wehub-resource-sync
903 lines
38 KiB
Python
903 lines
38 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Model definitions for UNet models.
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"""
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import torch
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from demo_diffusion.dynamic_import import import_from_diffusers
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from demo_diffusion.model import base_model, load, optimizer
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from diffusers import StableDiffusionXLControlNetPipeline
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# List of models to import from diffusers.models
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models_to_import = [
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"ControlNetModel",
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"UNet2DConditionModel",
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"UNetSpatioTemporalConditionModel",
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"StableCascadeUNet",
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]
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for model in models_to_import:
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globals()[model] = import_from_diffusers(model, "diffusers.models")
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def get_unet_embedding_dim(version, pipeline):
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if version in ("1.4", "dreamshaper-7"):
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return 768
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elif version in ("xl-1.0", "xl-turbo") and pipeline.is_sd_xl_base():
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return 2048
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elif version in ("cascade"):
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return 1280
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elif version in ("xl-1.0", "xl-turbo") and pipeline.is_sd_xl_refiner():
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return 1280
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elif pipeline.is_img2vid():
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return 1024
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else:
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raise ValueError(f"Invalid version {version} + pipeline {pipeline}")
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class UNet2DConditionControlNetModel(torch.nn.Module):
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def __init__(self, unet, controlnets) -> None:
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super().__init__()
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self.unet = unet
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self.controlnets = controlnets
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def forward(self, sample, timestep, encoder_hidden_states, images, controlnet_scales, added_cond_kwargs=None):
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for i, (image, conditioning_scale, controlnet) in enumerate(zip(images, controlnet_scales, self.controlnets)):
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down_samples, mid_sample = controlnet(
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sample,
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timestep,
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encoder_hidden_states=encoder_hidden_states,
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controlnet_cond=image,
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return_dict=False,
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added_cond_kwargs=added_cond_kwargs,
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)
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down_samples = [down_sample * conditioning_scale for down_sample in down_samples]
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mid_sample *= conditioning_scale
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# merge samples
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if i == 0:
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down_block_res_samples, mid_block_res_sample = down_samples, mid_sample
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else:
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down_block_res_samples = [
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samples_prev + samples_curr
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for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)
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]
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mid_block_res_sample += mid_sample
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noise_pred = self.unet(
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sample,
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timestep,
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encoder_hidden_states=encoder_hidden_states,
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down_block_additional_residuals=down_block_res_samples,
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mid_block_additional_residual=mid_block_res_sample,
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added_cond_kwargs=added_cond_kwargs,
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)
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return noise_pred
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class UNetModel(base_model.BaseModel):
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def __init__(
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self,
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version,
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pipeline,
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device,
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hf_token,
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verbose,
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framework_model_dir,
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fp16=False,
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int8=False,
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fp8=False,
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max_batch_size=16,
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text_maxlen=77,
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controlnets=None,
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do_classifier_free_guidance=False,
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):
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super(UNetModel, self).__init__(
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version,
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pipeline,
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device=device,
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hf_token=hf_token,
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verbose=verbose,
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framework_model_dir=framework_model_dir,
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fp16=fp16,
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int8=int8,
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fp8=fp8,
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max_batch_size=max_batch_size,
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text_maxlen=text_maxlen,
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embedding_dim=get_unet_embedding_dim(version, pipeline),
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)
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self.subfolder = "unet"
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self.controlnets = load.get_path(version, pipeline, controlnets) if controlnets else None
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self.unet_dim = 4
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self.xB = 2 if do_classifier_free_guidance else 1 # batch multiplier
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def get_model(self, torch_inference=""):
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model_opts = {"variant": "fp16", "torch_dtype": torch.float16} if self.fp16 else {}
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if self.controlnets:
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unet_model = UNet2DConditionModel.from_pretrained(
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self.path,
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subfolder=self.subfolder,
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use_safetensors=self.hf_safetensor,
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token=self.hf_token,
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**model_opts,
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).to(self.device)
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cnet_model_opts = {"torch_dtype": torch.float16} if self.fp16 else {}
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controlnets = torch.nn.ModuleList(
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[ControlNetModel.from_pretrained(path, **cnet_model_opts).to(self.device) for path in self.controlnets]
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)
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# FIXME - cache UNet2DConditionControlNetModel
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model = UNet2DConditionControlNetModel(unet_model, controlnets)
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else:
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unet_model_dir = load.get_checkpoint_dir(
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self.framework_model_dir, self.version, self.pipeline, self.subfolder
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)
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if not load.is_model_cached(unet_model_dir, model_opts, self.hf_safetensor):
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model = UNet2DConditionModel.from_pretrained(
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self.path,
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subfolder=self.subfolder,
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use_safetensors=self.hf_safetensor,
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token=self.hf_token,
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**model_opts,
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).to(self.device)
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model.save_pretrained(unet_model_dir, **model_opts)
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else:
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print(f"[I] Load UNet2DConditionModel model from: {unet_model_dir}")
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model = UNet2DConditionModel.from_pretrained(unet_model_dir, **model_opts).to(self.device)
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if torch_inference:
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model.to(memory_format=torch.channels_last)
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model = optimizer.optimize_checkpoint(model, torch_inference)
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return model
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def get_input_names(self):
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if self.controlnets is None:
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return ["sample", "timestep", "encoder_hidden_states"]
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else:
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return ["sample", "timestep", "encoder_hidden_states", "images", "controlnet_scales"]
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def get_output_names(self):
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return ["latent"]
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def get_dynamic_axes(self):
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xB = "2B" if self.xB == 2 else "B"
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if self.controlnets is None:
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return {
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"sample": {0: xB, 2: "H", 3: "W"},
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"encoder_hidden_states": {0: xB},
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"latent": {0: xB, 2: "H", 3: "W"},
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}
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else:
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return {
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"sample": {0: xB, 2: "H", 3: "W"},
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"encoder_hidden_states": {0: xB},
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"images": {1: xB, 3: "8H", 4: "8W"},
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"latent": {0: xB, 2: "H", 3: "W"},
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}
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def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
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# WAR to enable inference for H/W that are not multiples of 16
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# If building with Dynamic Shapes: ensure image height and width are not multiples of 16 for ONNX export and TensorRT engine build
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if not static_shape:
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image_height = image_height - 8 if image_height % 16 == 0 else image_height
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image_width = image_width - 8 if image_width % 16 == 0 else image_width
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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(
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min_batch,
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max_batch,
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min_image_height,
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max_image_height,
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min_image_width,
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max_image_width,
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min_latent_height,
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max_latent_height,
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min_latent_width,
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max_latent_width,
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) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
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if self.controlnets is None:
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return {
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"sample": [
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(self.xB * min_batch, self.unet_dim, min_latent_height, min_latent_width),
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(self.xB * batch_size, self.unet_dim, latent_height, latent_width),
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(self.xB * max_batch, self.unet_dim, max_latent_height, max_latent_width),
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],
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"encoder_hidden_states": [
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(self.xB * min_batch, self.text_maxlen, self.embedding_dim),
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(self.xB * batch_size, self.text_maxlen, self.embedding_dim),
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(self.xB * max_batch, self.text_maxlen, self.embedding_dim),
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],
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}
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else:
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return {
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"sample": [
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(self.xB * min_batch, self.unet_dim, min_latent_height, min_latent_width),
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(self.xB * batch_size, self.unet_dim, latent_height, latent_width),
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(self.xB * max_batch, self.unet_dim, max_latent_height, max_latent_width),
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],
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"encoder_hidden_states": [
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(self.xB * min_batch, self.text_maxlen, self.embedding_dim),
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(self.xB * batch_size, self.text_maxlen, self.embedding_dim),
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(self.xB * max_batch, self.text_maxlen, self.embedding_dim),
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],
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"images": [
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(len(self.controlnets), self.xB * min_batch, 3, min_image_height, min_image_width),
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(len(self.controlnets), self.xB * batch_size, 3, image_height, image_width),
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(len(self.controlnets), self.xB * max_batch, 3, max_image_height, max_image_width),
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],
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}
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def get_shape_dict(self, batch_size, image_height, image_width):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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if self.controlnets is None:
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return {
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"sample": (self.xB * batch_size, self.unet_dim, latent_height, latent_width),
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"encoder_hidden_states": (self.xB * batch_size, self.text_maxlen, self.embedding_dim),
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"latent": (self.xB * batch_size, 4, latent_height, latent_width),
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}
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else:
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return {
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"sample": (self.xB * batch_size, self.unet_dim, latent_height, latent_width),
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"encoder_hidden_states": (self.xB * batch_size, self.text_maxlen, self.embedding_dim),
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"images": (len(self.controlnets), self.xB * batch_size, 3, image_height, image_width),
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"latent": (self.xB * batch_size, 4, latent_height, latent_width),
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}
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def get_sample_input(self, batch_size, image_height, image_width, static_shape):
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# WAR to enable inference for H/W that are not multiples of 16
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# If building with Dynamic Shapes: ensure image height and width are not multiples of 16 for ONNX export and TensorRT engine build
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if not static_shape:
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image_height = image_height - 8 if image_height % 16 == 0 else image_height
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image_width = image_width - 8 if image_width % 16 == 0 else image_width
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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dtype = torch.float16 if self.fp16 else torch.float32
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if self.controlnets is None:
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return (
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torch.randn(batch_size, self.unet_dim, latent_height, latent_width, dtype=dtype, device=self.device),
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torch.tensor([1.0], dtype=dtype, device=self.device),
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torch.randn(batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device),
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)
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else:
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return (
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torch.randn(batch_size, self.unet_dim, latent_height, latent_width, dtype=dtype, device=self.device),
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torch.tensor(999, dtype=dtype, device=self.device),
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torch.randn(batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device),
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torch.randn(
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len(self.controlnets), batch_size, 3, image_height, image_width, dtype=dtype, device=self.device
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),
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torch.randn(len(self.controlnets), dtype=dtype, device=self.device),
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)
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def optimize(self, onnx_graph):
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if self.fp8:
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return super().optimize(onnx_graph, modify_fp8_graph=True)
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if self.int8:
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return super().optimize(onnx_graph, modify_int8_graph=True)
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return super().optimize(onnx_graph)
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class UNetXLModel(base_model.BaseModel):
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def __init__(
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self,
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version,
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pipeline,
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device,
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hf_token,
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verbose,
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framework_model_dir,
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fp16=False,
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int8=False,
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fp8=False,
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max_batch_size=16,
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text_maxlen=77,
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do_classifier_free_guidance=False,
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):
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super(UNetXLModel, self).__init__(
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version,
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pipeline,
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device=device,
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hf_token=hf_token,
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verbose=verbose,
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framework_model_dir=framework_model_dir,
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fp16=fp16,
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int8=int8,
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fp8=fp8,
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max_batch_size=max_batch_size,
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text_maxlen=text_maxlen,
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embedding_dim=get_unet_embedding_dim(version, pipeline),
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)
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self.subfolder = "unet"
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self.unet_dim = 4
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self.time_dim = 5 if pipeline.is_sd_xl_refiner() else 6
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self.xB = 2 if do_classifier_free_guidance else 1 # batch multiplier
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def get_model(self, torch_inference=""):
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model_opts = {"variant": "fp16", "torch_dtype": torch.float16} if self.fp16 else {}
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unet_model_dir = load.get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
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if not load.is_model_cached(unet_model_dir, model_opts, self.hf_safetensor):
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model = UNet2DConditionModel.from_pretrained(
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self.path,
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subfolder=self.subfolder,
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use_safetensors=self.hf_safetensor,
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token=self.hf_token,
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**model_opts,
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).to(self.device)
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# Use default attention processor for ONNX export
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if not torch_inference:
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model.set_default_attn_processor()
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model.save_pretrained(unet_model_dir, **model_opts)
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else:
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print(f"[I] Load UNet2DConditionModel model from: {unet_model_dir}")
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model = UNet2DConditionModel.from_pretrained(unet_model_dir, **model_opts).to(self.device)
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model = optimizer.optimize_checkpoint(model, torch_inference)
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return model
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def get_input_names(self):
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return ["sample", "timestep", "encoder_hidden_states", "text_embeds", "time_ids"]
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def get_output_names(self):
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return ["latent"]
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def get_dynamic_axes(self):
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xB = "2B" if self.xB == 2 else "B"
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return {
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"sample": {0: xB, 2: "H", 3: "W"},
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"encoder_hidden_states": {0: xB},
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"latent": {0: xB, 2: "H", 3: "W"},
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"text_embeds": {0: xB},
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"time_ids": {0: xB},
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}
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def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
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# WAR to enable inference for H/W that are not multiples of 16
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# If building with Dynamic Shapes: ensure image height and width are not multiples of 16 for ONNX export and TensorRT engine build
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if not static_shape:
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image_height = image_height - 8 if image_height % 16 == 0 else image_height
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image_width = image_width - 8 if image_width % 16 == 0 else image_width
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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min_batch, max_batch, _, _, _, _, min_latent_height, max_latent_height, min_latent_width, max_latent_width = (
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self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
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)
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return {
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"sample": [
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(self.xB * min_batch, self.unet_dim, min_latent_height, min_latent_width),
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(self.xB * batch_size, self.unet_dim, latent_height, latent_width),
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(self.xB * max_batch, self.unet_dim, max_latent_height, max_latent_width),
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],
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"encoder_hidden_states": [
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(self.xB * min_batch, self.text_maxlen, self.embedding_dim),
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(self.xB * batch_size, self.text_maxlen, self.embedding_dim),
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(self.xB * max_batch, self.text_maxlen, self.embedding_dim),
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],
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"text_embeds": [(self.xB * min_batch, 1280), (self.xB * batch_size, 1280), (self.xB * max_batch, 1280)],
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"time_ids": [
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(self.xB * min_batch, self.time_dim),
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(self.xB * batch_size, self.time_dim),
