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torch2coreml_custom.py
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torch2coreml_custom.py
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#
# For licensing see accompanying LICENSE.md file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#
from python_coreml_stable_diffusion import (
unet, controlnet, chunk_mlprogram
)
import argparse
from collections import OrderedDict, defaultdict
from copy import deepcopy
import coremltools as ct
from diffusers import (
StableDiffusionPipeline,
DiffusionPipeline,
ControlNetModel,
UNet2DConditionModel
)
import gc
import logging
logging.basicConfig()
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
import numpy as np
import os
import requests
import shutil
import time
import re
import pathlib
import torch
import torch.nn as nn
import torch.nn.functional as F
torch.set_grad_enabled(False)
from types import MethodType
def _get_coreml_inputs(sample_inputs, args):
return [
ct.TensorType(
name=k,
shape=v.shape,
dtype=v.numpy().dtype if isinstance(v, torch.Tensor) else v.dtype,
) for k, v in sample_inputs.items()
]
def compute_psnr(a, b):
""" Compute Peak-Signal-to-Noise-Ratio across two numpy.ndarray objects
"""
max_b = np.abs(b).max()
sumdeltasq = 0.0
sumdeltasq = ((a - b) * (a - b)).sum()
sumdeltasq /= b.size
sumdeltasq = np.sqrt(sumdeltasq)
eps = 1e-5
eps2 = 1e-10
psnr = 20 * np.log10((max_b + eps) / (sumdeltasq + eps2))
return psnr
ABSOLUTE_MIN_PSNR = 35
def report_correctness(original_outputs, final_outputs, log_prefix):
""" Report PSNR values across two compatible tensors
"""
original_psnr = compute_psnr(original_outputs, original_outputs)
final_psnr = compute_psnr(original_outputs, final_outputs)
dB_change = final_psnr - original_psnr
logger.info(
f"{log_prefix}: PSNR changed by {dB_change:.1f} dB ({original_psnr:.1f} -> {final_psnr:.1f})"
)
if final_psnr < ABSOLUTE_MIN_PSNR:
raise ValueError(f"{final_psnr:.1f} dB is too low!")
else:
logger.info(
f"{final_psnr:.1f} dB > {ABSOLUTE_MIN_PSNR} dB (minimum allowed) parity check passed"
)
return final_psnr
def _get_out_path(args, submodule_name):
fname = f"Stable_Diffusion_version_{args.model_version}_{submodule_name}.mlpackage"
fname = fname.replace("/", "_")
return os.path.join(args.o, fname)
def _convert_to_coreml(submodule_name, torchscript_module, sample_inputs,
output_names, args, out_path=None, precision=None, compute_unit=None):
if out_path is None:
out_path = _get_out_path(args, submodule_name)
compute_unit = compute_unit or ct.ComputeUnit[args.compute_unit]
if os.path.exists(out_path):
logger.info(f"Skipping export because {out_path} already exists")
logger.info(f"Loading model from {out_path}")
start = time.time()
# Note: Note that each model load will trigger a model compilation which takes up to a few minutes.
# The Swifty CLI we provide uses precompiled Core ML models (.mlmodelc) which incurs compilation only
# upon first load and mitigates the load time in subsequent runs.
coreml_model = ct.models.MLModel(
out_path, compute_units=compute_unit)
logger.info(
f"Loading {out_path} took {time.time() - start:.1f} seconds")
coreml_model.compute_unit = compute_unit
else:
logger.info(f"Converting {submodule_name} to CoreML..")
