import argparse import pathlib from typing import Any, Dict import torch from accelerate import init_empty_weights from huggingface_hub import snapshot_download from transformers import T5EncoderModel, T5TokenizerFast from diffusers import ( AutoencoderKLCosmos, AutoencoderKLWan, Cosmos2TextToImagePipeline, Cosmos2VideoToWorldPipeline, CosmosTextToWorldPipeline, CosmosTransformer3DModel, CosmosVideoToWorldPipeline, EDMEulerScheduler, FlowMatchEulerDiscreteScheduler, ) def remove_keys_(key: str, state_dict: Dict[str, Any]): state_dict.pop(key) def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> Dict[str, Any]: state_dict[new_key] = state_dict.pop(old_key) def rename_transformer_blocks_(key: str, state_dict: Dict[str, Any]): block_index = int(key.split(".")[1].removeprefix("block")) new_key = key old_prefix = f"blocks.block{block_index}" new_prefix = f"transformer_blocks.{block_index}" new_key = new_prefix + new_key.removeprefix(old_prefix) state_dict[new_key] = state_dict.pop(key) TRANSFORMER_KEYS_RENAME_DICT_COSMOS_1_0 = { "t_embedder.1": "time_embed.t_embedder", "affline_norm": "time_embed.norm", ".blocks.0.block.attn": ".attn1", ".blocks.1.block.attn": ".attn2", ".blocks.2.block": ".ff", ".blocks.0.adaLN_modulation.1": ".norm1.linear_1", ".blocks.0.adaLN_modulation.2": ".norm1.linear_2", ".blocks.1.adaLN_modulation.1": ".norm2.linear_1", ".blocks.1.adaLN_modulation.2": ".norm2.linear_2", ".blocks.2.adaLN_modulation.1": ".norm3.linear_1", ".blocks.2.adaLN_modulation.2": ".norm3.linear_2", "to_q.0": "to_q", "to_q.1": "norm_q", "to_k.0": "to_k", "to_k.1": "norm_k", "to_v.0": "to_v", "layer1": "net.0.proj", "layer2": "net.2", "proj.1": "proj", "x_embedder": "patch_embed", "extra_pos_embedder": "learnable_pos_embed", "final_layer.adaLN_modulation.1": "norm_out.linear_1", "final_layer.adaLN_modulation.2": "norm_out.linear_2", "final_layer.linear": "proj_out", } TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_1_0 = { "blocks.block": rename_transformer_blocks_, "logvar.0.freqs": remove_keys_, "logvar.0.phases": remove_keys_, "logvar.1.weight": remove_keys_, "pos_embedder.seq": remove_keys_, } TRANSFORMER_KEYS_RENAME_DICT_COSMOS_2_0 = { "t_embedder.1": "time_embed.t_embedder", "t_embedding_norm": "time_embed.norm", "blocks": "transformer_blocks", "adaln_modulation_self_attn.1": "norm1.linear_1", "adaln_modulation_self_attn.2": "norm1.linear_2", "adaln_modulation_cross_attn.1": "norm2.linear_1", "adaln_modulation_cross_attn.2": "norm2.linear_2", "adaln_modulation_mlp.1": "norm3.linear_1", "adaln_modulation_mlp.2": "norm3.linear_2", "self_attn": "attn1", "cross_attn": "attn2", "q_proj": "to_q", "k_proj": "to_k", "v_proj": "to_v", "output_proj": "to_out.0", "q_norm": "norm_q", "k_norm": "norm_k", "mlp.layer1": "ff.net.0.proj", "mlp.layer2": "ff.net.2", "x_embedder.proj.1": "patch_embed.proj", # "extra_pos_embedder": "learnable_pos_embed", "final_layer.adaln_modulation.1": "norm_out.linear_1", "final_layer.adaln_modulation.2": "norm_out.linear_2", "final_layer.linear": "proj_out", } TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_2_0 = { "accum_video_sample_counter": remove_keys_, "accum_image_sample_counter": remove_keys_, "accum_iteration": remove_keys_, "accum_train_in_hours": remove_keys_, "pos_embedder.seq": remove_keys_, "pos_embedder.dim_spatial_range": remove_keys_, "pos_embedder.dim_temporal_range": remove_keys_, "_extra_state": remove_keys_, } TRANSFORMER_CONFIGS = { "Cosmos-1.0-Diffusion-7B-Text2World": { "in_channels": 16, "out_channels": 16, "num_attention_heads": 32, "attention_head_dim": 128, "num_layers": 28, "mlp_ratio": 4.0, "text_embed_dim": 1024, "adaln_lora_dim": 256, "max_size": (128, 240, 240), "patch_size": (1, 2, 2), "rope_scale": (2.0, 1.0, 1.0), "concat_padding_mask": True, "extra_pos_embed_type": "learnable", }, "Cosmos-1.0-Diffusion-7B-Video2World": { "in_channels": 16 + 1, "out_channels": 16, "num_attention_heads": 32, "attention_head_dim": 128, "num_layers": 28, "mlp_ratio": 4.0, "text_embed_dim": 1024, "adaln_lora_dim": 256, "max_size": (128, 240, 240), "patch_size": (1, 2, 2), "rope_scale": (2.0, 1.0, 1.0), "concat_padding_mask": True, "extra_pos_embed_type": "learnable", }, "Cosmos-1.0-Diffusion-14B-Text2World": { "in_channels": 16, "out_channels": 16, "num_attention_heads": 40, "attention_head_dim": 128, "num_layers": 36, "mlp_ratio": 4.0, "text_embed_dim": 1024, "adaln_lora_dim": 256, "max_size": (128, 240, 240), "patch_size": (1, 2, 2), "rope_scale": (2.0, 2.0, 2.0), "concat_padding_mask": True, "extra_pos_embed_type": "learnable", }, "Cosmos-1.0-Diffusion-14B-Video2World": { "in_channels": 16 + 1, "out_channels": 16, "num_attention_heads": 40, "attention_head_dim": 128, "num_layers": 36, "mlp_ratio": 4.0, "text_embed_dim": 1024, "adaln_lora_dim": 256, "max_size": (128, 240, 240), "patch_size": (1, 2, 2), "rope_scale": (2.0, 2.0, 2.0), "concat_padding_mask": True, "extra_pos_embed_type": "learnable", }, "Cosmos-2.0-Diffusion-2B-Text2Image": { "in_channels": 16, "out_channels": 16, "num_attention_heads": 16, "attention_head_dim": 128, "num_layers": 28, "mlp_ratio": 4.0, "text_embed_dim": 1024, "adaln_lora_dim": 256, "max_size": (128, 240, 240), "patch_size": (1, 2, 2), "rope_scale": (1.0, 4.0, 4.0), "concat_padding_mask": True, "extra_pos_embed_type": None, }, "Cosmos-2.0-Diffusion-14B-Text2Image": { "in_channels": 16, "out_channels": 16, "num_attention_heads": 40, "attention_head_dim": 128, "num_layers": 36, "mlp_ratio": 4.0, "text_embed_dim": 1024, "adaln_lora_dim": 256, "max_size": (128, 240, 240), "patch_size": (1, 2, 2), "rope_scale": (1.0, 4.0, 4.0), "concat_padding_mask": True, "extra_pos_embed_type": None, }, "Cosmos-2.0-Diffusion-2B-Video2World": { "in_channels": 16 + 1, "out_channels": 16, "num_attention_heads": 16, "attention_head_dim": 128, "num_layers": 28, "mlp_ratio": 4.0, "text_embed_dim": 1024, "adaln_lora_dim": 256, "max_size": (128, 240, 240), "patch_size": (1, 2, 2), "rope_scale": (1.0, 3.0, 3.0), "concat_padding_mask": True, "extra_pos_embed_type": None, }, "Cosmos-2.0-Diffusion-14B-Video2World": { "in_channels": 16 + 1, "out_channels": 16, "num_attention_heads": 40, "attention_head_dim": 128, "num_layers": 36, "mlp_ratio": 4.0, "text_embed_dim": 1024, "adaln_lora_dim": 256, "max_size": (128, 240, 240), "patch_size": (1, 2, 2), "rope_scale": (20 / 24, 2.0, 2.0), "concat_padding_mask": True, "extra_pos_embed_type": None, }, } VAE_KEYS_RENAME_DICT = { "down.0": "down_blocks.0", "down.1": "down_blocks.1", "down.2": "down_blocks.2", "up.0": "up_blocks.2", "up.1": "up_blocks.1", "up.2": "up_blocks.0", ".block.": ".resnets.", "downsample": "downsamplers.0", "upsample": "upsamplers.0", "mid.block_1": "mid_block.resnets.0", "mid.attn_1.0": "mid_block.attentions.0", "mid.attn_1.1": "mid_block.temp_attentions.0", "mid.block_2": "mid_block.resnets.1", ".q.conv3d": ".to_q", ".k.conv3d": ".to_k", ".v.conv3d": ".to_v", ".proj_out.conv3d": ".to_out.0", ".0.conv3d": ".conv_s", ".1.conv3d": ".conv_t", "conv1.conv3d": "conv1", "conv2.conv3d": "conv2", "conv3.conv3d": "conv3", "nin_shortcut.conv3d": "conv_shortcut", "quant_conv.conv3d": "quant_conv", "post_quant_conv.conv3d": "post_quant_conv", } VAE_SPECIAL_KEYS_REMAP = { "wavelets": remove_keys_, "_arange": remove_keys_, "patch_size_buffer": remove_keys_, } VAE_CONFIGS = { "CV8x8x8-0.1": { "name": "nvidia/Cosmos-0.1-Tokenizer-CV8x8x8", "diffusers_config": { "in_channels": 3, "out_channels": 3, "latent_channels": 16, "encoder_block_out_channels": (128, 256, 512, 512), "decode_block_out_channels": (256, 512, 512, 512), "attention_resolutions": (32,), "resolution": 1024, "num_layers": 2, "patch_size": 4, "patch_type": "haar", "scaling_factor": 1.0, "spatial_compression_ratio": 8, "temporal_compression_ratio": 8, "latents_mean": None, "latents_std": None, }, }, "CV8x8x8-1.0": { "name": "nvidia/Cosmos-1.0-Tokenizer-CV8x8x8", "diffusers_config": { "in_channels": 3, "out_channels": 3, "latent_channels": 16, "encoder_block_out_channels": (128, 256, 512, 512), "decode_block_out_channels": (256, 512, 512, 512), "attention_resolutions": (32,), "resolution": 1024, "num_layers": 2, "patch_size": 4, "patch_type": "haar", "scaling_factor": 1.0, "spatial_compression_ratio": 8, "temporal_compression_ratio": 8, "latents_mean": None, "latents_std": None, }, }, } 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 convert_transformer(transformer_type: str, ckpt_path: str, weights_only: bool = True): PREFIX_KEY = "net." original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", weights_only=weights_only)) if "Cosmos-1.0" in transformer_type: TRANSFORMER_KEYS_RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT_COSMOS_1_0 TRANSFORMER_SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_1_0 elif "Cosmos-2.0" in transformer_type: TRANSFORMER_KEYS_RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT_COSMOS_2_0 TRANSFORMER_SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_2_0 else: assert False with init_empty_weights(): config = TRANSFORMER_CONFIGS[transformer_type] transformer = CosmosTransformer3DModel(**config) for key in list(original_state_dict.keys()): new_key = key[:] if new_key.startswith(PREFIX_KEY): new_key = new_key.removeprefix(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_(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(vae_type: str): model_name = VAE_CONFIGS[vae_type]["name"] snapshot_directory = snapshot_download(model_name, repo_type="model") directory = pathlib.Path(snapshot_directory) autoencoder_file = directory / "autoencoder.jit" mean_std_file = directory / "mean_std.pt" original_state_dict = torch.jit.load(autoencoder_file.as_posix()).state_dict() if mean_std_file.exists(): mean_std = torch.load(mean_std_file, map_location="cpu", weights_only=True) else: mean_std = (None, None) config = VAE_CONFIGS[vae_type]["diffusers_config"] config.update( { "latents_mean": mean_std[0].detach().cpu().numpy().tolist(), "latents_std": mean_std[1].detach().cpu().numpy().tolist(), } ) vae = AutoencoderKLCosmos(**config) 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_(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 save_pipeline_cosmos_1_0(args, transformer, vae): text_encoder = T5EncoderModel.from_pretrained(args.text_encoder_path, torch_dtype=torch.bfloat16) tokenizer = T5TokenizerFast.from_pretrained(args.tokenizer_path) # The original code initializes EDM config with sigma_min=0.0002, but does not make use of it anywhere directly. # So, the sigma_min values that is used is the default value of 0.002. scheduler = EDMEulerScheduler( sigma_min=0.002, sigma_max=80, sigma_data=0.5, sigma_schedule="karras", num_train_timesteps=1000, prediction_type="epsilon", rho=7.0, final_sigmas_type="sigma_min", ) pipe_cls = CosmosTextToWorldPipeline if "Text2World" in args.transformer_type else CosmosVideoToWorldPipeline pipe = pipe_cls( text_encoder=text_encoder, tokenizer=tokenizer, transformer=transformer, vae=vae, scheduler=scheduler, safety_checker=lambda *args, **kwargs: None, ) pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") def save_pipeline_cosmos_2_0(args, transformer, vae): text_encoder = T5EncoderModel.from_pretrained(args.text_encoder_path, torch_dtype=torch.bfloat16) tokenizer = T5TokenizerFast.from_pretrained(args.tokenizer_path) scheduler = FlowMatchEulerDiscreteScheduler(use_karras_sigmas=True) pipe_cls = Cosmos2TextToImagePipeline if "Text2Image" in args.transformer_type else Cosmos2VideoToWorldPipeline pipe = pipe_cls( text_encoder=text_encoder, tokenizer=tokenizer, transformer=transformer, vae=vae, scheduler=scheduler, safety_checker=lambda *args, **kwargs: None, ) pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") def get_args(): parser = argparse.ArgumentParser() parser.add_argument("--transformer_type", type=str, default=None, choices=list(TRANSFORMER_CONFIGS.keys())) parser.add_argument( "--transformer_ckpt_path", type=str, default=None, help="Path to original transformer checkpoint" ) parser.add_argument( "--vae_type", type=str, default=None, choices=["none", *list(VAE_CONFIGS.keys())], help="Type of VAE" ) parser.add_argument("--text_encoder_path", type=str, default="google-t5/t5-11b") parser.add_argument("--tokenizer_path", type=str, default="google-t5/t5-11b") 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="bf16", help="Torch dtype to save the transformer in.") return parser.parse_args() DTYPE_MAPPING = { "fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16, } if __name__ == "__main__": args = get_args() transformer = None dtype = DTYPE_MAPPING[args.dtype] if args.save_pipeline: assert args.transformer_ckpt_path is not None assert args.vae_type is not None assert args.text_encoder_path is not None assert args.tokenizer_path is not None if args.transformer_ckpt_path is not None: weights_only = "Cosmos-1.0" in args.transformer_type transformer = convert_transformer(args.transformer_type, args.transformer_ckpt_path, weights_only) transformer = transformer.to(dtype=dtype) if not args.save_pipeline: transformer.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") if args.vae_type is not None: if "Cosmos-1.0" in args.transformer_type: vae = convert_vae(args.vae_type) else: vae = AutoencoderKLWan.from_pretrained( "Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="vae", torch_dtype=torch.float32 ) if not args.save_pipeline: vae.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB") if args.save_pipeline: if "Cosmos-1.0" in args.transformer_type: save_pipeline_cosmos_1_0(args, transformer, vae) elif "Cosmos-2.0" in args.transformer_type: save_pipeline_cosmos_2_0(args, transformer, vae) else: assert False