models.py

#
# SPDX-FileCopyrightText: Copyright (c) 1993-2024 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.
#

from diffusers import DiffusionPipeline
from diffusers.loaders import LoraLoaderMixin
from diffusers.pipelines.wuerstchen import PaellaVQModel
import json
import numpy as np
import onnx
from onnx import numpy_helper, shape_inference
import onnx_graphsurgeon as gs
from safetensors import safe_open
import os
from polygraphy.backend.onnx.loader import fold_constants
import re
import tempfile
import torch
import torch.nn.functional as F
from transformers import (
    AutoConfig,
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionModelWithProjection,
    T5EncoderModel,
    T5TokenizerFast
)
from huggingface_hub import hf_hub_download
from utilities import merge_loras, import_from_diffusers
from utils_sd3.sd3_impls import BaseModel as BaseModelSD3
from utils_sd3.sd3_impls import SDVAE
from utils_sd3.other_impls import load_into, SDClipModel, SDXLClipG, T5XXLModel
from utils_modelopt import (
    convert_zp_fp8,
    cast_resize_io,
    convert_fp16_io,
    cast_fp8_mha_io,
)
from onnxmltools.utils.float16_converter import convert_float_to_float16

# List of models to import from diffusers.models
models_to_import = [
    'AutoencoderKL',
    'AutoencoderKLTemporalDecoder',
    'ControlNetModel',
    'UNet2DConditionModel',
    'UNetSpatioTemporalConditionModel',
    'StableCascadeUNet',
    'FluxTransformer2DModel'
]
for model in models_to_import:
    globals()[model] = import_from_diffusers(model, 'diffusers.models')

class Optimizer():
    def __init__(
        self,
        onnx_graph,
        verbose=False
    ):
        self.graph = gs.import_onnx(onnx_graph)
        self.verbose = verbose

    def info(self, prefix):
        if self.verbose:
            print(f"{prefix} .. {len(self.graph.nodes)} nodes, {len(self.graph.tensors().keys())} tensors, {len(self.graph.inputs)} inputs, {len(self.graph.outputs)} outputs")

    def cleanup(self, return_onnx=False):
        self.graph.cleanup().toposort()
        return gs.export_onnx(self.graph) if return_onnx else self.graph

    def select_outputs(self, keep, names=None):
        self.graph.outputs = [self.graph.outputs[o] for o in keep]
        if names:
            for i, name in enumerate(names):
                self.graph.outputs[i].name = name

    def fold_constants(self, return_onnx=False):
        onnx_graph = fold_constants(gs.export_onnx(self.graph), allow_onnxruntime_shape_inference=True)
        self.graph = gs.import_onnx(onnx_graph)
        if return_onnx:
            return onnx_graph

    def infer_shapes(self, return_onnx=False):
        onnx_graph = gs.export_onnx(self.graph)
        if onnx_graph.ByteSize() > 2147483648:
            temp_dir = tempfile.TemporaryDirectory().name
            os.makedirs(temp_dir, exist_ok=True)
            onnx_orig_path = os.path.join(temp_dir, 'model.onnx')
            onnx_inferred_path = os.path.join(temp_dir, 'inferred.onnx')
            onnx.save_model(onnx_graph,
                onnx_orig_path,
                save_as_external_data=True,
                all_tensors_to_one_file=True,
                convert_attribute=False)
            onnx.shape_inference.infer_shapes_path(onnx_orig_path, onnx_inferred_path)
            onnx_graph = onnx.load(onnx_inferred_path)
        else:
            onnx_graph = shape_inference.infer_shapes(onnx_graph)

        self.graph = gs.import_onnx(onnx_graph)
        if return_onnx:
            return onnx_graph

    def clip_add_hidden_states(self, hidden_layer_offset, return_onnx=False):
        hidden_layers = -1
        onnx_graph = gs.export_onnx(self.graph)
        for i in range(len(onnx_graph.graph.node)):
            for j in range(len(onnx_graph.graph.node[i].output)):
                name = onnx_graph.graph.node[i].output[j]
                if "layers" in name:
                    hidden_layers = max(int(name.split(".")[1].split("/")[0]), hidden_layers)
        for i in range(len(onnx_graph.graph.node)):
            for j in range(len(onnx_graph.graph.node[i].output)):
                if onnx_graph.graph.node[i].output[j] == "/text_model/encoder/layers.{}/Add_1_output_0".format(hidden_layers+hidden_layer_offset):
                    onnx_graph.graph.node[i].output[j] = "hidden_states"
            for j in range(len(onnx_graph.graph.node[i].input)):
                if onnx_graph.graph.node[i].input[j] == "/text_model/encoder/layers.{}/Add_1_output_0".format(hidden_layers+hidden_layer_offset):
                    onnx_graph.graph.node[i].input[j] = "hidden_states"
        if return_onnx:
            return onnx_graph

    def fuse_mha_qkv_int8_sq(self):
        tensors = self.graph.tensors()
        keys = tensors.keys()

        # mha  : fuse QKV QDQ nodes
        # mhca : fuse KV QDQ nodes
        q_pat = (
            "/down_blocks.\\d+/attentions.\\d+/transformer_blocks"
            ".\\d+/attn\\d+/to_q/input_quantizer/DequantizeLinear_output_0"
        )
        k_pat = (
            "/down_blocks.\\d+/attentions.\\d+/transformer_blocks"
            ".\\d+/attn\\d+/to_k/input_quantizer/DequantizeLinear_output_0"
        )
        v_pat = (
            "/down_blocks.\\d+/attentions.\\d+/transformer_blocks"
            ".\\d+/attn\\d+/to_v/input_quantizer/DequantizeLinear_output_0"
        )

        qs = list(sorted(map(
            lambda x: x.group(0),  # type: ignore
            filter(lambda x: x is not None, [re.match(q_pat, key) for key in keys]),
            )))
        ks = list(sorted(map(
            lambda x: x.group(0),  # type: ignore
            filter(lambda x: x is not None, [re.match(k_pat, key) for key in keys]),
            )))
        vs = list(sorted(map(
            lambda x: x.group(0),  # type: ignore
            filter(lambda x: x is not None, [re.match(v_pat, key) for key in keys]),
            )))

        removed = 0
        assert len(qs) == len(ks) == len(vs), "Failed to collect tensors"
        for q, k, v in zip(qs, ks, vs):
            is_mha = all(["attn1" in tensor for tensor in [q, k, v]])
            is_mhca = all(["attn2" in tensor for tensor in [q, k, v]])
            assert (is_mha or is_mhca) and (not (is_mha and is_mhca))

            if is_mha:
                tensors[k].outputs[0].inputs[0] = tensors[q]
                tensors[v].outputs[0].inputs[0] = tensors[q]
                del tensors[k]
                del tensors[v]
                removed += 2
            else:  # is_mhca
                tensors[k].outputs[0].inputs[0] = tensors[v]
                del tensors[k]
                removed += 1
        print(f"Removed {removed} QDQ nodes")
        return removed # expected 72 for L2.5

    def modify_fp8_graph(self):
        onnx_graph = gs.export_onnx(self.graph)
        # Convert INT8 Zero to FP8.
        onnx_graph = convert_zp_fp8(onnx_graph)
        # Convert weights and activations to FP16 and insert Cast nodes in FP8 MHA.
        onnx_graph = convert_float_to_float16(onnx_graph, keep_io_types=True, disable_shape_infer=True)
        self.graph = gs.import_onnx(onnx_graph)
        # Add cast nodes to Resize I/O.
        cast_resize_io(self.graph)
        # Convert model inputs and outputs to fp16 I/O.
        convert_fp16_io(self.graph)
        # Add cast nodes to MHA's BMM1 and BMM2's I/O.
        cast_fp8_mha_io(self.graph)

def get_path(version, pipeline, controlnets=None):
    if controlnets is not None:
        return ["lllyasviel/sd-controlnet-" + modality for modality in controlnets]

    if version in ("1.4", "1.5") and pipeline.is_inpaint():
        return "benjamin-paine/stable-diffusion-v1-5-inpainting"
    elif version == "1.4":
        return "CompVis/stable-diffusion-v1-4"
    elif version == "1.5":
        return "KiwiXR/stable-diffusion-v1-5"
    elif version == 'dreamshaper-7':
        return 'Lykon/dreamshaper-7'
    elif version in ("2.0-base", "2.0") and pipeline.is_inpaint():
        return "stabilityai/stable-diffusion-2-inpainting"
    elif version == "2.0-base":
        return "stabilityai/stable-diffusion-2-base"
    elif version == "2.0":
        return "stabilityai/stable-diffusion-2"
    elif version == "2.1-base":
        return "stabilityai/stable-diffusion-2-1-base"
    elif version == "2.1":
        return "stabilityai/stable-diffusion-2-1"
    elif version == 'xl-1.0' and pipeline.is_sd_xl_base():
        return "stabilityai/stable-diffusion-xl-base-1.0"
    elif version == 'xl-1.0' and pipeline.is_sd_xl_refiner():
        return "stabilityai/stable-diffusion-xl-refiner-1.0"
    # TODO SDXL turbo with refiner
    elif version == 'xl-turbo' and pipeline.is_sd_xl_base():
        return "stabilityai/sdxl-turbo"
    elif version == 'sd3':
        return "stabilityai/stable-diffusion-3-medium"
    elif version == 'svd-xt-1.1' and pipeline.is_img2vid():
        return "stabilityai/stable-video-diffusion-img2vid-xt-1-1"
    elif version == 'cascade':
        if pipeline.is_cascade_decoder():
            return "stabilityai/stable-cascade"
        else:
            return "stabilityai/stable-cascade-prior"
    elif version == 'flux.1-dev':
        return "black-forest-labs/FLUX.1-dev"
    else:
        raise ValueError(f"Unsupported version {version} + pipeline {pipeline.name}")

def get_clip_embedding_dim(version, pipeline):
    if version in ("1.4", "1.5", "dreamshaper-7", "flux.1-dev"):
        return 768
    elif version in ("2.0", "2.0-base", "2.1", "2.1-base"):
        return 1024
    elif version in ("xl-1.0", "xl-turbo") and pipeline.is_sd_xl_base():
        return 768
    elif version in ("sd3"):
        return 4096
    else:
        raise ValueError(f"Invalid version {version} + pipeline {pipeline}")

def get_clipwithproj_embedding_dim(version, pipeline):
    if version in ("xl-1.0", "xl-turbo", "cascade"):
        return 1280
    else:
        raise ValueError(f"Invalid version {version} + pipeline {pipeline}")

def get_unet_embedding_dim(version, pipeline):
    if version in ("1.4", "1.5", "dreamshaper-7"):
        return 768
    elif version in ("2.0", "2.0-base", "2.1", "2.1-base"):
        return 1024
    elif version in ("xl-1.0", "xl-turbo") and pipeline.is_sd_xl_base():
        return 2048
    elif version in ("cascade"):
        return 1280
    elif version in ("xl-1.0", "xl-turbo") and pipeline.is_sd_xl_refiner():
        return 1280
    elif pipeline.is_img2vid():
        return 1024
    else:
        raise ValueError(f"Invalid version {version} + pipeline {pipeline}")

