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"""
# Cosmos 2 Predict
Download checkpoint
```bash
hf download nvidia/Cosmos-Predict2-2B-Text2Image
```
convert checkpoint
```bash
transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2-2B-Text2Image/snapshots/acdb5fde992a73ef0355f287977d002cbfd127e0/model.pt
python scripts/convert_cosmos_to_diffusers.py \
--transformer_ckpt_path $transformer_ckpt_path \
--transformer_type Cosmos-2.0-Diffusion-2B-Text2Image \
--text_encoder_path google-t5/t5-11b \
--tokenizer_path google-t5/t5-11b \
--vae_type wan2.1 \
--output_path converted/cosmos-p2-t2i-2b \
--save_pipeline
```
# Cosmos 2.5 Predict
Download checkpoint
```bash
hf download nvidia/Cosmos-Predict2.5-2B
```
Convert checkpoint
```bash
# pre-trained
transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2.5-2B/snapshots/865baf084d4c9e850eac59a021277d5a9b9e8b63/base/pre-trained/d20b7120-df3e-4911-919d-db6e08bad31c_ema_bf16.pt
python scripts/convert_cosmos_to_diffusers.py \
--transformer_type Cosmos-2.5-Predict-Base-2B \
--transformer_ckpt_path $transformer_ckpt_path \
--vae_type wan2.1 \
--output_path converted/2b/d20b7120-df3e-4911-919d-db6e08bad31c \
--save_pipeline
# post-trained
transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2.5-2B/snapshots/865baf084d4c9e850eac59a021277d5a9b9e8b63/base/post-trained/81edfebe-bd6a-4039-8c1d-737df1a790bf_ema_bf16.pt
python scripts/convert_cosmos_to_diffusers.py \
--transformer_type Cosmos-2.5-Predict-Base-2B \
--transformer_ckpt_path $transformer_ckpt_path \
--vae_type wan2.1 \
--output_path converted/2b/81edfebe-bd6a-4039-8c1d-737df1a790bf \
--save_pipeline
```
## 14B
```bash
hf download nvidia/Cosmos-Predict2.5-14B
```
```bash
# pre-trained
transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2.5-14B/snapshots/71ebf3e8af30ecfe440bf0481115975fcc052b46/base/pre-trained/54937b8c-29de-4f04-862c-e67b04ec41e8_ema_bf16.pt
python scripts/convert_cosmos_to_diffusers.py \
--transformer_type Cosmos-2.5-Predict-Base-14B \
--transformer_ckpt_path $transformer_ckpt_path \
--vae_type wan2.1 \
--output_path converted/14b/54937b8c-29de-4f04-862c-e67b04ec41e8/ \
--save_pipeline
# post-trained
transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Predict2.5-14B/snapshots/71ebf3e8af30ecfe440bf0481115975fcc052b46/base/post-trained/e21d2a49-4747-44c8-ba44-9f6f9243715f_ema_bf16.pt
python scripts/convert_cosmos_to_diffusers.py \
--transformer_type Cosmos-2.5-Predict-Base-14B \
--transformer_ckpt_path $transformer_ckpt_path \
--vae_type wan2.1 \
--output_path converted/14b/e21d2a49-4747-44c8-ba44-9f6f9243715f/ \
--save_pipeline
```
# Cosmos 2.5 Transfer
Download checkpoint
```bash
hf download nvidia/Cosmos-Transfer2.5-2B
```
Convert checkpoint
```bash
# depth
transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Transfer2.5-2B/snapshots/eb5325b77d358944da58a690157dd2b8071bbf85/general/depth/626e6618-bfcd-4d9a-a077-1409e2ce353f_ema_bf16.pt
python scripts/convert_cosmos_to_diffusers.py \
--transformer_type Cosmos-2.5-Transfer-General-2B \
--transformer_ckpt_path $transformer_ckpt_path \
--vae_type wan2.1 \
--output_path converted/transfer/2b/general/depth/pipeline \
--save_pipeline
python scripts/convert_cosmos_to_diffusers.py \
--transformer_type Cosmos-2.5-Transfer-General-2B \
--transformer_ckpt_path $transformer_ckpt_path \
--vae_type wan2.1 \
--output_path converted/transfer/2b/general/depth/models
# edge
transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Transfer2.5-2B/snapshots/eb5325b77d358944da58a690157dd2b8071bbf85/general/edge/61f5694b-0ad5-4ecd-8ad7-c8545627d125_ema_bf16.pt
python scripts/convert_cosmos_to_diffusers.py \
--transformer_type Cosmos-2.5-Transfer-General-2B \
--transformer_ckpt_path $transformer_ckpt_path \
--vae_type wan2.1 \
--output_path converted/transfer/2b/general/edge/pipeline \
--save_pipeline
python scripts/convert_cosmos_to_diffusers.py \
--transformer_type Cosmos-2.5-Transfer-General-2B \
--transformer_ckpt_path $transformer_ckpt_path \
--vae_type wan2.1 \
--output_path converted/transfer/2b/general/edge/models
