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| # Copyright 2024 MIT Han Lab | |
| # | |
| # 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. | |
| # | |
| # SPDX-License-Identifier: Apache-2.0 | |
| from typing import Callable, Optional | |
| import diffusers | |
| import torch | |
| from huggingface_hub import PyTorchModelHubMixin | |
| from torch import nn | |
| from ..efficientvit.models.efficientvit.dc_ae import DCAE, DCAEConfig, dc_ae_f32c32, dc_ae_f64c128, dc_ae_f128c512 | |
| __all__ = ["create_dc_ae_model_cfg", "DCAE_HF", "AutoencoderKL"] | |
| REGISTERED_DCAE_MODEL: dict[str, tuple[Callable, Optional[str]]] = { | |
| "dc-ae-f32c32-in-1.0": (dc_ae_f32c32, None), | |
| "dc-ae-f64c128-in-1.0": (dc_ae_f64c128, None), | |
| "dc-ae-f128c512-in-1.0": (dc_ae_f128c512, None), | |
| ################################################################################################# | |
| "dc-ae-f32c32-mix-1.0": (dc_ae_f32c32, None), | |
| "dc-ae-f64c128-mix-1.0": (dc_ae_f64c128, None), | |
| "dc-ae-f128c512-mix-1.0": (dc_ae_f128c512, None), | |
| ################################################################################################# | |
| "dc-ae-f32c32-sana-1.0": (dc_ae_f32c32, None), | |
| } | |
| def create_dc_ae_model_cfg(name: str, pretrained_path: Optional[str] = None) -> DCAEConfig: | |
| assert name in REGISTERED_DCAE_MODEL, f"{name} is not supported" | |
| dc_ae_cls, default_pt_path = REGISTERED_DCAE_MODEL[name] | |
| pretrained_path = default_pt_path if pretrained_path is None else pretrained_path | |
| model_cfg = dc_ae_cls(name, pretrained_path) | |
| return model_cfg | |
| class DCAE_HF(DCAE, PyTorchModelHubMixin): | |
| def __init__(self, model_name: str): | |
| cfg = create_dc_ae_model_cfg(model_name) | |
| DCAE.__init__(self, cfg) | |
| class AutoencoderKL(nn.Module): | |
| def __init__(self, model_name: str): | |
| super().__init__() | |
| self.model_name = model_name | |
| if self.model_name in ["stabilityai/sd-vae-ft-ema"]: | |
| self.model = diffusers.models.AutoencoderKL.from_pretrained(self.model_name) | |
| self.spatial_compression_ratio = 8 | |
| elif self.model_name == "flux-vae": | |
| from diffusers import FluxPipeline | |
| pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16) | |
| self.model = diffusers.models.AutoencoderKL.from_pretrained(pipe.vae.config._name_or_path) | |
| self.spatial_compression_ratio = 8 | |
| else: | |
| raise ValueError(f"{self.model_name} is not supported for AutoencoderKL") | |
| def encode(self, x: torch.Tensor) -> torch.Tensor: | |
| if self.model_name in ["stabilityai/sd-vae-ft-ema", "flux-vae"]: | |
| return self.model.encode(x).latent_dist.sample() | |
| else: | |
| raise ValueError(f"{self.model_name} is not supported for AutoencoderKL") | |
| def decode(self, latent: torch.Tensor) -> torch.Tensor: | |
| if self.model_name in ["stabilityai/sd-vae-ft-ema", "flux-vae"]: | |
| return self.model.decode(latent).sample | |
| else: | |
| raise ValueError(f"{self.model_name} is not supported for AutoencoderKL") | |