coreml_model = ct.convert(
torchscript_module,
convert_to="mlprogram",
minimum_deployment_target=ct.target.macOS13,
inputs=_get_coreml_inputs(sample_inputs, args),
outputs=[ct.TensorType(name=name, dtype=np.float32) for name in output_names],
compute_units=compute_unit,
compute_precision=precision,
skip_model_load=not args.check_output_correctness,
)
del torchscript_module
gc.collect()
return coreml_model, out_path
def quantize_weights(args):
""" Quantize weights to args.quantize_nbits using a palette (look-up table)
"""
for model_name in ["text_encoder", "text_encoder_2", "unet", "refiner", "control-unet", "safety_checker"]:
logger.info(f"Quantizing {model_name} to {args.quantize_nbits}-bit precision")
out_path = _get_out_path(args, model_name)
_quantize_weights(
out_path,
model_name,
args.quantize_nbits
)
if args.convert_controlnet:
for controlnet_model_version in args.convert_controlnet:
controlnet_model_name = controlnet_model_version.replace("/", "_")
logger.info(f"Quantizing {controlnet_model_name} to {args.quantize_nbits}-bit precision")
fname = f"ControlNet_{controlnet_model_name}.mlpackage"
out_path = os.path.join(args.o, fname)
_quantize_weights(
out_path,
controlnet_model_name,
args.quantize_nbits
)
def _quantize_weights(out_path, model_name, nbits):
if os.path.exists(out_path):
logger.info(f"Quantizing {model_name}")
mlmodel = ct.models.MLModel(out_path,
compute_units=ct.ComputeUnit.CPU_ONLY)
op_config = ct.optimize.coreml.OpPalettizerConfig(
mode="kmeans",
nbits=nbits,
)
config = ct.optimize.coreml.OptimizationConfig(
global_config=op_config,
op_type_configs={
"gather": None # avoid quantizing the embedding table
}
)
model = ct.optimize.coreml.palettize_weights(mlmodel, config=config).save(out_path)
logger.info("Done")
else:
logger.info(
f"Skipped quantizing {model_name} (Not found at {out_path})")
def _compile_coreml_model(source_model_path, output_dir, final_name):
""" Compiles Core ML models using the coremlcompiler utility from Xcode toolchain
"""
target_path = os.path.join(output_dir, f"{final_name}.mlmodelc")
if os.path.exists(target_path):
logger.warning(
f"Found existing compiled model at {target_path}! Skipping..")
return target_path
logger.info(f"Compiling {source_model_path}")
source_model_name = os.path.basename(
os.path.splitext(source_model_path)[0])
os.system(f"xcrun coremlcompiler compile {source_model_path} {output_dir}")
compiled_output = os.path.join(output_dir, f"{source_model_name}.mlmodelc")
shutil.move(compiled_output, target_path)
return target_path
def bundle_resources_for_swift_cli(args):
"""
- Compiles Core ML models from mlpackage into mlmodelc format
- Download tokenizer resources for the text encoder
"""
resources_dir = os.path.join(args.o, "Resources")
if not os.path.exists(resources_dir):
os.makedirs(resources_dir, exist_ok=True)
logger.info(f"Created {resources_dir} for Swift CLI assets")
# Compile model using coremlcompiler (Significantly reduces the load time for unet)
for source_name, target_name in [("text_encoder", "TextEncoder"),
("text_encoder_2", "TextEncoder2"),
("vae_decoder", "VAEDecoder"),
("vae_encoder", "VAEEncoder"),
("unet", "Unet"),
("unet_chunk1", "UnetChunk1"),
("unet_chunk2", "UnetChunk2"),
("refiner", "UnetRefiner"),
("refiner_chunk1", "UnetRefinerChunk1"),
("refiner_chunk2", "UnetRefinerChunk2"),
("control-unet", "ControlledUnet"),
("control-unet_chunk1", "ControlledUnetChunk1"),
("control-unet_chunk2", "ControlledUnetChunk2"),
("safety_checker", "SafetyChecker")]:
source_path = _get_out_path(args, source_name)
if os.path.exists(source_path):
target_path = _compile_coreml_model(source_path, resources_dir,
target_name)
logger.info(f"Compiled {source_path} to {target_path}")
else:
logger.warning(
f"{source_path} not found, skipping compilation to {target_name}.mlmodelc"
)
if args.convert_controlnet:
for controlnet_model_version in args.convert_controlnet:
controlnet_model_name = controlnet_model_version.replace("/", "_")
fname = f"ControlNet_{controlnet_model_name}.mlpackage"
source_path = os.path.join(args.o, fname)
controlnet_dir = os.path.join(resources_dir, "controlnet")
target_name = "".join([word.title() for word in re.split('_|-', controlnet_model_name)])
if os.path.exists(source_path):
target_path = _compile_coreml_model(source_path, controlnet_dir,
target_name)
logger.info(f"Compiled {source_path} to {target_path}")
else:
logger.warning(
f"{source_path} not found, skipping compilation to {target_name}.mlmodelc"
)
# Fetch and save vocabulary JSON file for text tokenizer
logger.info("Downloading and saving tokenizer vocab.json")
with open(os.path.join(resources_dir, "vocab.json"), "wb") as f:
f.write(requests.get(args.text_encoder_vocabulary_url).content)
logger.info("Done")
# Fetch and save merged pairs JSON file for text tokenizer
logger.info("Downloading and saving tokenizer merges.txt")
with open(os.path.join(resources_dir, "merges.txt"), "wb") as f:
f.write(requests.get(args.text_encoder_merges_url).content)
logger.info("Done")
return resources_dir
from transformers.models.clip import modeling_clip
# Copied from https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/clip/modeling_clip.py#L677C1-L692C1
def patched_make_causal_mask(input_ids_shape, dtype, device, past_key_values_length: int = 0):
""" Patch to replace torch.finfo(dtype).min with -1e4
"""
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), torch.tensor(-1e4, device=device), device=device)
mask_cond = torch.arange(mask.size(-1), device=device)
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
mask = mask.to(dtype)
if past_key_values_length > 0:
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
modeling_clip._make_causal_mask = patched_make_causal_mask
def convert_text_encoder(text_encoder, tokenizer, submodule_name, args):
""" Converts the text encoder component of Stable Diffusion
"""
text_encoder = text_encoder.to(dtype=torch.float32)
out_path = _get_out_path(args, submodule_name)
if os.path.exists(out_path):
logger.info(
f"`{submodule_name}` already exists at {out_path}, skipping conversion."
