diffusers/src/diffusers/loaders/unet_loader_utils.py at main · fredatgithub/diffusers · GitHub

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# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import copy

from typing import TYPE_CHECKING, Dict, List, Union

from torch import nn

from ..utils import logging

if TYPE_CHECKING:

# import here to avoid circular imports

from ..models import UNet2DConditionModel

logger = logging.get_logger(__name__) # pylint: disable=invalid-name

def _translate_into_actual_layer_name(name):

"""Translate user-friendly name (e.g. 'mid') into actual layer name (e.g. 'mid_block.attentions.0')"""

if name == "mid":

return "mid_block.attentions.0"

updown, block, attn = name.split(".")

updown = updown.replace("down", "down_blocks").replace("up", "up_blocks")

block = block.replace("block_", "")

attn = "attentions." + attn

return ".".join((updown, block, attn))

def _maybe_expand_lora_scales(

unet: "UNet2DConditionModel", weight_scales: List[Union[float, Dict]], default_scale=1.0

blocks_with_transformer = {

"down": [i for i, block in enumerate(unet.down_blocks) if hasattr(block, "attentions")],

"up": [i for i, block in enumerate(unet.up_blocks) if hasattr(block, "attentions")],

}

transformer_per_block = {"down": unet.config.layers_per_block, "up": unet.config.layers_per_block + 1}

expanded_weight_scales = [

_maybe_expand_lora_scales_for_one_adapter(

weight_for_adapter,

blocks_with_transformer,

transformer_per_block,

model=unet,

default_scale=default_scale,

)

for weight_for_adapter in weight_scales

]

return expanded_weight_scales

def _maybe_expand_lora_scales_for_one_adapter(

scales: Union[float, Dict],

blocks_with_transformer: Dict[str, int],

transformer_per_block: Dict[str, int],

model: nn.Module,

default_scale: float = 1.0,

"""

Expands the inputs into a more granular dictionary. See the example below for more details.

Parameters:

scales (`Union[float, Dict]`):

Scales dict to expand.

blocks_with_transformer (`Dict[str, int]`):

Dict with keys 'up' and 'down', showing which blocks have transformer layers

transformer_per_block (`Dict[str, int]`):

Dict with keys 'up' and 'down', showing how many transformer layers each block has

E.g. turns

```python

scales = {"down": 2, "mid": 3, "up": {"block_0": 4, "block_1": [5, 6, 7]}}

blocks_with_transformer = {"down": [1, 2], "up": [0, 1]}

transformer_per_block = {"down": 2, "up": 3}

```

into

```python

{

"down.block_1.0": 2,

"down.block_1.1": 2,

"down.block_2.0": 2,

"down.block_2.1": 2,

"mid": 3,

"up.block_0.0": 4,

"up.block_0.1": 4,

"up.block_0.2": 4,

"up.block_1.0": 5,

"up.block_1.1": 6,

"up.block_1.2": 7,

}

```

"""

if sorted(blocks_with_transformer.keys()) != ["down", "up"]:

raise ValueError("blocks_with_transformer needs to be a dict with keys `'down' and `'up'`")

if sorted(transformer_per_block.keys()) != ["down", "up"]:

raise ValueError("transformer_per_block needs to be a dict with keys `'down' and `'up'`")

if not isinstance(scales, dict):

# don't expand if scales is a single number

return scales

scales = copy.deepcopy(scales)

if "mid" not in scales:

scales["mid"] = default_scale

elif isinstance(scales["mid"], list):

if len(scales["mid"]) == 1:

scales["mid"] = scales["mid"][0]

else:

raise ValueError(f"Expected 1 scales for mid, got {len(scales['mid'])}.")

for updown in ["up", "down"]:

if updown not in scales:

scales[updown] = default_scale

# eg {"down": 1} to {"down": {"block_1": 1, "block_2": 1}}}

if not isinstance(scales[updown], dict):

scales[updown] = {f"block_{i}": copy.deepcopy(scales[updown]) for i in blocks_with_transformer[updown]}

# eg {"down": {"block_1": 1}} to {"down": {"block_1": [1, 1]}}

for i in blocks_with_transformer[updown]:

block = f"block_{i}"

# set not assigned blocks to default scale

if block not in scales[updown]:

scales[updown][block] = default_scale

if not isinstance(scales[updown][block], list):

scales[updown][block] = [scales[updown][block] for _ in range(transformer_per_block[updown])]

elif len(scales[updown][block]) == 1:

# a list specifying scale to each masked IP input

scales[updown][block] = scales[updown][block] * transformer_per_block[updown]

elif len(scales[updown][block]) != transformer_per_block[updown]:

raise ValueError(

f"Expected {transformer_per_block[updown]} scales for {updown}.{block}, got {len(scales[updown][block])}."

)

# eg {"down": "block_1": [1, 1]}} to {"down.block_1.0": 1, "down.block_1.1": 1}

for i in blocks_with_transformer[updown]:

block = f"block_{i}"

for tf_idx, value in enumerate(scales[updown][block]):

scales[f"{updown}.{block}.{tf_idx}"] = value

del scales[updown]

state_dict = model.state_dict()

for layer in scales.keys():

if not any(_translate_into_actual_layer_name(layer) in module for module in state_dict.keys()):

raise ValueError(

f"Can't set lora scale for layer {layer}. It either doesn't exist in this unet or it has no attentions."

)

return {_translate_into_actual_layer_name(name): weight for name, weight in scales.items()}

diffusers/src/diffusers/loaders/unet_loader_utils.py at main · fredatgithub/diffusers · GitHub

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