# FIXME serialization not supported for torch.compile
def get_checkpoint_dir(framework_model_dir, version, pipeline, subfolder):
    return os.path.join(framework_model_dir, version, pipeline, subfolder)

torch_inference_modes = ['default', 'reduce-overhead', 'max-autotune']
# FIXME update callsites after serialization support for torch.compile is added
def optimize_checkpoint(model, torch_inference):
    if not torch_inference or torch_inference == 'eager':
        return model
    assert torch_inference in torch_inference_modes
    return torch.compile(model, mode=torch_inference, dynamic=False, fullgraph=False)

class LoraLoader(LoraLoaderMixin):
    def __init__(self,
        paths,
    ):
        self.paths = paths
        self.state_dict = dict()
        self.network_alphas = dict()

        for path in paths:
            state_dict, network_alphas = self.lora_state_dict(path)
            is_correct_format = all("lora" in key for key in state_dict.keys())
            if not is_correct_format:
                raise ValueError("Invalid LoRA checkpoint.")

            self.state_dict[path] = state_dict
            self.network_alphas[path] = network_alphas

    def get_dicts(self,
        prefix='unet',
        convert_to_diffusers=False,
    ):
        state_dict = dict()
        network_alphas = dict()

        for path in self.paths:
            keys = list(self.state_dict[path].keys())
            if all(key.startswith(('unet', 'text_encoder')) for key in keys):
                keys = [k for k in keys if k.startswith(prefix)]
                if keys:
                    print(f"Processing {prefix} LoRA: {path}")
                state_dict[path] = {k.replace(f"{prefix}.", ""): v for k, v in self.state_dict[path].items() if k in keys}

                network_alphas[path] = None
                if path in self.network_alphas and self.network_alphas[path] is not None:
                    alpha_keys = [k for k in self.network_alphas[path].keys() if k.startswith(prefix)]
                    network_alphas[path] = {
                        k.replace(f"{prefix}.", ""): v for k, v in self.network_alphas[path].items() if k in alpha_keys
                    }

            else:
                # Otherwise, we're dealing with the old format.
                warn_message = "You have saved the LoRA weights using the old format. To convert LoRA weights to the new format, first load them in a dictionary and then create a new dictionary as follows: `new_state_dict = {f'unet.{module_name}': params for module_name, params in old_state_dict.items()}`."
                print(warn_message)

        return state_dict, network_alphas


class BaseModel():
    def __init__(self,
        version='1.5',
        pipeline=None,
        device='cuda',
        hf_token='',
        verbose=True,
        framework_model_dir='pytorch_model',
        fp16=False,
        tf32=False,
        bf16=False,
        int8=False,
        fp8=False,
        max_batch_size=16,
        text_maxlen=77,
        embedding_dim=768,
        compression_factor=8
    ):

        self.name = self.__class__.__name__
        self.pipeline = pipeline.name
        self.version = version
        self.path = get_path(version, pipeline)
        self.device = device
        self.hf_token = hf_token
        self.hf_safetensor = not (pipeline.is_inpaint() and version in ("1.4", "1.5"))
        self.verbose = verbose
        self.framework_model_dir = framework_model_dir

        self.fp16 = fp16
        self.tf32 = tf32
        self.bf16 = bf16
        self.int8 = int8
        self.fp8 = fp8

        self.compression_factor = compression_factor
        self.min_batch = 1
        self.max_batch = max_batch_size
        self.min_image_shape = 256   # min image resolution: 256x256
        self.max_image_shape = 1344  # max image resolution: 1344x1344
        self.min_latent_shape = self.min_image_shape // self.compression_factor
        self.max_latent_shape = self.max_image_shape // self.compression_factor

        self.text_maxlen = text_maxlen
        self.embedding_dim = embedding_dim
        self.extra_output_names = []

        self.lora_dict = None
        self.do_constant_folding = True

    def get_pipeline(self):
        model_opts = {'variant': 'fp16', 'torch_dtype': torch.float16} if self.fp16 else {}
        model_opts = {'variant': 'bf16', 'torch_dtype': torch.bfloat16} if self.bf16 else model_opts
        return DiffusionPipeline.from_pretrained(
            self.path,
            use_safetensors=self.hf_safetensor,
            token=self.hf_token,
            **model_opts,
        ).to(self.device)

    def get_model_path(self, model_dir, model_opts, model_name="diffusion_pytorch_model"):
        variant = "." + model_opts.get("variant") if "variant" in model_opts else ""
        suffix = ".safetensors" if self.hf_safetensor else ".bin"
        model_file = model_name + variant + suffix
        return os.path.join(model_dir, model_file)

    def get_model(self, torch_inference=''):
        pass

    def get_input_names(self):
        pass

    def get_output_names(self):
        pass

    def get_dynamic_axes(self):
        return None

    def get_sample_input(self, batch_size, image_height, image_width, static_shape):
        pass

    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
        return None

    def get_shape_dict(self, batch_size, image_height, image_width):
        return None

    # Helper utility for ONNX export
    def export_onnx(
        self,
        onnx_path,
        onnx_opt_path,
        onnx_opset,
        opt_image_height,
        opt_image_width,
        custom_model=None,
        enable_lora_merge=False,
        static_shape=False,
    ):
        onnx_opt_graph = None
        # Export optimized ONNX model (if missing)
        if not os.path.exists(onnx_opt_path):
            if not os.path.exists(onnx_path):
                print(f"[I] Exporting ONNX model: {onnx_path}")
                def export_onnx(model):
                    if enable_lora_merge:
                        model = merge_loras(model, self.lora_dict, self.lora_alphas, self.lora_scales)
                    inputs = self.get_sample_input(1, opt_image_height, opt_image_width, static_shape)
                    torch.onnx.export(model,
                        inputs,
                        onnx_path,
                        export_params=True,
                        opset_version=onnx_opset,
                        do_constant_folding=self.do_constant_folding,
                        input_names=self.get_input_names(),
                        output_names=self.get_output_names(),
                        dynamic_axes=self.get_dynamic_axes(),
                    )
                if custom_model:
                    with torch.inference_mode():
                        export_onnx(custom_model)
                else:
                    with torch.inference_mode(), torch.autocast("cuda"):
                        export_onnx(self.get_model())
            else:
                print(f"[I] Found cached ONNX model: {onnx_path}")

            print(f"[I] Optimizing ONNX model: {onnx_opt_path}")
            onnx_opt_graph = self.optimize(onnx.load(onnx_path))
            if onnx_opt_graph.ByteSize() > 2147483648:
                onnx.save_model(
                    onnx_opt_graph,
                    onnx_opt_path,
                    save_as_external_data=True,
                    all_tensors_to_one_file=True,
                    convert_attribute=False)
            else:
                onnx.save(onnx_opt_graph, onnx_opt_path)
        else:
            print(f"[I] Found cached optimized ONNX model: {onnx_opt_path} ")

    # Helper utility for weights map
    def export_weights_map(self, onnx_opt_path, weights_map_path):
        if not os.path.exists(weights_map_path):
            onnx_opt_dir = os.path.dirname(onnx_opt_path)
            onnx_opt_model = onnx.load(onnx_opt_path)
            state_dict = self.get_model().state_dict()
            # Create initializer data hashes
            initializer_hash_mapping = {}
            for initializer in onnx_opt_model.graph.initializer:
                initializer_data = numpy_helper.to_array(initializer, base_dir=onnx_opt_dir).astype(np.float16)
                initializer_hash = hash(initializer_data.data.tobytes())
                initializer_hash_mapping[initializer.name] = (initializer_hash, initializer_data.shape)

            weights_name_mapping = {}
            weights_shape_mapping = {}
            # set to keep track of initializers already added to the name_mapping dict
            initializers_mapped = set()
            for wt_name, wt in state_dict.items():
                # get weight hash
                wt = wt.cpu().detach().numpy().astype(np.float16)
                wt_hash = hash(wt.data.tobytes())
                wt_t_hash = hash(np.transpose(wt).data.tobytes())

                for initializer_name, (initializer_hash, initializer_shape) in initializer_hash_mapping.items():
                    # Due to constant folding, some weights are transposed during export
                    # To account for the transpose op, we compare the initializer hash to the
                    # hash for the weight and its transpose
                    if wt_hash == initializer_hash or wt_t_hash == initializer_hash:
                        # The assert below ensures there is a 1:1 mapping between
                        # PyTorch and ONNX weight names. It can be removed in cases where 1:many
                        # mapping is found and name_mapping[wt_name] = list()
                        assert initializer_name not in initializers_mapped
                        weights_name_mapping[wt_name] = initializer_name
                        initializers_mapped.add(initializer_name)
                        is_transpose = False if wt_hash == initializer_hash else True
                        weights_shape_mapping[wt_name] = (initializer_shape, is_transpose)

                # Sanity check: Were any weights not matched
                if wt_name not in weights_name_mapping:
                    print(f'[I] PyTorch weight {wt_name} not matched with any ONNX initializer')
            print(f'[I] {len(weights_name_mapping.keys())} PyTorch weights were matched with ONNX initializers')
            assert weights_name_mapping.keys() == weights_shape_mapping.keys()
            with open(weights_map_path, 'w') as fp:
                json.dump([weights_name_mapping, weights_shape_mapping], fp)
        else:
            print(f"[I] Found cached weights map: {weights_map_path} ")

    def optimize(self, onnx_graph, return_onnx=True, **kwargs):
        opt = Optimizer(onnx_graph, verbose=self.verbose)
        opt.info(self.name + ': original')
        opt.cleanup()
        opt.info(self.name + ': cleanup')
        if kwargs.get('modify_fp8_graph', False):
            opt.modify_fp8_graph()
            opt.info(self.name + ': modify fp8 graph')
        else:
            opt.fold_constants()
            opt.info(self.name + ': fold constants')
            opt.infer_shapes()
            opt.info(self.name + ': shape inference')
            if kwargs.get('fuse_mha_qkv_int8', False):
                opt.fuse_mha_qkv_int8_sq()
                opt.info(self.name + ': fuse QKV nodes')
        onnx_opt_graph = opt.cleanup(return_onnx=return_onnx)
        opt.info(self.name + ': finished')
        return onnx_opt_graph

    def check_dims(self, batch_size, image_height, image_width):
        assert batch_size >= self.min_batch and batch_size <= self.max_batch
        latent_height = image_height // self.compression_factor
        latent_width = image_width // self.compression_factor
        assert latent_height >= self.min_latent_shape and latent_height <= self.max_latent_shape
        assert latent_width >= self.min_latent_shape and latent_width <= self.max_latent_shape
        return (latent_height, latent_width)

    def get_minmax_dims(self, batch_size, image_height, image_width, static_batch, static_shape):
        min_batch = batch_size if static_batch else self.min_batch
        max_batch = batch_size if static_batch else self.max_batch
        latent_height = image_height // self.compression_factor
        latent_width = image_width // self.compression_factor
        min_image_height = image_height if static_shape else self.min_image_shape
        max_image_height = image_height if static_shape else self.max_image_shape
        min_image_width = image_width if static_shape else self.min_image_shape
        max_image_width = image_width if static_shape else self.max_image_shape
        min_latent_height = latent_height if static_shape else self.min_latent_shape
        max_latent_height = latent_height if static_shape else self.max_latent_shape
        min_latent_width = latent_width if static_shape else self.min_latent_shape
        max_latent_width = latent_width if static_shape else self.max_latent_shape
        return (min_batch, max_batch, min_image_height, max_image_height, min_image_width, max_image_width, min_latent_height, max_latent_height, min_latent_width, max_latent_width)


class CLIPModel(BaseModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        max_batch_size,
        embedding_dim,
        fp16=False,
        tf32=False,
        bf16=False,
        output_hidden_states=False,
        keep_pooled_output=False,
        subfolder="text_encoder",
        lora_dict=None,
        lora_alphas=None,
    ):
        super(CLIPModel, 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, embedding_dim=embedding_dim)
        self.subfolder = subfolder
        self.hidden_layer_offset = 0 if pipeline.is_cascade() else -1
        self.keep_pooled_output = keep_pooled_output

        # Output the final hidden state
        if output_hidden_states:
            self.extra_output_names = ['hidden_states']

    def get_model(self, torch_inference=''):
        model_opts = {'torch_dtype': torch.float16} if self.fp16 else {}
        clip_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        if not os.path.exists(clip_model_dir):
            model = CLIPTextModel.from_pretrained(self.path,
                subfolder=self.subfolder,
                use_safetensors=self.hf_safetensor,
                token=self.hf_token,
                **model_opts).to(self.device)
            model.save_pretrained(clip_model_dir, **model_opts)
        else:
            print(f"[I] Load CLIPTextModel model from: {clip_model_dir}")
            model = CLIPTextModel.from_pretrained(clip_model_dir, **model_opts).to(self.device)
        model = optimize_checkpoint(model, torch_inference)
        return model

    def get_input_names(self):
        return ['input_ids']

    def get_output_names(self):
        output_names = ['text_embeddings']
        if self.keep_pooled_output:
            output_names += ['pooled_embeddings']
        return output_names

    def get_dynamic_axes(self):
        dynamic_axes =  {
            'input_ids': {0: 'B'},
            'text_embeddings': {0: 'B'},
        }
        if self.keep_pooled_output:
            dynamic_axes['pooled_embeddings'] = {0: 'B'}
        return dynamic_axes

    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
        self.check_dims(batch_size, image_height, image_width)
        min_batch, max_batch, _, _, _, _, _, _, _, _ = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
        return {
            'input_ids': [(min_batch, self.text_maxlen), (batch_size, self.text_maxlen), (max_batch, self.text_maxlen)]
        }

    def get_shape_dict(self, batch_size, image_height, image_width):
        self.check_dims(batch_size, image_height, image_width)
        output = {
            'input_ids': (batch_size, self.text_maxlen),
            'text_embeddings': (batch_size, self.text_maxlen, self.embedding_dim),
        }
        if self.keep_pooled_output:
            output['pooled_embeddings'] = (batch_size, self.embedding_dim)
        if 'hidden_states' in self.extra_output_names:
            output['hidden_states'] = (batch_size, self.text_maxlen, self.embedding_dim)
        return output

    def get_sample_input(self, batch_size, image_height, image_width, static_shape):
        self.check_dims(batch_size, image_height, image_width)
        return torch.zeros(batch_size, self.text_maxlen, dtype=torch.int32, device=self.device)

    def optimize(self, onnx_graph):
        opt = Optimizer(onnx_graph, verbose=self.verbose)
        opt.info(self.name + ': original')
        keep_outputs = [0, 1] if self.keep_pooled_output else [0]
        opt.select_outputs(keep_outputs)
        opt.cleanup()
        opt.fold_constants()
        opt.info(self.name + ': fold constants')
        opt.infer_shapes()
        opt.info(self.name + ': shape inference')
        opt.select_outputs(keep_outputs, names=self.get_output_names()) # rename network outputs
        opt.info(self.name + ': rename network output(s)')
        opt_onnx_graph = opt.cleanup(return_onnx=True)
        if 'hidden_states' in self.extra_output_names:
            opt_onnx_graph = opt.clip_add_hidden_states(self.hidden_layer_offset, return_onnx=True)
            opt.info(self.name + ': added hidden_states')
        opt.info(self.name + ': finished')
        return opt_onnx_graph


class CLIPWithProjModel(CLIPModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        fp16=False,
        bf16=False,
        max_batch_size=16,
        output_hidden_states=False,
        subfolder="text_encoder_2",
        lora_dict=None,
        lora_alphas=None,
    ):

        super(CLIPWithProjModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, fp16=fp16, bf16=bf16, max_batch_size=max_batch_size, embedding_dim=get_clipwithproj_embedding_dim(version, pipeline), output_hidden_states=output_hidden_states)
        self.subfolder = subfolder

    def get_model(self, torch_inference=''):
        model_opts = {'variant': 'bf16', 'torch_dtype': torch.bfloat16} if self.bf16 else {}
        clip_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        clip_path = self.get_model_path(clip_model_dir, model_opts, model_name='model')
        if not os.path.exists(clip_path):
            model = CLIPTextModelWithProjection.from_pretrained(self.path,
                subfolder=self.subfolder,
                use_safetensors=self.hf_safetensor,
                token=self.hf_token,
                **model_opts).to(self.device)
            model.save_pretrained(clip_model_dir, **model_opts)
        else:
            print(f"[I] Load CLIPTextModelWithProjection model from: {clip_path}")
            model = CLIPTextModelWithProjection.from_pretrained(clip_model_dir, **model_opts).to(self.device)
        model = optimize_checkpoint(model, torch_inference)
        return model

    def get_input_names(self):
        return ['input_ids', 'attention_mask']

    def get_output_names(self):
       return ['text_embeddings']

    def get_dynamic_axes(self):
        return {
            'input_ids': {0: 'B'},
            'attention_mask': {0: 'B'},
            'text_embeddings': {0: 'B'}
        }

    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
        self.check_dims(batch_size, image_height, image_width)
        min_batch, max_batch, _, _, _, _, _, _, _, _ = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
        return {
            'input_ids': [(min_batch, self.text_maxlen), (batch_size, self.text_maxlen), (max_batch, self.text_maxlen)],
            'attention_mask': [(min_batch, self.text_maxlen), (batch_size, self.text_maxlen), (max_batch, self.text_maxlen)]
        }

    def get_shape_dict(self, batch_size, image_height, image_width):
        self.check_dims(batch_size, image_height, image_width)
        output = {
            'input_ids': (batch_size, self.text_maxlen),
            'attention_mask': (batch_size, self.text_maxlen),
            'text_embeddings': (batch_size, self.embedding_dim)
        }
        if 'hidden_states' in self.extra_output_names:
            output["hidden_states"] = (batch_size, self.text_maxlen, self.embedding_dim)
        return output

    def get_sample_input(self, batch_size, image_height, image_width, static_shape):
        self.check_dims(batch_size, image_height, image_width)
        return (
            torch.zeros(batch_size, self.text_maxlen, dtype=torch.int32, device=self.device),
            torch.zeros(batch_size, self.text_maxlen, dtype=torch.int32, device=self.device)
        )

class T5Model(BaseModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        max_batch_size,
        fp16=False,
        tf32=False,
        bf16=False,
        subfolder="text_encoder",
        text_maxlen=512,
    ):
        super(T5Model, 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, text_maxlen=text_maxlen)
        self.subfolder = subfolder
        self.config = AutoConfig.from_pretrained(self.path, subfolder=self.subfolder, token=self.hf_token)

    def get_model(self, torch_inference=''):
        model_opts = {'torch_dtype': torch.float16} if self.fp16 else {}
        t5_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        if not os.path.exists(t5_model_dir):
            model = T5EncoderModel.from_pretrained(self.path,
                subfolder=self.subfolder,
                use_safetensors=self.hf_safetensor,
                token=self.hf_token,
                **model_opts).to(self.device)
            model.save_pretrained(t5_model_dir, **model_opts)
        else:
            print(f"[I] Load T5EncoderModel model from: {t5_model_dir}")
            model = T5EncoderModel.from_pretrained(t5_model_dir, **model_opts).to(self.device)
        model = optimize_checkpoint(model, torch_inference)
        return model

    def get_input_names(self):
        return ['input_ids']

    def get_output_names(self):
       return ['text_embeddings']

    def get_dynamic_axes(self):
        return {
            'input_ids': {0: 'B'},
            'text_embeddings': {0: 'B'}
        }

    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
        self.check_dims(batch_size, image_height, image_width)
        min_batch, max_batch, _, _, _, _, _, _, _, _ = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
        return {
            'input_ids': [(min_batch, self.text_maxlen), (batch_size, self.text_maxlen), (max_batch, self.text_maxlen)]
        }

    def get_shape_dict(self, batch_size, image_height, image_width):
        self.check_dims(batch_size, image_height, image_width)
        output = {
            'input_ids': (batch_size, self.text_maxlen),
            'text_embeddings': (batch_size, self.text_maxlen, self.config.d_model)
        }
        return output

    def get_sample_input(self, batch_size, image_height, image_width, static_shape):
        self.check_dims(batch_size, image_height, image_width)
        return torch.zeros(batch_size, self.text_maxlen, dtype=torch.int32, device=self.device)

class SD3_CLIPGModel(CLIPModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        max_batch_size,
        embedding_dim=None,
        fp16=False,
        pooled_output=False,
    ):
        self.CLIPG_CONFIG = {
            "hidden_act": "gelu",
            "hidden_size": 1280,
            "intermediate_size": 5120,
            "num_attention_heads": 20,
            "num_hidden_layers": 32
        }
        super(SD3_CLIPGModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, fp16=fp16, max_batch_size=max_batch_size, embedding_dim=self.CLIPG_CONFIG["hidden_size"] if embedding_dim is None else embedding_dim)
        self.subfolder = 'text_encoders'
        if pooled_output:
            self.extra_output_names = ['pooled_output']

    def get_model(self, torch_inference=''):
        clip_g_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        clip_g_filename="clip_g.safetensors"
        clip_g_model_path = f"{clip_g_model_dir}/{clip_g_filename}"
        if not os.path.exists(clip_g_model_path):
            hf_hub_download(
                repo_id=self.path,
                filename=clip_g_filename,
                local_dir=get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, ''),
                subfolder=self.subfolder
            )
        with safe_open(clip_g_model_path, framework="pt", device=self.device) as f:
            dtype = torch.float16 if self.fp16 else torch.float32
            model = SDXLClipG(self.CLIPG_CONFIG, device=self.device, dtype=dtype)
            load_into(f, model.transformer, "", self.device, dtype)
        model = optimize_checkpoint(model, torch_inference)
        return model

    def get_shape_dict(self, batch_size, image_height, image_width):
        self.check_dims(batch_size, image_height, image_width)
        output = {
            'input_ids': (batch_size, self.text_maxlen),
            'text_embeddings': (batch_size, self.text_maxlen, self.embedding_dim)
        }
        if 'pooled_output' in self.extra_output_names:
            output["pooled_output"] = (batch_size, self.embedding_dim)

        return output

    def optimize(self, onnx_graph):
        opt = Optimizer(onnx_graph, verbose=self.verbose)
        opt.info(self.name + ': original')
        opt.select_outputs([0, 1])
        opt.cleanup()
        opt.fold_constants()
        opt.info(self.name + ': fold constants')
        opt.infer_shapes()
        opt.info(self.name + ': shape inference')
        opt.select_outputs([0, 1], names=['text_embeddings', 'pooled_output']) # rename network output
        opt.info(self.name + ': rename output[0] and output[1]')
        opt_onnx_graph = opt.cleanup(return_onnx=True)
        opt.info(self.name + ': finished')
        return opt_onnx_graph

class SD3_CLIPLModel(SD3_CLIPGModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        max_batch_size,
        fp16=False,
        pooled_output=False,
    ):
        self.CLIPL_CONFIG = {
            "hidden_act": "quick_gelu",
            "hidden_size": 768,
            "intermediate_size": 3072,
            "num_attention_heads": 12,
            "num_hidden_layers": 12
        }
        super(SD3_CLIPLModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, fp16=fp16, max_batch_size=max_batch_size, embedding_dim=self.CLIPL_CONFIG["hidden_size"])
        self.subfolder = 'text_encoders'
        if pooled_output:
            self.extra_output_names = ['pooled_output']

    def get_model(self, torch_inference=''):
        clip_l_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        clip_l_filename="clip_l.safetensors"
        clip_l_model_path = f"{clip_l_model_dir}/{clip_l_filename}"
        if not os.path.exists(clip_l_model_path):
            hf_hub_download(
                repo_id=self.path,
                filename=clip_l_filename,
                local_dir=get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, ''),
                subfolder=self.subfolder
            )
        with safe_open(clip_l_model_path, framework="pt", device=self.device) as f:
            dtype = torch.float16 if self.fp16 else torch.float32
            model = SDClipModel(layer="hidden", layer_idx=-2, device=self.device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, textmodel_json_config=self.CLIPL_CONFIG)
            load_into(f, model.transformer, "", self.device, dtype)
        model = optimize_checkpoint(model, torch_inference)
        return model

class SD3_T5XXLModel(CLIPModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        max_batch_size,
        embedding_dim,
        fp16=False,
    ):
        super(SD3_T5XXLModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, fp16=fp16, max_batch_size=max_batch_size, embedding_dim=embedding_dim)
        self.T5_CONFIG = {
            "d_ff": 10240,
            "d_model": 4096,
            "num_heads": 64,
            "num_layers": 24,
            "vocab_size": 32128
        }
        self.subfolder = 'text_encoders'

    def get_model(self, torch_inference=''):
        t5xxl_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        t5xxl_filename="t5xxl_fp16.safetensors"
        t5xxl_model_path = f"{t5xxl_model_dir}/{t5xxl_filename}"
        if not os.path.exists(t5xxl_model_path):
            hf_hub_download(
                repo_id=self.path,
                filename=t5xxl_filename,
                local_dir=get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, ''),
                subfolder=self.subfolder
            )
        with safe_open(t5xxl_model_path, framework="pt", device=self.device) as f:
            dtype = torch.float16 if self.fp16 else torch.float32
            model = T5XXLModel(self.T5_CONFIG, device=self.device, dtype=dtype)
            load_into(f, model.transformer, "", self.device, dtype)
        model = optimize_checkpoint(model, torch_inference)
        return model

class CLIPVisionWithProjModel(BaseModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        max_batch_size=1,
        subfolder="image_encoder",
    ):

        super(CLIPVisionWithProjModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, max_batch_size=max_batch_size)
        self.subfolder = subfolder

    def get_model(self, torch_inference=''):
        clip_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        if not os.path.exists(clip_model_dir):
            model = CLIPVisionModelWithProjection.from_pretrained(self.path,
                subfolder=self.subfolder,
                use_safetensors=self.hf_safetensor,
                token=self.hf_token).to(self.device)
            model.save_pretrained(clip_model_dir)
        else:
            print(f"[I] Load CLIPVisionModelWithProjection model from: {clip_model_dir}")
            model = CLIPVisionModelWithProjection.from_pretrained(clip_model_dir).to(self.device)
        model = optimize_checkpoint(model, torch_inference)
        return model


class CLIPImageProcessorModel(BaseModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        max_batch_size=1,
        subfolder="feature_extractor",
    ):

        super(CLIPImageProcessorModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, max_batch_size=max_batch_size)
        self.subfolder = subfolder

    def get_model(self, torch_inference=''):
        clip_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        # NOTE to(device) not supported
        if not os.path.exists(clip_model_dir):
            model = CLIPImageProcessor.from_pretrained(self.path,
                subfolder=self.subfolder,
                use_safetensors=self.hf_safetensor,
                token=self.hf_token)
            model.save_pretrained(clip_model_dir)
        else:
            print(f"[I] Load CLIPImageProcessor model from: {clip_model_dir}")
            model = CLIPImageProcessor.from_pretrained(clip_model_dir)
        model = optimize_checkpoint(model, torch_inference)
        return model


class UNet2DConditionControlNetModel(torch.nn.Module):
    def __init__(self, unet, controlnets) -> None:
        super().__init__()
        self.unet = unet
        self.controlnets = controlnets

    def forward(self, sample, timestep, encoder_hidden_states, images, controlnet_scales):
        for i, (image, conditioning_scale, controlnet) in enumerate(zip(images, controlnet_scales, self.controlnets)):
            down_samples, mid_sample = controlnet(
                sample,
                timestep,
                encoder_hidden_states=encoder_hidden_states,
                controlnet_cond=image,
                return_dict=False,
            )

            down_samples = [
                    down_sample * conditioning_scale
                    for down_sample in down_samples
                ]
            mid_sample *= conditioning_scale

            # merge samples
            if i == 0:
                down_block_res_samples, mid_block_res_sample = down_samples, mid_sample
            else:
                down_block_res_samples = [
                    samples_prev + samples_curr
                    for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)
                ]
                mid_block_res_sample += mid_sample

        noise_pred = self.unet(
            sample,
            timestep,
            encoder_hidden_states=encoder_hidden_states,
            down_block_additional_residuals=down_block_res_samples,
            mid_block_additional_residual=mid_block_res_sample
        )
        return noise_pred


class UNetModel(BaseModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        fp16 = False,
        int8 = False,
        fp8 = False,
        max_batch_size = 16,
        text_maxlen = 77,
        controlnets = None,
        lora_scales = None,
        lora_dict = None,
        lora_alphas = None,
        do_classifier_free_guidance = False,
    ):

        super(UNetModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, fp16=fp16, int8=int8, fp8=fp8, max_batch_size=max_batch_size, text_maxlen=text_maxlen, embedding_dim=get_unet_embedding_dim(version, pipeline))
        self.subfolder = 'unet'
        self.controlnets = get_path(version, pipeline, controlnets) if controlnets else None
        self.unet_dim = (9 if pipeline.is_inpaint() else 4)
        self.lora_scales = lora_scales
        self.lora_dict = lora_dict
        self.lora_alphas = lora_alphas
        self.xB = 2 if do_classifier_free_guidance else 1 # batch multiplier

    def get_model(self, torch_inference=''):
        model_opts = {'variant': 'fp16', 'torch_dtype': torch.float16} if self.fp16 else {}
        if self.controlnets:
            unet_model = UNet2DConditionModel.from_pretrained(self.path,
                subfolder=self.subfolder,
                use_safetensors=self.hf_safetensor,
                token=self.hf_token,
                **model_opts).to(self.device)
            cnet_model_opts = {'torch_dtype': torch.float16} if self.fp16 else {}
            controlnets = torch.nn.ModuleList([ControlNetModel.from_pretrained(path, **cnet_model_opts).to(self.device) for path in self.controlnets])
            # FIXME - cache UNet2DConditionControlNetModel
            model = UNet2DConditionControlNetModel(unet_model, controlnets)
        else:
            unet_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
            unet_path = self.get_model_path(unet_model_dir, model_opts)
            if not os.path.exists(unet_path):
                model = UNet2DConditionModel.from_pretrained(self.path,
                    subfolder=self.subfolder,
                    use_safetensors=self.hf_safetensor,
                    token=self.hf_token,
                    **model_opts).to(self.device)
                model.save_pretrained(unet_model_dir, **model_opts)
            else:
                print(f"[I] Load UNet2DConditionModel  model from: {unet_path}")
                model = UNet2DConditionModel.from_pretrained(unet_model_dir, **model_opts).to(self.device)
            if torch_inference:
                model.to(memory_format=torch.channels_last)
        model = optimize_checkpoint(model, torch_inference)
        return model

    def get_input_names(self):
        if self.controlnets is None:
            return ['sample', 'timestep', 'encoder_hidden_states']
        else:
            return ['sample', 'timestep', 'encoder_hidden_states', 'images', 'controlnet_scales']

    def get_output_names(self):
       return ['latent']

    def get_dynamic_axes(self):
        xB = '2B' if self.xB == 2 else 'B'
        if self.controlnets is None:
            return {
                'sample': {0: xB, 2: 'H', 3: 'W'},
                'encoder_hidden_states': {0: xB},
                'latent': {0: xB, 2: 'H', 3: 'W'}
            }
        else:
            return {
                'sample': {0: xB, 2: 'H', 3: 'W'},
                'encoder_hidden_states': {0: xB},
                'images': {1: xB, 3: '8H', 4: '8W'},
                'latent': {0: xB, 2: 'H', 3: 'W'}
            }

    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
        # WAR to enable inference for H/W that are not multiples of 16
        # If building with Dynamic Shapes: ensure image height and width are not multiples of 16 for ONNX export and TensorRT engine build
        if not static_shape:
            image_height = image_height - 8 if image_height % 16 == 0 else image_height
            image_width = image_width - 8 if image_width % 16 == 0 else image_width
        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
        min_batch, max_batch, min_image_height, max_image_height, min_image_width, max_image_width, 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)
        if self.controlnets is None:
            return {
                'sample': [(self.xB*min_batch, self.unet_dim, min_latent_height, min_latent_width), (self.xB*batch_size, self.unet_dim, latent_height, latent_width), (self.xB*max_batch, self.unet_dim, max_latent_height, max_latent_width)],
                'encoder_hidden_states': [(self.xB*min_batch, self.text_maxlen, self.embedding_dim), (self.xB*batch_size, self.text_maxlen, self.embedding_dim), (self.xB*max_batch, self.text_maxlen, self.embedding_dim)]
            }
        else:
            return {
                'sample': [(self.xB*min_batch, self.unet_dim, min_latent_height, min_latent_width),
                           (self.xB*batch_size, self.unet_dim, latent_height, latent_width),
                           (self.xB*max_batch, self.unet_dim, max_latent_height, max_latent_width)],
                'encoder_hidden_states': [(self.xB*min_batch, self.text_maxlen, self.embedding_dim),
                                          (self.xB*batch_size, self.text_maxlen, self.embedding_dim),
                                          (self.xB*max_batch, self.text_maxlen, self.embedding_dim)],
                'images': [(len(self.controlnets), self.xB*min_batch, 3, min_image_height, min_image_width),
                          (len(self.controlnets), self.xB*batch_size, 3, image_height, image_width),
                          (len(self.controlnets), self.xB*max_batch, 3, max_image_height, max_image_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)
        if self.controlnets is None:
            return {
                'sample': (self.xB*batch_size, self.unet_dim, latent_height, latent_width),
                'encoder_hidden_states': (self.xB*batch_size, self.text_maxlen, self.embedding_dim),
                'latent': (self.xB*batch_size, 4, latent_height, latent_width)
            }
        else:
            return {
                'sample': (self.xB*batch_size, self.unet_dim, latent_height, latent_width),
                'encoder_hidden_states': (self.xB*batch_size, self.text_maxlen, self.embedding_dim),
                'images': (len(self.controlnets), self.xB*batch_size, 3, image_height, image_width),
                'latent': (self.xB*batch_size, 4, latent_height, latent_width)
                }

    def get_sample_input(self, batch_size, image_height, image_width, static_shape):
        # WAR to enable inference for H/W that are not multiples of 16
        # If building with Dynamic Shapes: ensure image height and width are not multiples of 16 for ONNX export and TensorRT engine build
        if not static_shape:
            image_height = image_height - 8 if image_height % 16 == 0 else image_height
            image_width = image_width - 8 if image_width % 16 == 0 else image_width
        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
        dtype = torch.float16 if self.fp16 else torch.float32
        if self.controlnets is None:
            return (
                torch.randn(batch_size, self.unet_dim, latent_height, latent_width, dtype=dtype, device=self.device),
                torch.tensor([1.], dtype=dtype, device=self.device),
                torch.randn(batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device)
            )
        else:
            return (
                torch.randn(batch_size, self.unet_dim, latent_height, latent_width, dtype=dtype, device=self.device),
                torch.tensor(999, dtype=dtype, device=self.device),
                torch.randn(batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device),
                torch.randn(len(self.controlnets), batch_size, 3, image_height, image_width, dtype=dtype, device=self.device),
                torch.randn(len(self.controlnets), dtype=dtype, device=self.device)
            )

    def optimize(self, onnx_graph):
        if self.fp8:
            return super().optimize(onnx_graph, modify_fp8_graph=True)
        if self.int8:
            return super().optimize(onnx_graph, fuse_mha_qkv_int8=True)
        return super().optimize(onnx_graph)


class UNetXLModel(BaseModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        fp16 = False,
        int8 = False,
        fp8 = False,
        max_batch_size = 16,
        text_maxlen = 77,
        lora_scales = None,
        lora_dict = None,
        lora_alphas = None,
        do_classifier_free_guidance = False,
    ):
        super(UNetXLModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, fp16=fp16, int8=int8, fp8=fp8, max_batch_size=max_batch_size, text_maxlen=text_maxlen, embedding_dim=get_unet_embedding_dim(version, pipeline))
        self.subfolder = 'unet'
        self.unet_dim = (9 if pipeline.is_inpaint() else 4)
        self.time_dim = (5 if pipeline.is_sd_xl_refiner() else 6)
        self.lora_scales = lora_scales
        self.lora_dict = lora_dict
        self.lora_alphas = lora_alphas
        self.xB = 2 if do_classifier_free_guidance else 1 # batch multiplier

    def get_model(self, torch_inference=''):
        model_opts = {'variant': 'fp16', 'torch_dtype': torch.float16} if self.fp16 else {}
        unet_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        unet_path = self.get_model_path(unet_model_dir, model_opts)
        if not os.path.exists(unet_path):
            model = UNet2DConditionModel.from_pretrained(self.path,
                subfolder=self.subfolder,
                use_safetensors=self.hf_safetensor,
                token=self.hf_token,
                **model_opts).to(self.device)
            # Use default attention processor for ONNX export
            if not torch_inference:
                model.set_default_attn_processor()
            model.save_pretrained(unet_model_dir, **model_opts)
        else:
            print(f"[I] Load UNet2DConditionModel model from: {unet_path}")
            model = UNet2DConditionModel.from_pretrained(unet_model_dir, **model_opts).to(self.device)
        model = optimize_checkpoint(model, torch_inference)
        return model

    def get_input_names(self):
        return ['sample', 'timestep', 'encoder_hidden_states', 'text_embeds', 'time_ids']

    def get_output_names(self):
       return ['latent']

    def get_dynamic_axes(self):
        xB = '2B' if self.xB == 2 else 'B'
        return {
            'sample': {0: xB, 2: 'H', 3: 'W'},
            'encoder_hidden_states': {0: xB},
            'latent': {0: xB, 2: 'H', 3: 'W'},
            'text_embeds': {0: xB},
            'time_ids': {0: xB}
        }

    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
        # WAR to enable inference for H/W that are not multiples of 16
        # If building with Dynamic Shapes: ensure image height and width are not multiples of 16 for ONNX export and TensorRT engine build
        if not static_shape:
            image_height = image_height - 8 if image_height % 16 == 0 else image_height
            image_width = image_width - 8 if image_width % 16 == 0 else image_width
        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 {
            'sample': [(self.xB*min_batch, self.unet_dim, min_latent_height, min_latent_width), (self.xB*batch_size, self.unet_dim, latent_height, latent_width), (self.xB*max_batch, self.unet_dim, max_latent_height, max_latent_width)],
            'encoder_hidden_states': [(self.xB*min_batch, self.text_maxlen, self.embedding_dim), (self.xB*batch_size, self.text_maxlen, self.embedding_dim), (self.xB*max_batch, self.text_maxlen, self.embedding_dim)],
            'text_embeds': [(self.xB*min_batch, 1280), (self.xB*batch_size, 1280), (self.xB*max_batch, 1280)],
            'time_ids': [(self.xB*min_batch, self.time_dim), (self.xB*batch_size, self.time_dim), (self.xB*max_batch, self.time_dim)]
        }

    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 {
            'sample': (self.xB*batch_size, self.unet_dim, latent_height, latent_width),
            'encoder_hidden_states': (self.xB*batch_size, self.text_maxlen, self.embedding_dim),
            'latent': (self.xB*batch_size, 4, latent_height, latent_width),
            'text_embeds': (self.xB*batch_size, 1280),
            'time_ids': (self.xB*batch_size, self.time_dim)
        }

    def get_sample_input(self, batch_size, image_height, image_width, static_shape):
        # WAR to enable inference for H/W that are not multiples of 16
        # If building with Dynamic Shapes: ensure image height and width are not multiples of 16 for ONNX export and TensorRT engine build
        if not static_shape:
            image_height = image_height - 8 if image_height % 16 == 0 else image_height
            image_width = image_width - 8 if image_width % 16 == 0 else image_width
        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
        dtype = torch.float16 if self.fp16 else torch.float32
        return (
            torch.randn(self.xB*batch_size, self.unet_dim, latent_height, latent_width, dtype=dtype, device=self.device),
            torch.tensor([1.], dtype=dtype, device=self.device),
            torch.randn(self.xB*batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device),
            {
                'added_cond_kwargs': {
                    'text_embeds': torch.randn(self.xB*batch_size, 1280, dtype=dtype, device=self.device),
                    'time_ids' : torch.randn(self.xB*batch_size, self.time_dim, dtype=dtype, device=self.device)
                }
            }
        )

    def optimize(self, onnx_graph):
        if self.fp8:
            return super().optimize(onnx_graph, modify_fp8_graph=True)
        if self.int8:
            return super().optimize(onnx_graph, fuse_mha_qkv_int8=True)
        return super().optimize(onnx_graph)

class SD3_MMDiTModel(BaseModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        shift=1.0,
        fp16 = False,
        max_batch_size = 16,
        text_maxlen = 77,
    ):

        super(SD3_MMDiTModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, fp16=fp16, max_batch_size=max_batch_size, text_maxlen=text_maxlen)
        self.subfolder = 'sd3'
        self.mmdit_dim = 16
        self.shift = shift
        self.xB = 2

    def get_model(self, torch_inference=''):
        sd3_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        sd3_filename="sd3_medium.safetensors"
        sd3_model_path = f"{sd3_model_dir}/{sd3_filename}"
        if not os.path.exists(sd3_model_path):
            hf_hub_download(repo_id=self.path, filename=sd3_filename, local_dir=sd3_model_dir)
        with safe_open(sd3_model_path, framework="pt", device=self.device) as f:
            model = BaseModelSD3(shift=self.shift, file=f, prefix="model.diffusion_model.", device=self.device, dtype=torch.float16).eval()
            load_into(f, model, "model.", self.device, torch.float16)
        model = optimize_checkpoint(model, torch_inference)
        return model

    def get_input_names(self):
        return ['sample', 'sigma', 'c_crossattn', 'y']

    def get_output_names(self):
       return ['latent']

    def get_dynamic_axes(self):
        xB = '2B' if self.xB == 2 else 'B'
        return {
            'sample': {0: xB, 2: 'H', 3: 'W'},
            'sigma': {0: xB},
            'c_crossattn': {0: xB},
            'y': {0: xB},
            'latent': {0: xB, 2: 'H', 3: 'W'}
        }