)
return
# Create sample inputs for tracing, conversion and correctness verification
text_encoder_sequence_length = tokenizer.model_max_length
sample_text_encoder_inputs = {
"input_ids":
torch.randint(
text_encoder.config.vocab_size,
(1, text_encoder_sequence_length),
# https://github.com/apple/coremltools/issues/1423
dtype=torch.float32,
)
}
sample_text_encoder_inputs_spec = {
k: (v.shape, v.dtype)
for k, v in sample_text_encoder_inputs.items()
}
logger.info(f"Sample inputs spec: {sample_text_encoder_inputs_spec}")
class TextEncoder(nn.Module):
def __init__(self, with_hidden_states_for_layer=None):
super().__init__()
self.text_encoder = text_encoder
self.with_hidden_states_for_layer = with_hidden_states_for_layer
def forward(self, input_ids):
if self.with_hidden_states_for_layer is not None:
output = self.text_encoder(input_ids, output_hidden_states=True)
hidden_embeds = output.hidden_states[self.with_hidden_states_for_layer]
if "text_embeds" in output:
return (hidden_embeds, output.text_embeds)
else:
return (hidden_embeds, output.pooler_output)
else:
return self.text_encoder(input_ids, return_dict=False)
# SD XL uses the hidden states after the encoder layers from both encoders,
# and the pooled `text_embeds` output of the second encoder.
hidden_layer = -2 if args.xl_version else None
reference_text_encoder = TextEncoder(with_hidden_states_for_layer=hidden_layer).eval()
logger.info(f"JIT tracing {submodule_name}..")
reference_text_encoder = torch.jit.trace(
reference_text_encoder,
(sample_text_encoder_inputs["input_ids"].to(torch.int32), ),
)
logger.info("Done.")
if args.xl_version:
output_names = ["hidden_embeds", "pooled_outputs"]
else:
output_names = ["last_hidden_state", "pooled_outputs"]
coreml_text_encoder, out_path = _convert_to_coreml(
submodule_name, reference_text_encoder, sample_text_encoder_inputs,
output_names, args)
# Set model metadata
coreml_text_encoder.author = f"Please refer to the Model Card available at huggingface.co/{args.model_version}"
if args.xl_version:
coreml_text_encoder.license = "OpenRAIL++-M (https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md)"
else:
coreml_text_encoder.license = "OpenRAIL (https://huggingface.co/spaces/CompVis/stable-diffusion-license)"
coreml_text_encoder.version = args.model_version
coreml_text_encoder.short_description = \
"Stable Diffusion generates images conditioned on text and/or other images as input through the diffusion process. " \
"Please refer to https://arxiv.org/abs/2112.10752 for details."
