mirror of
https://github.com/finegrain-ai/refiners.git
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208 lines
7.5 KiB
Python
208 lines
7.5 KiB
Python
import torch
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from diffusers import ControlNetModel # type: ignore
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from refiners.fluxion.utils import (
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forward_order_of_execution,
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verify_shape_match,
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convert_state_dict,
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save_to_safetensors,
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)
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from refiners.foundationals.latent_diffusion.controlnet import Controlnet
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from refiners.foundationals.latent_diffusion.schedulers.dpm_solver import DPMSolver
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from refiners.foundationals.latent_diffusion import UNet
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@torch.no_grad()
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def convert(controlnet_src: ControlNetModel) -> dict[str, torch.Tensor]:
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controlnet = Controlnet(name="mycn")
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condition = torch.randn(1, 3, 512, 512)
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controlnet.set_controlnet_condition(condition=condition)
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unet = UNet(in_channels=4, clip_embedding_dim=768)
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unet.insert(index=0, module=controlnet)
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clip_text_embedding = torch.rand(1, 77, 768)
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unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding)
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scheduler = DPMSolver(num_inference_steps=10)
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timestep = scheduler.timesteps[0].unsqueeze(dim=0)
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unet.set_timestep(timestep=timestep.unsqueeze(dim=0))
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x = torch.randn(1, 4, 64, 64)
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# We need the hack below because our implementation is not strictly equivalent
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# to diffusers in order, since we compute the residuals inline instead of
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# in a separate step.
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source_order = forward_order_of_execution(module=controlnet_src, example_args=(x, timestep, clip_text_embedding, condition)) # type: ignore
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target_order = forward_order_of_execution(module=controlnet, example_args=(x,))
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broken_k = ("Conv2d", (torch.Size([320, 320, 1, 1]), torch.Size([320])))
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expected_source_order = [
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"down_blocks.0.attentions.0.proj_in",
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"down_blocks.0.attentions.0.proj_out",
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"down_blocks.0.attentions.1.proj_in",
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"down_blocks.0.attentions.1.proj_out",
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"controlnet_down_blocks.0",
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"controlnet_down_blocks.1",
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"controlnet_down_blocks.2",
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"controlnet_down_blocks.3",
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]
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expected_target_order = [
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"DownBlocks.Chain_1.Passthrough.Conv2d",
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"DownBlocks.Chain_2.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
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"DownBlocks.Chain_2.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
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"DownBlocks.Chain_2.Passthrough.Conv2d",
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"DownBlocks.Chain_3.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
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"DownBlocks.Chain_3.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
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"DownBlocks.Chain_3.Passthrough.Conv2d",
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"DownBlocks.Chain_4.Passthrough.Conv2d",
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]
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fixed_source_order = [
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"controlnet_down_blocks.0",
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"down_blocks.0.attentions.0.proj_in",
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"down_blocks.0.attentions.0.proj_out",
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"controlnet_down_blocks.1",
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"down_blocks.0.attentions.1.proj_in",
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"down_blocks.0.attentions.1.proj_out",
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"controlnet_down_blocks.2",
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"controlnet_down_blocks.3",
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]
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assert source_order[broken_k] == expected_source_order
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assert target_order[broken_k] == expected_target_order
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source_order[broken_k] = fixed_source_order
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broken_k = ("Conv2d", (torch.Size([640, 640, 1, 1]), torch.Size([640])))
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expected_source_order = [
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"down_blocks.1.attentions.0.proj_in",
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"down_blocks.1.attentions.0.proj_out",
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"down_blocks.1.attentions.1.proj_in",
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"down_blocks.1.attentions.1.proj_out",
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"controlnet_down_blocks.4",
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"controlnet_down_blocks.5",
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"controlnet_down_blocks.6",
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]
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expected_target_order = [
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"DownBlocks.Chain_5.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
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"DownBlocks.Chain_5.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
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"DownBlocks.Chain_5.Passthrough.Conv2d",
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"DownBlocks.Chain_6.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
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"DownBlocks.Chain_6.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
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"DownBlocks.Chain_6.Passthrough.Conv2d",
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"DownBlocks.Chain_7.Passthrough.Conv2d",
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]
