[DC-AE] Add the official Deep Compression Autoencoder code(32x,64x,128x compression ratio);

docs/source/en/_toctree.yml

@@ -314,6 +314,8 @@
         title: AutoencoderKLMochi
       - local: api/models/asymmetricautoencoderkl
         title: AsymmetricAutoencoderKL
+      - local: api/models/autoencoder_dc
+        title: AutoencoderDC
       - local: api/models/consistency_decoder_vae
         title: ConsistencyDecoderVAE
       - local: api/models/autoencoder_oobleck

docs/source/en/api/models/autoencoder_dc.md

@@ -0,0 +1,50 @@
+<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+
+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. -->
+
+# AutoencoderDC
+
+The 2D Autoencoder model used in [SANA](https://huggingface.co/papers/2410.10629) and introduced in [DCAE](https://huggingface.co/papers/2410.10733) by authors Junyu Chen\*, Han Cai\*, Junsong Chen, Enze Xie, Shang Yang, Haotian Tang, Muyang Li, Yao Lu, Song Han from MIT HAN Lab.
+
+The abstract from the paper is:
+
+*We present Deep Compression Autoencoder (DC-AE), a new family of autoencoder models for accelerating high-resolution diffusion models. Existing autoencoder models have demonstrated impressive results at a moderate spatial compression ratio (e.g., 8x), but fail to maintain satisfactory reconstruction accuracy for high spatial compression ratios (e.g., 64x). We address this challenge by introducing two key techniques: (1) Residual Autoencoding, where we design our models to learn residuals based on the space-to-channel transformed features to alleviate the optimization difficulty of high spatial-compression autoencoders; (2) Decoupled High-Resolution Adaptation, an efficient decoupled three-phases training strategy for mitigating the generalization penalty of high spatial-compression autoencoders. With these designs, we improve the autoencoder's spatial compression ratio up to 128 while maintaining the reconstruction quality. Applying our DC-AE to latent diffusion models, we achieve significant speedup without accuracy drop. For example, on ImageNet 512x512, our DC-AE provides 19.1x inference speedup and 17.9x training speedup on H100 GPU for UViT-H while achieving a better FID, compared with the widely used SD-VAE-f8 autoencoder. Our code is available at [this https URL](https://github.com/mit-han-lab/efficientvit).*
+
+The following DCAE models are released and supported in Diffusers.
+
+| Diffusers format | Original format |
+|:----------------:|:---------------:|
+| [`mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers) | [`mit-han-lab/dc-ae-f32c32-sana-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.0)
+| [`mit-han-lab/dc-ae-f32c32-in-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-in-1.0-diffusers) | [`mit-han-lab/dc-ae-f32c32-in-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-in-1.0)
+| [`mit-han-lab/dc-ae-f32c32-mix-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-mix-1.0-diffusers) | [`mit-han-lab/dc-ae-f32c32-mix-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f32c32-mix-1.0)
+| [`mit-han-lab/dc-ae-f64c128-in-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f64c128-in-1.0-diffusers) | [`mit-han-lab/dc-ae-f64c128-in-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f64c128-in-1.0)
+| [`mit-han-lab/dc-ae-f64c128-mix-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f64c128-mix-1.0-diffusers) | [`mit-han-lab/dc-ae-f64c128-mix-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f64c128-mix-1.0)
+| [`mit-han-lab/dc-ae-f128c512-in-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f128c512-in-1.0-diffusers) | [`mit-han-lab/dc-ae-f128c512-in-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f128c512-in-1.0)
+| [`mit-han-lab/dc-ae-f128c512-mix-1.0-diffusers`](https://huggingface.co/mit-han-lab/dc-ae-f128c512-mix-1.0-diffusers) | [`mit-han-lab/dc-ae-f128c512-mix-1.0`](https://huggingface.co/mit-han-lab/dc-ae-f128c512-mix-1.0)
+
+Load a model in Diffusers format with [`~ModelMixin.from_pretrained`].
+
+```python
+from diffusers import AutoencoderDC
+
+ae = AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers", torch_dtype=torch.float32).to("cuda")
+```
+
+## AutoencoderDC
+
+[[autodoc]] AutoencoderDC
+  - encode
+  - decode
+  - all
+
+## DecoderOutput
+
+[[autodoc]] models.autoencoders.vae.DecoderOutput
+

