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https://github.com/finegrain-ai/refiners.git
synced 2024-09-19 19:05:28 +00:00
write ControlLora weight conversion script
This commit is contained in:
Laurent
Laureηt
parent
41a5ce2052
commit
5fee723cd1
349
scripts/conversion/convert_fooocus_control_lora.py
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349
scripts/conversion/convert_fooocus_control_lora.py
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@ -0,0 +1,349 @@
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import argparse
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import logging
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from logging import info
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from pathlib import Path
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from huggingface_hub import hf_hub_download # type: ignore
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from torch import Tensor
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from torch.nn import Parameter as TorchParameter
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from refiners.fluxion.adapters.lora import Lora, LoraAdapter, auto_attach_loras
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from refiners.fluxion.layers import Conv2d
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from refiners.fluxion.layers.linear import Linear
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from refiners.fluxion.utils import load_from_safetensors, save_to_safetensors
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from refiners.foundationals.latent_diffusion.lora import SDLoraManager
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.control_lora import (
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ConditionEncoder,
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ControlLora,
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ControlLoraAdapter,
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ZeroConvolution,
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)
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL
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def sort_keys(key: str, /) -> tuple[str, int]:
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"""Compute the score of a key, relatively to its suffix.
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When used by [`sorted`][sorted], the keys will only be sorted "at the suffix level".
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Args:
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key: The key to sort.
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Returns:
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The padded suffix of the key.
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The score of the key's suffix.
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"""
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if "time_embed" in key: # HACK: will place the "time_embed" layers at very start of the list
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return ("", -2)
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if "label_emb" in key: # HACK: will place the "label_emb" layers right after "time_embed"
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return ("", -1)
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if "proj_out" in key: # HACK: will place the "proj_out" layers at the end of each "transformer_blocks"
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return (key.removesuffix("proj_out") + "transformer_blocks.99.ff.net.2", 10)
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return SDLoraManager.sort_keys(key)
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def load_lora_layers(
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name: str,
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state_dict: dict[str, Tensor],
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control_lora: ControlLora,
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) -> dict[str, Lora[Linear | Conv2d]]:
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"""Load the LoRA layers from the state_dict into the ControlLora.
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Args:
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name: The name of the LoRA.
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state_dict: The state_dict of the LoRA.
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control_lora: The ControlLora to load the LoRA layers into.
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"""
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# filter from the state_dict the layers that will be used for the LoRA layers
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lora_weights = {f"{key}.weight": value for key, value in state_dict.items() if ".up" in key or ".down" in key}
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# move the tensors to the device and dtype of the ControlLora
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lora_weights = {
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key: value.to(
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dtype=control_lora.dtype,
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device=control_lora.device,
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)
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for key, value in lora_weights.items()
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}
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# load every LoRA layers from the filtered state_dict
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lora_layers = Lora.from_dict(name, state_dict=lora_weights)
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# sort all the LoRA's keys using the `sort_keys` method
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lora_layers = {
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key: lora_layers[key]
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for key in sorted(
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lora_layers.keys(),
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key=sort_keys,
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)
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}
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# auto-attach the LoRA layers to the U-Net
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failed_keys = auto_attach_loras(lora_layers, control_lora, exclude=["ZeroConvolution", "ConditionEncoder"])
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assert not failed_keys, f"Failed to auto-attach {len(failed_keys)}/{len(lora_layers)} LoRA layers."
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# eject all the LoRA adapters from the U-Net
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# because we need each target path as if the adapter wasn't injected
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for lora_layer in lora_layers.values():
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lora_adapter = lora_layer.parent
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assert isinstance(lora_adapter, LoraAdapter)
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lora_adapter.eject()
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return lora_layers
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def load_condition_encoder(
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state_dict: dict[str, Tensor],
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control_lora: ControlLora,
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) -> None:
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"""Load the ConditionEncoder's Conv2d layers from the state_dict into the ControlLora.
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Args:
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state_dict: The state_dict of the ConditionEncoder.
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control_lora: The control_lora to load the ConditionEncoder's Conv2d layers into.
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"""
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# filter from the state_dict the layers that will be used for the ConditionEncoder
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condition_encoder_tensors = {key: value for key, value in state_dict.items() if "input_hint_block" in key}
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# move the tensors to the device and dtype of the ControlLora
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condition_encoder_tensors = {
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key: value.to(
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dtype=control_lora.dtype,
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device=control_lora.device,
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)
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for key, value in condition_encoder_tensors.items()
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}
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# find the ConditionEncoder's Conv2d layers
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condition_encoder_layer = control_lora.ensure_find(ConditionEncoder)
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condition_encoder_conv2ds = list(condition_encoder_layer.layers(Conv2d))
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# replace the Conv2d layers' weights and biases with the ones from the state_dict
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for i, layer in enumerate(condition_encoder_conv2ds):
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layer.weight = TorchParameter(condition_encoder_tensors[f"input_hint_block.{i*2}.weight"])
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layer.bias = TorchParameter(condition_encoder_tensors[f"input_hint_block.{i*2}.bias"])
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def load_zero_convolutions(
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state_dict: dict[str, Tensor],
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control_lora: ControlLora,
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) -> None:
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"""Load the ZeroConvolution's Conv2d layers from the state_dict into the ControlLora.
