285 lines
14 KiB
Python
285 lines
14 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import re
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from ..utils import logging
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logger = logging.get_logger(__name__)
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def _maybe_map_sgm_blocks_to_diffusers(state_dict, unet_config, delimiter="_", block_slice_pos=5):
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# 1. get all state_dict_keys
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all_keys = list(state_dict.keys())
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sgm_patterns = ["input_blocks", "middle_block", "output_blocks"]
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# 2. check if needs remapping, if not return original dict
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is_in_sgm_format = False
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for key in all_keys:
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if any(p in key for p in sgm_patterns):
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is_in_sgm_format = True
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break
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if not is_in_sgm_format:
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return state_dict
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# 3. Else remap from SGM patterns
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new_state_dict = {}
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inner_block_map = ["resnets", "attentions", "upsamplers"]
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# Retrieves # of down, mid and up blocks
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input_block_ids, middle_block_ids, output_block_ids = set(), set(), set()
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for layer in all_keys:
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if "text" in layer:
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new_state_dict[layer] = state_dict.pop(layer)
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else:
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layer_id = int(layer.split(delimiter)[:block_slice_pos][-1])
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if sgm_patterns[0] in layer:
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input_block_ids.add(layer_id)
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elif sgm_patterns[1] in layer:
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middle_block_ids.add(layer_id)
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elif sgm_patterns[2] in layer:
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output_block_ids.add(layer_id)
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else:
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raise ValueError(f"Checkpoint not supported because layer {layer} not supported.")
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input_blocks = {
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layer_id: [key for key in state_dict if f"input_blocks{delimiter}{layer_id}" in key]
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for layer_id in input_block_ids
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}
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middle_blocks = {
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layer_id: [key for key in state_dict if f"middle_block{delimiter}{layer_id}" in key]
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for layer_id in middle_block_ids
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}
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output_blocks = {
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layer_id: [key for key in state_dict if f"output_blocks{delimiter}{layer_id}" in key]
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for layer_id in output_block_ids
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}
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# Rename keys accordingly
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for i in input_block_ids:
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block_id = (i - 1) // (unet_config.layers_per_block + 1)
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layer_in_block_id = (i - 1) % (unet_config.layers_per_block + 1)
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for key in input_blocks[i]:
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inner_block_id = int(key.split(delimiter)[block_slice_pos])
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inner_block_key = inner_block_map[inner_block_id] if "op" not in key else "downsamplers"
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inner_layers_in_block = str(layer_in_block_id) if "op" not in key else "0"
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new_key = delimiter.join(
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key.split(delimiter)[: block_slice_pos - 1]
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+ [str(block_id), inner_block_key, inner_layers_in_block]
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+ key.split(delimiter)[block_slice_pos + 1 :]
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)
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new_state_dict[new_key] = state_dict.pop(key)
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for i in middle_block_ids:
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key_part = None
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if i == 0:
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key_part = [inner_block_map[0], "0"]
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elif i == 1:
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key_part = [inner_block_map[1], "0"]
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elif i == 2:
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key_part = [inner_block_map[0], "1"]
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else:
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raise ValueError(f"Invalid middle block id {i}.")
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for key in middle_blocks[i]:
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new_key = delimiter.join(
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key.split(delimiter)[: block_slice_pos - 1] + key_part + key.split(delimiter)[block_slice_pos:]
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)
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new_state_dict[new_key] = state_dict.pop(key)
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for i in output_block_ids:
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block_id = i // (unet_config.layers_per_block + 1)
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layer_in_block_id = i % (unet_config.layers_per_block + 1)
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for key in output_blocks[i]:
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inner_block_id = int(key.split(delimiter)[block_slice_pos])
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inner_block_key = inner_block_map[inner_block_id]
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inner_layers_in_block = str(layer_in_block_id) if inner_block_id < 2 else "0"
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new_key = delimiter.join(
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key.split(delimiter)[: block_slice_pos - 1]
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+ [str(block_id), inner_block_key, inner_layers_in_block]
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+ key.split(delimiter)[block_slice_pos + 1 :]
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)
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new_state_dict[new_key] = state_dict.pop(key)
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if len(state_dict) > 0:
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raise ValueError("At this point all state dict entries have to be converted.")
