File size: 5,426 Bytes
af758d1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from einops import rearrange
import os
from cosmos_predict1.tokenizer.inference.video_lib import CausalVideoTokenizer
from cosmos_predict1.tokenizer.networks import TokenizerConfigs, TokenizerModels
def load_cosmos_1_decoder(vae_path: str, decoder_cosmos_kwargs):
tokenizer_cosmos, tokenizer_config = load_cosmos_1_tokenizer(
checkpoint_path=vae_path,
load_encoder=False,
load_decoder=True,
load_jit=False,
return_tokenizer_config=True,
add_tokenizer_kwargs=decoder_cosmos_kwargs,
)
decoder = tokenizer_cosmos.decoder
return decoder, tokenizer_config
def get_tokenizer_config(checkpoint_path: str):
model_name = os.path.basename(checkpoint_path)
model_name = model_name.split('Cosmos-Tokenize1-')[1].replace("-", "_")
tokenizer_config = TokenizerConfigs[model_name].value
return tokenizer_config
def load_cosmos_1_tokenizer(checkpoint_path: str, load_encoder: bool = True, load_decoder: bool = False, load_jit: bool = True, return_tokenizer_config: bool = False, add_tokenizer_kwargs = None):
tokenizer_kwargs = {}
if return_tokenizer_config or not load_jit:
tokenizer_config = get_tokenizer_config(checkpoint_path)
tokenizer_name = tokenizer_config["name"]
else:
tokenizer_config = None
if load_encoder:
tokenizer_kwargs['checkpoint_enc'] = f'{checkpoint_path}/encoder.jit'
if load_decoder:
tokenizer_kwargs['checkpoint_dec'] = f'{checkpoint_path}/decoder.jit'
if not load_jit:
if add_tokenizer_kwargs:
for k, v in add_tokenizer_kwargs.items():
tokenizer_config[k] = v
tokenizer = TokenizerModels[tokenizer_name].value(**tokenizer_config)
else:
tokenizer = CausalVideoTokenizer(**tokenizer_kwargs)
if return_tokenizer_config:
return tokenizer, tokenizer_config
else:
return tokenizer
def load_cosmos_latent_statistics(vae_path: str, pixel_chunk_duration: int = 121, device: torch.device = 'cpu', weight_dtype: torch.dtype = None):
tokenizer_config = get_tokenizer_config(vae_path)
latent_chunk_duration = (pixel_chunk_duration - 1) // tokenizer_config['temporal_compression'] + 1
latent_mean, latent_std = get_cosmos_diffusion_mean_std(vae_path, weight_dtype, tokenizer_config['latent_channels'], latent_chunk_duration)
latent_mean = latent_mean.to(device)
latent_std = latent_std.to(device)
return latent_mean, latent_std
def get_cosmos_diffusion_mean_std(vae_dir: str, dtype: torch.dtype, latent_ch: int, latent_chunk_duration: int):
latent_mean, latent_std = torch.load(os.path.join(vae_dir, "mean_std.pt"), weights_only=True)
if dtype is None:
dtype = latent_mean.dtype
target_shape = [1, latent_ch, latent_chunk_duration, 1, 1]
latent_mean = latent_mean.view(latent_ch, -1)
latent_std = latent_std.view(latent_ch, -1)
latent_mean = latent_mean.to(dtype).reshape(*target_shape)
latent_std = latent_std.to(dtype).reshape(*target_shape)
return latent_mean, latent_std
def denormalize_latents(model_input: torch.Tensor, latent_std: torch.Tensor, latent_mean: torch.Tensor, num_input_multi_views: int = 1, sigma_data: float = 0.5):
# Add batch dimension
if len(model_input.shape) == 4:
model_input = model_input.unsqueeze(0)
unsqueeze = True
else:
unsqueeze = False
# Use same statistics across views
model_input = rearrange(model_input, 'b (v t) c h w -> (b v) t c h w', v=num_input_multi_views)
model_input = model_input / sigma_data
model_input = model_input * latent_std + latent_mean
# Convert from generated internal cosmos (B T C H W) to cosmos-predict (B C T H W)
model_input = model_input.transpose(1, 2)
# Reshape frames and views again in one dimension
model_input = rearrange(model_input, '(b v) t c h w -> b (v t) c h w', v=num_input_multi_views)
# Remove batch dimension
if unsqueeze:
model_input = model_input.squeeze(0)
return model_input
if __name__ == '__main__':
model_name = 'Cosmos-Tokenize1-CV8x8x8-720p'
# model_name = 'Cosmos-Tokenize1-CV4x8x8-360p'
checkpoint_path = f'checkpoints/cosmos_predict1/{model_name}'
tokenizer = load_cosmos_1_tokenizer(checkpoint_path, load_encoder=True, load_decoder=True)
input_tensor = torch.rand(1, 3, 9, 512, 512).to('cuda').to(torch.bfloat16) # [B, C, T, H, W]
input_tensor = input_tensor * 2. - 1. # Normalize to [-1..1]
(latent,) = tokenizer.encode(input_tensor)
torch.testing.assert_close(latent.shape, (1, 16, 3, 64, 64))
reconstructed_tensor = tokenizer.decode(latent)
torch.testing.assert_close(reconstructed_tensor.shape, input_tensor.shape) |