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refiners/scripts/conversion/convert_diffusers_controlnet.py
limiteinductive 88efa117bf fix model comparison with custom layers
2023-09-05 12:34:38 +02:00

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# pyright: reportPrivateUsage=false
import argparse
from pathlib import Path
import torch
from torch import nn
from diffusers import ControlNetModel # type: ignore
from refiners.fluxion.utils import save_to_safetensors
from refiners.fluxion.model_converter import ModelConverter
from refiners.foundationals.latent_diffusion import (
SD1UNet,
SD1ControlnetAdapter,
DPMSolver,
)
class Args(argparse.Namespace):
source_path: str
output_path: str | None
@torch.no_grad()
def convert(args: Args) -> dict[str, torch.Tensor]:
controlnet_src: nn.Module = ControlNetModel.from_pretrained(pretrained_model_name_or_path=args.source_path) # type: ignore
unet = SD1UNet(in_channels=4)
adapter = SD1ControlnetAdapter(unet, name="mycn").inject()
controlnet = unet.Controlnet
condition = torch.randn(1, 3, 512, 512)
adapter.set_controlnet_condition(condition=condition)
clip_text_embedding = torch.rand(1, 77, 768)
unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding)
scheduler = DPMSolver(num_inference_steps=10)
timestep = scheduler.timesteps[0].unsqueeze(dim=0)
unet.set_timestep(timestep=timestep.unsqueeze(dim=0))
x = torch.randn(1, 4, 64, 64)
# We need the hack below because our implementation is not strictly equivalent
# to diffusers in order, since we compute the residuals inline instead of
# in a separate step.
converter = ModelConverter(
source_model=controlnet_src, target_model=controlnet, skip_output_check=True, verbose=False
)
source_order = converter._trace_module_execution_order(
module=controlnet_src, args=(x, timestep, clip_text_embedding, condition), keys_to_skip=[]
)
target_order = converter._trace_module_execution_order(module=controlnet, args=(x,), keys_to_skip=[])
broken_k = (nn.Conv2d, (torch.Size([320, 320, 1, 1]), torch.Size([320])))
expected_source_order = [
"down_blocks.0.attentions.0.proj_in",
"down_blocks.0.attentions.0.proj_out",
"down_blocks.0.attentions.1.proj_in",
"down_blocks.0.attentions.1.proj_out",
"controlnet_down_blocks.0",
"controlnet_down_blocks.1",
"controlnet_down_blocks.2",
"controlnet_down_blocks.3",
]
expected_target_order = [
"DownBlocks.Chain_1.Passthrough.Conv2d",
"DownBlocks.Chain_2.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
"DownBlocks.Chain_2.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
"DownBlocks.Chain_2.Passthrough.Conv2d",
"DownBlocks.Chain_3.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
"DownBlocks.Chain_3.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
"DownBlocks.Chain_3.Passthrough.Conv2d",
"DownBlocks.Chain_4.Passthrough.Conv2d",
]
fixed_source_order = [
"controlnet_down_blocks.0",
"down_blocks.0.attentions.0.proj_in",
"down_blocks.0.attentions.0.proj_out",
"controlnet_down_blocks.1",
"down_blocks.0.attentions.1.proj_in",
"down_blocks.0.attentions.1.proj_out",
"controlnet_down_blocks.2",
"controlnet_down_blocks.3",
]
assert source_order[broken_k] == expected_source_order
assert target_order[broken_k] == expected_target_order
source_order[broken_k] = fixed_source_order
broken_k = (nn.Conv2d, (torch.Size([640, 640, 1, 1]), torch.Size([640])))
expected_source_order = [
"down_blocks.1.attentions.0.proj_in",
"down_blocks.1.attentions.0.proj_out",
"down_blocks.1.attentions.1.proj_in",
"down_blocks.1.attentions.1.proj_out",
"controlnet_down_blocks.4",
"controlnet_down_blocks.5",
"controlnet_down_blocks.6",
]
expected_target_order = [
"DownBlocks.Chain_5.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
"DownBlocks.Chain_5.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
"DownBlocks.Chain_5.Passthrough.Conv2d",
"DownBlocks.Chain_6.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
