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[docs] Loader APIs (#5813) 

* first draft

* remove old loader doc

* start adding lora code examples

* finish

* add link to loralinearlayer

* feedback

* fix
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docs/source/en/_toctree.yml
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......@@ -186,13 +186,21 @@
- sections:
- local: api/configuration
title: Configuration
- local: api/loaders
title: Loaders
- local: api/logging
title: Logging
- local: api/outputs
title: Outputs
title: Main Classes
- sections:
- local: api/loaders/lora
title: LoRA
- local: api/loaders/single_file
title: Single files
- local: api/loaders/textual_inversion
title: Textual Inversion
- local: api/loaders/unet
title: UNet
title: Loaders
- sections:
- local: api/models/overview
title: Overview
......
docs/source/en/api/loaders/lora.md 0 → 100644
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<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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.
-->
# LoRA
LoRA is a fast and lightweight training method that inserts and trains a significantly smaller number of parameters instead of all the model parameters. This produces a smaller file (~100 MBs) and makes it easier to quickly train a model to learn a new concept. LoRA weights are typically loaded into the UNet, text encoder or both. There are two classes for loading LoRA weights:
- [`LoraLoaderMixin`] provides functions for loading and unloading, fusing and unfusing, enabling and disabling, and more functions for managing LoRA weights. This class can be used with any model.
- [`StableDiffusionXLLoraLoaderMixin`] is a [Stable Diffusion (SDXL)](../../api/pipelines/stable_diffusion/stable_diffusion_xl) version of the [`LoraLoaderMixin`] class for loading and saving LoRA weights. It can only be used with the SDXL model.
<Tip>
To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
</Tip>
## LoraLoaderMixin
[[autodoc]] loaders.lora.LoraLoaderMixin
## StableDiffusionXLLoraLoaderMixin
[[autodoc]] loaders.lora.StableDiffusionXLLoraLoaderMixin
\ No newline at end of file
docs/source/en/api/loaders.md → docs/source/en/api/loaders/single_file.md
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......@@ -10,40 +10,28 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
specific language governing permissions and limitations under the License.
-->
# Loaders
# Single files
Adapters (textual inversion, LoRA, hypernetworks) allow you to modify a diffusion model to generate images in a specific style without training or finetuning the entire model. The adapter weights are very portable because they're typically only a tiny fraction of the pretrained model weights. 🤗 Diffusers provides an easy-to-use `LoaderMixin` API to load adapter weights.
Diffusers supports loading pretrained pipeline (or model) weights stored in a single file, such as a `ckpt` or `safetensors` file. These single file types are typically produced from community trained models. There are three classes for loading single file weights:
<Tip warning={true}>
- [`FromSingleFileMixin`] supports loading pretrained pipeline weights stored in a single file, which can either be a `ckpt` or `safetensors` file.
- [`FromOriginalVAEMixin`] supports loading a pretrained [`AutoencoderKL`] from pretrained ControlNet weights stored in a single file, which can either be a `ckpt` or `safetensors` file.
- [`FromOriginalControlnetMixin`] supports loading pretrained ControlNet weights stored in a single file, which can either be a `ckpt` or `safetensors` file.
🧪 The `LoaderMixin`s are highly experimental and prone to future changes. To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `huggingface-cli login`.
<Tip>
</Tip>
## UNet2DConditionLoadersMixin
[[autodoc]] loaders.UNet2DConditionLoadersMixin
## TextualInversionLoaderMixin
[[autodoc]] loaders.TextualInversionLoaderMixin
## StableDiffusionXLLoraLoaderMixin
[[autodoc]] loaders.StableDiffusionXLLoraLoaderMixin
To learn more about how to load single file weights, see the [Load different Stable Diffusion formats](../../using-diffusers/other-formats) loading guide.
## LoraLoaderMixin
[[autodoc]] loaders.LoraLoaderMixin
</Tip>
## FromSingleFileMixin
[[autodoc]] loaders.FromSingleFileMixin
[[autodoc]] loaders.single_file.FromSingleFileMixin
## FromOriginalControlnetMixin
## FromOriginalVAEMixin
[[autodoc]] loaders.FromOriginalControlnetMixin
[[autodoc]] loaders.single_file.FromOriginalVAEMixin
## FromOriginalVAEMixin
## FromOriginalControlnetMixin
[[autodoc]] loaders.FromOriginalVAEMixin
[[autodoc]] loaders.single_file.FromOriginalControlnetMixin
\ No newline at end of file
docs/source/en/api/loaders/textual_inversion.md 0 → 100644
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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.
-->
# Textual Inversion
Textual Inversion is a training method for personalizing models by learning new text embeddings from a few example images. The file produced from training is extremely small (a few KBs) and the new embeddings can be loaded into the text encoder.
[`TextualInversionLoaderMixin`] provides a function for loading Textual Inversion embeddings from Diffusers and Automatic1111 into the text encoder and loading a special token to activate the embeddings.
<Tip>
To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/loading_adapters#textual-inversion) loading guide.
</Tip>
## TextualInversionLoaderMixin
[[autodoc]] loaders.textual_inversion.TextualInversionLoaderMixin
\ No newline at end of file
docs/source/en/api/loaders/unet.md 0 → 100644
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<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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.
-->
# UNet
Some training methods - like LoRA and Custom Diffusion - typically target the UNet's attention layers, but these training methods can also target other non-attention layers. Instead of training all of a model's parameters, only a subset of the parameters are trained, which is faster and more efficient. This class is useful if you're *only* loading weights into a UNet. If you need to load weights into the text encoder or a text encoder and UNet, try using the [`~loaders.LoraLoaderMixin.load_lora_weights`] function instead.
The [`UNet2DConditionLoadersMixin`] class provides functions for loading and saving weights, fusing and unfusing LoRAs, disabling and enabling LoRAs, and setting and deleting adapters.
<Tip>
To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
</Tip>
## UNet2DConditionLoadersMixin
[[autodoc]] loaders.unet.UNet2DConditionLoadersMixin
\ No newline at end of file
src/diffusers/loaders/lora.py
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......@@ -68,8 +68,7 @@ LORA_DEPRECATION_MESSAGE = "You are using an old version of LoRA backend. This w
class LoraLoaderMixin:
r"""
Load LoRA layers into [`UNet2DConditionModel`] and
[`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).
Load LoRA layers into [`UNet2DConditionModel`] and [`~transformers.CLIPTextModel`].
"""
text_encoder_name = TEXT_ENCODER_NAME
......@@ -95,12 +94,28 @@ class LoraLoaderMixin:
Parameters:
pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
See [`~loaders.LoraLoaderMixin.lora_state_dict`].
A string (model id of a pretrained model hosted on the Hub), a path to a directory containing the model
weights, or a [torch state
dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
kwargs (`dict`, *optional*):
See [`~loaders.LoraLoaderMixin.lora_state_dict`].
adapter_name (`str`, *optional*):
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
`default_{i}` where i is the total number of adapters being loaded.
Name for referencing the loaded adapter model. If not specified, it will use `default_{i}` where `i` is
the total number of adapters being loaded. Must have PEFT installed to use.
Example:
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to(
"cuda"
)
pipeline.load_lora_weights(
"Yntec/pineappleAnimeMix", weight_name="pineappleAnimeMix_pineapple10.1.safetensors", adapter_name="anime"
)
```
"""
# First, ensure that the checkpoint is a compatible one and can be successfully loaded.
state_dict, network_alphas = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
......@@ -138,15 +153,7 @@ class LoraLoaderMixin:
**kwargs,
):
r"""
Return state dict for lora weights and the network alphas.
<Tip warning={true}>
We support loading A1111 formatted LoRA checkpoints in a limited capacity.
This function is experimental and might change in the future.
</Tip>
Return state dict and network alphas of the LoRA weights.
Parameters:
pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
......@@ -154,8 +161,7 @@ class LoraLoaderMixin:
- A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
the Hub.
- A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
with [`ModelMixin.save_pretrained`].
- A path to a *directory* (for example `./my_model_directory`) containing the model weights.
- A [torch state
dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
......@@ -191,7 +197,6 @@ class LoraLoaderMixin:
Mirror source to resolve accessibility issues if you're downloading a model in China. We do not
guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
information.