scripts/convert_dcae_to_diffusers.py

@@ -0,0 +1,323 @@
+import argparse
+from typing import Any, Dict
+
+import torch
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+
+from diffusers import AutoencoderDC
+
+
+def remap_qkv_(key: str, state_dict: Dict[str, Any]):
+    qkv = state_dict.pop(key)
+    q, k, v = torch.chunk(qkv, 3, dim=0)
+    parent_module, _, _ = key.rpartition(".qkv.conv.weight")
+    state_dict[f"{parent_module}.to_q.weight"] = q.squeeze()
+    state_dict[f"{parent_module}.to_k.weight"] = k.squeeze()
+    state_dict[f"{parent_module}.to_v.weight"] = v.squeeze()
+
+
+def remap_proj_conv_(key: str, state_dict: Dict[str, Any]):
+    parent_module, _, _ = key.rpartition(".proj.conv.weight")
+    state_dict[f"{parent_module}.to_out.weight"] = state_dict.pop(key).squeeze()
+
+
+AE_KEYS_RENAME_DICT = {
+    # common
+    "main.": "",
+    "op_list.": "",
+    "context_module": "attn",
+    "local_module": "conv_out",
+    # NOTE: The below two lines work because scales in the available configs only have a tuple length of 1
+    # If there were more scales, there would be more layers, so a loop would be better to handle this
+    "aggreg.0.0": "to_qkv_multiscale.0.proj_in",
+    "aggreg.0.1": "to_qkv_multiscale.0.proj_out",
+    "depth_conv.conv": "conv_depth",
+    "inverted_conv.conv": "conv_inverted",
+    "point_conv.conv": "conv_point",
+    "point_conv.norm": "norm",
+    "conv.conv.": "conv.",
+    "conv1.conv": "conv1",
+    "conv2.conv": "conv2",
+    "conv2.norm": "norm",
+    "proj.norm": "norm_out",
+    # encoder
+    "encoder.project_in.conv": "encoder.conv_in",
+    "encoder.project_out.0.conv": "encoder.conv_out",
+    "encoder.stages": "encoder.down_blocks",
+    # decoder
+    "decoder.project_in.conv": "decoder.conv_in",
+    "decoder.project_out.0": "decoder.norm_out",
+    "decoder.project_out.2.conv": "decoder.conv_out",
+    "decoder.stages": "decoder.up_blocks",
+}
+
+AE_F32C32_KEYS = {
+    # encoder
+    "encoder.project_in.conv": "encoder.conv_in.conv",
+    # decoder
+    "decoder.project_out.2.conv": "decoder.conv_out.conv",
+}
+
+AE_F64C128_KEYS = {
+    # encoder
+    "encoder.project_in.conv": "encoder.conv_in.conv",
+    # decoder
+    "decoder.project_out.2.conv": "decoder.conv_out.conv",
+}
+
+AE_F128C512_KEYS = {
+    # encoder
+    "encoder.project_in.conv": "encoder.conv_in.conv",
+    # decoder
+    "decoder.project_out.2.conv": "decoder.conv_out.conv",
+}
+
+AE_SPECIAL_KEYS_REMAP = {
+    "qkv.conv.weight": remap_qkv_,
+    "proj.conv.weight": remap_proj_conv_,
+}
+
+
+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 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 convert_ae(config_name: str, dtype: torch.dtype):
+    config = get_ae_config(config_name)
+    hub_id = f"mit-han-lab/{config_name}"
+    ckpt_path = hf_hub_download(hub_id, "model.safetensors")
+    original_state_dict = get_state_dict(load_file(ckpt_path))
+
+    ae = AutoencoderDC(**config).to(dtype=dtype)
+
+    for key in list(original_state_dict.keys()):
+        new_key = key[:]
+        for replace_key, rename_key in AE_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 AE_SPECIAL_KEYS_REMAP.items():
+            if special_key not in key:
+                continue
+            handler_fn_inplace(key, original_state_dict)
+
+    ae.load_state_dict(original_state_dict, strict=True)
+    return ae
+
+
+def get_ae_config(name: str):
+    if name in ["dc-ae-f32c32-sana-1.0"]:
+        config = {
+            "latent_channels": 32,
+            "encoder_block_types": (
+                "ResBlock",
+                "ResBlock",