    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 {
            'sample': [(self.xB*min_batch, self.mmdit_dim, min_latent_height, min_latent_width), (self.xB*batch_size, self.mmdit_dim, latent_height, latent_width), (self.xB*max_batch, self.mmdit_dim, max_latent_height, max_latent_width)],
            'sigma': [(self.xB*min_batch,), (self.xB*batch_size,), (self.xB*max_batch,)],
            'c_crossattn': [(self.xB*min_batch, 154, 4096), (self.xB*batch_size, 154, 4096), (self.xB*max_batch, 154, 4096)],
            'y': [(self.xB*min_batch, 2048), (self.xB*batch_size, 2048), (self.xB*max_batch, 2048)]
        }

    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 {
            'sample': (self.xB*batch_size, self.mmdit_dim, latent_height, latent_width),
            'sigma': (self.xB*batch_size,),
            'c_crossattn': (self.xB*batch_size, 154, 4096),
            'y': (self.xB*batch_size, 2048),
            'latent': (self.xB*batch_size, self.mmdit_dim, latent_height, latent_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.float32
        return (
            torch.randn(batch_size, self.mmdit_dim, latent_height, latent_width, dtype=dtype, device=self.device),
            torch.randn(batch_size, dtype=dtype, device=self.device),
            {
                'c_crossattn': torch.randn(batch_size, 154, 4096, dtype=dtype, device=self.device),
                'y': torch.randn(batch_size, 2048, dtype=dtype, device=self.device),
            }
        )


class UNetTemporalModel(BaseModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        fp16 = False,
        max_batch_size = 16,
        num_frames = 14,
        do_classifier_free_guidance = True,
    ):
        super(UNetTemporalModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, fp16=fp16, max_batch_size=max_batch_size, embedding_dim=get_unet_embedding_dim(version, pipeline))
        self.subfolder = 'unet'
        self.unet_dim = 4
        self.num_frames = num_frames
        self.out_channels = 4
        self.cross_attention_dim = 1024
        self.xB = 2 if do_classifier_free_guidance else 1 # batch multiplier

    def get_model(self, torch_inference=''):
        model_opts = {'torch_dtype': torch.float16} if self.fp16 else {}
        unet_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        unet_path = self.get_model_path(unet_model_dir, model_opts)
        if not os.path.exists(unet_path):
            model = UNetSpatioTemporalConditionModel.from_pretrained(self.path,
                subfolder=self.subfolder,
                use_safetensors=self.hf_safetensor,
                token=self.hf_token,
                **model_opts).to(self.device)
            model.save_pretrained(unet_model_dir, **model_opts)
        else:
            print(f"[I] Load UNetSpatioTemporalConditionModel model from: {unet_path}")
            model = UNetSpatioTemporalConditionModel.from_pretrained(unet_model_dir, **model_opts).to(self.device)
        model = optimize_checkpoint(model, torch_inference)
        return model

    def get_input_names(self):
        return ['sample', 'timestep', 'encoder_hidden_states', 'added_time_ids']

    def get_output_names(self):
       return ['latent']

    def get_dynamic_axes(self):
        xB = str(self.xB)+'B'
        return {
            'sample': {0: xB, 1: 'num_frames', 3: 'H', 4: 'W'},
            'encoder_hidden_states': {0: xB},
            'added_time_ids': {0: xB}
        }

    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_image_height, max_image_height, min_image_width, max_image_width, 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 {
            'sample': [(self.xB*min_batch, self.num_frames, 2*self.out_channels, min_latent_height, min_latent_width), (self.xB*batch_size, self.num_frames, 2*self.out_channels, latent_height, latent_width), (self.xB*max_batch, self.num_frames, 2*self.out_channels, max_latent_height, max_latent_width)],
            'encoder_hidden_states': [(self.xB*min_batch, 1, self.cross_attention_dim), (self.xB*batch_size, 1, self.cross_attention_dim), (self.xB*max_batch, 1, self.cross_attention_dim)],
            'added_time_ids': [(self.xB*min_batch, 3), (self.xB*batch_size, 3), (self.xB*max_batch, 3)],
        }


    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 {
            'sample': (self.xB*batch_size, self.num_frames, 2*self.out_channels, latent_height, latent_width),
            'timestep': (1,),
            'encoder_hidden_states': (self.xB*batch_size, 1, self.cross_attention_dim),
            'added_time_ids': (self.xB*batch_size, 3),
        }

    def get_sample_input(self, batch_size, image_height, image_width):
        # TODO chunk_size if forward_chunking is used
        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)

        dtype = torch.float16 if self.fp16 else torch.float32
        return (
            torch.randn(self.xB*batch_size, self.num_frames, 2*self.out_channels, latent_height, latent_width, dtype=dtype, device=self.device),
            torch.tensor([1.], dtype=torch.float32, device=self.device),
            torch.randn(self.xB*batch_size, 1, self.cross_attention_dim, dtype=dtype, device=self.device),
            torch.randn(self.xB*batch_size, 3, dtype=dtype, device=self.device),
        )


class UNetCascadeModel(BaseModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        fp16 = False,
        bf16 = False,
        max_batch_size = 16,
        text_maxlen = 77,
        do_classifier_free_guidance = False,
        compression_factor=42,
        latent_dim_scale=10.67,
        image_embedding_dim=768,
        lite=False
    ):
        super(UNetCascadeModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, fp16=fp16, bf16=bf16, max_batch_size=max_batch_size, text_maxlen=text_maxlen, embedding_dim=get_unet_embedding_dim(version, pipeline), compression_factor=compression_factor)
        self.is_prior = True if pipeline.is_cascade_prior() else False
        self.subfolder = 'prior' if self.is_prior else 'decoder'
        if lite:
            self.subfolder += '_lite'
        self.prior_dim = 16
        self.decoder_dim = 4
        self.xB = 2 if do_classifier_free_guidance else 1 # batch multiplier
        self.latent_dim_scale = latent_dim_scale
        self.min_latent_shape = self.min_image_shape // self.compression_factor
        self.max_latent_shape = self.max_image_shape // self.compression_factor
        self.do_constant_folding = False
        self.image_embedding_dim = image_embedding_dim

    def get_model(self, torch_inference=''):
        # FP16 variant doesn't exist
        model_opts = {'torch_dtype': torch.float16} if self.fp16 else {}
        model_opts = {'variant': 'bf16', 'torch_dtype': torch.bfloat16} if self.bf16 else model_opts
        unet_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        unet_path = self.get_model_path(unet_model_dir, model_opts)
        if not os.path.exists(unet_path):
            model = StableCascadeUNet.from_pretrained(self.path,
                subfolder=self.subfolder,
                use_safetensors=self.hf_safetensor,
                token=self.hf_token,
                **model_opts).to(self.device)
            model.save_pretrained(unet_model_dir, **model_opts)
        else:
            print(f"[I] Load Stable Cascade UNet pytorch model from: {unet_path}")
            model = StableCascadeUNet.from_pretrained(unet_model_dir, **model_opts).to(self.device)
        model = optimize_checkpoint(model, torch_inference)
        return model

    def get_input_names(self):
        if self.is_prior:
            return ['sample', 'timestep_ratio', 'clip_text_pooled', 'clip_text', 'clip_img']
        else:
            return ['sample', 'timestep_ratio', 'clip_text_pooled', 'effnet']

    def get_output_names(self):
       return ['latent']

    def get_dynamic_axes(self):
        xB = '2B' if self.xB == 2 else 'B'
        if self.is_prior:
            return {
                'sample': {0: xB, 2: 'H', 3: 'W'},
                'timestep_ratio': {0: xB},
                'clip_text_pooled': {0: xB},
                'clip_text': {0: xB},
                'clip_img': {0: xB},
                'latent': {0: xB, 2: 'H', 3: 'W'}
            }
        else:
            return {
                'sample': {0: xB, 2: 'H', 3: 'W'},
                'timestep_ratio': {0: xB},
                'clip_text_pooled': {0: xB},
                'effnet': {0: xB, 2: 'H_effnet', 3: 'W_effnet'},
                'latent': {0: xB, 2: 'H', 3: 'W'}
            }

    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)
        if self.is_prior:
            return {
                'sample': [(self.xB*min_batch, self.prior_dim, min_latent_height, min_latent_width), (self.xB*batch_size, self.prior_dim, latent_height, latent_width), (self.xB*max_batch, self.prior_dim, max_latent_height, max_latent_width)],
                'timestep_ratio': [(self.xB*min_batch,), (self.xB*batch_size,), (self.xB*max_batch,)],
                'clip_text_pooled': [(self.xB*min_batch, 1, self.embedding_dim), (self.xB*batch_size, 1, self.embedding_dim), (self.xB*max_batch, 1, self.embedding_dim)],
                'clip_text': [(self.xB*min_batch, self.text_maxlen, self.embedding_dim), (self.xB*batch_size, self.text_maxlen, self.embedding_dim), (self.xB*max_batch, self.text_maxlen, self.embedding_dim)],
                'clip_img': [(self.xB*min_batch, 1, self.image_embedding_dim), (self.xB*batch_size, 1, self.image_embedding_dim), (self.xB*max_batch, 1, self.image_embedding_dim)],
            }
        else:
            return {
                'sample': [(self.xB*min_batch, self.decoder_dim, int(min_latent_height * self.latent_dim_scale), int(min_latent_width * self.latent_dim_scale)),
                    (self.xB*batch_size, self.decoder_dim, int(latent_height * self.latent_dim_scale), int(latent_width * self.latent_dim_scale)),
                    (self.xB*max_batch, self.decoder_dim, int(max_latent_height * self.latent_dim_scale), int(max_latent_width * self.latent_dim_scale))],
                'timestep_ratio': [(self.xB*min_batch,), (self.xB*batch_size,), (self.xB*max_batch,)],
                'clip_text_pooled': [(self.xB*min_batch, 1, self.embedding_dim), (self.xB*batch_size, 1, self.embedding_dim), (self.xB*max_batch, 1, self.embedding_dim)],
                'effnet': [(self.xB*min_batch, self.prior_dim, min_latent_height, min_latent_width), (self.xB*batch_size, self.prior_dim, latent_height, latent_width), (self.xB*max_batch, self.prior_dim, 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)
        if self.is_prior:
            return {
                'sample': (self.xB*batch_size, self.prior_dim, latent_height, latent_width),
                'timestep_ratio': (self.xB*batch_size,),
                'clip_text_pooled': (self.xB*batch_size, 1, self.embedding_dim),
                'clip_text': (self.xB*batch_size, self.text_maxlen, self.embedding_dim),
                'clip_img': (self.xB*batch_size, 1, self.image_embedding_dim),
                'latent': (self.xB*batch_size, self.prior_dim, latent_height, latent_width)
            }
        else:
            return {
                'sample': (self.xB*batch_size, self.decoder_dim, int(latent_height * self.latent_dim_scale), int(latent_width * self.latent_dim_scale)),
                'timestep_ratio': (self.xB*batch_size,),
                'clip_text_pooled': (self.xB*batch_size, 1, self.embedding_dim),
                'effnet': (self.xB*batch_size, self.prior_dim, latent_height, latent_width),
                'latent': (self.xB*batch_size, self.decoder_dim, int(latent_height * self.latent_dim_scale), int(latent_width * self.latent_dim_scale))
            }

