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# 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.
"""A library for image tokenizers inference."""
from typing import Any
import numpy as np
import torch
from cosmos_predict1.tokenizer.inference.utils import (
load_decoder_model,
load_encoder_model,
load_model,
numpy2tensor,
pad_image_batch,
tensor2numpy,
unpad_image_batch,
)
class ImageTokenizer(torch.nn.Module):
def __init__(
self,
checkpoint: str = None,
checkpoint_enc: str = None,
checkpoint_dec: str = None,
tokenizer_config: dict[str, Any] = None,
device: str = "cuda",
dtype: str = "bfloat16",
) -> None:
super().__init__()
self._device = device
self._dtype = getattr(torch, dtype)
self._full_model = (
load_model(checkpoint, tokenizer_config, device).to(self._dtype) if checkpoint is not None else None
)
self._enc_model = (
load_encoder_model(checkpoint_enc, tokenizer_config, device).to(self._dtype)
if checkpoint_enc is not None
else None
)
self._dec_model = (
load_decoder_model(checkpoint_dec, tokenizer_config, device).to(self._dtype)
if checkpoint_dec is not None
else None
)
@torch.no_grad()
def autoencode(self, input_tensor: torch.Tensor) -> torch.Tensor:
"""Reconstrcuts a batch of image tensors after embedding into a latent.
Args:
input_tensor: The input image Bx3xHxW layout, range [-1..1].
Returns:
The reconstructed tensor, layout Bx3xHxW, range [-1..1].
"""
if self._full_model is not None:
output_tensor = self._full_model(input_tensor)
output_tensor = output_tensor[0] if isinstance(output_tensor, tuple) else output_tensor
else:
output_latent = self.encode(input_tensor)[0]
output_tensor = self.decode(output_latent)
return output_tensor
@torch.no_grad()
def decode(self, input_latent: torch.Tensor) -> torch.Tensor:
"""Decodes an image from a provided latent embedding.
Args:
input_latent: The continuous latent Bx16xhxw for CI,
or the discrete indices Bxhxw for DI.
Returns:
The output tensor in Bx3xHxW, range [-1..1].
"""
return self._dec_model(input_latent)
@torch.no_grad()
def encode(self, input_tensor: torch.Tensor) -> tuple[torch.Tensor]:
"""Encodes an image into a latent embedding or code.
Args:
input_tensor: The input tensor Bx3xHxW layout, range [-1..1].
Returns:
For continuous image (CI) tokenizer, the tuple contains:
- The latent embedding, Bx16x(h)x(w), where the compression
rate is (H/h x W/w), and channel dimension of 16.
For discrete image (DI) tokenizer, the tuple contains:
- The indices, Bx(h)x(w), from a codebook of size 64K, which
corresponds to FSQ levels of (8,8,8,5,5,5).
- The discrete code, Bx6x(h)x(w), where the compression rate is
again (H/h x W/w), and channel dimension of 6.
"""
output_latent = self._enc_model(input_tensor)
if isinstance(output_latent, torch.Tensor):
return output_latent
return output_latent[:-1]
@torch.no_grad()
def forward(self, image: np.ndarray) -> np.ndarray:
"""Reconstructs an image using a pre-trained tokenizer.
Args:
image: The input image BxHxWxC layout, range [0..255].
Returns:
The reconstructed image in range [0..255], layout BxHxWxC.
"""
padded_input_image, crop_region = pad_image_batch(image)
input_tensor = numpy2tensor(padded_input_image, dtype=self._dtype, device=self._device)
output_tensor = self.autoencode(input_tensor)
padded_output_image = tensor2numpy(output_tensor)
return unpad_image_batch(padded_output_image, crop_region)
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