# Set the input descriptions
coreml_text_encoder.input_description[
"input_ids"] = "The token ids that represent the input text"
# Set the output descriptions
if args.xl_version:
coreml_text_encoder.output_description[
"hidden_embeds"] = "Hidden states after the encoder layers"
else:
coreml_text_encoder.output_description[
"last_hidden_state"] = "The token embeddings as encoded by the Transformer model"
coreml_text_encoder.output_description[
"pooled_outputs"] = "The version of the `last_hidden_state` output after pooling"
coreml_text_encoder.save(out_path)
logger.info(f"Saved text_encoder into {out_path}")
# Parity check PyTorch vs CoreML
if args.check_output_correctness:
baseline_out = text_encoder(
sample_text_encoder_inputs["input_ids"].to(torch.int32),
output_hidden_states=args.xl_version,
return_dict=True,
)
if args.xl_version:
# TODO: maybe check pooled_outputs too
baseline_out = baseline_out.hidden_states[hidden_layer].numpy()
else:
baseline_out = baseline_out.last_hidden_state.numpy()
coreml_out = coreml_text_encoder.predict(
{k: v.numpy() for k, v in sample_text_encoder_inputs.items()}
)
coreml_out = coreml_out["hidden_embeds" if args.xl_version else "last_hidden_state"]
report_correctness(
baseline_out, coreml_out,
"text_encoder baseline PyTorch to reference CoreML")
del reference_text_encoder, coreml_text_encoder
gc.collect()
def modify_coremltools_torch_frontend_badbmm():
"""
Modifies coremltools torch frontend for baddbmm to be robust to the `beta` argument being of non-float dtype:
e.g. https://github.com/huggingface/diffusers/blob/v0.8.1/src/diffusers/models/attention.py#L315
"""
from coremltools.converters.mil import register_torch_op
from coremltools.converters.mil.mil import Builder as mb
from coremltools.converters.mil.frontend.torch.ops import _get_inputs
from coremltools.converters.mil.frontend.torch.torch_op_registry import _TORCH_OPS_REGISTRY
if "baddbmm" in _TORCH_OPS_REGISTRY:
del _TORCH_OPS_REGISTRY["baddbmm"]
@register_torch_op
def baddbmm(context, node):
"""
baddbmm(Tensor input, Tensor batch1, Tensor batch2, Scalar beta=1, Scalar alpha=1)
output = beta * input + alpha * batch1 * batch2
Notice that batch1 and batch2 must be 3-D tensors each containing the same number of matrices.
If batch1 is a (b×n×m) tensor, batch2 is a (b×m×p) tensor, then input must be broadcastable with a (b×n×p) tensor
and out will be a (b×n×p) tensor.
"""
assert len(node.outputs) == 1
inputs = _get_inputs(context, node, expected=5)
bias, batch1, batch2, beta, alpha = inputs
if beta.val != 1.0:
# Apply scaling factor beta to the bias.
if beta.val.dtype == np.int32:
beta = mb.cast(x=beta, dtype="fp32")
logger.warning(
f"Casted the `beta`(value={beta.val}) argument of `baddbmm` op "
"from int32 to float32 dtype for conversion!")
bias = mb.mul(x=beta, y=bias, name=bias.name + "_scaled")
context.add(bias)
if alpha.val != 1.0:
# Apply scaling factor alpha to the input.
batch1 = mb.mul(x=alpha, y=batch1, name=batch1.name + "_scaled")
context.add(batch1)
bmm_node = mb.matmul(x=batch1, y=batch2, name=node.name + "_bmm")
context.add(bmm_node)
baddbmm_node = mb.add(x=bias, y=bmm_node, name=node.name)
context.add(baddbmm_node)
def convert_vae_decoder(pipe, args):
""" Converts the VAE Decoder component of Stable Diffusion
"""
out_path = _get_out_path(args, "vae_decoder")
if os.path.exists(out_path):
logger.info(
f"`vae_decoder` already exists at {out_path}, skipping conversion."
)
return
if not hasattr(pipe, "unet"):
raise RuntimeError(
"convert_unet() deletes pipe.unet to save RAM. "
"Please use convert_vae_decoder() before convert_unet()")
z_shape = (
1, # B
pipe.vae.config.latent_channels, # C
args.latent_h or pipe.unet.config.sample_size, # H
args.latent_w or pipe.unet.config.sample_size, # W
)
if args.custom_vae_version is None and args.xl_version:
inputs_dtype = torch.float32
compute_precision = ct.precision.FLOAT32
# FIXME: Hardcoding to CPU_AND_GPU since ANE doesn't support FLOAT32
compute_unit = ct.ComputeUnit.CPU_AND_GPU
else:
inputs_dtype = torch.float16
compute_precision = None
compute_unit = None
sample_vae_decoder_inputs = {
"z": torch.rand(*z_shape, dtype=inputs_dtype)
}
class VAEDecoder(nn.Module):
""" Wrapper nn.Module wrapper for pipe.decode() method
"""
def __init__(self):
super().__init__()
self.post_quant_conv = pipe.vae.post_quant_conv.to(dtype=torch.float32)
self.decoder = pipe.vae.decoder.to(dtype=torch.float32)
def forward(self, z):
return self.decoder(self.post_quant_conv(z))
baseline_decoder = VAEDecoder().eval()
# No optimization needed for the VAE Decoder as it is a pure ConvNet
traced_vae_decoder = torch.jit.trace(
baseline_decoder, (sample_vae_decoder_inputs["z"].to(torch.float32), ))
modify_coremltools_torch_frontend_badbmm()
coreml_vae_decoder, out_path = _convert_to_coreml(
"vae_decoder", traced_vae_decoder, sample_vae_decoder_inputs,
["image"], args, precision=compute_precision, compute_unit=compute_unit)
# Set model metadata
coreml_vae_decoder.author = f"Please refer to the Model Card available at huggingface.co/{args.model_version}"
if args.xl_version:
coreml_vae_decoder.license = "OpenRAIL++-M (https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md)"
else:
coreml_vae_decoder.license = "OpenRAIL (https://huggingface.co/spaces/CompVis/stable-diffusion-license)"
coreml_vae_decoder.version = args.model_version
coreml_vae_decoder.short_description = \
"Stable Diffusion generates images conditioned on text and/or other images as input through the diffusion process. " \
"Please refer to https://arxiv.org/abs/2112.10752 for details."