+                "ResBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+            ),
+            "decoder_block_types": (
+                "ResBlock",
+                "ResBlock",
+                "ResBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+            ),
+            "encoder_block_out_channels": (128, 256, 512, 512, 1024, 1024),
+            "decoder_block_out_channels": (128, 256, 512, 512, 1024, 1024),
+            "encoder_qkv_multiscales": ((), (), (), (5,), (5,), (5,)),
+            "decoder_qkv_multiscales": ((), (), (), (5,), (5,), (5,)),
+            "encoder_layers_per_block": (2, 2, 2, 3, 3, 3),
+            "decoder_layers_per_block": [3, 3, 3, 3, 3, 3],
+            "downsample_block_type": "conv",
+            "upsample_block_type": "interpolate",
+            "decoder_norm_types": "rms_norm",
+            "decoder_act_fns": "silu",
+            "scaling_factor": 0.41407,
+        }
+    elif name in ["dc-ae-f32c32-in-1.0", "dc-ae-f32c32-mix-1.0"]:
+        AE_KEYS_RENAME_DICT.update(AE_F32C32_KEYS)
+        config = {
+            "latent_channels": 32,
+            "encoder_block_types": [
+                "ResBlock",
+                "ResBlock",
+                "ResBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+            ],
+            "decoder_block_types": [
+                "ResBlock",
+                "ResBlock",
+                "ResBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+            ],
+            "encoder_block_out_channels": [128, 256, 512, 512, 1024, 1024],
+            "decoder_block_out_channels": [128, 256, 512, 512, 1024, 1024],
+            "encoder_layers_per_block": [0, 4, 8, 2, 2, 2],
+            "decoder_layers_per_block": [0, 5, 10, 2, 2, 2],
+            "encoder_qkv_multiscales": ((), (), (), (), (), ()),
+            "decoder_qkv_multiscales": ((), (), (), (), (), ()),
+            "decoder_norm_types": ["batch_norm", "batch_norm", "batch_norm", "rms_norm", "rms_norm", "rms_norm"],
+            "decoder_act_fns": ["relu", "relu", "relu", "silu", "silu", "silu"],
+        }
+        if name == "dc-ae-f32c32-in-1.0":
+            config["scaling_factor"] = 0.3189
+        elif name == "dc-ae-f32c32-mix-1.0":
+            config["scaling_factor"] = 0.4552
+    elif name in ["dc-ae-f64c128-in-1.0", "dc-ae-f64c128-mix-1.0"]:
+        AE_KEYS_RENAME_DICT.update(AE_F64C128_KEYS)
+        config = {
+            "latent_channels": 128,
+            "encoder_block_types": [
+                "ResBlock",
+                "ResBlock",
+                "ResBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+            ],
+            "decoder_block_types": [
+                "ResBlock",
+                "ResBlock",
+                "ResBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+            ],
+            "encoder_block_out_channels": [128, 256, 512, 512, 1024, 1024, 2048],
+            "decoder_block_out_channels": [128, 256, 512, 512, 1024, 1024, 2048],
+            "encoder_layers_per_block": [0, 4, 8, 2, 2, 2, 2],
+            "decoder_layers_per_block": [0, 5, 10, 2, 2, 2, 2],
+            "encoder_qkv_multiscales": ((), (), (), (), (), (), ()),
+            "decoder_qkv_multiscales": ((), (), (), (), (), (), ()),
+            "decoder_norm_types": [
+                "batch_norm",
+                "batch_norm",
+                "batch_norm",
+                "rms_norm",
+                "rms_norm",
+                "rms_norm",
+                "rms_norm",
+            ],
+            "decoder_act_fns": ["relu", "relu", "relu", "silu", "silu", "silu", "silu"],
+        }
