| 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_model"] = ["FromOriginalModelMixin"] |
|
|
| _import_structure["unet"] = ["UNet2DConditionLoadersMixin"] |
| _import_structure["utils"] = ["AttnProcsLayers"] |
| if is_transformers_available(): |
| _import_structure["single_file"] = ["FromSingleFileMixin"] |
| _import_structure["lora_pipeline"] = [ |
| "AmusedLoraLoaderMixin", |
| "StableDiffusionLoraLoaderMixin", |
| "SD3LoraLoaderMixin", |
| "StableDiffusionXLLoraLoaderMixin", |
| "LoraLoaderMixin", |
| "FluxLoraLoaderMixin", |
| ] |
| _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_model import FromOriginalModelMixin |
| from .unet import UNet2DConditionLoadersMixin |
| from .utils import AttnProcsLayers |
|
|
| if is_transformers_available(): |
| from .ip_adapter import IPAdapterMixin |
| from .lora_pipeline import ( |
| AmusedLoraLoaderMixin, |
| FluxLoraLoaderMixin, |
| LoraLoaderMixin, |
| SD3LoraLoaderMixin, |
| StableDiffusionLoraLoaderMixin, |
| 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__) |
|
|