# blur
transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Transfer2.5-2B/snapshots/eb5325b77d358944da58a690157dd2b8071bbf85/general/blur/ba2f44f2-c726-4fe7-949f-597069d9b91c_ema_bf16.pt
python scripts/convert_cosmos_to_diffusers.py \
--transformer_type Cosmos-2.5-Transfer-General-2B \
--transformer_ckpt_path $transformer_ckpt_path \
--vae_type wan2.1 \
--output_path converted/transfer/2b/general/blur/pipeline \
--save_pipeline
python scripts/convert_cosmos_to_diffusers.py \
--transformer_type Cosmos-2.5-Transfer-General-2B \
--transformer_ckpt_path $transformer_ckpt_path \
--vae_type wan2.1 \
--output_path converted/transfer/2b/general/blur/models
# seg
transformer_ckpt_path=~/.cache/huggingface/hub/models--nvidia--Cosmos-Transfer2.5-2B/snapshots/eb5325b77d358944da58a690157dd2b8071bbf85/general/seg/5136ef49-6d8d-42e8-8abf-7dac722a304a_ema_bf16.pt
python scripts/convert_cosmos_to_diffusers.py \
--transformer_type Cosmos-2.5-Transfer-General-2B \
--transformer_ckpt_path $transformer_ckpt_path \
--vae_type wan2.1 \
--output_path converted/transfer/2b/general/seg/pipeline \
--save_pipeline
python scripts/convert_cosmos_to_diffusers.py \
--transformer_type Cosmos-2.5-Transfer-General-2B \
--transformer_ckpt_path $transformer_ckpt_path \
--vae_type wan2.1 \
--output_path converted/transfer/2b/general/seg/models
```
"""
import argparse
import pathlib
import sys
from typing import Any, Dict, Optional
import torch
from accelerate import init_empty_weights
from huggingface_hub import snapshot_download
from transformers import AutoTokenizer, Qwen2_5_VLForConditionalGeneration, T5EncoderModel, T5TokenizerFast
from diffusers import (
AutoencoderKLCosmos,
AutoencoderKLWan,
Cosmos2TextToImagePipeline,
Cosmos2VideoToWorldPipeline,
CosmosControlNetModel,
CosmosTextToWorldPipeline,
CosmosTransformer3DModel,
CosmosVideoToWorldPipeline,
EDMEulerScheduler,
FlowMatchEulerDiscreteScheduler,
UniPCMultistepScheduler,
)
from diffusers.pipelines.cosmos.pipeline_cosmos2_5_predict import Cosmos2_5_PredictBasePipeline
from diffusers.pipelines.cosmos.pipeline_cosmos2_5_transfer import Cosmos2_5_TransferPipeline
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",
"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,
},
"Cosmos-2.5-Predict-Base-2B": {
"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,
# NOTE: source config has pos_emb_learnable: 'True' - but params are missing
"extra_pos_embed_type": None,
"use_crossattn_projection": True,
"crossattn_proj_in_channels": 100352,
"encoder_hidden_states_channels": 1024,
},
"Cosmos-2.5-Predict-Base-14B": {
"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": (1.0, 3.0, 3.0),
"concat_padding_mask": True,
# NOTE: source config has pos_emb_learnable: 'True' - but params are missing
"extra_pos_embed_type": None,
"use_crossattn_projection": True,
"crossattn_proj_in_channels": 100352,
"encoder_hidden_states_channels": 1024,
},
"Cosmos-2.5-Transfer-General-2B": {
"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,
"use_crossattn_projection": True,
"crossattn_proj_in_channels": 100352,
"encoder_hidden_states_channels": 1024,
"controlnet_block_every_n": 7,
"img_context_dim_in": 1152,
"img_context_dim_out": 2048,
"img_context_num_tokens": 256,
},
}
CONTROLNET_CONFIGS = {
"Cosmos-2.5-Transfer-General-2B": {
"n_controlnet_blocks": 4,
"model_channels": 2048,
"in_channels": 130,
"latent_channels": 18, # (16 latent + 1 condition_mask) + 1 padding_mask = 18
"num_attention_heads": 16,
"attention_head_dim": 128,
"mlp_ratio": 4.0,
"text_embed_dim": 1024,
"adaln_lora_dim": 256,
"patch_size": (1, 2, 2),
"max_size": (128, 240, 240),
"rope_scale": (1.0, 3.0, 3.0),
"extra_pos_embed_type": None,
"img_context_dim_in": 1152,
"img_context_dim_out": 2048,
"use_crossattn_projection": True,
"crossattn_proj_in_channels": 100352,
"encoder_hidden_states_channels": 1024,
},
}
CONTROLNET_KEYS_RENAME_DICT = {
**TRANSFORMER_KEYS_RENAME_DICT_COSMOS_2_0,
"blocks": "blocks",
"control_embedder.proj.1": "patch_embed.proj",
}
CONTROLNET_SPECIAL_KEYS_REMAP = {**TRANSFORMER_SPECIAL_KEYS_REMAP_COSMOS_2_0}
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,
state_dict: Optional[Dict[str, Any]] = None,
weights_only: bool = True,
):
PREFIX_KEY = "net."
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
elif "Cosmos-2.5" 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)
old2new = {}
new2old = {}
for key in list(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)
print(key, "->", new_key, flush=True)
assert new_key not in new2old, f"new key {new_key} already mapped"
assert key not in old2new, f"old key {key} already mapped"
old2new[key] = new_key
new2old[new_key] = key
update_state_dict_(state_dict, key, new_key)
for key in list(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, state_dict)
expected_keys = set(transformer.state_dict().keys())
mapped_keys = set(state_dict.keys())
missing_keys = expected_keys - mapped_keys
unexpected_keys = mapped_keys - expected_keys
if missing_keys:
print(f"ERROR: missing keys ({len(missing_keys)} from state_dict:", flush=True, file=sys.stderr)
for k in missing_keys:
print(k)
sys.exit(1)
if unexpected_keys:
print(f"ERROR: unexpected keys ({len(unexpected_keys)}) from state_dict:", flush=True, file=sys.stderr)
for k in unexpected_keys:
print(k)
sys.exit(2)
transformer.load_state_dict(state_dict, strict=True, assign=True)
return transformer
def convert_controlnet(
transformer_type: str,
control_state_dict: Dict[str, Any],
base_state_dict: Dict[str, Any],
weights_only: bool = True,
):
"""
Convert controlnet weights.
Args:
transformer_type: The type of transformer/controlnet
control_state_dict: State dict containing controlnet-specific weights
base_state_dict: State dict containing base transformer weights (for shared modules)
weights_only: Whether to use weights_only loading
"""
if transformer_type not in CONTROLNET_CONFIGS:
raise AssertionError(f"{transformer_type} does not define a ControlNet config")
PREFIX_KEY = "net."
# Process control-specific keys
for key in list(control_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 CONTROLNET_KEYS_RENAME_DICT.items():
new_key = new_key.replace(replace_key, rename_key)
update_state_dict_(control_state_dict, key, new_key)
for key in list(control_state_dict.keys()):
for special_key, handler_fn_inplace in CONTROLNET_SPECIAL_KEYS_REMAP.items():
if special_key not in key:
continue
handler_fn_inplace(key, control_state_dict)
# Copy shared weights from base transformer to controlnet
# These are the duplicated modules: patch_embed_base, time_embed, learnable_pos_embed, img_context_proj, crossattn_proj
shared_module_mappings = {
# transformer key prefix -> controlnet key prefix
"patch_embed.": "patch_embed_base.",
"time_embed.": "time_embed.",
"learnable_pos_embed.": "learnable_pos_embed.",
"img_context_proj.": "img_context_proj.",
"crossattn_proj.": "crossattn_proj.",
}
for key in list(base_state_dict.keys()):
for transformer_prefix, controlnet_prefix in shared_module_mappings.items():
if key.startswith(transformer_prefix):
controlnet_key = controlnet_prefix + key[len(transformer_prefix) :]
control_state_dict[controlnet_key] = base_state_dict[key].clone()
print(f"Copied shared weight: {key} -> {controlnet_key}", flush=True)
break
cfg = CONTROLNET_CONFIGS[transformer_type]
controlnet = CosmosControlNetModel(**cfg)
expected_keys = set(controlnet.state_dict().keys())
mapped_keys = set(control_state_dict.keys())
missing_keys = expected_keys - mapped_keys
unexpected_keys = mapped_keys - expected_keys
if missing_keys:
print(f"WARNING: missing controlnet keys ({len(missing_keys)}):", file=sys.stderr, flush=True)
for k in sorted(missing_keys):
print(k, file=sys.stderr)
sys.exit(3)
if unexpected_keys:
print(f"WARNING: unexpected controlnet keys ({len(unexpected_keys)}):", file=sys.stderr, flush=True)
for k in sorted(unexpected_keys):
print(k, file=sys.stderr)
sys.exit(4)
controlnet.load_state_dict(control_state_dict, strict=True, assign=True)
return controlnet
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 save_pipeline_cosmos2_5_predict(args, transformer, vae):
text_encoder_path = args.text_encoder_path or "nvidia/Cosmos-Reason1-7B"
tokenizer_path = args.tokenizer_path or "Qwen/Qwen2.5-VL-7B-Instruct"
text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
text_encoder_path, torch_dtype="auto", device_map="cpu"