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fixed_source_order = [
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"down_blocks.1.attentions.0.proj_in",
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"down_blocks.1.attentions.0.proj_out",
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"controlnet_down_blocks.4",
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"down_blocks.1.attentions.1.proj_in",
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"down_blocks.1.attentions.1.proj_out",
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"controlnet_down_blocks.5",
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"controlnet_down_blocks.6",
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]
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assert source_order[broken_k] == expected_source_order
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assert target_order[broken_k] == expected_target_order
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source_order[broken_k] = fixed_source_order
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broken_k = ("Conv2d", (torch.Size([1280, 1280, 1, 1]), torch.Size([1280])))
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expected_source_order = [
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"down_blocks.2.attentions.0.proj_in",
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"down_blocks.2.attentions.0.proj_out",
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"down_blocks.2.attentions.1.proj_in",
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"down_blocks.2.attentions.1.proj_out",
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"mid_block.attentions.0.proj_in",
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"mid_block.attentions.0.proj_out",
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"controlnet_down_blocks.7",
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"controlnet_down_blocks.8",
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"controlnet_down_blocks.9",
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"controlnet_down_blocks.10",
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"controlnet_down_blocks.11",
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"controlnet_mid_block",
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]
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expected_target_order = [
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"DownBlocks.Chain_8.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
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"DownBlocks.Chain_8.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
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"DownBlocks.Chain_8.Passthrough.Conv2d",
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"DownBlocks.Chain_9.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
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"DownBlocks.Chain_9.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
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"DownBlocks.Chain_9.Passthrough.Conv2d",
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"DownBlocks.Chain_10.Passthrough.Conv2d",
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"DownBlocks.Chain_11.Passthrough.Conv2d",
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"DownBlocks.Chain_12.Passthrough.Conv2d",
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"MiddleBlock.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
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"MiddleBlock.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
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"MiddleBlock.Passthrough.Conv2d",
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]
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fixed_source_order = [
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"down_blocks.2.attentions.0.proj_in",
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"down_blocks.2.attentions.0.proj_out",
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"controlnet_down_blocks.7",
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"down_blocks.2.attentions.1.proj_in",
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"down_blocks.2.attentions.1.proj_out",
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"controlnet_down_blocks.8",
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"controlnet_down_blocks.9",
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"controlnet_down_blocks.10",
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"controlnet_down_blocks.11",
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"mid_block.attentions.0.proj_in",
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"mid_block.attentions.0.proj_out",
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"controlnet_mid_block",
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]
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assert source_order[broken_k] == expected_source_order
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assert target_order[broken_k] == expected_target_order
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source_order[broken_k] = fixed_source_order
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assert verify_shape_match(source_order=source_order, target_order=target_order)
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mapping: dict[str, str] = {}
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for model_type_shape in source_order:
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source_keys = source_order[model_type_shape]
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target_keys = target_order[model_type_shape]
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mapping.update(zip(target_keys, source_keys))
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state_dict = convert_state_dict(
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source_state_dict=controlnet_src.state_dict(),
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target_state_dict=controlnet.state_dict(),
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state_dict_mapping=mapping,
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)
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return {k: v.half() for k, v in state_dict.items()}
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def main() -> None:
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--from",
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type=str,
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dest="source",
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required=True,
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help="Source model",
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)
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parser.add_argument(
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"--output-file",
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type=str,
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required=False,
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default="output.safetensors",
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help="Path for the output file",
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)
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args = parser.parse_args()
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controlnet_src = ControlNetModel.from_pretrained(pretrained_model_name_or_path=args.source) # type: ignore
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tensors = convert(controlnet_src=controlnet_src) # type: ignore
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save_to_safetensors(path=args.output_file, tensors=tensors)
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if __name__ == "__main__":
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main()
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