335 lines
11 KiB
Python
335 lines
11 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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#
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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 Dict
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import torch
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from diffusers import (
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OnnxRuntimeModel,
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OnnxStableDiffusionPipeline,
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StableDiffusionPipeline,
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)
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from onnx import load, save_model
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from torch.onnx import export
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from ..diffusion.pipeline_onnx_stable_diffusion_upscale import (
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OnnxStableDiffusionUpscalePipeline,
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)
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from .utils import ConversionContext
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logger = getLogger(__name__)
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def onnx_export(
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model,
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model_args: tuple,
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output_path: Path,
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ordered_input_names,
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output_names,
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dynamic_axes,
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opset,
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use_external_data_format=False,
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):
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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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output_path.parent.mkdir(parents=True, exist_ok=True)
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export(
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model,
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model_args,
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f=output_path.as_posix(),
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input_names=ordered_input_names,
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output_names=output_names,
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dynamic_axes=dynamic_axes,
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do_constant_folding=True,
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opset_version=opset,
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)
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@torch.no_grad()
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def convert_diffusion_stable(
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ctx: ConversionContext,
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model: Dict,
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source: str,
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):
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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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name = model.get("name")
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source = source or model.get("source")
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single_vae = model.get("single_vae")
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dtype = torch.float16 if ctx.half else torch.float32
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dest_path = path.join(ctx.model_path, name)
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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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if path.exists(dest_path):
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logger.info("ONNX model already exists, skipping.")
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return
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pipeline = StableDiffusionPipeline.from_pretrained(
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source,
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torch_dtype=dtype,
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use_auth_token=ctx.token,
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# cache_dir=path.join(ctx.cache_path, name)
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).to(ctx.training_device)
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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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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=ctx.training_device, dtype=torch.int32)
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),
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output_path=output_path / "text_encoder" / "model.onnx",
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ordered_input_names=["input_ids"],
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output_names=["last_hidden_state", "pooler_output"],
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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=ctx.opset,
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)
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del pipeline.text_encoder
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logger.debug("UNET config: %s", pipeline.unet.config)
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# UNET
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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(
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device=ctx.training_device, dtype=torch.long
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)
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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(
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device=ctx.training_device, dtype=torch.bool
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)
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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" / "model.onnx"
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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=ctx.training_device, dtype=dtype
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),
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torch.randn(2).to(device=ctx.training_device, dtype=dtype),
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torch.randn(2, num_tokens, text_hidden_size).to(
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device=ctx.training_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=ctx.opset,
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use_external_data_format=True, # UNet is > 2GB, so the weights need to be split
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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(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="weights.pb",
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convert_attribute=False,
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)
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del pipeline.unet
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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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vae_out_channels = vae_only.config.out_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=ctx.training_device, dtype=dtype),
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False,
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),
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output_path=output_path / "vae" / "model.onnx",
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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=ctx.opset,
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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(
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sample, return_dict
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)[0].sample()
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onnx_export(
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vae_encoder,
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model_args=(
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torch.randn(1, vae_in_channels, vae_sample_size, vae_sample_size).to(
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device=ctx.training_device, dtype=dtype
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),
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False,
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),
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output_path=output_path / "vae_encoder" / "model.onnx",
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ordered_input_names=["sample", "return_dict"],
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output_names=["latent_sample"],
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dynamic_axes={
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"sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
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},
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opset=ctx.opset,
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)
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# VAE DECODER
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vae_decoder = pipeline.vae
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vae_latent_channels = vae_decoder.config.latent_channels
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vae_out_channels = vae_decoder.config.out_channels
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# forward only through the decoder part
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vae_decoder.forward = vae_encoder.decode
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onnx_export(
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vae_decoder,
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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=ctx.training_device, dtype=dtype),
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False,
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),
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output_path=output_path / "vae_decoder" / "model.onnx",
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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=ctx.opset,
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)
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del pipeline.vae
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# SAFETY CHECKER
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if pipeline.safety_checker is not None:
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safety_checker = pipeline.safety_checker
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clip_num_channels = safety_checker.config.vision_config.num_channels
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clip_image_size = safety_checker.config.vision_config.image_size
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safety_checker.forward = safety_checker.forward_onnx
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onnx_export(
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pipeline.safety_checker,
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model_args=(
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torch.randn(
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1,
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clip_num_channels,
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clip_image_size,
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clip_image_size,
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).to(device=ctx.training_device, dtype=dtype),
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torch.randn(1, vae_sample_size, vae_sample_size, vae_out_channels).to(
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device=ctx.training_device, dtype=dtype
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),
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),
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output_path=output_path / "safety_checker" / "model.onnx",
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ordered_input_names=["clip_input", "images"],
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output_names=["out_images", "has_nsfw_concepts"],
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dynamic_axes={
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"clip_input": {0: "batch", 1: "channels", 2: "height", 3: "width"},
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"images": {0: "batch", 1: "height", 2: "width", 3: "channels"},
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},
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opset=ctx.opset,
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)
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del pipeline.safety_checker
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safety_checker = OnnxRuntimeModel.from_pretrained(
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output_path / "safety_checker"
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)
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feature_extractor = pipeline.feature_extractor
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else:
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safety_checker = None
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feature_extractor = None
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if single_vae:
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onnx_pipeline = OnnxStableDiffusionUpscalePipeline(
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vae=OnnxRuntimeModel.from_pretrained(output_path / "vae"),
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text_encoder=OnnxRuntimeModel.from_pretrained(output_path / "text_encoder"),
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tokenizer=pipeline.tokenizer,
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low_res_scheduler=pipeline.scheduler,
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unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"),
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scheduler=pipeline.scheduler,
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)
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else:
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onnx_pipeline = OnnxStableDiffusionPipeline(
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vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder"),
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vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_decoder"),
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text_encoder=OnnxRuntimeModel.from_pretrained(output_path / "text_encoder"),
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tokenizer=pipeline.tokenizer,
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unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"),
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scheduler=pipeline.scheduler,
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safety_checker=safety_checker,
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feature_extractor=feature_extractor,
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requires_safety_checker=safety_checker is not None,
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)
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logger.info("exporting ONNX model")
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onnx_pipeline.save_pretrained(output_path)
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logger.info("ONNX pipeline saved to %s", output_path)
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del pipeline
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del onnx_pipeline
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if single_vae:
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_ = OnnxStableDiffusionUpscalePipeline.from_pretrained(
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output_path, provider="CPUExecutionProvider"
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)
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else:
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_ = OnnxStableDiffusionPipeline.from_pretrained(
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output_path, provider="CPUExecutionProvider"
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)
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logger.info("ONNX pipeline is loadable")
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