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from typing import TYPE_CHECKING | |
from ..utils import DIFFUSERS_SLOW_IMPORT, _LazyModule, deprecate | |
from ..utils.import_utils import is_peft_available, is_torch_available, is_transformers_available | |
def text_encoder_lora_state_dict(text_encoder): | |
deprecate( | |
"text_encoder_load_state_dict in `models`", | |
"0.27.0", | |
"`text_encoder_lora_state_dict` is deprecated and will be removed in 0.27.0. Make sure to retrieve the weights using `get_peft_model`. See https://huggingface.co/docs/peft/v0.6.2/en/quicktour#peftmodel for more information.", | |
) | |
state_dict = {} | |
for name, module in text_encoder_attn_modules(text_encoder): | |
for k, v in module.q_proj.lora_linear_layer.state_dict().items(): | |
state_dict[f"{name}.q_proj.lora_linear_layer.{k}"] = v | |
for k, v in module.k_proj.lora_linear_layer.state_dict().items(): | |
state_dict[f"{name}.k_proj.lora_linear_layer.{k}"] = v | |
for k, v in module.v_proj.lora_linear_layer.state_dict().items(): | |
state_dict[f"{name}.v_proj.lora_linear_layer.{k}"] = v | |
for k, v in module.out_proj.lora_linear_layer.state_dict().items(): | |
state_dict[f"{name}.out_proj.lora_linear_layer.{k}"] = v | |
return state_dict | |
if is_transformers_available(): | |
def text_encoder_attn_modules(text_encoder): | |
deprecate( | |
"text_encoder_attn_modules in `models`", | |
"0.27.0", | |
"`text_encoder_lora_state_dict` is deprecated and will be removed in 0.27.0. Make sure to retrieve the weights using `get_peft_model`. See https://huggingface.co/docs/peft/v0.6.2/en/quicktour#peftmodel for more information.", | |
) | |
from transformers import CLIPTextModel, CLIPTextModelWithProjection | |
attn_modules = [] | |
if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)): | |
for i, layer in enumerate(text_encoder.text_model.encoder.layers): | |
name = f"text_model.encoder.layers.{i}.self_attn" | |
mod = layer.self_attn | |
attn_modules.append((name, mod)) | |
else: | |
raise ValueError(f"do not know how to get attention modules for: {text_encoder.__class__.__name__}") | |
return attn_modules | |
_import_structure = {} | |
if is_torch_available(): | |
_import_structure["single_file"] = ["FromOriginalControlnetMixin", "FromOriginalVAEMixin"] | |
_import_structure["unet"] = ["UNet2DConditionLoadersMixin"] | |
_import_structure["utils"] = ["AttnProcsLayers"] | |
if is_transformers_available(): | |
_import_structure["single_file"].extend(["FromSingleFileMixin"]) | |
_import_structure["lora"] = ["LoraLoaderMixin", "StableDiffusionXLLoraLoaderMixin"] | |
_import_structure["textual_inversion"] = ["TextualInversionLoaderMixin"] | |
_import_structure["ip_adapter"] = ["IPAdapterMixin"] | |
_import_structure["peft"] = ["PeftAdapterMixin"] | |
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: | |
if is_torch_available(): | |
from .single_file import FromOriginalControlnetMixin, FromOriginalVAEMixin | |
from .unet import UNet2DConditionLoadersMixin | |
from .utils import AttnProcsLayers | |
if is_transformers_available(): | |
from .ip_adapter import IPAdapterMixin | |
from .lora import LoraLoaderMixin, StableDiffusionXLLoraLoaderMixin | |
from .single_file import FromSingleFileMixin | |
from .textual_inversion import TextualInversionLoaderMixin | |
from .peft import PeftAdapterMixin | |
else: | |
import sys | |
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | |