| """ |
| # 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, |
| |
| "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, |
| |
| "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, |
| "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." |
|
|
| |
| 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) |
|
|
| |
| |
| shared_module_mappings = { |
| |
| "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) |
| |
| |
| 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 |
|
|
| |
| transformer = convert_transformer( |
| args.transformer_type, state_dict=base_state_dict, weights_only=weights_only |
| ) |
| transformer = transformer.to(dtype=dtype) |
|
|
| |
| converted_base_state_dict = transformer.state_dict() |
|
|
| |
| 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") |
|
|