"""
# Load the main state dict first which has the LoRA layers for either of
# UNet and text encoder or both.
......@@ -468,25 +473,27 @@ class LoraLoaderMixin:
cls, state_dict, network_alphas, unet, low_cpu_mem_usage=None, adapter_name=None, _pipeline=None
):
"""
This will load the LoRA layers specified in `state_dict` into `unet`.
Load LoRA layers specified in `state_dict` into `unet`.
Parameters:
state_dict (`dict`):
A standard state dict containing the lora layer parameters. The keys can either be indexed directly
into the unet or prefixed with an additional `unet` which can be used to distinguish between text
encoder lora layers.
A standard state dict containing the LoRA layer parameters. The keys can either be indexed directly
into the `unet` or prefixed with an additional `unet`, which can be used to distinguish between text
encoder LoRA layers.
network_alphas (`Dict[str, float]`):
See `LoRALinearLayer` for more details.
See
[`LoRALinearLayer`](https://github.com/huggingface/diffusers/blob/c697f524761abd2314c030221a3ad2f7791eab4e/src/diffusers/models/lora.py#L182)
for more details.
unet (`UNet2DConditionModel`):
The UNet model to load the LoRA layers into.
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
Speed up model loading only loading the pretrained weights and not initializing the weights. This also
tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
argument to `True` will raise an error.
Only load and not initialize the pretrained weights. This can speedup model loading and also tries to
not use more than 1x model size in CPU memory (including peak memory) while loading the model. Only
supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this argument to
`True` will raise an error.
adapter_name (`str`, *optional*):
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
`default_{i}` where i is the total number of adapters being loaded.
Name for referencing the loaded adapter model. If not specified, it will use `default_{i}` where `i` is
the total number of adapters being loaded.
"""
low_cpu_mem_usage = low_cpu_mem_usage if low_cpu_mem_usage is not None else _LOW_CPU_MEM_USAGE_DEFAULT
# If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
......@@ -580,26 +587,27 @@ class LoraLoaderMixin:
_pipeline=None,
):
"""
This will load the LoRA layers specified in `state_dict` into `text_encoder`
Load LoRA layers specified in `state_dict` into `text_encoder`.
Parameters:
state_dict (`dict`):
A standard state dict containing the lora layer parameters. The key should be prefixed with an
additional `text_encoder` to distinguish between unet lora layers.
A standard state dict containing the LoRA layer parameters. The key should be prefixed with an
additional `text_encoder` to distinguish between UNet LoRA layers.
network_alphas (`Dict[str, float]`):
See `LoRALinearLayer` for more details.
See
[`LoRALinearLayer`](https://github.com/huggingface/diffusers/blob/c697f524761abd2314c030221a3ad2f7791eab4e/src/diffusers/models/lora.py#L182)
for more details.
text_encoder (`CLIPTextModel`):
The text encoder model to load the LoRA layers into.
prefix (`str`):
Expected prefix of the `text_encoder` in the `state_dict`.
lora_scale (`float`):
How much to scale the output of the lora linear layer before it is added with the output of the regular
lora layer.
Scale of `LoRALinearLayer`'s output before it is added with the output of the regular LoRA layer.
low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
Speed up model loading only loading the pretrained weights and not initializing the weights. This also
tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this
argument to `True` will raise an error.
Only load and not initialize the pretrained weights. This can speedup model loading and also tries to
not use more than 1x model size in CPU memory (including peak memory) while loading the model. Only
supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this argument to
`True` will raise an error.
adapter_name (`str`, *optional*):
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
`default_{i}` where i is the total number of adapters being loaded.
......@@ -884,11 +892,11 @@ class LoraLoaderMixin:
safe_serialization: bool = True,
):
r"""
Save the LoRA parameters corresponding to the UNet and text encoder.
Save the UNet and text encoder LoRA parameters.
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to save LoRA parameters to. Will be created if it doesn't exist.
