wehub-resource-sync
672 lines
27 KiB
Python
672 lines
27 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import torch
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from huggingface_hub import hf_hub_download
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from safetensors import safe_open
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from demo_diffusion.dynamic_import import import_from_diffusers
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from demo_diffusion.model import base_model, load, optimizer
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from demo_diffusion.utils_sd3.other_impls import load_into
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from demo_diffusion.utils_sd3.sd3_impls import SDVAE
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# List of models to import from diffusers.models
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models_to_import = ["AutoencoderKL", "AutoencoderKLTemporalDecoder", "AutoencoderKLWan"]
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for model in models_to_import:
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globals()[model] = import_from_diffusers(model, "diffusers.models")
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# Import FluxKontextUtil from pipeline module
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# Using a deferred import to avoid circular dependencies
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def _get_flux_kontext_util():
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from demo_diffusion.pipeline.flux_pipeline import FluxKontextUtil
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return FluxKontextUtil
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class VAEModel(base_model.BaseModel):
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def __init__(
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self,
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version,
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pipeline,
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device,
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hf_token,
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verbose,
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framework_model_dir,
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fp16=False,
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tf32=False,
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bf16=False,
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max_batch_size=16,
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):
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super(VAEModel, self).__init__(
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version,
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pipeline,
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device=device,
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hf_token=hf_token,
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verbose=verbose,
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framework_model_dir=framework_model_dir,
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fp16=fp16,
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tf32=tf32,
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bf16=bf16,
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max_batch_size=max_batch_size,
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)
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self.do_constant_folding = False
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self.subfolder = "vae"
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self.vae_decoder_model_dir = load.get_checkpoint_dir(
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self.framework_model_dir, self.version, self.pipeline, self.subfolder
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)
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if not os.path.exists(self.vae_decoder_model_dir):
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self.config = AutoencoderKL.load_config(self.path, subfolder=self.subfolder, token=self.hf_token)
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else:
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print(f"[I] Load AutoencoderKL (decoder) config from: {self.vae_decoder_model_dir}")
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self.config = AutoencoderKL.load_config(self.vae_decoder_model_dir)
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def get_model(self, torch_inference=""):
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model_opts = (
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{"torch_dtype": torch.float16} if self.fp16 else {"torch_dtype": torch.bfloat16} if self.bf16 else {}
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)
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if not load.is_model_cached(self.vae_decoder_model_dir, model_opts, self.hf_safetensor):
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model = AutoencoderKL.from_pretrained(
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self.path,
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subfolder=self.subfolder,
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use_safetensors=self.hf_safetensor,
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token=self.hf_token,
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**model_opts,
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).to(self.device)
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model.save_pretrained(self.vae_decoder_model_dir, **model_opts)
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else:
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print(f"[I] Load AutoencoderKL (decoder) model from: {self.vae_decoder_model_dir}")
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model = AutoencoderKL.from_pretrained(self.vae_decoder_model_dir, **model_opts).to(self.device)
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model.forward = model.decode
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model = optimizer.optimize_checkpoint(model, torch_inference)
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return model
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def get_input_names(self):
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return ["latent"]
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def get_output_names(self):
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return ["images"]
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def get_dynamic_axes(self):
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return {"latent": {0: "B", 2: "H", 3: "W"}, "images": {0: "B", 2: "8H", 3: "8W"}}
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def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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min_batch, max_batch, _, _, _, _, min_latent_height, max_latent_height, min_latent_width, max_latent_width = (
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self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
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)
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return {
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"latent": [
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(min_batch, self.config["latent_channels"], min_latent_height, min_latent_width),
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(batch_size, self.config["latent_channels"], latent_height, latent_width),
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(max_batch, self.config["latent_channels"], max_latent_height, max_latent_width),
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]
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}
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def get_shape_dict(self, batch_size, image_height, image_width):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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return {
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"latent": (batch_size, self.config["latent_channels"], latent_height, latent_width),
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"images": (batch_size, 3, image_height, image_width),
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}
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def get_sample_input(self, batch_size, image_height, image_width, static_shape):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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dtype = torch.float16 if self.fp16 else torch.bfloat16 if self.bf16 else torch.float32
