Skip to content

Improve control net block index for sd3 #9758

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 16 commits into from
Nov 20, 2024
Merged

Improve control net block index for sd3 #9758

Show file tree
Hide file tree
Changes from 15 commits
Commits
Show all changes
16 commits
Select commit Hold shift + click to select a range
a0d199a
improve control net index
linjiapro Oct 23, 2024
48b4b62
wip
linjiapro Oct 24, 2024
d1a1ebe
wip
linjiapro Oct 24, 2024
bd32b2b
wip
linjiapro Oct 24, 2024
a7ffec7
wip
linjiapro Oct 24, 2024
235b800
wip
linjiapro Oct 24, 2024
50b4db9
wip
linjiapro Oct 26, 2024
933ecf3
add rms_norm to controlnet test
linjiapro Oct 26, 2024
9d33417
wip
linjiapro Oct 29, 2024
cd3069c
wip
linjiapro Oct 29, 2024
e40bd61
wip
linjiapro Nov 15, 2024
4c1d293
merge
linjiapro Nov 15, 2024
d8006c3
format
linjiapro Nov 15, 2024
b4983cb
wip
linjiapro Nov 15, 2024
5f560ca
Update src/diffusers/models/controlnets/controlnet_sd3.py
linjiapro Nov 20, 2024
570558b
style
yiyixuxu Nov 20, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 7 additions & 1 deletion src/diffusers/models/controlnets/controlnet_sd3.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -56,6 +56,10 @@ def __init__(
out_channels: int = 16,
pos_embed_max_size: int = 96,
extra_conditioning_channels: int = 0,
dual_attention_layers: Tuple[
int, ...
] = (),
qk_norm: Optional[str] = None,
):
super().__init__()
default_out_channels = in_channels
Expand Down Expand Up @@ -84,6 +88,8 @@ def __init__(
num_attention_heads=num_attention_heads,
attention_head_dim=self.config.attention_head_dim,
context_pre_only=False,
qk_norm=qk_norm,
use_dual_attention=True if i in dual_attention_layers else False,
)
for i in range(num_layers)
]
Expand Down Expand Up @@ -248,7 +254,7 @@ def from_transformer(
config = transformer.config
config["num_layers"] = num_layers or config.num_layers
config["extra_conditioning_channels"] = num_extra_conditioning_channels
controlnet = cls(**config)
controlnet = cls.from_config(config)

if load_weights_from_transformer:
controlnet.pos_embed.load_state_dict(transformer.pos_embed.state_dict())
Expand Down
4 changes: 3 additions & 1 deletion src/diffusers/models/transformers/transformer_sd3.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@

import torch
import torch.nn as nn
import numpy as np

from ...configuration_utils import ConfigMixin, register_to_config
from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
Expand Down Expand Up @@ -344,7 +345,8 @@ def custom_forward(*inputs):

# controlnet residual
if block_controlnet_hidden_states is not None and block.context_pre_only is False:
interval_control = len(self.transformer_blocks) // len(block_controlnet_hidden_states)
interval_control = len(self.transformer_blocks) / len(block_controlnet_hidden_states)
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

why are we making this change? it is not the same so a breaking change, no?

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

@yiyixuxu Good question.

The revised code adapts the strategy used by ControlNet for Flux, introducing a significant improvement in flexibility. Here's why this change matters:

In the old code, the number of transformer layers is divisible by the number of ControlNet layers. For example, with SD3.5 Large, which has 38 transformer layers, there were only two valid options for the number of ControlNet layers: 2 and 19. Setting the number of ControlNet layers to anything else, such as 5, would cause the old code to crash.

However, the Flux ControlNet approach removes this restriction, allowing greater flexibility in choosing the number of layers. The revised logic essentially mirrors the Flux implementation, enabling more versatile configurations.

Importantly, the new code maintains compatibility with existing setups. If the number of transformer layers is divisible by the number of ControlNet layers, the interval_control remains unchanged, ensuring all previous configurations continue to function seamlessly.

Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

thanks! I think it's indeed better, I'm just wondering if it would cause issue for controlnet is trained with the current logic
cc @haofanwang here

Copy link
Contributor Author

@linjiapro linjiapro Nov 20, 2024 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I don't think it will cause any issue with the trained controlnet using old code before this PR.

The reason is that for the controlnet to be trained with the old code, the number of layers of the transformer has to be divisible by the number of layers of the controlnet, and the new logic after this PR does not change the behavior for the above scenario.

interval_control = int(np.ceil(interval_control))
hidden_states = hidden_states + block_controlnet_hidden_states[index_block // interval_control]

hidden_states = self.norm_out(hidden_states, temb)
Expand Down
8 changes: 5 additions & 3 deletions tests/pipelines/controlnet_sd3/test_controlnet_sd3.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@
import numpy as np
import pytest
import torch
from typing import Optional
from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel

from diffusers import (
Expand Down Expand Up @@ -58,8 +59,8 @@ class StableDiffusion3ControlNetPipelineFastTests(unittest.TestCase, PipelineTes
]
)
batch_params = frozenset(["prompt", "negative_prompt"])

def get_dummy_components(self):
def get_dummy_components(self, num_controlnet_layers: int = 3, qk_norm: Optional[str] = "rms_norm"):
torch.manual_seed(0)
transformer = SD3Transformer2DModel(
sample_size=32,
Expand All @@ -72,14 +73,15 @@ def get_dummy_components(self):
caption_projection_dim=32,
pooled_projection_dim=64,
out_channels=8,
qk_norm=qk_norm,
)

torch.manual_seed(0)
controlnet = SD3ControlNetModel(
sample_size=32,
patch_size=1,
in_channels=8,
num_layers=1,
num_layers=num_controlnet_layers,
attention_head_dim=8,
num_attention_heads=4,
joint_attention_dim=32,
Expand Down
Loading