Directory to save LoRA parameters to (will be created if it doesn't exist).
unet_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
State dict of the LoRA layers corresponding to the `unet`.
text_encoder_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
......@@ -899,11 +907,30 @@ class LoraLoaderMixin:
need to call this function on all processes. In this case, set `is_main_process=True` only on the main
process to avoid race conditions.
save_function (`Callable`):
The function to use to save the state dictionary. Useful during distributed training when you need to
replace `torch.save` with another method. Can be configured with the environment variable
The function to use to save the state dict. Useful during distributed training when you need to replace
`torch.save` with another method. Can be configured with the environment variable
`DIFFUSERS_SAVE_MODE`.
safe_serialization (`bool`, *optional*, defaults to `True`):
Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
Whether to save the model using `safetensors` or with `pickle`.
Example:
```py
from diffusers import StableDiffusionXLPipeline
from peft.utils import get_peft_model_state_dict
import torch
pipeline = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
pipeline.fuse_lora()
# get and save unet state dict
unet_state_dict = get_peft_model_state_dict(pipeline.unet, adapter_name="pixel")
pipeline.save_lora_weights("fused-model", unet_lora_layers=unet_state_dict)
pipeline.load_lora_weights("fused-model", weight_name="pytorch_lora_weights.safetensors")
```
"""
# Create a flat dictionary.
state_dict = {}
......@@ -1139,14 +1166,19 @@ class LoraLoaderMixin:
def unload_lora_weights(self):
"""
Unloads the LoRA parameters.
Unload the LoRA parameters from a pipeline.
Examples:
```python
>>> # Assuming `pipeline` is already loaded with the LoRA parameters.
>>> pipeline.unload_lora_weights()
>>> ...
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
pipeline.unload_lora_weights()
```
"""
if not USE_PEFT_BACKEND:
......@@ -1175,7 +1207,7 @@ class LoraLoaderMixin:
safe_fusing: bool = False,
):
r"""
Fuses the LoRA parameters into the original parameters of the corresponding blocks.
Fuse the LoRA parameters with the original parameters in their corresponding blocks.
<Tip warning={true}>
......@@ -1189,9 +1221,23 @@ class LoraLoaderMixin:
Whether to fuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
LoRA parameters then it won't have any effect.
lora_scale (`float`, defaults to 1.0):
Controls how much to influence the outputs with the LoRA parameters.
Controls LoRA influence on the outputs.
safe_fusing (`bool`, defaults to `False`):
Whether to check fused weights for NaN values before fusing and if values are NaN not fusing them.
Whether to check fused weights for `NaN` values before fusing and if values are `NaN`, then don't fuse
them.
Example:
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
pipeline.fuse_lora(lora_scale=0.7)
```
"""
if fuse_unet or fuse_text_encoder:
self.num_fused_loras += 1
......@@ -1240,8 +1286,7 @@ class LoraLoaderMixin:
def unfuse_lora(self, unfuse_unet: bool = True, unfuse_text_encoder: bool = True):
r"""
Reverses the effect of
[`pipe.fuse_lora()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraLoaderMixin.fuse_lora).
Unfuse the LoRA parameters from the original parameters in their corresponding blocks.
<Tip warning={true}>
......@@ -1254,6 +1299,20 @@ class LoraLoaderMixin:
unfuse_text_encoder (`bool`, defaults to `True`):
Whether to unfuse the text encoder LoRA parameters. If the text encoder wasn't monkey-patched with the
LoRA parameters then it won't have any effect.
Example:
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
pipeline.fuse_lora(lora_scale=0.7)
pipeline.unfuse_lora()
```
"""
if unfuse_unet:
if not USE_PEFT_BACKEND:
......@@ -1305,16 +1364,32 @@ class LoraLoaderMixin:
text_encoder_weights: List[float] = None,
):
"""
Sets the adapter layers for the text encoder.
Set the currently active adapter for use in the text encoder.
Args:
adapter_names (`List[str]` or `str`):
The names of the adapters to use.
The adapter to activate.
text_encoder (`torch.nn.Module`, *optional*):
The text encoder module to set the adapter layers for. If `None`, it will try to get the `text_encoder`
attribute.
The text encoder module to activate the adapter layers for. If `None`, it will try to get the
`text_encoder` attribute.
text_encoder_weights (`List[float]`, *optional*):
The weights to use for the text encoder. If `None`, the weights are set to `1.0` for all the adapters.
