564 lines
20 KiB
Python
564 lines
20 KiB
Python
###
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# Parts of this file are copied or derived from:
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# https://github.com/huggingface/diffusers/blob/main/scripts/convert_stable_diffusion_checkpoint_to_onnx.py
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#
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# Originally by https://github.com/huggingface
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# Those portions *are not* covered by the MIT licensed used for the rest of the onnx-web project.
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# ...diffusers.pipelines.pipeline_onnx_stable_diffusion_upscale
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# HuggingFace code used under the Apache License, Version 2.0
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# https://github.com/huggingface/diffusers/blob/main/LICENSE
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###
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from logging import getLogger
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from os import mkdir, path
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from pathlib import Path
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from shutil import rmtree
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from typing import Any, Dict, Optional, Tuple, Union
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import torch
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from diffusers import (
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AutoencoderKL,
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OnnxRuntimeModel,
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OnnxStableDiffusionPipeline,
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StableDiffusionControlNetPipeline,
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StableDiffusionInstructPix2PixPipeline,
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StableDiffusionPipeline,
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StableDiffusionUpscalePipeline,
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)
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from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
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download_from_original_stable_diffusion_ckpt,
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)
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from onnx import load_model, save_model
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from ...constants import ONNX_MODEL, ONNX_WEIGHTS
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from ...diffusers.load import optimize_pipeline
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from ...diffusers.pipelines.upscale import OnnxStableDiffusionUpscalePipeline
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from ...diffusers.version_safe_diffusers import AttnProcessor
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from ...models.cnet import UNet2DConditionModel_CNet
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from ..utils import ConversionContext, is_torch_2_0, load_tensor, onnx_export
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logger = getLogger(__name__)
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available_pipelines = {
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"controlnet": StableDiffusionControlNetPipeline,
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"img2img": StableDiffusionPipeline,
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"inpaint": StableDiffusionPipeline,
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"lpw": StableDiffusionPipeline,
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"panorama": StableDiffusionPipeline,
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"pix2pix": StableDiffusionInstructPix2PixPipeline,
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"txt2img": StableDiffusionPipeline,
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"upscale": StableDiffusionUpscalePipeline,
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}
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def get_model_version(
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source,
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map_location,
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size = None,
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version = None,
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) -> Tuple[bool, Dict[str, Union[bool, int, str]]]:
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v2 = version is not None and "v2" in version
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opts = {
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"extract_ema": True,
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}
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try:
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checkpoint = load_tensor(source, map_location=map_location)
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if "global_step" in checkpoint:
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global_step = checkpoint["global_step"]
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else:
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print("global_step key not found in model")
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global_step = None
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if size is None:
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# NOTE: For stable diffusion 2 base one has to pass `image_size==512`
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# as it relies on a brittle global step parameter here
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size = 512 if global_step == 875000 else 768
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opts["image_size"] = size
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key_name = (
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"model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
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)
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if key_name in checkpoint and checkpoint[key_name].shape[-1] == 1024:
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v2 = True
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if size != 512:
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# v2.1 needs to upcast attention
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logger.debug("setting upcast_attention")
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opts["upcast_attention"] = True
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if v2 and size != 512:
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opts["model_type"] = "FrozenOpenCLIPEmbedder"
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opts["prediction_type"] = "v_prediction"
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else:
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opts["model_type"] = "FrozenCLIPEmbedder"
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opts["prediction_type"] = "epsilon"
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except:
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logger.debug("unable to load tensor for version check")
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pass
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return (v2, opts)
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def convert_diffusion_diffusers_cnet(
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conversion: ConversionContext,