)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
scheduler = UniPCMultistepScheduler(
use_karras_sigmas=True,
use_flow_sigmas=True,
prediction_type="flow_prediction",
sigma_max=200.0,
sigma_min=0.01,
)
pipe = Cosmos2_5_PredictBasePipeline(
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_cosmos2_5_transfer(args, transformer, controlnet, vae):
text_encoder_path = args.text_encoder_path or "nvidia/Cosmos-Reason1-7B"
tokenizer_path = args.tokenizer_path or "Qwen/Qwen2.5-VL-7B-Instruct"
text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
text_encoder_path, torch_dtype="auto", device_map="cpu"
)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
scheduler = UniPCMultistepScheduler(
use_karras_sigmas=True,
use_flow_sigmas=True,
prediction_type="flow_prediction",
sigma_max=200.0,
sigma_min=0.01,
)
pipe = Cosmos2_5_TransferPipeline(
text_encoder=text_encoder,
tokenizer=tokenizer,
transformer=transformer,
controlnet=controlnet,
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="wan2.1", choices=["wan2.1", *list(VAE_CONFIGS.keys())], help="Type of VAE"
)
parser.add_argument("--text_encoder_path", type=str, default=None)
parser.add_argument("--tokenizer_path", type=str, default=None)
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
controlnet = 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
raw_state_dict = None
if args.transformer_ckpt_path is not None:
weights_only = "Cosmos-1.0" in args.transformer_type
raw_state_dict = get_state_dict(
torch.load(args.transformer_ckpt_path, map_location="cpu", weights_only=weights_only)
)
if raw_state_dict is not None:
if "Transfer" in args.transformer_type:
base_state_dict = {}
control_state_dict = {}
for k, v in raw_state_dict.items():
plain_key = k.removeprefix("net.") if k.startswith("net.") else k
if "control" in plain_key.lower():
control_state_dict[k] = v
else:
base_state_dict[k] = v
assert len(base_state_dict.keys() & control_state_dict.keys()) == 0
# Convert transformer first to get the processed base state dict
transformer = convert_transformer(
args.transformer_type, state_dict=base_state_dict, weights_only=weights_only
)
transformer = transformer.to(dtype=dtype)
# Get converted transformer state dict to copy shared weights to controlnet
converted_base_state_dict = transformer.state_dict()
# Convert controlnet with both control-specific and shared weights from transformer
controlnet = convert_controlnet(
args.transformer_type, control_state_dict, converted_base_state_dict, weights_only=weights_only
)
controlnet = controlnet.to(dtype=dtype)
if not args.save_pipeline:
transformer.save_pretrained(
pathlib.Path(args.output_path) / "transformer", safe_serialization=True, max_shard_size="5GB"
)
controlnet.save_pretrained(
pathlib.Path(args.output_path) / "controlnet", safe_serialization=True, max_shard_size="5GB"
)
else:
transformer = convert_transformer(
args.transformer_type, state_dict=raw_state_dict, weights_only=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)
elif "Cosmos-2.0" in args.transformer_type or "Cosmos-2.5" in args.transformer_type:
vae = AutoencoderKLWan.from_pretrained(
"Wan-AI/Wan2.1-T2V-1.3B-Diffusers", subfolder="vae", torch_dtype=torch.float32
)
else:
raise AssertionError(f"{args.transformer_type} not supported")
if not args.save_pipeline:
vae.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")
else:
vae = None
if args.save_pipeline:
if "Cosmos-1.0" in args.transformer_type:
assert args.text_encoder_path is not None
assert args.tokenizer_path is not None
save_pipeline_cosmos_1_0(args, transformer, vae)
elif "Cosmos-2.0" in args.transformer_type:
assert args.text_encoder_path is not None
assert args.tokenizer_path is not None
save_pipeline_cosmos_2_0(args, transformer, vae)
elif "Cosmos-2.5" in args.transformer_type:
if "Predict" in args.transformer_type:
save_pipeline_cosmos2_5_predict(args, transformer, vae)
elif "Transfer" in args.transformer_type:
save_pipeline_cosmos2_5_transfer(args, transformer, None, vae)
else:
raise AssertionError(f"{args.transformer_type} not supported")
else:
raise AssertionError(f"{args.transformer_type} not supported")