+        if name == "dc-ae-f64c128-in-1.0":
+            config["scaling_factor"] = 0.2889
+        elif name == "dc-ae-f64c128-mix-1.0":
+            config["scaling_factor"] = 0.4538
+    elif name in ["dc-ae-f128c512-in-1.0", "dc-ae-f128c512-mix-1.0"]:
+        AE_KEYS_RENAME_DICT.update(AE_F128C512_KEYS)
+        config = {
+            "latent_channels": 512,
+            "encoder_block_types": [
+                "ResBlock",
+                "ResBlock",
+                "ResBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+            ],
+            "decoder_block_types": [
+                "ResBlock",
+                "ResBlock",
+                "ResBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+                "EfficientViTBlock",
+            ],
+            "encoder_block_out_channels": [128, 256, 512, 512, 1024, 1024, 2048, 2048],
+            "decoder_block_out_channels": [128, 256, 512, 512, 1024, 1024, 2048, 2048],
+            "encoder_layers_per_block": [0, 4, 8, 2, 2, 2, 2, 2],
+            "decoder_layers_per_block": [0, 5, 10, 2, 2, 2, 2, 2],
+            "encoder_qkv_multiscales": ((), (), (), (), (), (), (), ()),
+            "decoder_qkv_multiscales": ((), (), (), (), (), (), (), ()),
+            "decoder_norm_types": [
+                "batch_norm",
+                "batch_norm",
+                "batch_norm",
+                "rms_norm",
+                "rms_norm",
+                "rms_norm",
+                "rms_norm",
+                "rms_norm",
+            ],
+            "decoder_act_fns": ["relu", "relu", "relu", "silu", "silu", "silu", "silu", "silu"],
+        }
+        if name == "dc-ae-f128c512-in-1.0":
+            config["scaling_factor"] = 0.4883
+        elif name == "dc-ae-f128c512-mix-1.0":
+            config["scaling_factor"] = 0.3620
+    else:
+        raise ValueError("Invalid config name provided.")
+
+    return config
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--config_name",
+        type=str,
+        default="dc-ae-f32c32-sana-1.0",
+        choices=[
+            "dc-ae-f32c32-sana-1.0",
+            "dc-ae-f32c32-in-1.0",
+            "dc-ae-f32c32-mix-1.0",
+            "dc-ae-f64c128-in-1.0",
+            "dc-ae-f64c128-mix-1.0",
+            "dc-ae-f128c512-in-1.0",
+            "dc-ae-f128c512-mix-1.0",
+        ],
+        help="The DCAE checkpoint to convert",
+    )
+    parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved")
+    parser.add_argument("--dtype", default="fp32", help="Torch dtype to save the model in.")
+    return parser.parse_args()
+
+
+DTYPE_MAPPING = {
+    "fp32": torch.float32,
+    "fp16": torch.float16,
+    "bf16": torch.bfloat16,
+}
+
+VARIANT_MAPPING = {
+    "fp32": None,
+    "fp16": "fp16",
+    "bf16": "bf16",
+}
+
+
+if __name__ == "__main__":
+    args = get_args()
+
+    dtype = DTYPE_MAPPING[args.dtype]
+    variant = VARIANT_MAPPING[args.dtype]
+
+    ae = convert_ae(args.config_name, dtype)
+    ae.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB", variant=variant)

src/diffusers/__init__.py

@@ -80,6 +80,7 @@
             "AllegroTransformer3DModel",
             "AsymmetricAutoencoderKL",
             "AuraFlowTransformer2DModel",
+            "AutoencoderDC",
             "AutoencoderKL",
             "AutoencoderKLAllegro",
             "AutoencoderKLCogVideoX",
@@ -572,6 +573,7 @@
             AllegroTransformer3DModel,
             AsymmetricAutoencoderKL,
             AuraFlowTransformer2DModel,
+            AutoencoderDC,
             AutoencoderKL,
             AutoencoderKLAllegro,
             AutoencoderKLCogVideoX,