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[draft v2] AutoPipeline (#4138)

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* initial

* style

* from ...pipelines -> from ..pipeline_util

* make style

* fix-copies

* fix value_guided_sampling oops

* style

* add test

* Show failing test

* update from_pipe

* fix

* add controlnet, additional test and register unused original config

* update for controlnet

* Apply suggestions from code review

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* store unused config as private attribute and pass if can

* add doc

* kandinsky inpaint pipeline does not work with decoder checkpoint

* update doc

* Apply suggestions from code review

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* style

* Apply suggestions from code review

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* fix

* Apply suggestions from code review

---------

Co-authored-by: yiyixuxu <yixu310@gmail,com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
This commit is contained in:
YiYi Xu
2023-07-25 01:20:35 -10:00
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2
docs/source/en/_toctree.yml
View File

@@ -182,6 +182,8 @@
title: Audio Diffusion
- local: api/pipelines/audioldm
title: AudioLDM
- local: api/pipelines/auto_pipeline
title: AutoPipeline
- local: api/pipelines/consistency_models
title: Consistency Models
- local: api/pipelines/controlnet

68
docs/source/en/api/pipelines/auto_pipeline.mdx Normal file
View File

@@ -0,0 +1,68 @@
<!--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.
-->
# AutoPipeline
In many cases, one checkpoint can be used for multiple tasks. For example, you may be able to use the same checkpoint for Text-to-Image, Image-to-Image, and Inpainting. However, you'll need to know the pipeline class names linked to your checkpoint.
AutoPipeline is designed to make it easy for you to use multiple pipelines in your workflow. We currently provide 3 AutoPipeline classes to perform three different tasks, i.e. [`AutoPipelineForText2Image`], [`AutoPipelineForImage2Image`], and [`AutoPipelineForInpainting`]. You'll need to choose the AutoPipeline class based on the task you want to perform and use it to automatically retrieve the relevant pipeline given the name/path to the pre-trained weights.
For example, to perform Image-to-Image with the SD1.5 checkpoint, you can do
```python
from diffusers import PipelineForImageToImage
pipe_i2i = PipelineForImageoImage.from_pretrained("runwayml/stable-diffusion-v1-5")
```
It will also help you switch between tasks seamlessly using the same checkpoint without reallocating additional memory. For example, to re-use the Image-to-Image pipeline we just created for inpainting, you can do
```python
from diffusers import PipelineForInpainting
pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_i2i)
```
All the components will be transferred to the inpainting pipeline with zero cost.
Currently AutoPipeline support the Text-to-Image, Image-to-Image, and Inpainting tasks for below diffusion models:
- [stable Diffusion](./stable_diffusion)
- [Stable Diffusion Controlnet](./api/pipelines/controlnet)
- [Stable Diffusion XL](./stable_diffusion/stable_diffusion_xl)
- [IF](./if)
- [Kandinsky](./kandinsky)(./kandinsky)(./kandinsky)(./kandinsky)(./kandinsky)
- [Kandinsky 2.2]()(./kandinsky)
## AutoPipelineForText2Image
[[autodoc]] AutoPipelineForText2Image
- all
- from_pretrained
- from_pipe
## AutoPipelineForImage2Image
[[autodoc]] AutoPipelineForImage2Image
- all
- from_pretrained
- from_pipe
## AutoPipelineForInpainting
[[autodoc]] AutoPipelineForInpainting
- all
- from_pretrained
- from_pipe

3
src/diffusers/__init__.py
View File

@@ -62,6 +62,9 @@ else:
)
from .pipelines import (
AudioPipelineOutput,
AutoPipelineForImage2Image,
AutoPipelineForInpainting,
AutoPipelineForText2Image,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,

1
src/diffusers/pipelines/__init__.py
View File

@@ -17,6 +17,7 @@ try:
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .auto_pipeline import AutoPipelineForImage2Image, AutoPipelineForInpainting, AutoPipelineForText2Image
from .consistency_models import ConsistencyModelPipeline
from .dance_diffusion import DanceDiffusionPipeline
from .ddim import DDIMPipeline

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src/diffusers/pipelines/auto_pipeline.py Normal file
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@@ -0,0 +1,834 @@
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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 inspect
from collections import OrderedDict
from ..configuration_utils import ConfigMixin
from .controlnet import (
StableDiffusionControlNetImg2ImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionXLControlNetPipeline,
)
from .deepfloyd_if import IFImg2ImgPipeline, IFInpaintingPipeline, IFPipeline
from .kandinsky import KandinskyImg2ImgPipeline, KandinskyInpaintPipeline, KandinskyPipeline
from .kandinsky2_2 import KandinskyV22Img2ImgPipeline, KandinskyV22InpaintPipeline, KandinskyV22Pipeline
from .stable_diffusion import (
StableDiffusionImg2ImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionPipeline,
)
from .stable_diffusion_xl import (
StableDiffusionXLImg2ImgPipeline,
StableDiffusionXLInpaintPipeline,
StableDiffusionXLPipeline,
)
AUTO_TEXT2IMAGE_PIPELINES_MAPPING = OrderedDict(
[
("stable-diffusion", StableDiffusionPipeline),
("stable-diffusion-xl", StableDiffusionXLPipeline),
("if", IFPipeline),
("kandinsky", KandinskyPipeline),
("kandinsky22", KandinskyV22Pipeline),
("stable-diffusion-controlnet", StableDiffusionControlNetPipeline),
("stable-diffusion-xl-controlnet", StableDiffusionXLControlNetPipeline),
]
)
AUTO_IMAGE2IMAGE_PIPELINES_MAPPING = OrderedDict(
[
("stable-diffusion", StableDiffusionImg2ImgPipeline),
("stable-diffusion-xl", StableDiffusionXLImg2ImgPipeline),
("if", IFImg2ImgPipeline),
("kandinsky", KandinskyImg2ImgPipeline),
("kandinsky22", KandinskyV22Img2ImgPipeline),
("stable-diffusion-controlnet", StableDiffusionControlNetImg2ImgPipeline),
]
)
AUTO_INPAINT_PIPELINES_MAPPING = OrderedDict(
[
("stable-diffusion", StableDiffusionInpaintPipeline),
("stable-diffusion-xl", StableDiffusionXLInpaintPipeline),
("if", IFInpaintingPipeline),
("kandinsky", KandinskyInpaintPipeline),
("kandinsky22", KandinskyV22InpaintPipeline),
("stable-diffusion-controlnet", StableDiffusionControlNetInpaintPipeline),
]
)
SUPPORTED_TASKS_MAPPINGS = [
AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
AUTO_INPAINT_PIPELINES_MAPPING,
]
def _get_task_class(mapping, pipeline_class_name):
def get_model(pipeline_class_name):
for task_mapping in SUPPORTED_TASKS_MAPPINGS:
for model_name, pipeline in task_mapping.items():
if pipeline.__name__ == pipeline_class_name:
return model_name
model_name = get_model(pipeline_class_name)
if model_name is not None:
task_class = mapping.get(model_name, None)
if task_class is not None:
return task_class
raise ValueError(f"AutoPipeline can't find a pipeline linked to {pipeline_class_name} for {model_name}")
def _get_signature_keys(obj):
parameters = inspect.signature(obj.__init__).parameters
required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}
optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
expected_modules = set(required_parameters.keys()) - {"self"}
return expected_modules, optional_parameters
class AutoPipelineForText2Image(ConfigMixin):
r"""
AutoPipeline for text-to-image generation.
[`AutoPipelineForText2Image`] is a generic pipeline class that will be instantiated as one of the text-to-image
pipeline class in diffusers.
The pipeline type (for example [`StableDiffusionPipeline`]) is automatically selected when created with the
AutoPipelineForText2Image.from_pretrained(pretrained_model_name_or_path) or
AutoPipelineForText2Image.from_pipe(pipeline) class methods .
This class cannot be instantiated using __init__() (throws an error).
Class attributes:
- **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
diffusion pipeline's components.
"""
config_name = "model_index.json"
def __init__(self, *args, **kwargs):
raise EnvironmentError(
f"{self.__class__.__name__} is designed to be instantiated "
f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
f"`{self.__class__.__name__}.from_pipe(pipeline)` methods."
)
@classmethod
def from_pretrained(cls, pretrained_model_or_path, **kwargs):
r"""
Instantiates a text-to-image Pytorch diffusion pipeline from pretrained pipeline weight.
The from_pretrained() method takes care of returning the correct pipeline class instance by:
1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
config object
2. Find the text-to-image pipeline linked to the pipeline class using pattern matching on pipeline class
name.
If a `controlnet` argument is passed, it will instantiate a [`StableDiffusionControlNetPipeline`] object.
The pipeline is set in evaluation mode (`model.eval()`) by default.