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source: str,
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device: str,
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output_path: Path,
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dtype,
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unet_in_channels,
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unet_sample_size,
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num_tokens,
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text_hidden_size,
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unet: Optional[Any] = None,
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):
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# CNet
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if unet is not None:
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pipe_cnet = UNet2DConditionModel_CNet.from_config(unet.config)
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else:
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pipe_cnet = UNet2DConditionModel_CNet.from_pretrained(source, subfolder="unet")
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pipe_cnet = pipe_cnet.to(device=device, dtype=dtype)
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if is_torch_2_0:
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pipe_cnet.set_attn_processor(AttnProcessor())
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optimize_pipeline(conversion, pipe_cnet)
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cnet_path = output_path / "cnet" / ONNX_MODEL
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onnx_export(
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pipe_cnet,
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model_args=(
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torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(
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device=device, dtype=dtype
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),
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torch.randn(2).to(device=device, dtype=dtype),
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torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype),
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torch.randn(2, 320, unet_sample_size, unet_sample_size).to(
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device=device, dtype=dtype
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),
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torch.randn(2, 320, unet_sample_size, unet_sample_size).to(
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device=device, dtype=dtype
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),
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torch.randn(2, 320, unet_sample_size, unet_sample_size).to(
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device=device, dtype=dtype
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),
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torch.randn(2, 320, unet_sample_size // 2, unet_sample_size // 2).to(
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device=device, dtype=dtype
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),
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torch.randn(2, 640, unet_sample_size // 2, unet_sample_size // 2).to(
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device=device, dtype=dtype
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),
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torch.randn(2, 640, unet_sample_size // 2, unet_sample_size // 2).to(
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device=device, dtype=dtype
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),
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torch.randn(2, 640, unet_sample_size // 4, unet_sample_size // 4).to(
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device=device, dtype=dtype
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),
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torch.randn(2, 1280, unet_sample_size // 4, unet_sample_size // 4).to(
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device=device, dtype=dtype
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),
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torch.randn(2, 1280, unet_sample_size // 4, unet_sample_size // 4).to(
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device=device, dtype=dtype
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),
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torch.randn(2, 1280, unet_sample_size // 8, unet_sample_size // 8).to(
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device=device, dtype=dtype
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),
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torch.randn(2, 1280, unet_sample_size // 8, unet_sample_size // 8).to(
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device=device, dtype=dtype
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),
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torch.randn(2, 1280, unet_sample_size // 8, unet_sample_size // 8).to(
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device=device, dtype=dtype
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),
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torch.randn(2, 1280, unet_sample_size // 8, unet_sample_size // 8).to(
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device=device, dtype=dtype
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),
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False,
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),
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output_path=cnet_path,
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ordered_input_names=[
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"sample",
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"timestep",
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"encoder_hidden_states",
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"down_block_0",
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"down_block_1",
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"down_block_2",
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"down_block_3",
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"down_block_4",
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"down_block_5",
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"down_block_6",
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"down_block_7",
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"down_block_8",
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"down_block_9",
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"down_block_10",
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"down_block_11",
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"mid_block_additional_residual",
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"return_dict",
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],
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output_names=[
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"out_sample"
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], # has to be different from "sample" for correct tracing
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dynamic_axes={
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"sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
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"timestep": {0: "batch"},
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"encoder_hidden_states": {0: "batch", 1: "sequence"},