# Set the input descriptions
coreml_vae_decoder.input_description["z"] = \
"The denoised latent embeddings from the unet model after the last step of reverse diffusion"
# Set the output descriptions
coreml_vae_decoder.output_description[
"image"] = "Generated image normalized to range [-1, 1]"
coreml_vae_decoder.save(out_path)
logger.info(f"Saved vae_decoder into {out_path}")
# Parity check PyTorch vs CoreML
if args.check_output_correctness:
baseline_out = baseline_decoder(
z=sample_vae_decoder_inputs["z"].to(torch.float32)).numpy()
coreml_out = list(
coreml_vae_decoder.predict(
{k: v.numpy()
for k, v in sample_vae_decoder_inputs.items()}).values())[0]
report_correctness(baseline_out, coreml_out,
"vae_decoder baseline PyTorch to baseline CoreML")
del traced_vae_decoder, pipe.vae.decoder, coreml_vae_decoder
gc.collect()
def convert_vae_encoder(pipe, args):
""" Converts the VAE Encoder component of Stable Diffusion
"""
out_path = _get_out_path(args, "vae_encoder")
if os.path.exists(out_path):
logger.info(
f"`vae_encoder` already exists at {out_path}, skipping conversion."
)
return
if not hasattr(pipe, "unet"):
raise RuntimeError(
"convert_unet() deletes pipe.unet to save RAM. "
"Please use convert_vae_encoder() before convert_unet()")
height = (args.latent_h or pipe.unet.config.sample_size) * 8
width = (args.latent_w or pipe.unet.config.sample_size) * 8
x_shape = (
1, # B
3, # C (RGB range from -1 to 1)
height, # H
width, # w
)
if args.xl_version:
inputs_dtype = torch.float32
compute_precision = ct.precision.FLOAT32
# FIXME: Hardcoding to CPU_AND_GPU since ANE doesn't support FLOAT32
compute_unit = ct.ComputeUnit.CPU_AND_GPU
else:
inputs_dtype = torch.float16
compute_precision = None
compute_unit = None
sample_vae_encoder_inputs = {
"x": torch.rand(*x_shape, dtype=inputs_dtype)
}
class VAEEncoder(nn.Module):
""" Wrapper nn.Module wrapper for pipe.encode() method
"""
def __init__(self):
super().__init__()
self.quant_conv = pipe.vae.quant_conv.to(dtype=torch.float32)
self.encoder = pipe.vae.encoder.to(dtype=torch.float32)
def forward(self, x):
return self.quant_conv(self.encoder(x))
baseline_encoder = VAEEncoder().eval()
# No optimization needed for the VAE Encoder as it is a pure ConvNet
traced_vae_encoder = torch.jit.trace(
baseline_encoder, (sample_vae_encoder_inputs["x"].to(torch.float32), ))
modify_coremltools_torch_frontend_badbmm()
coreml_vae_encoder, out_path = _convert_to_coreml(
"vae_encoder", traced_vae_encoder, sample_vae_encoder_inputs,
["latent"], args, precision=compute_precision, compute_unit=compute_unit)
# Set model metadata
coreml_vae_encoder.author = f"Please refer to the Model Card available at huggingface.co/{args.model_version}"
if args.xl_version:
coreml_vae_encoder.license = "OpenRAIL++-M (https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md)"
else:
coreml_vae_encoder.license = "OpenRAIL (https://huggingface.co/spaces/CompVis/stable-diffusion-license)"
coreml_vae_encoder.version = args.model_version
coreml_vae_encoder.short_description = \
"Stable Diffusion generates images conditioned on text and/or other images as input through the diffusion process. " \
"Please refer to https://arxiv.org/abs/2112.10752 for details."
