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import argparse
from pathlib import Path
from typing import Any, Dict
import torch
from accelerate import init_empty_weights
from safetensors.torch import load_file
from transformers import T5EncoderModel, T5Tokenizer
from diffusers import (
AutoencoderKLLTXVideo,
FlowMatchEulerDiscreteScheduler,
LTXConditionPipeline,
LTXLatentUpsamplePipeline,
LTXPipeline,
LTXVideoTransformer3DModel,
)
from diffusers.pipelines.ltx.modeling_latent_upsampler import LTXLatentUpsamplerModel
def remove_keys_(key: str, state_dict: Dict[str, Any]):
state_dict.pop(key)
TOKENIZER_MAX_LENGTH = 128
TRANSFORMER_KEYS_RENAME_DICT = {
"patchify_proj": "proj_in",
"adaln_single": "time_embed",
"q_norm": "norm_q",
"k_norm": "norm_k",
}
TRANSFORMER_SPECIAL_KEYS_REMAP = {
"vae": remove_keys_,
}
VAE_KEYS_RENAME_DICT = {
# decoder
"up_blocks.0": "mid_block",
"up_blocks.1": "up_blocks.0",
"up_blocks.2": "up_blocks.1.upsamplers.0",
"up_blocks.3": "up_blocks.1",
"up_blocks.4": "up_blocks.2.conv_in",
"up_blocks.5": "up_blocks.2.upsamplers.0",
"up_blocks.6": "up_blocks.2",
"up_blocks.7": "up_blocks.3.conv_in",
"up_blocks.8": "up_blocks.3.upsamplers.0",
"up_blocks.9": "up_blocks.3",
# encoder
"down_blocks.0": "down_blocks.0",
"down_blocks.1": "down_blocks.0.downsamplers.0",
"down_blocks.2": "down_blocks.0.conv_out",
"down_blocks.3": "down_blocks.1",
"down_blocks.4": "down_blocks.1.downsamplers.0",
"down_blocks.5": "down_blocks.1.conv_out",
"down_blocks.6": "down_blocks.2",
"down_blocks.7": "down_blocks.2.downsamplers.0",
"down_blocks.8": "down_blocks.3",
"down_blocks.9": "mid_block",
# common
"conv_shortcut": "conv_shortcut.conv",
"res_blocks": "resnets",
"norm3.norm": "norm3",
"per_channel_statistics.mean-of-means": "latents_mean",
"per_channel_statistics.std-of-means": "latents_std",
}
VAE_091_RENAME_DICT = {
# 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",
"up_blocks.7": "up_blocks.3.upsamplers.0",
"up_blocks.8": "up_blocks.3",
# common
"last_time_embedder": "time_embedder",
"last_scale_shift_table": "scale_shift_table",
}
VAE_095_RENAME_DICT = {
# 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",
"up_blocks.7": "up_blocks.3.upsamplers.0",
"up_blocks.8": "up_blocks.3",
# 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",
# common
"last_time_embedder": "time_embedder",
"last_scale_shift_table": "scale_shift_table",
}
VAE_SPECIAL_KEYS_REMAP = {
"per_channel_statistics.channel": remove_keys_,
"per_channel_statistics.mean-of-means": remove_keys_,
"per_channel_statistics.mean-of-stds": remove_keys_,
"model.diffusion_model": remove_keys_,
}
def get_state_dict(saved_dict: Dict[str, Any]) -> Dict[str, Any]:
state_dict = saved_dict
if "model" in saved_dict.keys():
state_dict = state_dict["model"]
if "module" in saved_dict.keys():
state_dict = state_dict["module"]
if "state_dict" in saved_dict.keys():
state_dict = state_dict["state_dict"]
return state_dict
def update_state_dict_inplace(state_dict: Dict[str, Any], old_key: str, new_key: str) -> Dict[str, Any]:
state_dict[new_key] = state_dict.pop(old_key)
def convert_transformer(ckpt_path: str, config, dtype: torch.dtype):
PREFIX_KEY = "model.diffusion_model."