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"down_block_0": {0: "batch", 2: "height", 3: "width"},
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"down_block_1": {0: "batch", 2: "height", 3: "width"},
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"down_block_2": {0: "batch", 2: "height", 3: "width"},
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"down_block_3": {0: "batch", 2: "height2", 3: "width2"},
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"down_block_4": {0: "batch", 2: "height2", 3: "width2"},
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"down_block_5": {0: "batch", 2: "height2", 3: "width2"},
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"down_block_6": {0: "batch", 2: "height4", 3: "width4"},
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"down_block_7": {0: "batch", 2: "height4", 3: "width4"},
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"down_block_8": {0: "batch", 2: "height4", 3: "width4"},
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"down_block_9": {0: "batch", 2: "height8", 3: "width8"},
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"down_block_10": {0: "batch", 2: "height8", 3: "width8"},
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"down_block_11": {0: "batch", 2: "height8", 3: "width8"},
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"mid_block_additional_residual": {0: "batch", 2: "height8", 3: "width8"},
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},
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opset=conversion.opset,
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half=conversion.half,
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external_data=True, # UNet is > 2GB, so the weights need to be split
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)
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cnet_model_path = str(cnet_path.absolute().as_posix())
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cnet_dir = path.dirname(cnet_model_path)
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cnet = load_model(cnet_model_path)
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# clean up existing tensor files
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rmtree(cnet_dir)
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mkdir(cnet_dir)
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# collate external tensor files into one
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save_model(
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cnet,
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cnet_model_path,
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save_as_external_data=True,
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all_tensors_to_one_file=True,
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location=ONNX_WEIGHTS,
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convert_attribute=False,
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)
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del pipe_cnet
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@torch.no_grad()
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def convert_diffusion_diffusers(
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conversion: ConversionContext,
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model: Dict,
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source: str,
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) -> Tuple[bool, str]:
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"""
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From https://github.com/huggingface/diffusers/blob/main/scripts/convert_stable_diffusion_checkpoint_to_onnx.py
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"""
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config = model.get("config", None)
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image_size = model.get("image_size", None)
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name = model.get("name")
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pipe_type = model.get("pipeline", "txt2img")
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single_vae = model.get("single_vae")
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source = source or model.get("source")
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replace_vae = model.get("vae")
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version = model.get("version", None)
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device = conversion.training_device
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dtype = conversion.torch_dtype()
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logger.debug("using Torch dtype %s for pipeline", dtype)
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config_path = (
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None if config is None else path.join(conversion.model_path, "config", config)
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)
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dest_path = path.join(conversion.model_path, name)
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model_index = path.join(dest_path, "model_index.json")
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model_cnet = path.join(dest_path, "cnet", ONNX_MODEL)
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# diffusers go into a directory rather than .onnx file
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logger.info(
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"converting Stable Diffusion model %s: %s -> %s/", name, source, dest_path
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)
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if single_vae:
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logger.info("converting model with single VAE")
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cnet_only = False
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if path.exists(dest_path) and path.exists(model_index):
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if not path.exists(model_cnet):
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logger.info(
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"ONNX model was converted without a ControlNet UNet, converting one"
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)
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cnet_only = True
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else:
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logger.info("ONNX model already exists, skipping")
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return (False, dest_path)
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pipe_class = available_pipelines.get(pipe_type)
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v2, pipe_args = get_model_version(source, conversion.map_location, size=image_size, version=version)
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if pipe_type == "inpaint":
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pipe_args["num_in_channels"] = 9
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if path.exists(source) and path.isdir(source):
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logger.debug("loading pipeline from diffusers directory: %s", source)
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pipeline = pipe_class.from_pretrained(
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source,
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torch_dtype=dtype,
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use_auth_token=conversion.token,
