import argparse from typing import Any, Dict import torch from safetensors.torch import load_file from transformers import T5EncoderModel, T5Tokenizer from diffusers import AutoencoderKLLTXVideo, FlowMatchEulerDiscreteScheduler, LTXPipeline, LTXVideoTransformer3DModel 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_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_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_, } 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, dtype: torch.dtype, ): PREFIX_KEY = "" original_state_dict = get_state_dict(load_file(ckpt_path)) transformer = LTXVideoTransformer3DModel().to(dtype=dtype) for key in list(original_state_dict.keys()): 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) return transformer def convert_vae(ckpt_path: str, dtype: torch.dtype): original_state_dict = get_state_dict(load_file(ckpt_path)) vae = AutoencoderKLLTXVideo().to(dtype=dtype) for key in list(original_state_dict.keys()): new_key = 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) return vae 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( "--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.") 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] if args.save_pipeline: assert args.transformer_ckpt_path is not None and args.vae_ckpt_path is not None if args.transformer_ckpt_path is not None: transformer: LTXVideoTransformer3DModel = convert_transformer(args.transformer_ckpt_path, dtype) if not args.save_pipeline: transformer.save_pretrained( args.output_path, safe_serialization=True, max_shard_size="5GB", variant=variant ) if args.vae_ckpt_path is not None: vae: AutoencoderKLLTXVideo = convert_vae(args.vae_ckpt_path, dtype) if not args.save_pipeline: vae.save_pretrained(args.output_path, 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() 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, ) pipe = LTXPipeline( scheduler=scheduler, vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, transformer=transformer, ) pipe.save_pretrained(args.output_path, safe_serialization=True, variant=variant, max_shard_size="5GB")