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* update * make style * update * update * update * make style * single file related changes * update * fix * update single file urls and docs * update * fix
298 lines
10 KiB
Python
298 lines
10 KiB
Python
import argparse
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from pathlib import Path
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from typing import Any, Dict
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import torch
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from accelerate import init_empty_weights
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from safetensors.torch import load_file
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from transformers import T5EncoderModel, T5Tokenizer
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from diffusers import AutoencoderKLLTXVideo, FlowMatchEulerDiscreteScheduler, LTXPipeline, LTXVideoTransformer3DModel
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def remove_keys_(key: str, state_dict: Dict[str, Any]):
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state_dict.pop(key)
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TOKENIZER_MAX_LENGTH = 128
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TRANSFORMER_KEYS_RENAME_DICT = {
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"patchify_proj": "proj_in",
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"adaln_single": "time_embed",
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"q_norm": "norm_q",
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"k_norm": "norm_k",
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}
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TRANSFORMER_SPECIAL_KEYS_REMAP = {
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"vae": remove_keys_,
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}
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VAE_KEYS_RENAME_DICT = {
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# decoder
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"up_blocks.0": "mid_block",
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"up_blocks.1": "up_blocks.0",
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"up_blocks.2": "up_blocks.1.upsamplers.0",
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"up_blocks.3": "up_blocks.1",
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"up_blocks.4": "up_blocks.2.conv_in",
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"up_blocks.5": "up_blocks.2.upsamplers.0",
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"up_blocks.6": "up_blocks.2",
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"up_blocks.7": "up_blocks.3.conv_in",
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"up_blocks.8": "up_blocks.3.upsamplers.0",
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"up_blocks.9": "up_blocks.3",
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# encoder
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"down_blocks.0": "down_blocks.0",
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"down_blocks.1": "down_blocks.0.downsamplers.0",
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"down_blocks.2": "down_blocks.0.conv_out",
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"down_blocks.3": "down_blocks.1",
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"down_blocks.4": "down_blocks.1.downsamplers.0",
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"down_blocks.5": "down_blocks.1.conv_out",
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"down_blocks.6": "down_blocks.2",
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"down_blocks.7": "down_blocks.2.downsamplers.0",
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"down_blocks.8": "down_blocks.3",
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"down_blocks.9": "mid_block",
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# common
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"conv_shortcut": "conv_shortcut.conv",
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"res_blocks": "resnets",
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"norm3.norm": "norm3",
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"per_channel_statistics.mean-of-means": "latents_mean",
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"per_channel_statistics.std-of-means": "latents_std",
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}
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VAE_091_RENAME_DICT = {
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# decoder
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"up_blocks.0": "mid_block",
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"up_blocks.1": "up_blocks.0.upsamplers.0",
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"up_blocks.2": "up_blocks.0",
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"up_blocks.3": "up_blocks.1.upsamplers.0",
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"up_blocks.4": "up_blocks.1",
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"up_blocks.5": "up_blocks.2.upsamplers.0",
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"up_blocks.6": "up_blocks.2",
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"up_blocks.7": "up_blocks.3.upsamplers.0",
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"up_blocks.8": "up_blocks.3",
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# common
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"last_time_embedder": "time_embedder",
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"last_scale_shift_table": "scale_shift_table",
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}
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VAE_SPECIAL_KEYS_REMAP = {
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"per_channel_statistics.channel": remove_keys_,
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"per_channel_statistics.mean-of-means": remove_keys_,
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"per_channel_statistics.mean-of-stds": remove_keys_,
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"model.diffusion_model": remove_keys_,
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}
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VAE_091_SPECIAL_KEYS_REMAP = {
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"timestep_scale_multiplier": remove_keys_,
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}
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def get_state_dict(saved_dict: Dict[str, Any]) -> Dict[str, Any]:
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state_dict = saved_dict
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if "model" in saved_dict.keys():
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state_dict = state_dict["model"]
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if "module" in saved_dict.keys():
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state_dict = state_dict["module"]
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if "state_dict" in saved_dict.keys():
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state_dict = state_dict["state_dict"]
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return state_dict
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def update_state_dict_inplace(state_dict: Dict[str, Any], old_key: str, new_key: str) -> Dict[str, Any]:
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state_dict[new_key] = state_dict.pop(old_key)
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def convert_transformer(
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ckpt_path: str,
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dtype: torch.dtype,
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):
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PREFIX_KEY = "model.diffusion_model."
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original_state_dict = get_state_dict(load_file(ckpt_path))
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with init_empty_weights():
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transformer = LTXVideoTransformer3DModel()
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for key in list(original_state_dict.keys()):
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new_key = key[:]
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if new_key.startswith(PREFIX_KEY):
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new_key = key[len(PREFIX_KEY) :]
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for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items():
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new_key = new_key.replace(replace_key, rename_key)
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update_state_dict_inplace(original_state_dict, key, new_key)
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for key in list(original_state_dict.keys()):
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for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items():
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if special_key not in key:
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continue
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handler_fn_inplace(key, original_state_dict)
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transformer.load_state_dict(original_state_dict, strict=True, assign=True)
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return transformer
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def convert_vae(ckpt_path: str, config, dtype: torch.dtype):
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PREFIX_KEY = "vae."