If you get the error message below, you need to finetune the weights for your downstream task:
```
Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
- conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline
hosted on the Hub.
- A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights
saved using
[`~DiffusionPipeline.save_pretrained`].
torch_dtype (`str` or `torch.dtype`, *optional*):
Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
dtype is automatically derived from the model's weights.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
incompletely downloaded files are deleted.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`):
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only (`bool`, *optional*, defaults to `False`):
Whether to only load local model weights and configuration files or not. If set to `True`, the model
won't be downloaded from the Hub.
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
`diffusers-cli login` (stored in `~/.huggingface`) is used.
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.
custom_revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
`revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
mirror (`str`, *optional*):
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.
device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
A map that specifies where each submodule should go. It doesn’t need to be defined for each
parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
same device.
Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
more information about each option see [designing a device
map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
max_memory (`Dict`, *optional*):
A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
each GPU and the available CPU RAM if unset.
offload_folder (`str` or `os.PathLike`, *optional*):
The path to offload weights if device_map contains the value `"disk"`.
offload_state_dict (`bool`, *optional*):
If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
when there is some disk offload.
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.
use_safetensors (`bool`, *optional*, defaults to `None`):
If set to `None`, the safetensors weights are downloaded if they're available **and** if the
safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
weights. If set to `False`, safetensors weights are not loaded.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
class). The overwritten components are passed directly to the pipelines `__init__` method. See example
below for more information.
variant (`str`, *optional*):
Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
loading `from_flax`.
<Tip>
To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
`huggingface-cli login`.
</Tip>
Examples:
```py
>>> from diffusers import AutoPipelineForTextToImage
>>> pipeline = AutoPipelineForTextToImage.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> print(pipeline.__class__)
```
"""
config = cls.load_config(pretrained_model_or_path)
orig_class_name = config["_class_name"]
if "controlnet" in kwargs:
orig_class_name = config["_class_name"].replace("Pipeline", "ControlNetPipeline")
text_2_image_cls = _get_task_class(AUTO_TEXT2IMAGE_PIPELINES_MAPPING, orig_class_name)
return text_2_image_cls.from_pretrained(pretrained_model_or_path, **kwargs)
@classmethod
def from_pipe(cls, pipeline, **kwargs):
r"""
Instantiates a text-to-image Pytorch diffusion pipeline from another instantiated diffusion pipeline class.
The from_pipe() method takes care of returning the correct pipeline class instance by finding the text-to-image
pipeline linked to the pipeline class using pattern matching on pipeline class name.
All the modules the pipeline contains will be used to initialize the new pipeline without reallocating
additional memoery.
The pipeline is set in evaluation mode (`model.eval()`) by default.
Parameters:
pipeline (`DiffusionPipeline`):
an instantiated `DiffusionPipeline` object
```py
>>> from diffusers import AutoPipelineForTextToImage, AutoPipelineForImageToImage
>>> pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
... "runwayml/stable-diffusion-v1-5", requires_safety_checker=False
... )
>>> pipe_t2i = AutoPipelineForTextToImage.from_pipe(pipe_t2i)
```
"""
original_config = dict(pipeline.config)
original_cls_name = pipeline.__class__.__name__
# derive the pipeline class to instantiate
text_2_image_cls = _get_task_class(AUTO_TEXT2IMAGE_PIPELINES_MAPPING, original_cls_name)
# define expected module and optional kwargs given the pipeline signature
expected_modules, optional_kwargs = _get_signature_keys(text_2_image_cls)