Example:
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
pipeline.load_lora_weights(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
)
pipeline.set_adapters_for_text_encoder("pixel")
```
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
......@@ -1342,12 +1417,25 @@ class LoraLoaderMixin:
def disable_lora_for_text_encoder(self, text_encoder: Optional["PreTrainedModel"] = None):
"""
Disables the LoRA layers for the text encoder.
Disable the text encoder's LoRA layers.
Args:
text_encoder (`torch.nn.Module`, *optional*):
The text encoder module to disable the LoRA layers for. If `None`, it will try to get the
`text_encoder` attribute.
Example:
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
pipeline.disable_lora_for_text_encoder()
```
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
......@@ -1359,12 +1447,25 @@ class LoraLoaderMixin:
def enable_lora_for_text_encoder(self, text_encoder: Optional["PreTrainedModel"] = None):
"""
Enables the LoRA layers for the text encoder.
Enables the text encoder's LoRA layers.
Args:
text_encoder (`torch.nn.Module`, *optional*):
The text encoder module to enable the LoRA layers for. If `None`, it will try to get the `text_encoder`
attribute.
Example:
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
pipeline.enable_lora_for_text_encoder()
```
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
......@@ -1415,10 +1516,24 @@ class LoraLoaderMixin:
def delete_adapters(self, adapter_names: Union[List[str], str]):
"""
Delete an adapter's LoRA layers from the UNet and text encoder(s).
Args:
Deletes the LoRA layers of `adapter_name` for the unet and text-encoder(s).
adapter_names (`Union[List[str], str]`):
The names of the adapter to delete. Can be a single string or a list of strings
The names (single string or list of strings) of the adapter to delete.
Example:
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
pipeline.delete_adapters("pixel")
```
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
......@@ -1438,7 +1553,7 @@ class LoraLoaderMixin:
def get_active_adapters(self) -> List[str]:
"""
Gets the list of the current active adapters.
Get a list of currently active adapters.
Example:
......@@ -1470,7 +1585,22 @@ class LoraLoaderMixin:
def get_list_adapters(self) -> Dict[str, List[str]]:
"""
Gets the current list of all available adapters in the pipeline.
Get a list of all currently available adapters for each component in the pipeline.
Example:
```py
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
).to("cuda")
pipeline.load_lora_weights(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
)
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
pipeline.get_list_adapters()
```
"""
if not USE_PEFT_BACKEND:
raise ValueError(
......@@ -1492,14 +1622,27 @@ class LoraLoaderMixin:
def set_lora_device(self, adapter_names: List[str], device: Union[torch.device, str, int]) -> None:
"""
Moves the LoRAs listed in `adapter_names` to a target device. Useful for offloading the LoRA to the CPU in case
you want to load multiple adapters and free some GPU memory.
Move a LoRA to a target device. Useful for offloading a LoRA to the CPU in case you want to load multiple
adapters and free some GPU memory.
Args:
adapter_names (`List[str]`):
List of adapters to send device to.
List of adapters to send to device.
device (`Union[torch.device, str, int]`):
Device to send the adapters to. Can be either a torch device, a str or an integer.
Device (can be a `torch.device`, `str` or `int`) to place adapters on.
Example:
```py
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
).to("cuda")
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
pipeline.set_lora_device(["pixel"], device="cuda")
```
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
......@@ -1531,7 +1674,7 @@ class LoraLoaderMixin:
class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):
"""This class overrides `LoraLoaderMixin` with LoRA loading/saving code that's specific to SDXL"""
"""This class overrides [`LoraLoaderMixin`] with LoRA loading/saving code that's specific to SDXL."""
# Overrride to properly handle the loading and unloading of the additional text encoder.
def load_lora_weights(
......@@ -1556,12 +1699,26 @@ class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):
Parameters:
pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
See [`~loaders.LoraLoaderMixin.lora_state_dict`].
adapter_name (`str`, *optional*):
Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
`default_{i}` where i is the total number of adapters being loaded.