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Args:
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state_dict: The state_dict of the ZeroConvolution.
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control_lora: The ControlLora to load the ZeroConvolution's Conv2d layers into.
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"""
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# filter from the state_dict the layers that will be used for the ZeroConvolution layers
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zero_convolution_tensors = {key: value for key, value in state_dict.items() if "zero_convs" in key}
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n = len(zero_convolution_tensors) // 2
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zero_convolution_tensors[f"zero_convs.{n}.0.weight"] = state_dict["middle_block_out.0.weight"]
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zero_convolution_tensors[f"zero_convs.{n}.0.bias"] = state_dict["middle_block_out.0.bias"]
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# move the tensors to the device and dtype of the ControlLora
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zero_convolution_tensors = {
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key: value.to(
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dtype=control_lora.dtype,
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device=control_lora.device,
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)
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for key, value in zero_convolution_tensors.items()
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}
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# find the ZeroConvolution's Conv2d layers
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zero_convolution_layers = list(control_lora.layers(ZeroConvolution))
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zero_convolution_conv2ds = [layer.ensure_find(Conv2d) for layer in zero_convolution_layers]
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# replace the Conv2d layers' weights and biases with the ones from the state_dict
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for i, layer in enumerate(zero_convolution_conv2ds):
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layer.weight = TorchParameter(zero_convolution_tensors[f"zero_convs.{i}.0.weight"])
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layer.bias = TorchParameter(zero_convolution_tensors[f"zero_convs.{i}.0.bias"])
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def simplify_key(key: str, prefix: str, index: int | None = None) -> str:
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"""Simplify a key by stripping everything to the left of the prefix.
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Also optionally add a zero-padded index to the prefix.
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Example:
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>>> simplify_key("foo.bar.ControlLora.something", "ControlLora", 1)
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"ControlLora_01.something"
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>>> simplify_key("foo.bar.ControlLora.DownBlocks.something", "ControlLora")
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"ControlLora.DownBlocks.something"
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Args:
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key: The key to simplify.
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prefix: The prefix to remove.
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index: The index to add.
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"""
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_, right = key.split(prefix, maxsplit=1)
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if index:
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return f"{prefix}_{index:02d}{right}"
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else:
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return f"{prefix}{right}"
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def convert_lora_layers(
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lora_layers: dict[str, Lora[Linear | Conv2d]],
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control_lora: ControlLora,
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refiners_state_dict: dict[str, Tensor],
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) -> None:
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"""Convert the LoRA layers to the refiners format.
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Args:
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lora_layers: The LoRA layers to convert.
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control_lora: The ControlLora to convert the LoRA layers from.
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refiners_state_dict: The refiners state dict to update with the converted LoRA layers.
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"""
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for lora_layer in lora_layers.values():
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# get the adapter associated with the LoRA layer
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lora_adapter = lora_layer.parent
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assert isinstance(lora_adapter, LoraAdapter)
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# get the path of the adapter's target in the ControlLora
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target = lora_adapter.target
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path = target.get_path(parent=control_lora.ensure_find_parent(target))
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state_dict = {
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f"{path}.down": lora_layer.down.weight,
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f"{path}.up": lora_layer.up.weight,
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}
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state_dict = {simplify_key(key, "ControlLora"): param for key, param in state_dict.items()}
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refiners_state_dict.update(state_dict)
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def convert_zero_convolutions(
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control_lora: ControlLora,
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refiners_state_dict: dict[str, Tensor],
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) -> None:
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"""Convert the ZeroConvolution layers to the refiners format.
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Args:
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control_lora: The ControlLora to convert the ZeroConvolution layers from.
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refiners_state_dict: The refiners state dict to update with the converted ZeroConvolution layers.
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"""
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zero_convolution_layers = list(control_lora.layers(ZeroConvolution))
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for i, zero_convolution_layer in enumerate(zero_convolution_layers):
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state_dict = zero_convolution_layer.state_dict()
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path = zero_convolution_layer.get_path()
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state_dict = {f"{path}.{key}": param for key, param in state_dict.items()}
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state_dict = {simplify_key(key, "ZeroConvolution", i + 1): param for key, param in state_dict.items()}
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refiners_state_dict.update(state_dict)
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def convert_condition_encoder(
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control_lora: ControlLora,
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refiners_state_dict: dict[str, Tensor],
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) -> None:
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"""Convert the ConditionEncoder to the refiners format.
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Args:
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control_lora: The ControlLora to convert the ConditionEncoder from.
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refiners_state_dict: The refiners state dict to update with the converted ConditionEncoder.