# Set the input descriptions
coreml_vae_encoder.input_description["x"] = \
"The input image to base the initial latents on normalized to range [-1, 1]"
# Set the output descriptions
coreml_vae_encoder.output_description["latent"] = "The latent embeddings from the unet model from the input image."
coreml_vae_encoder.save(out_path)
logger.info(f"Saved vae_encoder into {out_path}")
# Parity check PyTorch vs CoreML
if args.check_output_correctness:
baseline_out = baseline_encoder(
x=sample_vae_encoder_inputs["x"].to(torch.float32)).numpy()
coreml_out = list(
coreml_vae_encoder.predict(
{k: v.numpy()
for k, v in sample_vae_encoder_inputs.items()}).values())[0]
report_correctness(baseline_out, coreml_out,
"vae_encoder baseline PyTorch to baseline CoreML")
del traced_vae_encoder, pipe.vae.encoder, coreml_vae_encoder
gc.collect()
def convert_unet(pipe, args, model_name = None):
""" Converts the UNet component of Stable Diffusion
"""
if args.unet_support_controlnet:
unet_name = "control-unet"
else:
unet_name = model_name or "unet"
out_path = _get_out_path(args, unet_name)
# Check if Unet was previously exported and then chunked
unet_chunks_exist = all(
os.path.exists(
out_path.replace(".mlpackage", f"_chunk{idx+1}.mlpackage"))
for idx in range(2))
if args.chunk_unet and unet_chunks_exist:
logger.info("`unet` chunks already exist, skipping conversion.")
del pipe.unet
gc.collect()
return
# If original Unet does not exist, export it from PyTorch+diffusers
elif not os.path.exists(out_path):
# Prepare sample input shapes and values
batch_size = 2 # for classifier-free guidance
sample_shape = (
batch_size, # B
pipe.unet.config.in_channels, # C
args.latent_h or pipe.unet.config.sample_size, # H
args.latent_w or pipe.unet.config.sample_size, # W
)
if not hasattr(pipe, "text_encoder"):
raise RuntimeError(
"convert_text_encoder() deletes pipe.text_encoder to save RAM. "
"Please use convert_unet() before convert_text_encoder()")
if hasattr(pipe, "text_encoder") and pipe.text_encoder is not None:
text_token_sequence_length = pipe.text_encoder.config.max_position_embeddings
hidden_size = pipe.text_encoder.config.hidden_size,
elif hasattr(pipe, "text_encoder_2") and pipe.text_encoder_2 is not None:
text_token_sequence_length = pipe.text_encoder_2.config.max_position_embeddings
hidden_size = pipe.text_encoder_2.config.hidden_size,
encoder_hidden_states_shape = (
batch_size,
args.text_encoder_hidden_size or pipe.unet.config.cross_attention_dim or hidden_size,
1,
args.text_token_sequence_length or text_token_sequence_length,
)
# Create the scheduled timesteps for downstream use
DEFAULT_NUM_INFERENCE_STEPS = 50
pipe.scheduler.set_timesteps(DEFAULT_NUM_INFERENCE_STEPS)
sample_unet_inputs = OrderedDict([
("sample", torch.rand(*sample_shape)),
("timestep",
torch.tensor([pipe.scheduler.timesteps[0].item()] *
(batch_size)).to(torch.float32)),
("encoder_hidden_states", torch.rand(*encoder_hidden_states_shape))
])
# Prepare inputs
baseline_sample_unet_inputs = deepcopy(sample_unet_inputs)
baseline_sample_unet_inputs[
"encoder_hidden_states"] = baseline_sample_unet_inputs[
"encoder_hidden_states"].squeeze(2).transpose(1, 2)
# Initialize reference unet
if args.xl_version:
unet_cls = unet.UNet2DConditionModelXL
# Sample time_ids
height = (args.latent_h or pipe.unet.config.sample_size) * 8
width = (args.latent_w or pipe.unet.config.sample_size) * 8
original_size = (height, width) # output_resolution
crops_coords_top_left = (0, 0) # topleft_crop_cond
target_size = (height, width) # resolution_cond
if hasattr(pipe.config, "requires_aesthetics_score") and pipe.config.requires_aesthetics_score:
# Part of SDXL's micro-conditioning as explained in section 2.2 of
# [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to
# simulate an aesthetic score of the generated image by influencing the positive and negative text conditions.
aesthetic_score = 6.0 # default aesthetic_score
negative_aesthetic_score = 2.5 # default negative_aesthetic_score
add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))
add_neg_time_ids = list(original_size + crops_coords_top_left + (negative_aesthetic_score,))
else:
add_time_ids = list(original_size + crops_coords_top_left + target_size)
add_neg_time_ids = list(original_size + crops_coords_top_left + target_size)
time_ids = [
add_neg_time_ids,
add_time_ids
]
# Pooled text embedding from text_encoder_2
text_embeds_shape = (
batch_size,
pipe.text_encoder_2.config.hidden_size
)
additional_xl_inputs = OrderedDict([
("time_ids", torch.tensor(time_ids).to(torch.float32)),
("text_embeds", torch.rand(*text_embeds_shape)),
])
sample_unet_inputs.update(additional_xl_inputs)
baseline_sample_unet_inputs['added_cond_kwargs'] = additional_xl_inputs
else:
unet_cls = unet.UNet2DConditionModel
reference_unet = unet_cls(**pipe.unet.config).eval()
load_state_dict_summary = reference_unet.load_state_dict(
pipe.unet.state_dict(), strict=False)
if args.unet_support_controlnet:
from .unet import calculate_conv2d_output_shape
additional_residuals_shapes = []
# conv_in
out_h, out_w = calculate_conv2d_output_shape(
(args.latent_h or pipe.unet.config.sample_size),
(args.latent_w or pipe.unet.config.sample_size),
reference_unet.conv_in,
)
additional_residuals_shapes.append(
(batch_size, reference_unet.conv_in.out_channels, out_h, out_w))
# down_blocks
for down_block in reference_unet.down_blocks:
additional_residuals_shapes += [
(batch_size, resnet.out_channels, out_h, out_w) for resnet in down_block.resnets
]
if hasattr(down_block, "downsamplers") and down_block.downsamplers is not None:
for downsampler in down_block.downsamplers:
out_h, out_w = calculate_conv2d_output_shape(out_h, out_w, downsampler.conv)
additional_residuals_shapes.append(
(batch_size, down_block.downsamplers[-1].conv.out_channels, out_h, out_w))
# mid_block
additional_residuals_shapes.append(
(batch_size, reference_unet.mid_block.resnets[-1].out_channels, out_h, out_w)
)
baseline_sample_unet_inputs["down_block_additional_residuals"] = ()
for i, shape in enumerate(additional_residuals_shapes):
sample_residual_input = torch.rand(*shape)
sample_unet_inputs[f"additional_residual_{i}"] = sample_residual_input
if i == len(additional_residuals_shapes) - 1:
baseline_sample_unet_inputs["mid_block_additional_residual"] = sample_residual_input
else:
baseline_sample_unet_inputs["down_block_additional_residuals"] += (sample_residual_input, )
sample_unet_inputs_spec = {
k: (v.shape, v.dtype)
for k, v in sample_unet_inputs.items()
}
logger.info(f"Sample UNet inputs spec: {sample_unet_inputs_spec}")
# JIT trace
logger.info("JIT tracing..")
reference_unet = torch.jit.trace(reference_unet,
list(sample_unet_inputs.values()))
logger.info("Done.")
if args.check_output_correctness:
baseline_out = pipe.unet.to(torch.float32)(**baseline_sample_unet_inputs,
return_dict=False)[0].numpy()
reference_out = reference_unet(*sample_unet_inputs.values())[0].numpy()
report_correctness(baseline_out, reference_out,
"unet baseline to reference PyTorch")
del pipe.unet
gc.collect()
coreml_sample_unet_inputs = {
k: v.numpy().astype(np.float16)
for k, v in sample_unet_inputs.items()
}
coreml_unet, out_path = _convert_to_coreml(unet_name, reference_unet,
coreml_sample_unet_inputs,
["noise_pred"], args)
del reference_unet
gc.collect()
# Set model metadata
coreml_unet.author = f"Please refer to the Model Card available at huggingface.co/{args.model_version}"
if args.xl_version:
coreml_unet.license = "OpenRAIL++-M (https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md)"
else:
coreml_unet.license = "OpenRAIL (https://huggingface.co/spaces/CompVis/stable-diffusion-license)"
coreml_unet.version = args.model_version if model_name != "refiner" or not hasattr(args, "refiner_version") else args.refiner_version
coreml_unet.short_description = \
"Stable Diffusion generates images conditioned on text or other images as input through the diffusion process. " \
"Please refer to https://arxiv.org/abs/2112.10752 for details."