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return torch.randn(
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batch_size, self.config["latent_channels"], latent_height, latent_width, dtype=dtype, device=self.device
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)
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class SD3_VAEDecoderModel(base_model.BaseModel):
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def __init__(
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self,
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version,
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pipeline,
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device,
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hf_token,
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verbose,
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framework_model_dir,
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max_batch_size,
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fp16=False,
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):
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super(SD3_VAEDecoderModel, self).__init__(
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version,
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pipeline,
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device=device,
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hf_token=hf_token,
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verbose=verbose,
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framework_model_dir=framework_model_dir,
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fp16=fp16,
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max_batch_size=max_batch_size,
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)
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self.subfolder = "sd3"
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def get_model(self, torch_inference=""):
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dtype = torch.float16 if self.fp16 else torch.float32
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sd3_model_dir = load.get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
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sd3_filename = "sd3_medium.safetensors"
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sd3_model_path = f"{sd3_model_dir}/{sd3_filename}"
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if not os.path.exists(sd3_model_path):
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hf_hub_download(repo_id=self.path, filename=sd3_filename, local_dir=sd3_model_dir)
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with safe_open(sd3_model_path, framework="pt", device=self.device) as f:
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model = SDVAE(device=self.device, dtype=dtype).eval().cuda()
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prefix = ""
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if any(k.startswith("first_stage_model.") for k in f.keys()):
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prefix = "first_stage_model."
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load_into(f, model, prefix, self.device, dtype)
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model.forward = model.decode
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model = optimizer.optimize_checkpoint(model, torch_inference)
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return model
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def get_input_names(self):
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return ["latent"]
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def get_output_names(self):
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return ["images"]
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def get_dynamic_axes(self):
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return {"latent": {0: "B", 2: "H", 3: "W"}, "images": {0: "B", 2: "8H", 3: "8W"}}
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def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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min_batch, max_batch, _, _, _, _, min_latent_height, max_latent_height, min_latent_width, max_latent_width = (
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self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
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)
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return {
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"latent": [
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(min_batch, 16, min_latent_height, min_latent_width),
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(batch_size, 16, latent_height, latent_width),
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(max_batch, 16, max_latent_height, max_latent_width),
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]
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}
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def get_shape_dict(self, batch_size, image_height, image_width):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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return {
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"latent": (batch_size, 16, latent_height, latent_width),
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"images": (batch_size, 3, image_height, image_width),
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}
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def get_sample_input(self, batch_size, image_height, image_width, static_shape):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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dtype = torch.float16 if self.fp16 else torch.float32
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return torch.randn(batch_size, 16, latent_height, latent_width, dtype=dtype, device=self.device)
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class VAEDecTemporalModel(base_model.BaseModel):
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def __init__(
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self,
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version,
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pipeline,
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device,
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hf_token,
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verbose,
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framework_model_dir,
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max_batch_size=16,
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decode_chunk_size=14,
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):
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super(VAEDecTemporalModel, self).__init__(
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version,
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pipeline,
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device=device,
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hf_token=hf_token,
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verbose=verbose,
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framework_model_dir=framework_model_dir,
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max_batch_size=max_batch_size,
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)
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self.subfolder = "vae"
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self.decode_chunk_size = decode_chunk_size
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def get_model(self, torch_inference=""):
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vae_decoder_model_path = load.get_checkpoint_dir(
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self.framework_model_dir, self.version, self.pipeline, self.subfolder
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)
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if not os.path.exists(vae_decoder_model_path):
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model = AutoencoderKLTemporalDecoder.from_pretrained(
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self.path, subfolder=self.subfolder, use_safetensors=self.hf_safetensor, token=self.hf_token
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).to(self.device)
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model.save_pretrained(vae_decoder_model_path)
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else:
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print(f"[I] Load AutoencoderKLTemporalDecoder model from: {vae_decoder_model_path}")