    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
        if self.is_prior:
            return (
                torch.randn(batch_size, self.prior_dim, latent_height, latent_width, dtype=dtype, device=self.device),
                torch.tensor([1.]*batch_size, dtype=dtype, device=self.device),
                torch.randn(batch_size, 1, self.embedding_dim, dtype=dtype, device=self.device),
                {
                    'clip_text': torch.randn(batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device),
                    'clip_img': torch.randn(batch_size, 1, self.image_embedding_dim, dtype=dtype, device=self.device),
                }
            )
        else:
            return (
                torch.randn(batch_size, self.decoder_dim, int(latent_height * self.latent_dim_scale), int(latent_width * self.latent_dim_scale), dtype=dtype, device=self.device),
                torch.tensor([1.]*batch_size, dtype=dtype, device=self.device),
                torch.randn(batch_size, 1, self.embedding_dim, dtype=dtype, device=self.device),
                {
                    'effnet': torch.randn(batch_size, self.prior_dim, latent_height, latent_width, dtype=dtype, device=self.device),
                }
            )

class FluxTransformerModel(BaseModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        fp16 = False,
        tf32=False,
        int8 = False,
        fp8 = False,
        max_batch_size = 16,
        text_maxlen = 77,
        build_strongly_typed=False
    ):

        super(FluxTransformerModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, fp16=fp16, tf32=tf32, int8=int8, fp8=fp8, max_batch_size=max_batch_size, text_maxlen=text_maxlen)
        self.subfolder = 'transformer'
        self.config = FluxTransformer2DModel.load_config(self.path, subfolder=self.subfolder, token=self.hf_token)
        self.build_strongly_typed = build_strongly_typed

    def get_model(self, torch_inference=''):
        model_opts = {'torch_dtype': torch.float16} if self.fp16 else {}
        transformer_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        transformer_path = self.get_model_path(transformer_model_dir, model_opts)
        if not os.path.exists(transformer_path):
            model = FluxTransformer2DModel.from_pretrained(self.path,
                subfolder=self.subfolder,
                use_safetensors=self.hf_safetensor,
                token=self.hf_token,
                **model_opts).to(self.device)
            model.save_pretrained(transformer_model_dir, **model_opts)
        else:
            print(f"[I] Load FluxTransformer2DModel  model from: {transformer_path}")
            model = FluxTransformer2DModel.from_pretrained(transformer_model_dir, **model_opts).to(self.device)
        if torch_inference:
            model.to(memory_format=torch.channels_last)
        model = optimize_checkpoint(model, torch_inference)
        return model

    def get_input_names(self):
            return ['hidden_states', 'encoder_hidden_states', 'pooled_projections', 'timestep', 'img_ids', 'txt_ids', 'guidance']

    def get_output_names(self):
       return ['latent']

    def get_dynamic_axes(self):
        return {
            'hidden_states': {0: 'B', 1: 'latent_dim'},
            'encoder_hidden_states': {0: 'B'},
            'pooled_projections': {0: 'B'},
            'timestep': {0: 'B'},
            'img_ids': {0: 'latent_dim'},
            'guidance': {0: 'B'},
        }

    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_image_height, max_image_height, min_image_width, max_image_width, 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 {
            'hidden_states': [(min_batch, (min_latent_height // 2) * (min_latent_width // 2), self.config['in_channels']), (batch_size, (latent_height // 2) * (latent_width // 2), self.config['in_channels']), (max_batch, (max_latent_height // 2) * (max_latent_width // 2), self.config['in_channels'])],
            'encoder_hidden_states': [(min_batch, self.text_maxlen, self.config['joint_attention_dim']), (batch_size, self.text_maxlen, self.config['joint_attention_dim']), (max_batch, self.text_maxlen, self.config['joint_attention_dim'])],
            'pooled_projections': [(min_batch, self.config['pooled_projection_dim']), (batch_size, self.config['pooled_projection_dim']), (max_batch, self.config['pooled_projection_dim'])],
            'timestep': [(min_batch,), (batch_size,), (max_batch,)],
            'img_ids': [((min_latent_height // 2) * (min_latent_width // 2), 3), ((latent_height // 2) * (latent_width // 2), 3), ((max_latent_height // 2) * (max_latent_width // 2), 3)],
            'txt_ids': [(self.text_maxlen, 3), (self.text_maxlen, 3), (self.text_maxlen, 3)],
            'guidance': [(min_batch,), (batch_size,), (max_batch,)],
        }


    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 {
            'hidden_states': (batch_size, (latent_height // 2) * (latent_width // 2), self.config['in_channels']),
            'encoder_hidden_states': (batch_size, self.text_maxlen, self.config['joint_attention_dim']),
            'pooled_projections': (batch_size, self.config['pooled_projection_dim']),
            'timestep': (batch_size,),
            'img_ids': ((latent_height // 2) * (latent_width // 2), 3),
            'txt_ids': (self.text_maxlen, 3),
            'latent': (batch_size, (latent_height // 2) * (latent_width // 2), self.config['in_channels']),
            'guidance': (batch_size,),
        }

    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.float32
        return (
            torch.randn(batch_size, (latent_height // 2) * (latent_width // 2), self.config['in_channels'], dtype=dtype, device=self.device),
            torch.randn(batch_size, self.text_maxlen, self.config['joint_attention_dim'], dtype=dtype, device=self.device),
            torch.randn(batch_size, self.config['pooled_projection_dim'], dtype=dtype, device=self.device),
            torch.tensor([1.]*batch_size, dtype=dtype, device=self.device),
            torch.randn((latent_height // 2) * (latent_width // 2), 3, dtype=dtype, device=self.device),
            torch.randn(self.text_maxlen, 3, dtype=dtype, device=self.device),
            {
                'guidance': torch.tensor([1.]*batch_size, dtype=dtype, device=self.device),
            }
        )

class VAEModel(BaseModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        fp16=False,
        tf32=False,
        max_batch_size=16,
    ):
        super(VAEModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, fp16=fp16, tf32=tf32, max_batch_size=max_batch_size)
        self.subfolder = 'vae'
        self.config = AutoencoderKL.load_config(self.path, subfolder=self.subfolder, token=self.hf_token)

    def get_model(self, torch_inference=''):
        vae_decoder_model_path = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        if not os.path.exists(vae_decoder_model_path):
            model = AutoencoderKL.from_pretrained(self.path,
                subfolder=self.subfolder,
                use_safetensors=self.hf_safetensor,
                token=self.hf_token).to(self.device)
            model.save_pretrained(vae_decoder_model_path)
        else:
            print(f"[I] Load AutoencoderKL (decoder) model from: {vae_decoder_model_path}")
            model = AutoencoderKL.from_pretrained(vae_decoder_model_path).to(self.device)
        model.forward = model.decode
        model = 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', 2: 'H', 3: 'W'},
            'images': {0: 'B', 2: '8H', 3: '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['latent_channels'], min_latent_height, min_latent_width),
                (batch_size, self.config['latent_channels'], latent_height, latent_width),
                (max_batch, self.config['latent_channels'], 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['latent_channels'], latent_height, latent_width),
            'images': (batch_size, 3, 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)
        return torch.randn(batch_size, self.config['latent_channels'], latent_height, latent_width, dtype=torch.float32, device=self.device)

class SD3_VAEDecoderModel(BaseModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        max_batch_size,
        fp16=False,
    ):
        super(SD3_VAEDecoderModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, fp16=fp16, max_batch_size=max_batch_size)
        self.subfolder = 'sd3'

    def get_model(self, torch_inference=''):
        dtype = torch.float16 if self.fp16 else torch.float32
        sd3_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        sd3_filename="sd3_medium.safetensors"
        sd3_model_path = f"{sd3_model_dir}/{sd3_filename}"
        if not os.path.exists(sd3_model_path):
            hf_hub_download(repo_id=self.path, filename=sd3_filename, local_dir=sd3_model_dir)
        with safe_open(sd3_model_path, framework="pt", device=self.device) as f:
            model = SDVAE(device=self.device, dtype=dtype).eval().cuda()
            prefix = ""
            if any(k.startswith("first_stage_model.") for k in f.keys()):
                prefix = "first_stage_model."
            load_into(f, model, prefix, self.device, dtype)
        model.forward = model.decode
        model = 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', 2: 'H', 3: 'W'},
            'images': {0: 'B', 2: '8H', 3: '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, 16, min_latent_height, min_latent_width), (batch_size, 16, latent_height, latent_width), (max_batch, 16, 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, 16, latent_height, latent_width),
            'images': (batch_size, 3, 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.float32
        return torch.randn(batch_size, 16, latent_height, latent_width, dtype=dtype, device=self.device)

class VAEDecTemporalModel(BaseModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        max_batch_size = 16,
        decode_chunk_size = 14,
    ):
        super(VAEDecTemporalModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, max_batch_size=max_batch_size)
        self.subfolder = 'vae'
        self.decode_chunk_size = decode_chunk_size