A string (model id of a pretrained model hosted on the Hub), a path to a directory containing the model
weights, or a [torch state
dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
kwargs (`dict`, *optional*):
See [`~loaders.LoraLoaderMixin.lora_state_dict`].
adapter_name (`str`, *optional*):
Name for referencing the loaded adapter model. If not specified, it will use `default_{i}` where `i` is
the total number of adapters being loaded. Must have PEFT installed to use.
Example:
```py
from diffusers import StableDiffusionXLPipeline
import torch
pipeline = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
```
"""
# We could have accessed the unet config from `lora_state_dict()` too. We pass
# it here explicitly to be able to tell that it's coming from an SDXL
......
src/diffusers/loaders/single_file.py
View file @ 7457aa67
......@@ -288,12 +288,15 @@ class FromSingleFileMixin:
class FromOriginalVAEMixin:
"""
Load pretrained ControlNet weights saved in the `.ckpt` or `.safetensors` format into an [`AutoencoderKL`].
"""
@classmethod
def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
r"""
Instantiate a [`AutoencoderKL`] from pretrained controlnet weights saved in the original `.ckpt` or
`.safetensors` format. The pipeline is format. The pipeline is set in evaluation mode (`model.eval()`) by
default.
Instantiate a [`AutoencoderKL`] from pretrained ControlNet weights saved in the original `.ckpt` or
`.safetensors` format. The pipeline is set in evaluation mode (`model.eval()`) by default.
Parameters:
pretrained_model_link_or_path (`str` or `os.PathLike`, *optional*):
......@@ -348,8 +351,8 @@ class FromOriginalVAEMixin:
<Tip warning={true}>
Make sure to pass both `image_size` and `scaling_factor` to `from_single_file()` if you want to load
a VAE that does accompany a stable diffusion model of v2 or higher or SDXL.
Make sure to pass both `image_size` and `scaling_factor` to `from_single_file()` if you're loading
a VAE from SDXL or a Stable Diffusion v2 model or higher.
</Tip>
......@@ -482,10 +485,14 @@ class FromOriginalVAEMixin:
class FromOriginalControlnetMixin:
"""
Load pretrained ControlNet weights saved in the `.ckpt` or `.safetensors` format into a [`ControlNetModel`].
"""
@classmethod
def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
r"""
Instantiate a [`ControlNetModel`] from pretrained controlnet weights saved in the original `.ckpt` or
Instantiate a [`ControlNetModel`] from pretrained ControlNet weights saved in the original `.ckpt` or
`.safetensors` format. The pipeline is set in evaluation mode (`model.eval()`) by default.
Parameters:
......
src/diffusers/loaders/textual_inversion.py
View file @ 7457aa67
......@@ -116,7 +116,7 @@ def load_textual_inversion_state_dicts(pretrained_model_name_or_paths, **kwargs)
class TextualInversionLoaderMixin:
r"""
Load textual inversion tokens and embeddings to the tokenizer and text encoder.
Load Textual Inversion tokens and embeddings to the tokenizer and text encoder.
"""
def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: "PreTrainedTokenizer"): # noqa: F821
......@@ -276,7 +276,7 @@ class TextualInversionLoaderMixin:
**kwargs,
):
r"""
Load textual inversion embeddings into the text encoder of [`StableDiffusionPipeline`] (both 🤗 Diffusers and
Load Textual Inversion embeddings into the text encoder of [`StableDiffusionPipeline`] (both 🤗 Diffusers and
Automatic1111 formats are supported).
Parameters:
......@@ -335,7 +335,7 @@ class TextualInversionLoaderMixin:
Example:
To load a textual inversion embedding vector in 🤗 Diffusers format:
To load a Textual Inversion embedding vector in 🤗 Diffusers format:
```py
from diffusers import StableDiffusionPipeline
......@@ -352,7 +352,7 @@ class TextualInversionLoaderMixin:
image.save("cat-backpack.png")
```
To load a textual inversion embedding vector in Automatic1111 format, make sure to download the vector first
To load a Textual Inversion embedding vector in Automatic1111 format, make sure to download the vector first
(for example from [civitAI](https://civitai.com/models/3036?modelVersionId=9857)) and then load the vector
locally:
......
src/diffusers/loaders/unet.py
View file @ 7457aa67
......@@ -53,6 +53,10 @@ CUSTOM_DIFFUSION_WEIGHT_NAME_SAFE = "pytorch_custom_diffusion_weights.safetensor
class UNet2DConditionLoadersMixin:
"""
Load LoRA layers into a [`UNet2DCondtionModel`].