# Set the input descriptions
coreml_unet.input_description["sample"] = \
"The low resolution latent feature maps being denoised through reverse diffusion"
coreml_unet.input_description["timestep"] = \
"A value emitted by the associated scheduler object to condition the model on a given noise schedule"
coreml_unet.input_description["encoder_hidden_states"] = \
"Output embeddings from the associated text_encoder model to condition to generated image on text. " \
"A maximum of 77 tokens (~40 words) are allowed. Longer text is truncated. " \
"Shorter text does not reduce computation."
if args.xl_version:
coreml_unet.input_description["time_ids"] = \
"Additional embeddings that if specified are added to the embeddings that are passed along to the UNet blocks."
coreml_unet.input_description["text_embeds"] = \
"Additional embeddings from text_encoder_2 that if specified are added to the embeddings that are passed along to the UNet blocks."
# Set the output descriptions
coreml_unet.output_description["noise_pred"] = \
"Same shape and dtype as the `sample` input. " \
"The predicted noise to facilitate the reverse diffusion (denoising) process"
# Set package version metadata
from python_coreml_stable_diffusion._version import __version__
coreml_unet.user_defined_metadata["com.github.apple.ml-stable-diffusion.version"] = __version__
coreml_unet.save(out_path)
logger.info(f"Saved unet into {out_path}")
# Parity check PyTorch vs CoreML
if args.check_output_correctness:
coreml_out = list(
coreml_unet.predict(coreml_sample_unet_inputs).values())[0]
report_correctness(baseline_out, coreml_out,
"unet baseline PyTorch to reference CoreML")
del coreml_unet
gc.collect()
else:
del pipe.unet
gc.collect()
logger.info(
f"`unet` already exists at {out_path}, skipping conversion.")
if args.chunk_unet and not unet_chunks_exist:
logger.info(f"Chunking {model_name} in two approximately equal MLModels")
args.mlpackage_path = out_path
args.remove_original = False
args.merge_chunks_in_pipeline_model = False
chunk_mlprogram.main(args)
def convert_safety_checker(pipe, args):
""" Converts the Safety Checker component of Stable Diffusion
"""
if pipe.safety_checker is None:
logger.warning(
f"diffusers pipeline for {args.model_version} does not have a `safety_checker` module! " \
"`--convert-safety-checker` will be ignored."
)
return
out_path = _get_out_path(args, "safety_checker")
if os.path.exists(out_path):
logger.info(
f"`safety_checker` already exists at {out_path}, skipping conversion."
)
return
pipe.safety_checker = pipe.safety_checker.to(torch.float32)
im_h = pipe.vae.config.sample_size
im_w = pipe.vae.config.sample_size
if args.latent_h is not None:
im_h = args.latent_h * 8
if args.latent_w is not None:
im_w = args.latent_w * 8
sample_image = np.random.randn(
1, # B
im_h, # H
im_w, # w
3 # C
).astype(np.float32)
# Note that pipe.feature_extractor is not an ML model. It simply
# preprocesses data for the pipe.safety_checker module.
safety_checker_input = pipe.feature_extractor(
pipe.numpy_to_pil(sample_image),
return_tensors="pt",
).pixel_values.to(torch.float32)
sample_safety_checker_inputs = OrderedDict([
("clip_input", safety_checker_input),
("images", torch.from_numpy(sample_image)),
("adjustment", torch.tensor([0]).to(torch.float32)),
])
sample_safety_checker_inputs_spec = {
k: (v.shape, v.dtype)
for k, v in sample_safety_checker_inputs.items()
}
logger.info(f"Sample inputs spec: {sample_safety_checker_inputs_spec}")
# Patch safety_checker's forward pass to be vectorized and avoid conditional blocks
# (similar to pipe.safety_checker.forward_onnx)
from diffusers.pipelines.stable_diffusion import safety_checker
def forward_coreml(self, clip_input, images, adjustment):
""" Forward pass implementation for safety_checker
"""
def cosine_distance(image_embeds, text_embeds):
return F.normalize(image_embeds) @ F.normalize(
text_embeds).transpose(0, 1)
pooled_output = self.vision_model(clip_input)[1] # pooled_output
image_embeds = self.visual_projection(pooled_output)
special_cos_dist = cosine_distance(image_embeds,
self.special_care_embeds)
cos_dist = cosine_distance(image_embeds, self.concept_embeds)
special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment
special_care = special_scores.gt(0).float().sum(dim=1).gt(0).float()
special_adjustment = special_care * 0.01
special_adjustment = special_adjustment.unsqueeze(1).expand(
-1, cos_dist.shape[1])
concept_scores = (cos_dist -
self.concept_embeds_weights) + special_adjustment
has_nsfw_concepts = concept_scores.gt(0).float().sum(dim=1).gt(0)[:,
None,
None,
None]
has_nsfw_concepts_inds, _ = torch.broadcast_tensors(
has_nsfw_concepts, images)