original_state_dict = get_state_dict(load_file(ckpt_path))
with init_empty_weights():
transformer = LTXVideoTransformer3DModel(**config)
for key in list(original_state_dict.keys()):
new_key = key[:]
if new_key.startswith(PREFIX_KEY):
new_key = key[len(PREFIX_KEY) :]
for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items():
new_key = new_key.replace(replace_key, rename_key)
update_state_dict_inplace(original_state_dict, key, new_key)
for key in list(original_state_dict.keys()):
for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items():
if special_key not in key:
continue
handler_fn_inplace(key, original_state_dict)
transformer.load_state_dict(original_state_dict, strict=True, assign=True)
return transformer
def convert_vae(ckpt_path: str, config, dtype: torch.dtype):
PREFIX_KEY = "vae."
original_state_dict = get_state_dict(load_file(ckpt_path))
with init_empty_weights():
vae = AutoencoderKLLTXVideo(**config)
for key in list(original_state_dict.keys()):
new_key = key[:]
if new_key.startswith(PREFIX_KEY):
new_key = key[len(PREFIX_KEY) :]
for replace_key, rename_key in VAE_KEYS_RENAME_DICT.items():
new_key = new_key.replace(replace_key, rename_key)
update_state_dict_inplace(original_state_dict, key, new_key)
for key in list(original_state_dict.keys()):
for special_key, handler_fn_inplace in VAE_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 convert_spatial_latent_upsampler(ckpt_path: str, config, dtype: torch.dtype):
original_state_dict = get_state_dict(load_file(ckpt_path))
with init_empty_weights():
latent_upsampler = LTXLatentUpsamplerModel(**config)
latent_upsampler.load_state_dict(original_state_dict, strict=True, assign=True)
latent_upsampler.to(dtype)
return latent_upsampler
def get_transformer_config(version: str) -> Dict[str, Any]:
if version == "0.9.7":
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,
"num_layers": 48,
"activation_fn": "gelu-approximate",
"qk_norm": "rms_norm_across_heads",
"norm_elementwise_affine": False,
"norm_eps": 1e-6,
"caption_channels": 4096,
"attention_bias": True,
"attention_out_bias": True,
}
else:
config = {
"in_channels": 128,
"out_channels": 128,
"patch_size": 1,
"patch_size_t": 1,
"num_attention_heads": 32,
"attention_head_dim": 64,
"cross_attention_dim": 2048,
"num_layers": 28,
"activation_fn": "gelu-approximate",
"qk_norm": "rms_norm_across_heads",
"norm_elementwise_affine": False,
"norm_eps": 1e-6,
"caption_channels": 4096,
"attention_bias": True,
"attention_out_bias": True,
}
return config
def get_vae_config(version: str) -> Dict[str, Any]:
if version in ["0.9.0"]:
config = {
"in_channels": 3,
"out_channels": 3,
"latent_channels": 128,
"block_out_channels": (128, 256, 512, 512),
"down_block_types": (
"LTXVideoDownBlock3D",
"LTXVideoDownBlock3D",
"LTXVideoDownBlock3D",
"LTXVideoDownBlock3D",
),
"decoder_block_out_channels": (128, 256, 512, 512),
"layers_per_block": (4, 3, 3, 3, 4),
"decoder_layers_per_block": (4, 3, 3, 3, 4),
"spatio_temporal_scaling": (True, True, True, False),
"decoder_spatio_temporal_scaling": (True, True, True, False),
"decoder_inject_noise": (False, False, False, False, False),
"downsample_type": ("conv", "conv", "conv", "conv"),
"upsample_residual": (False, False, False, False),
"upsample_factor": (1, 1, 1, 1),
"patch_size": 4,
"patch_size_t": 1,
"resnet_norm_eps": 1e-6,
"scaling_factor": 1.0,