"""
text_encoder_name = TEXT_ENCODER_NAME
unet_name = UNET_NAME
......@@ -107,6 +111,19 @@ class UNet2DConditionLoadersMixin:
guarantee the timeliness or safety of the source, and you should refer to the mirror site for more
information.
Example:
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.unet.load_attn_procs(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
)
```
"""
from ..models.attention_processor import CustomDiffusionAttnProcessor
from ..models.lora import LoRACompatibleConv, LoRACompatibleLinear, LoRAConv2dLayer, LoRALinearLayer
......@@ -393,12 +410,12 @@ class UNet2DConditionLoadersMixin:
**kwargs,
):
r"""
Save an attention processor to a directory so that it can be reloaded using the
Save attention processor layers to a directory so that it can be reloaded with the
[`~loaders.UNet2DConditionLoadersMixin.load_attn_procs`] method.
Arguments:
save_directory (`str` or `os.PathLike`):
Directory to save an attention processor to. Will be created if it doesn't exist.
Directory to save an attention processor to (will be created if it doesn't exist).
is_main_process (`bool`, *optional*, defaults to `True`):
Whether the process calling this is the main process or not. Useful during distributed training and you
need to call this function on all processes. In this case, set `is_main_process=True` only on the main
......@@ -408,7 +425,21 @@ class UNet2DConditionLoadersMixin:
replace `torch.save` with another method. Can be configured with the environment variable
`DIFFUSERS_SAVE_MODE`.
safe_serialization (`bool`, *optional*, defaults to `True`):
Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
Whether to save the model using `safetensors` or with `pickle`.
Example:
```py
import torch
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
).to("cuda")
pipeline.unet.load_attn_procs("path-to-save-model", weight_name="pytorch_custom_diffusion_weights.bin")
pipeline.unet.save_attn_procs("path-to-save-model", weight_name="pytorch_custom_diffusion_weights.bin")
```
"""
from ..models.attention_processor import (
CustomDiffusionAttnProcessor,
......@@ -507,14 +538,30 @@ class UNet2DConditionLoadersMixin:
weights: Optional[Union[List[float], float]] = None,
):
"""
Sets the adapter layers for the unet.
Set the currently active adapters for use in the UNet.
Args:
adapter_names (`List[str]` or `str`):
The names of the adapters to use.
weights (`Union[List[float], float]`, *optional*):
adapter_weights (`Union[List[float], float]`, *optional*):
The adapter(s) weights to use with the UNet. If `None`, the weights are set to `1.0` for all the
adapters.
Example:
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
)
pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
pipeline.set_adapters(["cinematic", "pixel"], adapter_weights=[0.5, 0.5])
```
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for `set_adapters()`.")
......@@ -535,7 +582,22 @@ class UNet2DConditionLoadersMixin:
def disable_lora(self):
"""
Disables the active LoRA layers for the unet.
Disable the UNet's active LoRA layers.
Example:
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
)
pipeline.disable_lora()
```
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
......@@ -543,7 +605,22 @@ class UNet2DConditionLoadersMixin:
def enable_lora(self):
"""
Enables the active LoRA layers for the unet.
Enable the UNet's active LoRA layers.
Example:
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
)
pipeline.enable_lora()
```
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
......@@ -551,10 +628,26 @@ class UNet2DConditionLoadersMixin:
def delete_adapters(self, adapter_names: Union[List[str], str]):
"""
Delete an adapter's LoRA layers from the UNet.
Args:
Deletes the LoRA layers of `adapter_name` for the unet.
adapter_names (`Union[List[str], str]`):
The names of the adapter to delete. Can be a single string or a list of strings
The names (single string or list of strings) of the adapter to delete.
Example:
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_names="cinematic"
)
pipeline.delete_adapters("cinematic")
```
"""
if not USE_PEFT_BACKEND:
raise ValueError("PEFT backend is required for this method.")
......
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