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"""
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condition_encoder_layer = control_lora.ensure_find(ConditionEncoder)
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path = condition_encoder_layer.get_path()
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state_dict = condition_encoder_layer.state_dict()
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state_dict = {f"{path}.{key}": param for key, param in state_dict.items()}
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state_dict = {simplify_key(key, "ConditionEncoder"): param for key, param in state_dict.items()}
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refiners_state_dict.update(state_dict)
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def convert(
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name: str,
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state_dict_path: Path,
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output_path: Path,
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) -> None:
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sdxl = StableDiffusion_XL()
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info("Stable Diffusion XL model initialized")
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fooocus_state_dict = load_from_safetensors(state_dict_path)
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info(f"Fooocus weights loaded from: {state_dict_path}")
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control_lora_adapter = ControlLoraAdapter(target=sdxl.unet, name=name).inject()
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control_lora = control_lora_adapter.control_lora
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info("ControlLoraAdapter initialized")
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lora_layers = load_lora_layers(name, fooocus_state_dict, control_lora)
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info("LoRA layers loaded")
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load_zero_convolutions(fooocus_state_dict, control_lora)
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info("ZeroConvolution layers loaded")
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load_condition_encoder(fooocus_state_dict, control_lora)
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info("ConditionEncoder loaded")
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refiners_state_dict: dict[str, Tensor] = {}
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convert_lora_layers(lora_layers, control_lora, refiners_state_dict)
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info("LoRA layers converted to refiners format")
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convert_zero_convolutions(control_lora, refiners_state_dict)
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info("ZeroConvolution layers converted to refiners format")
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convert_condition_encoder(control_lora, refiners_state_dict)
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info("ConditionEncoder converted to refiners format")
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output_path.parent.mkdir(parents=True, exist_ok=True)
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save_to_safetensors(path=output_path, tensors=refiners_state_dict)
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info(f"Converted ControlLora state dict saved to disk at: {output_path}")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Convert ControlLora (from Fooocus) weights to refiners.",
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)
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parser.add_argument(
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"--from",
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type=Path,
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dest="source_path",
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default="lllyasviel/misc:control-lora-canny-rank128.safetensors",
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help="Path to the state_dict of the ControlLora, or a Hugging Face model ID.",
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)
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parser.add_argument(
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"--to",
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type=Path,
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dest="output_path",
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help=(
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"Path to save the converted model (extension will be .safetensors)."
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"If not specified, the output path will be the source path with the extension changed to .safetensors."
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),
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)
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parser.add_argument(
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"--verbose",
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action="store_true",
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dest="verbose",
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default=False,
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help="Use this flag to print verbose output during conversion.",
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)
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args = parser.parse_args()
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if args.verbose:
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logging.basicConfig(
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level=logging.INFO,
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format="%(levelname)s: %(message)s",
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)
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if not args.source_path.exists():
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repo_id, filename = str(args.source_path).split(":")
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args.source_path = Path(
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hf_hub_download(
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repo_id=repo_id,
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filename=filename,
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)
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)
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if args.output_path is None:
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args.output_path = Path(f"refiners_{args.source_path.stem}.safetensors")
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convert(
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name=args.source_path.stem,
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state_dict_path=args.source_path,
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output_path=args.output_path,
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)
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@ -273,6 +273,20 @@ def download_controlnet():
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download_files(urls, mfidabel_folder)
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def download_control_lora_fooocus():
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base_folder = os.path.join(test_weights_dir, "lllyasviel", "misc")
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control_loras = [
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"control-lora-canny-rank128.safetensors",
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"fooocus_xl_cpds_128.safetensors",
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]
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for control_lora in control_loras:
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download_file(
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url=f"https://huggingface.co/lllyasviel/misc/resolve/main/{control_lora}",
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dest_folder=base_folder,
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)
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def download_unclip():
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base_folder = os.path.join(test_weights_dir, "stabilityai", "stable-diffusion-2-1-unclip")
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download_file(
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@ -624,6 +638,21 @@ def convert_dinov2():
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)
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def convert_control_lora_fooocus():
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run_conversion_script(
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"convert_fooocus_control_lora.py",
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"tests/weights/lllyasviel/misc/control-lora-canny-rank128.safetensors",
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"tests/weights/control_lora/refiners_control-lora-canny-rank128.safetensors",
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expected_hash="4d505134",
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)
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run_conversion_script(
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"convert_fooocus_control_lora.py",
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"tests/weights/lllyasviel/misc/fooocus_xl_cpds_128.safetensors",
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"tests/weights/control_lora/refiners_fooocus_xl_cpds_128.safetensors",
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expected_hash="d81aa461",
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)
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def download_all():
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print(f"\nAll weights will be downloaded to {test_weights_dir}\n")
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download_sd15("runwayml/stable-diffusion-v1-5")
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@ -639,6 +668,7 @@ def download_all():
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download_t2i_adapter()
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download_sam()
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download_dinov2()
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download_control_lora_fooocus()
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def convert_all():
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@ -654,6 +684,7 @@ def convert_all():
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convert_t2i_adapter()
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convert_sam()
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convert_dinov2()
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convert_control_lora_fooocus()
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def main():
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