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return new_state_dict
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def _convert_kohya_lora_to_diffusers(state_dict, unet_name="unet", text_encoder_name="text_encoder"):
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unet_state_dict = {}
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te_state_dict = {}
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te2_state_dict = {}
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network_alphas = {}
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# every down weight has a corresponding up weight and potentially an alpha weight
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lora_keys = [k for k in state_dict.keys() if k.endswith("lora_down.weight")]
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for key in lora_keys:
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lora_name = key.split(".")[0]
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lora_name_up = lora_name + ".lora_up.weight"
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lora_name_alpha = lora_name + ".alpha"
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if lora_name.startswith("lora_unet_"):
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diffusers_name = key.replace("lora_unet_", "").replace("_", ".")
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if "input.blocks" in diffusers_name:
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diffusers_name = diffusers_name.replace("input.blocks", "down_blocks")
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else:
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diffusers_name = diffusers_name.replace("down.blocks", "down_blocks")
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if "middle.block" in diffusers_name:
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diffusers_name = diffusers_name.replace("middle.block", "mid_block")
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else:
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diffusers_name = diffusers_name.replace("mid.block", "mid_block")
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if "output.blocks" in diffusers_name:
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diffusers_name = diffusers_name.replace("output.blocks", "up_blocks")
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else:
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diffusers_name = diffusers_name.replace("up.blocks", "up_blocks")
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diffusers_name = diffusers_name.replace("transformer.blocks", "transformer_blocks")
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diffusers_name = diffusers_name.replace("to.q.lora", "to_q_lora")
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diffusers_name = diffusers_name.replace("to.k.lora", "to_k_lora")
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diffusers_name = diffusers_name.replace("to.v.lora", "to_v_lora")
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diffusers_name = diffusers_name.replace("to.out.0.lora", "to_out_lora")
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diffusers_name = diffusers_name.replace("proj.in", "proj_in")
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diffusers_name = diffusers_name.replace("proj.out", "proj_out")
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diffusers_name = diffusers_name.replace("emb.layers", "time_emb_proj")
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# SDXL specificity.
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if "emb" in diffusers_name and "time.emb.proj" not in diffusers_name:
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pattern = r"\.\d+(?=\D*$)"
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diffusers_name = re.sub(pattern, "", diffusers_name, count=1)
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if ".in." in diffusers_name:
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diffusers_name = diffusers_name.replace("in.layers.2", "conv1")
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if ".out." in diffusers_name:
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diffusers_name = diffusers_name.replace("out.layers.3", "conv2")
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if "downsamplers" in diffusers_name or "upsamplers" in diffusers_name:
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diffusers_name = diffusers_name.replace("op", "conv")
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if "skip" in diffusers_name:
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diffusers_name = diffusers_name.replace("skip.connection", "conv_shortcut")
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# LyCORIS specificity.
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if "time.emb.proj" in diffusers_name:
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diffusers_name = diffusers_name.replace("time.emb.proj", "time_emb_proj")
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if "conv.shortcut" in diffusers_name:
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diffusers_name = diffusers_name.replace("conv.shortcut", "conv_shortcut")
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# General coverage.
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if "transformer_blocks" in diffusers_name:
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if "attn1" in diffusers_name or "attn2" in diffusers_name:
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diffusers_name = diffusers_name.replace("attn1", "attn1.processor")
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diffusers_name = diffusers_name.replace("attn2", "attn2.processor")
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unet_state_dict[diffusers_name] = state_dict.pop(key)
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unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
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elif "ff" in diffusers_name:
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unet_state_dict[diffusers_name] = state_dict.pop(key)
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unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
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elif any(key in diffusers_name for key in ("proj_in", "proj_out")):
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unet_state_dict[diffusers_name] = state_dict.pop(key)
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unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
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else:
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unet_state_dict[diffusers_name] = state_dict.pop(key)
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unet_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
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elif lora_name.startswith("lora_te_"):
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diffusers_name = key.replace("lora_te_", "").replace("_", ".")