"encoder_causal": True,
"decoder_causal": False,
"timestep_conditioning": False,
}
elif version in ["0.9.1"]:
config = {
"in_channels": 3,
"out_channels": 3,
"latent_channels": 128,
"block_out_channels": (128, 256, 512, 512),
"down_block_types": (
"LTXVideoDownBlock3D",
"LTXVideoDownBlock3D",
"LTXVideoDownBlock3D",
"LTXVideoDownBlock3D",
),
"decoder_block_out_channels": (256, 512, 1024),
"layers_per_block": (4, 3, 3, 3, 4),
"decoder_layers_per_block": (5, 6, 7, 8),
"spatio_temporal_scaling": (True, True, True, False),
"decoder_spatio_temporal_scaling": (True, True, True),
"decoder_inject_noise": (True, True, True, False),
"downsample_type": ("conv", "conv", "conv", "conv"),
"upsample_residual": (True, True, True),
"upsample_factor": (2, 2, 2),
"timestep_conditioning": True,
"patch_size": 4,
"patch_size_t": 1,
"resnet_norm_eps": 1e-6,
"scaling_factor": 1.0,
"encoder_causal": True,
"decoder_causal": False,
}
VAE_KEYS_RENAME_DICT.update(VAE_091_RENAME_DICT)
elif version in ["0.9.5"]:
config = {
"in_channels": 3,
"out_channels": 3,
"latent_channels": 128,
"block_out_channels": (128, 256, 512, 1024, 2048),
"down_block_types": (
"LTXVideo095DownBlock3D",
"LTXVideo095DownBlock3D",
"LTXVideo095DownBlock3D",
"LTXVideo095DownBlock3D",
),
"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": True,
"patch_size": 4,
"patch_size_t": 1,
"resnet_norm_eps": 1e-6,
"scaling_factor": 1.0,
"encoder_causal": True,
"decoder_causal": False,
"spatial_compression_ratio": 32,
"temporal_compression_ratio": 8,
}
VAE_KEYS_RENAME_DICT.update(VAE_095_RENAME_DICT)
elif version in ["0.9.7"]:
config = {
"in_channels": 3,
"out_channels": 3,
"latent_channels": 128,
"block_out_channels": (128, 256, 512, 1024, 2048),
"down_block_types": (
"LTXVideo095DownBlock3D",
"LTXVideo095DownBlock3D",
"LTXVideo095DownBlock3D",
"LTXVideo095DownBlock3D",
),
"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": True,
"patch_size": 4,
"patch_size_t": 1,
"resnet_norm_eps": 1e-6,
"scaling_factor": 1.0,
"encoder_causal": True,
"decoder_causal": False,
"spatial_compression_ratio": 32,
"temporal_compression_ratio": 8,
}
VAE_KEYS_RENAME_DICT.update(VAE_095_RENAME_DICT)
return config
def get_spatial_latent_upsampler_config(version: str) -> Dict[str, Any]:
if version == "0.9.7":
config = {
"in_channels": 128,
"mid_channels": 512,
"num_blocks_per_stage": 4,
"dims": 3,
"spatial_upsample": True,
"temporal_upsample": False,
}
else:
raise ValueError(f"Unsupported version: {version}")
return config
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--transformer_ckpt_path", type=str, default=None, help="Path to original transformer checkpoint"
)
parser.add_argument("--vae_ckpt_path", type=str, default=None, help="Path to original vae checkpoint")
parser.add_argument(
"--spatial_latent_upsampler_path",
type=str,
default=None,
help="Path to original spatial latent upsampler checkpoint",
)
parser.add_argument(
"--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory"
)
parser.add_argument(
"--typecast_text_encoder",
action="store_true",
default=False,
help="Whether or not to apply fp16/bf16 precision to text_encoder",
)
parser.add_argument("--save_pipeline", action="store_true")
parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved")
parser.add_argument("--dtype", default="fp32", help="Torch dtype to save the model in.")
