[DC-AE] Add the official Deep Compression Autoencoder code(32x,64x,128x compression ratio); #9708
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| <!-- Copyright 2024 The HuggingFace Team. All rights reserved. | ||
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| 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 | ||
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| http://www.apache.org/licenses/LICENSE-2.0 | ||
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| 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. --> | ||
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| # AutoencoderDC | ||
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| *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.* | ||
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| The following DCAE models are released and supported in Diffusers: | ||
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| | diffusers format | original format | | ||
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| |:----------------:|:---------------:| | ||
| | [`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) | ||
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| The models can be loaded with the following code snippet. | ||
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| ```python | ||
| from diffusers import AutoencoderDC | ||
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| ae = AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers", torch_dtype=torch.float32).to("cuda") | ||
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There was a problem hiding this comment. @lawrence-cj @chenjy2003 Will you be hosting the diffusers converted weights for the VAE as well? There was a problem hiding this comment. Oh yes. We will host the weight in the same place as the original ones. There was a problem hiding this comment. Cc: @chenjy2003 , please add our official diffusers version checkpoint into our huggingface collection. There was a problem hiding this comment. Sure. We can hold the converted weight. By the way, I noticed @a-r-r-o-w 's previous comments.
Could you please elaborate more? Do you mean that it is not necessary to hold the converted weights? Thanks in advance for your answer. There was a problem hiding this comment. Usually, whenever there is a conversion required to go from original implementation to diffusers version, we make sure to host the diffusers formats weights as well, so that it guarantees to work out of the box. With single file loading (already added support for it in this commit, and you can find the example in docs as well as here), one can load the original checkpoints directly as well. This, however, relies on the Using this value, we choose the appropriate config for initalizing the AE and rename the state-dict on the fly. There was a problem hiding this comment. I suppose what he mean is we can load the original weight using DCAE.from_single_file(xxx), but DCAE.from_pretrained(xxx) command still need a converted weight, right? There was a problem hiding this comment. Yes, |
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| ``` | ||
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| ## Single file loading | ||
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| The `AutoencoderDC` implementation supports loading checkpoints shipped in the original format by MIT HAN Lab. The following example demonstrates how to load the `f128c512` checkpoint: | ||
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| ```python | ||
| from diffusers import AutoencoderDC | ||
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| model_name = "dc-ae-f128c512-mix-1.0" | ||
| ae = AutoencoderDC.from_single_file( | ||
| f"https://huggingface.co/mit-han-lab/{model_name}/model.safetensors", | ||
| original_config=f"https://huggingface.co/mit-han-lab/{model_name}/resolve/main/config.json" | ||
| ) | ||
| ``` | ||
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| ## AutoencoderDC | ||
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| [[autodoc]] AutoencoderDC | ||
| - decode | ||
| - all | ||
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| ## DecoderOutput | ||
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| [[autodoc]] models.autoencoders.vae.DecoderOutput | ||
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| import argparse | ||
| from typing import Any, Dict | ||
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| import torch | ||
| from huggingface_hub import hf_hub_download | ||
| from safetensors.torch import load_file | ||
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| from diffusers import AutoencoderDC | ||
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| 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() | ||
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| 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() | ||
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| 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", | ||
| } | ||
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| AE_F32C32_KEYS = { | ||
| # encoder | ||
| "encoder.project_in.conv": "encoder.conv_in.conv", | ||
| # decoder | ||
| "decoder.project_out.2.conv": "decoder.conv_out.conv", | ||
| } | ||
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| AE_F64C128_KEYS = { | ||
| # encoder | ||
| "encoder.project_in.conv": "encoder.conv_in.conv", | ||
| # decoder | ||
| "decoder.project_out.2.conv": "decoder.conv_out.conv", | ||
| } | ||
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| AE_F128C512_KEYS = { | ||
| # encoder | ||
| "encoder.project_in.conv": "encoder.conv_in.conv", | ||
| # decoder | ||
| "decoder.project_out.2.conv": "decoder.conv_out.conv", | ||
| } | ||
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| AE_SPECIAL_KEYS_REMAP = { | ||
| "qkv.conv.weight": remap_qkv_, | ||
| "proj.conv.weight": remap_proj_conv_, | ||
| } | ||
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| 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 | ||
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| 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) | ||
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| 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)) | ||
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| ae = AutoencoderDC(**config).to(dtype=dtype) | ||
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| 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) | ||
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| 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) | ||
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| ae.load_state_dict(original_state_dict, strict=True) | ||
| return ae | ||
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| 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.") | ||
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| return config | ||
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| 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() | ||
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| DTYPE_MAPPING = { | ||
| "fp32": torch.float32, | ||
| "fp16": torch.float16, | ||
| "bf16": torch.bfloat16, | ||
| } | ||
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| VARIANT_MAPPING = { | ||
| "fp32": None, | ||
| "fp16": "fp16", | ||
| "bf16": "bf16", | ||
| } | ||
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| if __name__ == "__main__": | ||
| args = get_args() | ||
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| dtype = DTYPE_MAPPING[args.dtype] | ||
| variant = VARIANT_MAPPING[args.dtype] | ||
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| ae = convert_ae(args.config_name, dtype) | ||
| ae.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB", variant=variant) |
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