"DownBlocks.Chain_6.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
"DownBlocks.Chain_6.Passthrough.Conv2d",
"DownBlocks.Chain_7.Passthrough.Conv2d",
]
fixed_source_order = [
"down_blocks.1.attentions.0.proj_in",
"down_blocks.1.attentions.0.proj_out",
"controlnet_down_blocks.4",
"down_blocks.1.attentions.1.proj_in",
"down_blocks.1.attentions.1.proj_out",
"controlnet_down_blocks.5",
"controlnet_down_blocks.6",
]
assert source_order[broken_k] == expected_source_order
assert target_order[broken_k] == expected_target_order
source_order[broken_k] = fixed_source_order
broken_k = (nn.Conv2d, (torch.Size([1280, 1280, 1, 1]), torch.Size([1280])))
expected_source_order = [
"down_blocks.2.attentions.0.proj_in",
"down_blocks.2.attentions.0.proj_out",
"down_blocks.2.attentions.1.proj_in",
"down_blocks.2.attentions.1.proj_out",
"mid_block.attentions.0.proj_in",
"mid_block.attentions.0.proj_out",
"controlnet_down_blocks.7",
"controlnet_down_blocks.8",
"controlnet_down_blocks.9",
"controlnet_down_blocks.10",
"controlnet_down_blocks.11",
"controlnet_mid_block",
]
expected_target_order = [
"DownBlocks.Chain_8.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
"DownBlocks.Chain_8.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
"DownBlocks.Chain_8.Passthrough.Conv2d",
"DownBlocks.Chain_9.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
"DownBlocks.Chain_9.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
"DownBlocks.Chain_9.Passthrough.Conv2d",
"DownBlocks.Chain_10.Passthrough.Conv2d",
"DownBlocks.Chain_11.Passthrough.Conv2d",
"DownBlocks.Chain_12.Passthrough.Conv2d",
"MiddleBlock.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
"MiddleBlock.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
"MiddleBlock.Passthrough.Conv2d",
]
fixed_source_order = [
"down_blocks.2.attentions.0.proj_in",
"down_blocks.2.attentions.0.proj_out",
"controlnet_down_blocks.7",
"down_blocks.2.attentions.1.proj_in",
"down_blocks.2.attentions.1.proj_out",
"controlnet_down_blocks.8",
"controlnet_down_blocks.9",
"controlnet_down_blocks.10",
"controlnet_down_blocks.11",
"mid_block.attentions.0.proj_in",
"mid_block.attentions.0.proj_out",
"controlnet_mid_block",
]
assert source_order[broken_k] == expected_source_order
assert target_order[broken_k] == expected_target_order
source_order[broken_k] = fixed_source_order
assert converter._assert_shapes_aligned(source_order=source_order, target_order=target_order), "Shapes not aligned"
mapping: dict[str, str] = {}
for model_type_shape in source_order:
source_keys = source_order[model_type_shape]
target_keys = target_order[model_type_shape]
mapping.update(zip(target_keys, source_keys))
state_dict = converter._convert_state_dict(
source_state_dict=controlnet_src.state_dict(),
target_state_dict=controlnet.state_dict(),
state_dict_mapping=mapping,
)
return {k: v.half() for k, v in state_dict.items()}
def main() -> None:
parser = argparse.ArgumentParser(description="Convert a diffusers ControlNet model to a Refiners ControlNet model")
parser.add_argument(
"--from",
type=str,
dest="source_path",
default="lllyasviel/sd-controlnet-depth",
help=(
"Can be a path to a .bin, a .safetensors file, or a model identifier from Hugging Face Hub. Defaults to"
" lllyasviel/sd-controlnet-depth"
),
)
parser.add_argument(
"--to",
type=str,
dest="output_path",
required=False,
default=None,
help=(
"Output path (.safetensors) for converted model. If not provided, the output path will be the same as the"
" source path."
),
)
args = parser.parse_args(namespace=Args())
if args.output_path is None:
args.output_path = f"{Path(args.source_path).stem}-controlnet.safetensors"
state_dict = convert(args=args)
save_to_safetensors(path=args.output_path, tensors=state_dict)
if __name__ == "__main__":
main()
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