parser.add_argument(
"--version",
type=str,
default="0.9.0",
choices=["0.9.0", "0.9.1", "0.9.5", "0.9.7"],
help="Version of the LTX model",
)
return parser.parse_args()
DTYPE_MAPPING = {
"fp32": torch.float32,
"fp16": torch.float16,
"bf16": torch.bfloat16,
}
VARIANT_MAPPING = {
"fp32": None,
"fp16": "fp16",
"bf16": "bf16",
}
if __name__ == "__main__":
args = get_args()
transformer = None
dtype = DTYPE_MAPPING[args.dtype]
variant = VARIANT_MAPPING[args.dtype]
output_path = Path(args.output_path)
if args.transformer_ckpt_path is not None:
config = get_transformer_config(args.version)
transformer: LTXVideoTransformer3DModel = convert_transformer(args.transformer_ckpt_path, config, dtype)
if not args.save_pipeline:
transformer.save_pretrained(
output_path / "transformer", safe_serialization=True, max_shard_size="5GB", variant=variant
)
if args.vae_ckpt_path is not None:
config = get_vae_config(args.version)
vae: AutoencoderKLLTXVideo = convert_vae(args.vae_ckpt_path, config, dtype)
if not args.save_pipeline:
vae.save_pretrained(output_path / "vae", safe_serialization=True, max_shard_size="5GB", variant=variant)
if args.spatial_latent_upsampler_path is not None:
config = get_spatial_latent_upsampler_config(args.version)
latent_upsampler: LTXLatentUpsamplerModel = convert_spatial_latent_upsampler(
args.spatial_latent_upsampler_path, config, dtype
)
if not args.save_pipeline:
latent_upsampler.save_pretrained(
output_path / "latent_upsampler", safe_serialization=True, max_shard_size="5GB", variant=variant
)
if args.save_pipeline:
text_encoder_id = "google/t5-v1_1-xxl"
tokenizer = T5Tokenizer.from_pretrained(text_encoder_id, model_max_length=TOKENIZER_MAX_LENGTH)
text_encoder = T5EncoderModel.from_pretrained(text_encoder_id, cache_dir=args.text_encoder_cache_dir)
if args.typecast_text_encoder:
text_encoder = text_encoder.to(dtype=dtype)
# Apparently, the conversion does not work anymore without this :shrug:
for param in text_encoder.parameters():
param.data = param.data.contiguous()
if args.version in ["0.9.5", "0.9.7"]:
scheduler = FlowMatchEulerDiscreteScheduler(use_dynamic_shifting=False)
else:
scheduler = FlowMatchEulerDiscreteScheduler(
use_dynamic_shifting=True,
base_shift=0.95,
max_shift=2.05,
base_image_seq_len=1024,
max_image_seq_len=4096,
shift_terminal=0.1,
)
if args.version in ["0.9.0", "0.9.1", "0.9.5"]:
pipe = LTXPipeline(
scheduler=scheduler,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
transformer=transformer,
)
pipe.save_pretrained(
output_path.as_posix(), safe_serialization=True, variant=variant, max_shard_size="5GB"
)
elif args.version in ["0.9.7"]:
pipe = LTXConditionPipeline(
scheduler=scheduler,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
transformer=transformer,
)
pipe_upsample = LTXLatentUpsamplePipeline(
vae=vae,
latent_upsampler=latent_upsampler,
)
pipe.save_pretrained(
(output_path / "ltx_pipeline").as_posix(),
safe_serialization=True,
variant=variant,
max_shard_size="5GB",
)
pipe_upsample.save_pretrained(
(output_path / "ltx_upsample_pipeline").as_posix(),
safe_serialization=True,
variant=variant,
max_shard_size="5GB",
)
else:
raise ValueError(f"Unsupported version: {args.version}")