
handler_fn_inplace(key, original_state_dict)
vocoder.load_state_dict(original_state_dict, strict=True, assign=True)
return vocoder
def get_ltx2_spatial_latent_upsampler_config(version: str):
if version == "2.0":
config = {
"in_channels": 128,
"mid_channels": 1024,
"num_blocks_per_stage": 4,
"dims": 3,
"spatial_upsample": True,
"temporal_upsample": False,
"rational_spatial_scale": 2.0,
"use_rational_resampler": True,
}
elif version == "2.3":
config = {
"in_channels": 128,
"mid_channels": 1024,
"num_blocks_per_stage": 4,
"dims": 3,
"spatial_upsample": True,
"temporal_upsample": False,
"rational_spatial_scale": 2.0,
"use_rational_resampler": False,
}
else:
raise ValueError(f"Unsupported version: {version}")
return config
def convert_ltx2_spatial_latent_upsampler(
original_state_dict: dict[str, Any], config: dict[str, Any], dtype: torch.dtype
):
with init_empty_weights():
latent_upsampler = LTX2LatentUpsamplerModel(**config)
latent_upsampler.load_state_dict(original_state_dict, strict=True, assign=True)
latent_upsampler.to(dtype)
return latent_upsampler
def load_original_checkpoint(args, filename: str | None) -> dict[str, Any]:
if args.original_state_dict_repo_id is not None:
ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=filename)
elif args.checkpoint_path is not None:
ckpt_path = args.checkpoint_path
else:
raise ValueError("Please provide either `original_state_dict_repo_id` or a local `checkpoint_path`")
original_state_dict = safetensors.torch.load_file(ckpt_path)
return original_state_dict
def load_hub_or_local_checkpoint(repo_id: str | None = None, filename: str | None = None) -> dict[str, Any]:
if repo_id is None and filename is None:
raise ValueError("Please supply at least one of `repo_id` or `filename`")
if repo_id is not None:
if filename is None:
raise ValueError("If repo_id is specified, filename must also be specified.")
ckpt_path = hf_hub_download(repo_id=repo_id, filename=filename)
else:
ckpt_path = filename
_, ext = os.path.splitext(ckpt_path)
if ext in [".safetensors", ".sft"]:
state_dict = safetensors.torch.load_file(ckpt_path)
else:
state_dict = torch.load(ckpt_path, map_location="cpu")
return state_dict
def get_model_state_dict_from_combined_ckpt(combined_ckpt: dict[str, Any], prefix: str) -> dict[str, Any]:
# Ensure that the key prefix ends with a dot (.)
if not prefix.endswith("."):
prefix = prefix + "."
model_state_dict = {}
for param_name, param in combined_ckpt.items():
if param_name.startswith(prefix):
model_state_dict[param_name.removeprefix(prefix)] = param
if prefix == "model.diffusion_model.":
# Some checkpoints store the text connector projection outside the diffusion model prefix.
connector_prefixes = ["text_embedding_projection"]
for param_name, param in combined_ckpt.items():
for prefix in connector_prefixes:
if param_name.startswith(prefix):
# Check to make sure we're not overwriting an existing key
if param_name not in model_state_dict:
model_state_dict[param_name] = combined_ckpt[param_name]
return model_state_dict
def get_args():
parser = argparse.ArgumentParser()
def none_or_str(value: str):
if isinstance(value, str) and value.lower() == "none":
return None
return value
parser.add_argument(
"--original_state_dict_repo_id",
default="Lightricks/LTX-2",
type=none_or_str,
help="HF Hub repo id with LTX 2.0 checkpoint",
)
parser.add_argument(
"--checkpoint_path",
default=None,
type=str,
help="Local checkpoint path for LTX 2.0. Will be used if `original_state_dict_repo_id` is not specified.",
)
parser.add_argument(
"--version",
type=str,
default="2.0",
choices=["test", "2.0", "2.3"],
help="Version of the LTX 2.0 model",
)
parser.add_argument(
"--combined_filename",
default="ltx-2-19b-dev.safetensors",
type=none_or_str,
help="Filename for combined checkpoint with all LTX 2.0 models (VAE, DiT, etc.)",
)
parser.add_argument("--vae_prefix", default="vae.", type=str)
parser.add_argument("--audio_vae_prefix", default="audio_vae.", type=str)
parser.add_argument("--dit_prefix", default="model.diffusion_model.", type=str)
parser.add_argument("--vocoder_prefix", default="vocoder.", type=str)
parser.add_argument("--vae_filename", default=None, type=str, help="VAE filename; overrides combined ckpt if set")
parser.add_argument(
"--audio_vae_filename", default=None, type=str, help="Audio VAE filename; overrides combined ckpt if set"
)
parser.add_argument("--dit_filename", default=None, type=str, help="DiT filename; overrides combined ckpt if set")
parser.add_argument(
"--vocoder_filename", default=None, type=str, help="Vocoder filename; overrides combined ckpt if set"
)
parser.add_argument(
"--text_encoder_model_id",
default="google/gemma-3-12b-it-qat-q4_0-unquantized",
type=none_or_str,
help="HF Hub id for the LTX 2.0 base text encoder model",
)
parser.add_argument(
"--tokenizer_id",
default="google/gemma-3-12b-it-qat-q4_0-unquantized",
type=none_or_str,
help="HF Hub id for the LTX 2.0 text tokenizer",
)
parser.add_argument(
"--latent_upsampler_filename",
default="ltx-2-spatial-upscaler-x2-1.0.safetensors",
type=none_or_str,
help="Latent upsampler filename",
)
parser.add_argument(
"--timestep_conditioning", action="store_true", help="Whether to add timestep condition to the video VAE model"
)
parser.add_argument("--vae", action="store_true", help="Whether to convert the video VAE model")
parser.add_argument("--audio_vae", action="store_true", help="Whether to convert the audio VAE model")
parser.add_argument("--dit", action="store_true", help="Whether to convert the DiT model")
parser.add_argument("--connectors", action="store_true", help="Whether to convert the connector model")
parser.add_argument("--vocoder", action="store_true", help="Whether to convert the vocoder model")