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diffusers_name = diffusers_name.replace("text.model", "text_model")
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diffusers_name = diffusers_name.replace("self.attn", "self_attn")
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diffusers_name = diffusers_name.replace("q.proj.lora", "to_q_lora")
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diffusers_name = diffusers_name.replace("k.proj.lora", "to_k_lora")
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diffusers_name = diffusers_name.replace("v.proj.lora", "to_v_lora")
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diffusers_name = diffusers_name.replace("out.proj.lora", "to_out_lora")
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if "self_attn" in diffusers_name:
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te_state_dict[diffusers_name] = state_dict.pop(key)
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te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
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elif "mlp" in diffusers_name:
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# Be aware that this is the new diffusers convention and the rest of the code might
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# not utilize it yet.
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diffusers_name = diffusers_name.replace(".lora.", ".lora_linear_layer.")
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te_state_dict[diffusers_name] = state_dict.pop(key)
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te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
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# (sayakpaul): Duplicate code. Needs to be cleaned.
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elif lora_name.startswith("lora_te1_"):
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diffusers_name = key.replace("lora_te1_", "").replace("_", ".")
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diffusers_name = diffusers_name.replace("text.model", "text_model")
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diffusers_name = diffusers_name.replace("self.attn", "self_attn")
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diffusers_name = diffusers_name.replace("q.proj.lora", "to_q_lora")
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diffusers_name = diffusers_name.replace("k.proj.lora", "to_k_lora")
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diffusers_name = diffusers_name.replace("v.proj.lora", "to_v_lora")
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diffusers_name = diffusers_name.replace("out.proj.lora", "to_out_lora")
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if "self_attn" in diffusers_name:
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te_state_dict[diffusers_name] = state_dict.pop(key)
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te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
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elif "mlp" in diffusers_name:
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# Be aware that this is the new diffusers convention and the rest of the code might
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# not utilize it yet.
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diffusers_name = diffusers_name.replace(".lora.", ".lora_linear_layer.")
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te_state_dict[diffusers_name] = state_dict.pop(key)
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te_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
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# (sayakpaul): Duplicate code. Needs to be cleaned.
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elif lora_name.startswith("lora_te2_"):
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diffusers_name = key.replace("lora_te2_", "").replace("_", ".")
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diffusers_name = diffusers_name.replace("text.model", "text_model")
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diffusers_name = diffusers_name.replace("self.attn", "self_attn")
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diffusers_name = diffusers_name.replace("q.proj.lora", "to_q_lora")
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diffusers_name = diffusers_name.replace("k.proj.lora", "to_k_lora")
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diffusers_name = diffusers_name.replace("v.proj.lora", "to_v_lora")
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diffusers_name = diffusers_name.replace("out.proj.lora", "to_out_lora")
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if "self_attn" in diffusers_name:
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te2_state_dict[diffusers_name] = state_dict.pop(key)
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te2_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
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elif "mlp" in diffusers_name:
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# Be aware that this is the new diffusers convention and the rest of the code might
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# not utilize it yet.
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diffusers_name = diffusers_name.replace(".lora.", ".lora_linear_layer.")
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te2_state_dict[diffusers_name] = state_dict.pop(key)
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te2_state_dict[diffusers_name.replace(".down.", ".up.")] = state_dict.pop(lora_name_up)
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# Rename the alphas so that they can be mapped appropriately.
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if lora_name_alpha in state_dict:
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alpha = state_dict.pop(lora_name_alpha).item()
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if lora_name_alpha.startswith("lora_unet_"):
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prefix = "unet."
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elif lora_name_alpha.startswith(("lora_te_", "lora_te1_")):
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prefix = "text_encoder."
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else:
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prefix = "text_encoder_2."
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new_name = prefix + diffusers_name.split(".lora.")[0] + ".alpha"
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network_alphas.update({new_name: alpha})
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if len(state_dict) > 0:
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raise ValueError(f"The following keys have not been correctly be renamed: \n\n {', '.join(state_dict.keys())}")
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logger.info("Kohya-style checkpoint detected.")
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unet_state_dict = {f"{unet_name}.{module_name}": params for module_name, params in unet_state_dict.items()}
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te_state_dict = {f"{text_encoder_name}.{module_name}": params for module_name, params in te_state_dict.items()}
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te2_state_dict = (
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{f"text_encoder_2.{module_name}": params for module_name, params in te2_state_dict.items()}
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if len(te2_state_dict) > 0
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else None
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)
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if te2_state_dict is not None:
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te_state_dict.update(te2_state_dict)
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new_state_dict = {**unet_state_dict, **te_state_dict}
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return new_state_dict, network_alphas
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