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model = AutoencoderKLTemporalDecoder.from_pretrained(vae_decoder_model_path).to(self.device)
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model.forward = model.decode
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model = optimizer.optimize_checkpoint(model, torch_inference)
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return model
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def get_input_names(self):
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return ["latent", "num_frames_in"]
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def get_output_names(self):
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return ["frames"]
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def get_dynamic_axes(self):
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return {"latent": {0: "num_frames_in", 2: "H", 3: "W"}, "frames": {0: "num_frames_in", 2: "8H", 3: "8W"}}
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def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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assert batch_size == 1
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_, _, _, _, _, _, min_latent_height, max_latent_height, min_latent_width, max_latent_width = (
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self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
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)
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return {
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"latent": [
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(1, 4, min_latent_height, min_latent_width),
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(self.decode_chunk_size, 4, latent_height, latent_width),
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(self.decode_chunk_size, 4, max_latent_height, max_latent_width),
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],
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"num_frames_in": [(1,), (1,), (1,)],
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}
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def get_shape_dict(self, batch_size, image_height, image_width):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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assert batch_size == 1
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return {
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"latent": (self.decode_chunk_size, 4, latent_height, latent_width),
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#'num_frames_in': (1,),
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"frames": (self.decode_chunk_size, 3, image_height, image_width),
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}
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def get_sample_input(self, batch_size, image_height, image_width):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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assert batch_size == 1
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return (
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torch.randn(
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self.decode_chunk_size, 4, latent_height, latent_width, dtype=torch.float32, device=self.device
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),
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self.decode_chunk_size,
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)
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class TorchVAEEncoder(torch.nn.Module):
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def __init__(
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self,
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version,
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pipeline,
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hf_token,
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device,
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path,
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framework_model_dir,
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subfolder,
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fp16=False,
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bf16=False,
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hf_safetensor=False,
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):
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super().__init__()
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model_opts = {"torch_dtype": torch.float16} if fp16 else {"torch_dtype": torch.bfloat16} if bf16 else {}
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vae_encoder_model_dir = load.get_checkpoint_dir(framework_model_dir, version, pipeline, subfolder)
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if not load.is_model_cached(vae_encoder_model_dir, model_opts, hf_safetensor):
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self.vae_encoder = AutoencoderKL.from_pretrained(
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path, subfolder="vae", use_safetensors=hf_safetensor, token=hf_token, **model_opts
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).to(device)
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self.vae_encoder.save_pretrained(vae_encoder_model_dir, **model_opts)
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else:
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print(f"[I] Load AutoencoderKL (encoder) model from: {vae_encoder_model_dir}")
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self.vae_encoder = AutoencoderKL.from_pretrained(vae_encoder_model_dir, **model_opts).to(device)
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def forward(self, x):
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return self.vae_encoder.encode(x).latent_dist.sample()
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class VAEEncoderModel(base_model.BaseModel):
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def __init__(
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self,
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version,
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pipeline,
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device,
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hf_token,
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verbose,
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framework_model_dir,
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fp16=False,
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tf32=False,
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bf16=False,
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max_batch_size=16,
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do_classifier_free_guidance=False,
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kontext_resolution=None,
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):
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super(VAEEncoderModel, self).__init__(
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version,
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pipeline,
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device=device,
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hf_token=hf_token,
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verbose=verbose,
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framework_model_dir=framework_model_dir,
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fp16=fp16,
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tf32=tf32,
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bf16=bf16,
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max_batch_size=max_batch_size,
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)
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self.kontext_resolution = kontext_resolution
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self.subfolder = "vae"
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self.vae_encoder_model_dir = load.get_checkpoint_dir(
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framework_model_dir, version, self.pipeline, self.subfolder
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)
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if not os.path.exists(self.vae_encoder_model_dir):
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self.config = AutoencoderKL.load_config(self.path, subfolder=self.subfolder, token=self.hf_token)
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else:
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print(f"[I] Load AutoencoderKL (encoder) config from: {self.vae_encoder_model_dir}")
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self.config = AutoencoderKL.load_config(self.vae_encoder_model_dir)
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self.xB = 2 if do_classifier_free_guidance else 1 # batch multiplier
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def get_model(self, torch_inference=""):
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vae_encoder = TorchVAEEncoder(
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self.version,
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self.pipeline,
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self.hf_token,
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self.device,
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self.path,
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self.framework_model_dir,
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self.subfolder,
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self.fp16,
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self.bf16,
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hf_safetensor=self.hf_safetensor,
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)
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return vae_encoder
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def get_input_names(self):
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return ["images"]
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def get_output_names(self):
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return ["latent"]
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def get_dynamic_axes(self):
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xB = "2B" if self.xB == 2 else "B"
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return {"images": {0: xB, 2: "8H", 3: "8W"}, "latent": {0: xB, 2: "H", 3: "W"}}
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def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
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assert batch_size >= self.min_batch and batch_size <= self.max_batch
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min_batch = batch_size if static_batch else self.min_batch
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max_batch = batch_size if static_batch else self.max_batch
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self.check_dims(batch_size, image_height, image_width)
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min_batch, max_batch, min_image_height, max_image_height, min_image_width, max_image_width, _, _, _, _ = (
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self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
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)
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if self.version == "flux.1-kontext-dev":
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FluxKontextUtil = _get_flux_kontext_util()
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min_latent_dim, max_latent_dim = FluxKontextUtil.get_min_max_kontext_dimensions()
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return {
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"images": [
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(self.xB * min_batch, 3, min_latent_dim[1], min_latent_dim[0]),
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(self.xB * batch_size, 3, self.kontext_resolution[1], self.kontext_resolution[0]),
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(self.xB * max_batch, 3, max_latent_dim[1], max_latent_dim[0]),
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],
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}
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return {
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"images": [
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(self.xB * min_batch, 3, min_image_height, min_image_width),
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(self.xB * batch_size, 3, image_height, image_width),
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(self.xB * max_batch, 3, max_image_height, max_image_width),
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],
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}
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def get_shape_dict(self, batch_size, image_height, image_width):
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# Determine dimensions based on version
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if self.version == "flux.1-kontext-dev":
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img_h, img_w = self.kontext_resolution[1], self.kontext_resolution[0]
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else:
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img_h, img_w = image_height, image_width
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latent_height, latent_width = self.check_dims(batch_size, img_h, img_w)
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return {
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"images": (self.xB * batch_size, 3, img_h, img_w),
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"latent": (self.xB * batch_size, self.config["latent_channels"], latent_height, latent_width),
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}
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def get_sample_input(self, batch_size, image_height, image_width, static_shape):
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self.check_dims(batch_size, image_height, image_width)
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dtype = torch.float16 if self.fp16 else torch.bfloat16 if self.bf16 else torch.float32
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return torch.randn(self.xB * batch_size, 3, image_height, image_width, dtype=dtype, device=self.device)
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class SD3_VAEEncoderModel(base_model.BaseModel):
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def __init__(
|
|
self,
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|
version,
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|
pipeline,
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|
device,
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|
hf_token,
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|
verbose,
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|
framework_model_dir,
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|
max_batch_size,
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|
fp16=False,
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|
):
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|
super(SD3_VAEEncoderModel, self).__init__(
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|
version,
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|
pipeline,
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|
device=device,
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|
hf_token=hf_token,
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verbose=verbose,
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|
framework_model_dir=framework_model_dir,
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fp16=fp16,
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max_batch_size=max_batch_size,
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|
)
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self.subfolder = "sd3"
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|
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def get_model(self, torch_inference=""):
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dtype = torch.float16 if self.fp16 else torch.float32
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sd3_model_dir = load.get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
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sd3_filename = "sd3_medium.safetensors"
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sd3_model_path = f"{sd3_model_dir}/{sd3_filename}"
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if not os.path.exists(sd3_model_path):
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hf_hub_download(repo_id=self.path, filename=sd3_filename, local_dir=sd3_model_dir)
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with safe_open(sd3_model_path, framework="pt", device=self.device) as f:
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model = SDVAE(device=self.device, dtype=dtype).eval().cuda()
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prefix = ""
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if any(k.startswith("first_stage_model.") for k in f.keys()):
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prefix = "first_stage_model."
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load_into(f, model, prefix, self.device, dtype)
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model.forward = model.encode
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model = optimizer.optimize_checkpoint(model, torch_inference)
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return model
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|
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|
def get_input_names(self):
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return ["images"]
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|
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|
def get_output_names(self):
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|
return ["latent"]
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|
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|
def get_dynamic_axes(self):
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return {"images": {0: "B", 2: "8H", 3: "8W"}, "latent": {0: "B", 2: "H", 3: "W"}}
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|
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def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
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|
min_batch, max_batch, _, _, _, _, _, _, _, _ = self.get_minmax_dims(
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batch_size, image_height, image_width, static_batch, static_shape
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|
)
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return {
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"images": [
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|
(min_batch, 3, image_height, image_width),
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|
(batch_size, 3, image_height, image_width),
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|
(max_batch, 3, image_height, image_width),
|
|
]
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|
}
|
|
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|
def get_shape_dict(self, batch_size, image_height, image_width):
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|
latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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return {
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|
"images": (batch_size, 3, image_height, image_width),
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|
"latent": (batch_size, 16, latent_height, latent_width),
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|
}
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|
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|
def get_sample_input(self, batch_size, image_height, image_width, static_shape):
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|
dtype = torch.float16 if self.fp16 else torch.float32
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return torch.randn(batch_size, 3, image_height, image_width, dtype=dtype, device=self.device)
|
|
|
|
|
|
class AutoencoderKLWanModel(base_model.BaseModel):
|
|
|
|
def __init__(
|
|
self,
|
|
version,
|
|
pipeline,
|
|
device,
|
|
hf_token,
|
|
verbose,
|
|
framework_model_dir,
|
|
fp16=False,
|
|
tf32=False,
|
|
bf16=False,
|
|
max_batch_size=16,
|
|
):
|
|
super(AutoencoderKLWanModel, self).__init__(
|
|
version,
|
|
pipeline,
|
|
device=device,
|
|
hf_token=hf_token,
|
|
verbose=verbose,
|
|
framework_model_dir=framework_model_dir,
|
|
fp16=fp16,
|
|
tf32=tf32,
|
|
bf16=bf16,
|
|
max_batch_size=max_batch_size,
|
|
)
|
|
self.is_wan_pipeline = version.startswith("wan")
|
|
self.subfolder = "vae"
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|
self.vae_decoder_model_dir = load.get_checkpoint_dir(
|
|
self.framework_model_dir, self.version, self.pipeline, self.subfolder
|
|
)
|
|
if not os.path.exists(self.vae_decoder_model_dir):
|
|
self.config = AutoencoderKLWan.load_config(self.path, subfolder=self.subfolder, token=self.hf_token)
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|
else:
|
|
print(f"[I] Load AutoencoderKLWan (decoder) config from: {self.vae_decoder_model_dir}")
|
|
self.config = AutoencoderKLWan.load_config(self.vae_decoder_model_dir)
|
|
|
|
def get_model(self, torch_inference=""):
|
|
if self.is_wan_pipeline:
|
|
print(f"[I] Using float32 precision for Wan 2.2 VAE decoder")
|
|
model_opts = {"torch_dtype": torch.float32}
|
|
else:
|
|
model_opts = (