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).to(device)
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elif path.exists(source) and path.isfile(source):
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logger.debug("loading pipeline from SD checkpoint: %s", source)
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pipeline = download_from_original_stable_diffusion_ckpt(
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source,
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original_config_file=config_path,
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pipeline_class=pipe_class,
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torch_dtype=dtype,
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**pipe_args,
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).to(device, torch_dtype=dtype)
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else:
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logger.warning("pipeline source not found or not recognized: %s", source)
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raise ValueError(f"pipeline source not found or not recognized: {source}")
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optimize_pipeline(conversion, pipeline)
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output_path = Path(dest_path)
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# TEXT ENCODER
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num_tokens = pipeline.text_encoder.config.max_position_embeddings
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text_hidden_size = pipeline.text_encoder.config.hidden_size
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text_input = pipeline.tokenizer(
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"A sample prompt",
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padding="max_length",
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max_length=pipeline.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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if not cnet_only:
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onnx_export(
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pipeline.text_encoder,
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# casting to torch.int32 until the CLIP fix is released: https://github.com/huggingface/transformers/pull/18515/files
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model_args=(
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text_input.input_ids.to(device=device, dtype=torch.int32),
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None, # attention mask
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None, # position ids
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None, # output attentions
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torch.tensor(True).to(device=device, dtype=torch.bool),
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),
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output_path=output_path / "text_encoder" / ONNX_MODEL,
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ordered_input_names=["input_ids"],
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output_names=["last_hidden_state", "pooler_output", "hidden_states"],
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dynamic_axes={
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"input_ids": {0: "batch", 1: "sequence"},
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},
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opset=conversion.opset,
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half=conversion.half,
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)
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del pipeline.text_encoder
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# UNET
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logger.debug("UNET config: %s", pipeline.unet.config)
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if single_vae:
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unet_inputs = ["sample", "timestep", "encoder_hidden_states", "class_labels"]
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unet_scale = torch.tensor(4).to(device=device, dtype=torch.long)
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else:
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unet_inputs = ["sample", "timestep", "encoder_hidden_states", "return_dict"]
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unet_scale = torch.tensor(False).to(device=device, dtype=torch.bool)
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if is_torch_2_0:
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pipeline.unet.set_attn_processor(AttnProcessor())
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unet_in_channels = pipeline.unet.config.in_channels
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unet_sample_size = pipeline.unet.config.sample_size
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unet_path = output_path / "unet" / ONNX_MODEL
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if not cnet_only:
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onnx_export(
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pipeline.unet,
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model_args=(
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torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(
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device=device, dtype=dtype
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),
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torch.randn(2).to(device=device, dtype=dtype),
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torch.randn(2, num_tokens, text_hidden_size).to(
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device=device, dtype=dtype
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),
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unet_scale,
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),
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output_path=unet_path,
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ordered_input_names=unet_inputs,
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# has to be different from "sample" for correct tracing
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output_names=["out_sample"],
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dynamic_axes={
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"sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
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"timestep": {0: "batch"},
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"encoder_hidden_states": {0: "batch", 1: "sequence"},
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},
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opset=conversion.opset,
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half=conversion.half,
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external_data=True,
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)
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unet_model_path = str(unet_path.absolute().as_posix())
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unet_dir = path.dirname(unet_model_path)
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unet = load_model(unet_model_path)
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# clean up existing tensor files
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rmtree(unet_dir)
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mkdir(unet_dir)
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# collate external tensor files into one