    def get_model(self, torch_inference=''):
        vae_decoder_model_path = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        if not os.path.exists(vae_decoder_model_path):
            model = AutoencoderKLTemporalDecoder.from_pretrained(self.path,
                subfolder=self.subfolder,
                use_safetensors=self.hf_safetensor,
                token=self.hf_token).to(self.device)
            model.save_pretrained(vae_decoder_model_path)
        else:
            print(f"[I] Load AutoencoderKLTemporalDecoder model from: {vae_decoder_model_path}")
            model = AutoencoderKLTemporalDecoder.from_pretrained(vae_decoder_model_path).to(self.device)
        model.forward = model.decode
        model = optimize_checkpoint(model, torch_inference)
        return model

    def get_input_names(self):
        return ['latent', 'num_frames_in']

    def get_output_names(self):
       return ['frames']

    def get_dynamic_axes(self):
        return {
            'latent': {0: 'num_frames_in', 2: 'H', 3: 'W'},
            'frames': {0: 'num_frames_in', 2: '8H', 3: '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)
        assert batch_size == 1
        _, _, _, _, _, _, 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': [(1, 4, min_latent_height, min_latent_width), (self.decode_chunk_size, 4, latent_height, latent_width), (self.decode_chunk_size, 4, max_latent_height, max_latent_width)],
            'num_frames_in': [(1,), (1,), (1,)],
        }

    def get_shape_dict(self, batch_size, image_height, image_width):
        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
        assert batch_size == 1
        return {
            'latent': (self.decode_chunk_size, 4, latent_height, latent_width),
            #'num_frames_in': (1,),
            'frames': (self.decode_chunk_size, 3, image_height, image_width)
        }

    def get_sample_input(self, batch_size, image_height, image_width):
        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
        assert batch_size == 1
        return (
            torch.randn(self.decode_chunk_size, 4, latent_height, latent_width, dtype=torch.float32, device=self.device),
            self.decode_chunk_size,
        )


class TorchVAEEncoder(torch.nn.Module):
    def __init__(self, version, pipeline, hf_token, device, path, framework_model_dir, hf_safetensor=False):
        super().__init__()
        vae_encoder_model_dir = get_checkpoint_dir(framework_model_dir, version, pipeline, 'vae_encoder')
        if not os.path.exists(vae_encoder_model_dir):
            self.vae_encoder = AutoencoderKL.from_pretrained(path,
                subfolder='vae',
                use_safetensors=hf_safetensor,
                token=hf_token).to(device)
            self.vae_encoder.save_pretrained(vae_encoder_model_dir)
        else:
            print(f"[I] Load AutoencoderKL (encoder) model from: {vae_encoder_model_dir}")
            self.vae_encoder = AutoencoderKL.from_pretrained(vae_encoder_model_dir).to(device)

    def forward(self, x):
        return self.vae_encoder.encode(x).latent_dist.sample()


class VAEEncoderModel(BaseModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        fp16=False,
        max_batch_size=16,
    ):
        super(VAEEncoderModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, fp16=fp16, max_batch_size=max_batch_size)

    def get_model(self, torch_inference=''):
        vae_encoder = TorchVAEEncoder(self.version, self.pipeline, self.hf_token, self.device, self.path, self.framework_model_dir, hf_safetensor=self.hf_safetensor)
        return vae_encoder

    def get_input_names(self):
        return ['images']

    def get_output_names(self):
       return ['latent']

    def get_dynamic_axes(self):
        return {
            'images': {0: 'B', 2: '8H', 3: '8W'},
            'latent': {0: 'B', 2: 'H', 3: 'W'}
        }

    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
        assert batch_size >= self.min_batch and batch_size <= self.max_batch
        min_batch = batch_size if static_batch else self.min_batch
        max_batch = batch_size if static_batch else self.max_batch
        self.check_dims(batch_size, image_height, image_width)
        min_batch, max_batch, min_image_height, max_image_height, min_image_width, max_image_width, _, _, _, _ = \
            self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)

        return {
            'images': [(min_batch, 3, min_image_height, min_image_width), (batch_size, 3, image_height, image_width), (max_batch, 3, max_image_height, max_image_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 {
            'images': (batch_size, 3, image_height, image_width),
            'latent': (batch_size, 4, latent_height, latent_width)
        }

    def get_sample_input(self, batch_size, image_height, image_width, static_shape):
        self.check_dims(batch_size, image_height, image_width)
        return torch.randn(batch_size, 3, image_height, image_width, dtype=torch.float32, device=self.device)

class SD3_VAEEncoderModel(VAEEncoderModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        max_batch_size,
        fp16=False,
    ):
        super(SD3_VAEEncoderModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, fp16=fp16, max_batch_size=max_batch_size)
        self.subfolder = 'sd3'

    def get_model(self, torch_inference=''):
        dtype = torch.float16 if self.fp16 else torch.float32
        sd3_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        sd3_filename="sd3_medium.safetensors"
        sd3_model_path = f"{sd3_model_dir}/{sd3_filename}"
        if not os.path.exists(sd3_model_path):
            hf_hub_download(repo_id=self.path, filename=sd3_filename, local_dir=sd3_model_dir)
        with safe_open(sd3_model_path, framework="pt", device=self.device) as f:
            model = SDVAE(device=self.device, dtype=dtype).eval().cuda()
            prefix = ""
            if any(k.startswith("first_stage_model.") for k in f.keys()):
                prefix = "first_stage_model."
            load_into(f, model, prefix, self.device, dtype)
        model.forward = model.encode
        model = optimize_checkpoint(model, torch_inference)
        return model

    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
        min_batch, max_batch, _, _, _, _, _, _, _, _ = \
            self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
        return {
            'images': [(min_batch, 3, image_height, image_width), (batch_size, 3, image_height, image_width), (max_batch, 3, image_height, image_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 {
            'images': (batch_size, 3, image_height, image_width),
            'latent': (batch_size, 16, latent_height, latent_width)
        }

    def get_sample_input(self, batch_size, image_height, image_width, static_shape):
        dtype = torch.float16 if self.fp16 else torch.float32
        return torch.randn(batch_size, 3, image_height, image_width, dtype=dtype, device=self.device)

class VQGANModel(BaseModel):
    def __init__(self,
        version,
        pipeline,
        device,
        hf_token,
        verbose,
        framework_model_dir,
        fp16=False,
        bf16=False,
        max_batch_size=16,
        compression_factor=42,
        latent_dim_scale=10.67,
        scale_factor=0.3764
    ):
        super(VQGANModel, self).__init__(version, pipeline, device=device, hf_token=hf_token, verbose=verbose, framework_model_dir=framework_model_dir, fp16=fp16, bf16=bf16, max_batch_size=max_batch_size, compression_factor=compression_factor)
        self.subfolder = 'vqgan'
        self.latent_dim_scale = latent_dim_scale
        self.scale_factor = scale_factor

    def get_model(self, torch_inference=''):
        model_opts = {'variant': 'bf16', 'torch_dtype': torch.bfloat16} if self.bf16 else {}
        vqgan_model_dir = get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
        vqgan_path = self.get_model_path(vqgan_model_dir, model_opts, model_name='model')
        if not os.path.exists(vqgan_path):
            model = PaellaVQModel.from_pretrained(self.path,
                subfolder=self.subfolder,
                use_safetensors=self.hf_safetensor,
                token=self.hf_token,
                **model_opts).to(self.device)
            model.save_pretrained(vqgan_model_dir, **model_opts)
        else:
            print(f"[I] Load VQGAN pytorch model from: {vqgan_path}")
            model = PaellaVQModel.from_pretrained(vqgan_model_dir, **model_opts).to(self.device)
        model.forward = model.decode
        model = 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', 2: 'H', 3: 'W'},
            'images': {0: 'B', 2: '8H', 3: '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, 4, min_latent_height, min_latent_width), (batch_size, 4, latent_height, latent_width), (max_batch, 4, 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, 4, latent_height, latent_width),
            'images': (batch_size, 3, 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, 4, latent_height, latent_width, dtype=dtype, device=self.device)

    def check_dims(self, batch_size, image_height, image_width):
        latent_height, latent_width = super().check_dims(batch_size, image_height, image_width)
        latent_height = int(latent_height * self.latent_dim_scale)
        latent_width = int(latent_width * self.latent_dim_scale)
        return (latent_height, latent_width)

    def get_minmax_dims(self, batch_size, image_height, image_width, static_batch, static_shape):
        min_batch, max_batch, min_image_height, max_image_height, min_image_width, max_image_width, min_latent_height, max_latent_height, min_latent_width, max_latent_width = \
            super().get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
        min_latent_height = int(min_latent_height * self.latent_dim_scale)
        min_latent_width = int(min_latent_width * self.latent_dim_scale)
        max_latent_height = int(max_latent_height * self.latent_dim_scale)
        max_latent_width = int(max_latent_width * self.latent_dim_scale)
        return (min_batch, max_batch, min_image_height, max_image_height, min_image_width, max_image_width, min_latent_height, max_latent_height, min_latent_width, max_latent_width)

def make_tokenizer(version, pipeline, hf_token, framework_model_dir, subfolder="tokenizer", tokenizer_type="clip"):
    if tokenizer_type == "clip":
        tokenizer_class = CLIPTokenizer
    elif tokenizer_type == "t5":
        tokenizer_class = T5TokenizerFast
    else:
        raise ValueError(f"Unsupported tokenizer_type {tokenizer_type}. Only tokenizer_type clip and t5 are currently supported")
    tokenizer_model_dir = get_checkpoint_dir(framework_model_dir, version, pipeline.name, subfolder)
    if not os.path.exists(tokenizer_model_dir):
        model = tokenizer_class.from_pretrained(get_path(version, pipeline),
                subfolder=subfolder,
                use_safetensors=pipeline.is_sd_xl(),
                token=hf_token)
        model.save_pretrained(tokenizer_model_dir)
    else:
        print(f"[I] Load {tokenizer_class.__name__} model from: {tokenizer_model_dir}")
        model = tokenizer_class.from_pretrained(tokenizer_model_dir)
    return model

def make_scheduler(cls, version, pipeline, hf_token, framework_model_dir, subfolder="scheduler"):
    scheduler_dir = os.path.join(framework_model_dir, version, pipeline.name, next(iter({cls.__name__})).lower(), subfolder)
    if not os.path.exists(scheduler_dir):
        scheduler = cls.from_pretrained(get_path(version, pipeline), subfolder=subfolder, token=hf_token)
        scheduler.save_pretrained(scheduler_dir)
    else:
        print(f"[I] Load Scheduler {cls.__name__} from: {scheduler_dir}")
        scheduler = cls.from_pretrained(scheduler_dir)
    return scheduler