|
|
{"torch_dtype": torch.float16} if self.fp16 else {"torch_dtype": torch.bfloat16} if self.bf16 else {}
|
|
)
|
|
if not load.is_model_cached(self.vae_decoder_model_dir, model_opts, self.hf_safetensor):
|
|
model = AutoencoderKLWan.from_pretrained(
|
|
self.path,
|
|
subfolder=self.subfolder,
|
|
use_safetensors=self.hf_safetensor,
|
|
token=self.hf_token,
|
|
**model_opts,
|
|
).to(self.device)
|
|
model.save_pretrained(self.vae_decoder_model_dir, **model_opts)
|
|
else:
|
|
print(f"[I] Load AutoencoderKLWan (decoder) model from: {self.vae_decoder_model_dir}")
|
|
model = AutoencoderKLWan.from_pretrained(self.vae_decoder_model_dir, **model_opts).to(self.device)
|
|
model.forward = model.decode
|
|
model = optimizer.optimize_checkpoint(model, torch_inference)
|
|
return model
|
|
|
|
def get_input_names(self):
|
|
return ["latent"]
|
|
|
|
def get_output_names(self):
|
|
return ["images"]
|
|
|
|
def get_dynamic_axes(self):
|
|
return {"latent": {0: "B", 3: "H", 4: "W"}, "images": {0: "B", 3: "8H", 4: "8W"}}
|
|
|
|
def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
|
|
latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
|
|
min_batch, max_batch, _, _, _, _, min_latent_height, max_latent_height, min_latent_width, max_latent_width = (
|
|
self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
|
|
)
|
|
return {
|
|
"latent": [
|
|
(min_batch, self.config["z_dim"], 1, min_latent_height, min_latent_width),
|
|
(batch_size, self.config["z_dim"], 1, latent_height, latent_width),
|
|
(max_batch, self.config["z_dim"], 1, max_latent_height, max_latent_width),
|
|
]
|
|
}
|
|
|
|
def get_shape_dict(self, batch_size, image_height, image_width):
|
|
latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
|
|
return {
|
|
"latent": (batch_size, self.config["z_dim"], 1, latent_height, latent_width),
|
|
"images": (batch_size, 3, 1, image_height, image_width),
|
|
}
|
|
|
|
def get_sample_input(self, batch_size, image_height, image_width, static_shape):
|
|
latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
|
|
dtype = torch.float16 if self.fp16 else torch.bfloat16 if self.bf16 else torch.float32
|
|
return torch.randn(
|
|
batch_size, self.config["z_dim"], 1, latent_height, latent_width, dtype=dtype, device=self.device
|
|
)
|
|
|
|
class AutoencoderKLWanEncoderModelWrapper(torch.nn.Module):
|
|
def __init__(self, model):
|
|
super().__init__()
|
|
self.model = model
|
|
|
|
def forward(self, x):
|
|
return self.model.encode(x).latent_dist.sample()
|
|
|
|
|
|
class AutoencoderKLWanEncoderModel(base_model.BaseModel):
|
|
def __init__(
|
|
self,
|
|
version,
|
|
pipeline,
|
|
device,
|
|
hf_token,
|
|
verbose,
|
|
framework_model_dir,
|
|
fp16=False,
|
|
tf32=False,
|
|
bf16=False,
|
|
max_batch_size=16,
|
|
):
|
|
super(AutoencoderKLWanEncoderModel, self).__init__(
|
|
version,
|
|
pipeline,
|
|
device=device,
|
|
hf_token=hf_token,
|
|
verbose=verbose,
|
|
framework_model_dir=framework_model_dir,
|
|
fp16=fp16,
|
|
tf32=tf32,
|
|
bf16=bf16,
|
|
max_batch_size=max_batch_size,
|
|
)
|
|
self.subfolder = "vae"
|
|
self.vae_encoder_model_dir = load.get_checkpoint_dir(
|
|
self.framework_model_dir, self.version, self.pipeline, self.subfolder
|
|
)
|
|
if not os.path.exists(self.vae_encoder_model_dir):
|
|
self.config = AutoencoderKLWan.load_config(self.path, subfolder=self.subfolder, token=self.hf_token)
|
|
else:
|
|
print(f"[I] Load AutoencoderKLWan (encoder) config from: {self.vae_encoder_model_dir}")
|
|
self.config = AutoencoderKLWan.load_config(self.vae_encoder_model_dir)
|
|
|
|
def get_model(self, torch_inference=""):
|
|
model_opts = (
|
|
{"torch_dtype": torch.float16} if self.fp16 else {"torch_dtype": torch.bfloat16} if self.bf16 else {}
|
|
)
|
|
if not load.is_model_cached(self.vae_encoder_model_dir, model_opts, self.hf_safetensor):
|
|
model = AutoencoderKLWan.from_pretrained(
|
|
self.path,
|
|
subfolder=self.subfolder,
|
|
use_safetensors=self.hf_safetensor,
|
|
token=self.hf_token,
|
|
**model_opts,
|
|
).to(self.device)
|
|
model.save_pretrained(self.vae_encoder_model_dir, **model_opts)
|
|
else:
|
|
print(f"[I] Load AutoencoderKLWan (encoder) model from: {self.vae_encoder_model_dir}")
|
|
model = AutoencoderKLWan.from_pretrained(self.vae_encoder_model_dir, **model_opts).to(self.device)
|
|
model = AutoencoderKLWanEncoderModelWrapper(model)
|
|
model = optimizer.optimize_checkpoint(model, torch_inference)
|
|
return model
|