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(self.xB * max_batch, self.time_dim),
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],
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}
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def get_shape_dict(self, batch_size, image_height, image_width):
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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return {
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"sample": (self.xB * batch_size, self.unet_dim, latent_height, latent_width),
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"encoder_hidden_states": (self.xB * batch_size, self.text_maxlen, self.embedding_dim),
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"latent": (self.xB * batch_size, 4, latent_height, latent_width),
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"text_embeds": (self.xB * batch_size, 1280),
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"time_ids": (self.xB * batch_size, self.time_dim),
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}
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def get_sample_input(self, batch_size, image_height, image_width, static_shape):
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# WAR to enable inference for H/W that are not multiples of 16
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# If building with Dynamic Shapes: ensure image height and width are not multiples of 16 for ONNX export and TensorRT engine build
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if not static_shape:
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image_height = image_height - 8 if image_height % 16 == 0 else image_height
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image_width = image_width - 8 if image_width % 16 == 0 else image_width
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latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
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dtype = torch.float16 if self.fp16 else torch.float32
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return (
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torch.randn(
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self.xB * batch_size, self.unet_dim, latent_height, latent_width, dtype=dtype, device=self.device
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),
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torch.tensor([1.0], dtype=dtype, device=self.device),
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torch.randn(self.xB * batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device),
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{
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"added_cond_kwargs": {
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|
"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, modify_int8_graph=True)
|
|
return super().optimize(onnx_graph)
|
|
|
|
|
|
class UNetXLModelControlNet(UNetXLModel):
|
|
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,
|
|
do_classifier_free_guidance=False,
|
|
):
|
|
super().__init__(
|
|
version=version,
|
|
pipeline=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,
|
|
do_classifier_free_guidance=do_classifier_free_guidance,
|
|
)
|
|
self.controlnets = load.get_path(version, pipeline, controlnets) if controlnets else None
|
|
|
|
def get_pipeline(self):
|
|
cnet_model_opts = {"torch_dtype": torch.float16} if self.fp16 else {}
|
|
controlnets = [
|
|
ControlNetModel.from_pretrained(path, **cnet_model_opts).to(self.device) for path in self.controlnets
|
|
]
|
|
if self.bf16:
|
|
model_opts = {"torch_dtype": torch.bfloat16}
|
|
elif self.fp16:
|
|
model_opts = {"variant": "fp16", "torch_dtype": torch.float16}
|
|
else:
|
|
model_opts = {}
|
|
pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
|
|
self.path,
|
|
use_safetensors=self.hf_safetensor,
|
|
token=self.hf_token,
|
|
controlnet=controlnets,
|
|
**model_opts,
|
|
).to(self.device)
|
|
return pipeline
|
|
|
|
def get_model(self, torch_inference=""):
|
|
model_opts = {"variant": "fp16", "torch_dtype": torch.float16} if self.fp16 else {}
|
|
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)
|
|
model = optimizer.optimize_checkpoint(model, torch_inference)
|
|
return model
|
|
|
|
def get_input_names(self):
|
|
return ["sample", "timestep", "encoder_hidden_states", "images", "controlnet_scales", "text_embeds", "time_ids"]
|
|
|
|
def get_dynamic_axes(self):
|
|
xB = "2B" if self.xB == 2 else "B"
|
|
result = super().get_dynamic_axes()
|
|
result["images"] = {1: xB, 3: "8H", 4: "8W"}
|
|
return result
|
|
|
|
def get_input_profile(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, _, _, _, _ = (
|
|
self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
|
|
)
|
|
result = super().get_input_profile(batch_size, image_height, image_width, static_batch, static_shape)
|
|
result["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),
|
|
]
|
|
return result
|
|
|
|
def get_shape_dict(self, batch_size, image_height, image_width):
|
|
result = super().get_shape_dict(batch_size, image_height, image_width)
|
|
result["images"] = (len(self.controlnets), self.xB * batch_size, 3, image_height, image_width)
|
|
return result
|
|
|
|
def get_sample_input(self, batch_size, image_height, image_width, static_shape):
|
|
dtype = torch.float16 if self.fp16 else torch.float32
|
|
result = super().get_sample_input(batch_size, image_height, image_width, static_shape)
|
|
result = (
|
|
result[:-1]
|
|
+ (
|
|
torch.randn(
|
|
len(self.controlnets),
|
|
self.xB * batch_size,
|
|
3,
|
|
image_height,
|
|
image_width,
|
|
dtype=dtype,
|
|
device=self.device,
|
|
), # images
|
|
torch.randn(len(self.controlnets), dtype=dtype, device=self.device), # controlnet_scales
|
|
)
|
|
+ result[-1:]
|
|
)
|
|
return result
|
|
|
|
|
|
class UNetTemporalModel(base_model.BaseModel):
|
|
def __init__(
|
|
self,
|
|
version,
|
|
pipeline,
|
|
device,
|
|
hf_token,
|
|
verbose,
|
|
framework_model_dir,
|
|
fp16=False,
|
|
fp8=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,
|
|
fp8=fp8,
|
|
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 = load.get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
|
|
if not load.is_model_cached(unet_model_dir, model_opts, self.hf_safetensor):
|
|
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_model_dir}")
|
|
model = UNetSpatioTemporalConditionModel.from_pretrained(unet_model_dir, **model_opts).to(self.device)
|
|
model = optimizer.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, static_shape):
|
|
# 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([1.0], 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),
|
|
)
|
|
|
|
def optimize(self, onnx_graph):
|
|
return super().optimize(onnx_graph, modify_fp8_graph=self.fp8)
|
|
|
|
|
|
class UNetCascadeModel(base_model.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 = load.get_checkpoint_dir(self.framework_model_dir, self.version, self.pipeline, self.subfolder)
|
|
if not load.is_model_cached(unet_model_dir, model_opts, self.hf_safetensor):
|
|
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_model_dir}")
|
|
model = StableCascadeUNet.from_pretrained(unet_model_dir, **model_opts).to(self.device)
|
|
model = optimizer.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([1.0] * 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([1.0] * 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
|
|
),
|
|
},
|
|
)
|