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Bug fixes, Runtime & Performance optimization #332

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Bug fixes, Runtime & Performance optimization #332

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a73b02c
Bug fixes
DrHazemAli Jul 1, 2024
08400d4
Fix runtime crashes.
DrHazemAli Jul 1, 2024
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155 changes: 26 additions & 129 deletions python_coreml_stable_diffusion/controlnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,29 +2,18 @@
# For licensing see accompanying LICENSE.md file.
# Copyright (C) 2022 Apple Inc. All Rights Reserved.
#

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers import ModelMixin

import torch
import torch.nn as nn
import torch.nn.functional as F

from .unet import Timesteps, TimestepEmbedding, get_down_block, UNetMidBlock2DCrossAttn, linear_to_conv2d_map

class ControlNetConditioningEmbedding(nn.Module):

def __init__(
self,
conditioning_embedding_channels,
conditioning_channels=3,
block_out_channels=(16, 32, 96, 256),
):
def __init__(self, conditioning_embedding_channels, conditioning_channels=3, block_out_channels=(16, 32, 96, 256)):
super().__init__()

self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)

self.blocks = nn.ModuleList([])
self.blocks = nn.ModuleList()

for i in range(len(block_out_channels) - 1):
channel_in = block_out_channels[i]
Expand All @@ -43,86 +32,18 @@ def forward(self, conditioning):
embedding = F.silu(embedding)

embedding = self.conv_out(embedding)

return embedding

class ControlNetModel(ModelMixin, ConfigMixin):

@register_to_config
def __init__(
self,
in_channels=4,
flip_sin_to_cos=True,
freq_shift=0,
down_block_types=(
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
),
only_cross_attention=False,
block_out_channels=(320, 640, 1280, 1280),
layers_per_block=2,
downsample_padding=1,
mid_block_scale_factor=1,
act_fn="silu",
norm_num_groups=32,
norm_eps=1e-5,
cross_attention_dim=1280,
transformer_layers_per_block=1,
attention_head_dim=8,
use_linear_projection=False,
upcast_attention=False,
resnet_time_scale_shift="default",
conditioning_embedding_out_channels=(16, 32, 96, 256),
**kwargs,
):
def __init__(self, in_channels=4, flip_sin_to_cos=True, freq_shift=0, down_block_types=("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"),
only_cross_attention=False, block_out_channels=(320, 640, 1280, 1280), layers_per_block=2, downsample_padding=1, mid_block_scale_factor=1, act_fn="silu",
norm_num_groups=32, norm_eps=1e-5, cross_attention_dim=1280, transformer_layers_per_block=1, attention_head_dim=8, use_linear_projection=False,
upcast_attention=False, resnet_time_scale_shift="default", conditioning_embedding_out_channels=(16, 32, 96, 256), **kwargs):
super().__init__()

# Check inputs
if len(block_out_channels) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
)

if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
)

if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
)

self._register_load_state_dict_pre_hook(linear_to_conv2d_map)

# input
conv_in_kernel = 3
conv_in_padding = (conv_in_kernel - 1) // 2
self.conv_in = nn.Conv2d(
in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
)

# time
time_embed_dim = block_out_channels[0] * 4

self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
timestep_input_dim = block_out_channels[0]

self.time_embedding = TimestepEmbedding(
timestep_input_dim,
time_embed_dim,
)

# control net conditioning embedding
self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0],
block_out_channels=conditioning_embedding_out_channels,
)

self.down_blocks = nn.ModuleList([])
self.controlnet_down_blocks = nn.ModuleList([])
raise ValueError(f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.")

if isinstance(only_cross_attention, bool):
only_cross_attention = [only_cross_attention] * len(down_block_types)
Expand All @@ -133,11 +54,19 @@ def __init__(
if isinstance(transformer_layers_per_block, int):
transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)

# down
output_channel = block_out_channels[0]
self._register_load_state_dict_pre_hook(linear_to_conv2d_map)

controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
self.controlnet_down_blocks.append(controlnet_block)
self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=1)
time_embed_dim = block_out_channels[0] * 4
self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
self.time_embedding = TimestepEmbedding(block_out_channels[0], time_embed_dim)

self.controlnet_cond_embedding = ControlNetConditioningEmbedding(conditioning_embedding_channels=block_out_channels[0], block_out_channels=conditioning_embedding_out_channels)
self.down_blocks = nn.ModuleList()
self.controlnet_down_blocks = nn.ModuleList()

output_channel = block_out_channels[0]
self.controlnet_down_blocks.append(nn.Conv2d(output_channel, output_channel, kernel_size=1))

for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
Expand All @@ -161,19 +90,13 @@ def __init__(
self.down_blocks.append(down_block)

for _ in range(layers_per_block):
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
self.controlnet_down_blocks.append(controlnet_block)
self.controlnet_down_blocks.append(nn.Conv2d(output_channel, output_channel, kernel_size=1))

if not is_final_block:
controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
self.controlnet_down_blocks.append(controlnet_block)
self.controlnet_down_blocks.append(nn.Conv2d(output_channel, output_channel, kernel_size=1))

# mid
mid_block_channel = block_out_channels[-1]

controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)
self.controlnet_mid_block = controlnet_block

self.controlnet_mid_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)
self.mid_block = UNetMidBlock2DCrossAttn(
in_channels=mid_block_channel,
temb_channels=time_embed_dim,
Expand All @@ -196,55 +119,29 @@ def get_num_residuals(self):
num_res += len(down_block.downsamplers)
return num_res

def forward(
self,
sample,
timestep,
encoder_hidden_states,
controlnet_cond,
):
# 1. time
def forward(self, sample, timestep, encoder_hidden_states, controlnet_cond):
t_emb = self.time_proj(timestep)
emb = self.time_embedding(t_emb)

# 2. pre-process
sample = self.conv_in(sample)

controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)

sample += controlnet_cond

# 3. down
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "attentions") and downsample_block.attentions is not None:
sample, res_samples = downsample_block(
hidden_states=sample,
temb=emb,
encoder_hidden_states=encoder_hidden_states,
)
sample, res_samples = downsample_block(hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb)

down_block_res_samples += res_samples

# 4. mid
if self.mid_block is not None:
sample = self.mid_block(
sample,
emb,
encoder_hidden_states=encoder_hidden_states,
)
sample = self.mid_block(sample, emb, encoder_hidden_states=encoder_hidden_states)

# 5. Control net blocks
controlnet_down_block_res_samples = ()

for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
down_block_res_sample = controlnet_block(down_block_res_sample)
controlnet_down_block_res_samples += (down_block_res_sample,)

down_block_res_samples = controlnet_down_block_res_samples

mid_block_res_sample = self.controlnet_mid_block(sample)

return down_block_res_samples, mid_block_res_sample
return down_block_res_samples, mid_block_res_sample
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