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import argparse

from typing import Any, Dict

import torch

from transformers import T5EncoderModel, T5Tokenizer

from diffusers import AutoencoderKLCogVideoX, CogVideoXDDIMScheduler, CogVideoXPipeline, CogVideoXTransformer3DModel

def reassign_query_key_value_inplace(key: str, state_dict: Dict[str, Any]):

to_q_key = key.replace("query_key_value", "to_q")

to_k_key = key.replace("query_key_value", "to_k")

to_v_key = key.replace("query_key_value", "to_v")

to_q, to_k, to_v = torch.chunk(state_dict[key], chunks=3, dim=0)

state_dict[to_q_key] = to_q

state_dict[to_k_key] = to_k

state_dict[to_v_key] = to_v

state_dict.pop(key)

def reassign_query_key_layernorm_inplace(key: str, state_dict: Dict[str, Any]):

layer_id, weight_or_bias = key.split(".")[-2:]

if "query" in key:

new_key = f"transformer_blocks.{layer_id}.attn1.norm_q.{weight_or_bias}"

elif "key" in key:

new_key = f"transformer_blocks.{layer_id}.attn1.norm_k.{weight_or_bias}"

state_dict[new_key] = state_dict.pop(key)

def reassign_adaln_norm_inplace(key: str, state_dict: Dict[str, Any]):

layer_id, _, weight_or_bias = key.split(".")[-3:]

weights_or_biases = state_dict[key].chunk(12, dim=0)

norm1_weights_or_biases = torch.cat(weights_or_biases[0:3] + weights_or_biases[6:9])

norm2_weights_or_biases = torch.cat(weights_or_biases[3:6] + weights_or_biases[9:12])

norm1_key = f"transformer_blocks.{layer_id}.norm1.linear.{weight_or_bias}"

state_dict[norm1_key] = norm1_weights_or_biases

norm2_key = f"transformer_blocks.{layer_id}.norm2.linear.{weight_or_bias}"

state_dict[norm2_key] = norm2_weights_or_biases

state_dict.pop(key)

def remove_keys_inplace(key: str, state_dict: Dict[str, Any]):

state_dict.pop(key)

def replace_up_keys_inplace(key: str, state_dict: Dict[str, Any]):

key_split = key.split(".")

layer_index = int(key_split[2])

replace_layer_index = 4 - 1 - layer_index

key_split[1] = "up_blocks"

key_split[2] = str(replace_layer_index)

new_key = ".".join(key_split)

state_dict[new_key] = state_dict.pop(key)

TRANSFORMER_KEYS_RENAME_DICT = {

"transformer.final_layernorm": "norm_final",

"transformer": "transformer_blocks",

"attention": "attn1",

"mlp": "ff.net",

"dense_h_to_4h": "0.proj",

"dense_4h_to_h": "2",

".layers": "",

"dense": "to_out.0",

"input_layernorm": "norm1.norm",

"post_attn1_layernorm": "norm2.norm",

"time_embed.0": "time_embedding.linear_1",

"time_embed.2": "time_embedding.linear_2",

"mixins.patch_embed": "patch_embed",

"mixins.final_layer.norm_final": "norm_out.norm",

"mixins.final_layer.linear": "proj_out",

"mixins.final_layer.adaLN_modulation.1": "norm_out.linear",

}

TRANSFORMER_SPECIAL_KEYS_REMAP = {

"query_key_value": reassign_query_key_value_inplace,

"query_layernorm_list": reassign_query_key_layernorm_inplace,

"key_layernorm_list": reassign_query_key_layernorm_inplace,

"adaln_layer.adaLN_modulations": reassign_adaln_norm_inplace,

"embed_tokens": remove_keys_inplace,

}

VAE_KEYS_RENAME_DICT = {

"block.": "resnets.",

"down.": "down_blocks.",

"downsample": "downsamplers.0",

"upsample": "upsamplers.0",

"nin_shortcut": "conv_shortcut",

"encoder.mid.block_1": "encoder.mid_block.resnets.0",

"encoder.mid.block_2": "encoder.mid_block.resnets.1",

"decoder.mid.block_1": "decoder.mid_block.resnets.0",

"decoder.mid.block_2": "decoder.mid_block.resnets.1",

}

VAE_SPECIAL_KEYS_REMAP = {

"loss": remove_keys_inplace,

"up.": replace_up_keys_inplace,

}

TOKENIZER_MAX_LENGTH = 226

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):

PREFIX_KEY = "model.diffusion_model."

original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", mmap=True))

transformer = CogVideoXTransformer3DModel()

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):

original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", mmap=True))

vae = AutoencoderKLCogVideoX()

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("--output_path", type=str, required=True, help="Path where converted model should be saved")

parser.add_argument("--fp16", action="store_true", default=True, help="Whether to save the model weights in fp16")

parser.add_argument(

"--push_to_hub", action="store_true", default=False, help="Whether to push to HF Hub after saving"

)

parser.add_argument(

"--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory"

)

return parser.parse_args()

if __name__ == "__main__":

args = get_args()

transformer = None

vae = None

if args.transformer_ckpt_path is not None:

transformer = convert_transformer(args.transformer_ckpt_path)

if args.vae_ckpt_path is not None:

vae = convert_vae(args.vae_ckpt_path)

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)

scheduler = CogVideoXDDIMScheduler.from_config(

{

"snr_shift_scale": 3.0,

"beta_end": 0.012,

"beta_schedule": "scaled_linear",

"beta_start": 0.00085,

"clip_sample": False,

"num_train_timesteps": 1000,

"prediction_type": "v_prediction",

"rescale_betas_zero_snr": True,

"set_alpha_to_one": True,

"timestep_spacing": "linspace",

}

)

pipe = CogVideoXPipeline(

tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler

)

if args.fp16:

pipe = pipe.to(dtype=torch.float16)

pipe.save_pretrained(args.output_path, safe_serialization=True, push_to_hub=args.push_to_hub)