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original_state_dict = get_state_dict(load_file(ckpt_path))
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with init_empty_weights():
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vae = AutoencoderKLLTXVideo(**config)
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for key in list(original_state_dict.keys()):
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new_key = key[:]
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if new_key.startswith(PREFIX_KEY):
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new_key = key[len(PREFIX_KEY) :]
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for replace_key, rename_key in VAE_KEYS_RENAME_DICT.items():
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new_key = new_key.replace(replace_key, rename_key)
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update_state_dict_inplace(original_state_dict, key, new_key)
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for key in list(original_state_dict.keys()):
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for special_key, handler_fn_inplace in VAE_SPECIAL_KEYS_REMAP.items():
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if special_key not in key:
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continue
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handler_fn_inplace(key, original_state_dict)
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vae.load_state_dict(original_state_dict, strict=True, assign=True)
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return vae
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def get_vae_config(version: str) -> Dict[str, Any]:
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if version == "0.9.0":
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config = {
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"in_channels": 3,
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"out_channels": 3,
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"latent_channels": 128,
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"block_out_channels": (128, 256, 512, 512),
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"decoder_block_out_channels": (128, 256, 512, 512),
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"layers_per_block": (4, 3, 3, 3, 4),
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"decoder_layers_per_block": (4, 3, 3, 3, 4),
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"spatio_temporal_scaling": (True, True, True, False),
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"decoder_spatio_temporal_scaling": (True, True, True, False),
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"decoder_inject_noise": (False, False, False, False, False),
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"upsample_residual": (False, False, False, False),
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"upsample_factor": (1, 1, 1, 1),
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"patch_size": 4,
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"patch_size_t": 1,
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"resnet_norm_eps": 1e-6,
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"scaling_factor": 1.0,
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"encoder_causal": True,
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"decoder_causal": False,
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"timestep_conditioning": False,
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}
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elif version == "0.9.1":
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config = {
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"in_channels": 3,
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"out_channels": 3,
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"latent_channels": 128,
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"block_out_channels": (128, 256, 512, 512),
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"decoder_block_out_channels": (256, 512, 1024),
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"layers_per_block": (4, 3, 3, 3, 4),
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"decoder_layers_per_block": (5, 6, 7, 8),
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"spatio_temporal_scaling": (True, True, True, False),
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"decoder_spatio_temporal_scaling": (True, True, True),
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"decoder_inject_noise": (True, True, True, False),
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"upsample_residual": (True, True, True),
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"upsample_factor": (2, 2, 2),
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"timestep_conditioning": True,
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"patch_size": 4,
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"patch_size_t": 1,
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"resnet_norm_eps": 1e-6,
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"scaling_factor": 1.0,
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"encoder_causal": True,
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"decoder_causal": False,
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}
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VAE_KEYS_RENAME_DICT.update(VAE_091_RENAME_DICT)
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VAE_SPECIAL_KEYS_REMAP.update(VAE_091_SPECIAL_KEYS_REMAP)
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return config
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--transformer_ckpt_path", type=str, default=None, help="Path to original transformer checkpoint"
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)
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parser.add_argument("--vae_ckpt_path", type=str, default=None, help="Path to original vae checkpoint")
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parser.add_argument(
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"--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory"
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)
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parser.add_argument(
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"--typecast_text_encoder",
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action="store_true",
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default=False,
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help="Whether or not to apply fp16/bf16 precision to text_encoder",
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)
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parser.add_argument("--save_pipeline", action="store_true")
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parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved")
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parser.add_argument("--dtype", default="fp32", help="Torch dtype to save the model in.")
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parser.add_argument(
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"--version", type=str, default="0.9.0", choices=["0.9.0", "0.9.1"], help="Version of the LTX model"
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)
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return parser.parse_args()
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DTYPE_MAPPING = {
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"fp32": torch.float32,
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"fp16": torch.float16,
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"bf16": torch.bfloat16,
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}
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VARIANT_MAPPING = {
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"fp32": None,
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"fp16": "fp16",
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"bf16": "bf16",
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}
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if __name__ == "__main__":
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args = get_args()
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transformer = None
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dtype = DTYPE_MAPPING[args.dtype]
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variant = VARIANT_MAPPING[args.dtype]
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output_path = Path(args.output_path)
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if args.save_pipeline:
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assert args.transformer_ckpt_path is not None and args.vae_ckpt_path is not None
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if args.transformer_ckpt_path is not None:
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transformer: LTXVideoTransformer3DModel = convert_transformer(args.transformer_ckpt_path, dtype)
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if not args.save_pipeline:
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transformer.save_pretrained(
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output_path / "transformer", safe_serialization=True, max_shard_size="5GB", variant=variant
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)
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if args.vae_ckpt_path is not None:
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config = get_vae_config(args.version)
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vae: AutoencoderKLLTXVideo = convert_vae(args.vae_ckpt_path, config, dtype)
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if not args.save_pipeline:
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vae.save_pretrained(output_path / "vae", safe_serialization=True, max_shard_size="5GB", variant=variant)
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if args.save_pipeline:
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text_encoder_id = "google/t5-v1_1-xxl"
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tokenizer = T5Tokenizer.from_pretrained(text_encoder_id, model_max_length=TOKENIZER_MAX_LENGTH)
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text_encoder = T5EncoderModel.from_pretrained(text_encoder_id, cache_dir=args.text_encoder_cache_dir)
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if args.typecast_text_encoder:
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text_encoder = text_encoder.to(dtype=dtype)
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# Apparently, the conversion does not work anymore without this :shrug:
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for param in text_encoder.parameters():
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param.data = param.data.contiguous()
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scheduler = FlowMatchEulerDiscreteScheduler(
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use_dynamic_shifting=True,
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base_shift=0.95,
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max_shift=2.05,
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base_image_seq_len=1024,
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max_image_seq_len=4096,
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shift_terminal=0.1,
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)
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pipe = LTXPipeline(
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scheduler=scheduler,
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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transformer=transformer,
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)
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pipe.save_pretrained(args.output_path, safe_serialization=True, variant=variant, max_shard_size="5GB")
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