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save_model(
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unet,
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unet_model_path,
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save_as_external_data=True,
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all_tensors_to_one_file=True,
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location=ONNX_WEIGHTS,
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convert_attribute=False,
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)
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if not single_vae:
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# if converting only the CNet, the rest of the model has already been converted
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convert_diffusion_diffusers_cnet(
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conversion,
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source,
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device,
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output_path,
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dtype,
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unet_in_channels,
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unet_sample_size,
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num_tokens,
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text_hidden_size,
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unet=pipeline.unet,
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)
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else:
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logger.debug("skipping CNet for single-VAE model")
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del pipeline.unet
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if cnet_only:
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logger.info("done converting CNet")
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return (True, dest_path)
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# VAE
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if replace_vae is not None:
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logger.debug("loading custom VAE: %s", replace_vae)
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vae = AutoencoderKL.from_pretrained(replace_vae)
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pipeline.vae = vae
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if single_vae:
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logger.debug("VAE config: %s", pipeline.vae.config)
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# SINGLE VAE
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vae_only = pipeline.vae
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vae_latent_channels = vae_only.config.latent_channels
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# forward only through the decoder part
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vae_only.forward = vae_only.decode
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onnx_export(
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vae_only,
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model_args=(
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torch.randn(
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1, vae_latent_channels, unet_sample_size, unet_sample_size
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).to(device=device, dtype=dtype),
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False,
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),
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output_path=output_path / "vae" / ONNX_MODEL,
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ordered_input_names=["latent_sample", "return_dict"],
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output_names=["sample"],
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dynamic_axes={
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"latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
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},
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opset=conversion.opset,
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half=conversion.half,
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)
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else:
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# VAE ENCODER
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vae_encoder = pipeline.vae
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vae_in_channels = vae_encoder.config.in_channels
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vae_sample_size = vae_encoder.config.sample_size
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# need to get the raw tensor output (sample) from the encoder
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|
vae_encoder.forward = lambda sample, return_dict: vae_encoder.encode(
|
|
sample, return_dict
|
|
)[0].sample()
|
|
onnx_export(
|
|
vae_encoder,
|
|
model_args=(
|
|
torch.randn(1, vae_in_channels, vae_sample_size, vae_sample_size).to(
|
|
device=device, dtype=dtype
|
|
),
|
|
False,
|
|
),
|
|
output_path=output_path / "vae_encoder" / ONNX_MODEL,
|
|
ordered_input_names=["sample", "return_dict"],
|
|
output_names=["latent_sample"],
|
|
dynamic_axes={
|
|
"sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
|
|
},
|
|
opset=conversion.opset,
|
|
half=False, # https://github.com/ssube/onnx-web/issues/290
|
|
)
|
|
|
|
# VAE DECODER
|
|
vae_decoder = pipeline.vae
|
|
vae_latent_channels = vae_decoder.config.latent_channels
|
|
# forward only through the decoder part
|
|
vae_decoder.forward = vae_encoder.decode
|
|
onnx_export(
|
|
vae_decoder,
|
|
model_args=(
|
|
torch.randn(
|
|
1, vae_latent_channels, unet_sample_size, unet_sample_size
|
|
).to(device=device, dtype=dtype),
|
|
False,
|
|
),
|
|
output_path=output_path / "vae_decoder" / ONNX_MODEL,
|
|
ordered_input_names=["latent_sample", "return_dict"],
|
|
output_names=["sample"],
|
|
dynamic_axes={
|
|
"latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
|
|
},
|
|
opset=conversion.opset,
|
|
half=conversion.half,
|
|
)
|
|
|
|
del pipeline.vae
|
|
|
|
if single_vae:
|
|
onnx_pipeline = OnnxStableDiffusionUpscalePipeline(
|
|
vae=OnnxRuntimeModel.from_pretrained(output_path / "vae"),
|
|
text_encoder=OnnxRuntimeModel.from_pretrained(output_path / "text_encoder"),
|
|
tokenizer=pipeline.tokenizer,
|
|
low_res_scheduler=pipeline.scheduler,
|
|
unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"),
|
|
scheduler=pipeline.scheduler,
|
|
)
|
|
else:
|
|
onnx_pipeline = OnnxStableDiffusionPipeline(
|
|
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder"),
|
|
vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_decoder"),
|
|
text_encoder=OnnxRuntimeModel.from_pretrained(output_path / "text_encoder"),
|
|
tokenizer=pipeline.tokenizer,
|
|
unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"),
|
|
scheduler=pipeline.scheduler,
|
|
safety_checker=None,
|
|
feature_extractor=None,
|
|
requires_safety_checker=False,
|
|
)
|
|
|
|
logger.info("exporting ONNX model")
|
|
|
|
onnx_pipeline.save_pretrained(output_path)
|
|
logger.info("ONNX pipeline saved to %s", output_path)
|
|
|
|
del pipeline
|
|
del onnx_pipeline
|
|
|
|
if single_vae:
|
|
_ = OnnxStableDiffusionUpscalePipeline.from_pretrained(
|
|
output_path, provider="CPUExecutionProvider"
|
|
)
|
|
else:
|
|
_ = OnnxStableDiffusionPipeline.from_pretrained(
|
|
output_path, provider="CPUExecutionProvider"
|
|
)
|
|
|
|
logger.info("ONNX pipeline is loadable")
|
|
|
|
return (True, dest_path)
|