pretrained_model_name_or_path = original_config.pop("_name_or_path", None)
# allow users pass modules in `kwargs` to override the original pipeline's components
passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
original_class_obj = {
k: pipeline.components[k]
for k, v in pipeline.components.items()
if k in expected_modules and k not in passed_class_obj
}
# allow users pass optional kwargs to override the original pipelines config attribute
passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
original_pipe_kwargs = {
k: original_config[k]
for k, v in original_config.items()
if k in optional_kwargs and k not in passed_pipe_kwargs
}
# config that were not expected by original pipeline is stored as private attribute
# we will pass them as optional arguments if they can be accepted by the pipeline
additional_pipe_kwargs = [
k[1:]
for k in original_config.keys()
if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
]
for k in additional_pipe_kwargs:
original_pipe_kwargs[k] = original_config.pop(f"_{k}")
text_2_image_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}
# store unused config as private attribute
unused_original_config = {
f"{'' if k.startswith('_') else '_'}{k}": original_config[k]
for k, v in original_config.items()
if k not in text_2_image_kwargs
}
missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(text_2_image_kwargs.keys())
if len(missing_modules) > 0:
raise ValueError(
f"Pipeline {text_2_image_cls} expected {expected_modules}, but only {set(passed_class_obj.keys()) + set(original_class_obj.keys())} were passed"
)
model = text_2_image_cls(**text_2_image_kwargs)
model.register_to_config(_name_or_path=pretrained_model_name_or_path)
model.register_to_config(**unused_original_config)
return model
class AutoPipelineForImage2Image(ConfigMixin):
r"""
AutoPipeline for image-to-image generation.
[`AutoPipelineForImage2Image`] is a generic pipeline class that will be instantiated as one of the image-to-image
pipeline classes in diffusers.
The pipeline type (for example [`StableDiffusionImg2ImgPipeline`]) is automatically selected when created with the
`AutoPipelineForImage2Image.from_pretrained(pretrained_model_name_or_path)` or
`AutoPipelineForImage2Image.from_pipe(pipeline)` class methods.
This class cannot be instantiated using __init__() (throws an error).
Class attributes:
- **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
diffusion pipeline's components.
"""
config_name = "model_index.json"
def __init__(self, *args, **kwargs):
raise EnvironmentError(
f"{self.__class__.__name__} is designed to be instantiated "
f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
f"`{self.__class__.__name__}.from_pipe(pipeline)` methods."
)
@classmethod
def from_pretrained(cls, pretrained_model_or_path, **kwargs):
r"""
Instantiates a image-to-image Pytorch diffusion pipeline from pretrained pipeline weight.
The from_pretrained() method takes care of returning the correct pipeline class instance by:
1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
config object
2. Find the image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class
name.
If a `controlnet` argument is passed, it will instantiate a StableDiffusionControlNetImg2ImgPipeline object.
The pipeline is set in evaluation mode (`model.eval()`) by default.
If you get the error message below, you need to finetune the weights for your downstream task:
```
Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
- conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline
hosted on the Hub.
- A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights
saved using
[`~DiffusionPipeline.save_pretrained`].
torch_dtype (`str` or `torch.dtype`, *optional*):
Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
dtype is automatically derived from the model's weights.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
incompletely downloaded files are deleted.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`):
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only (`bool`, *optional*, defaults to `False`):
Whether to only load local model weights and configuration files or not. If set to `True`, the model
won't be downloaded from the Hub.
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
`diffusers-cli login` (stored in `~/.huggingface`) is used.
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.
custom_revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
`revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
mirror (`str`, *optional*):
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.
device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
A map that specifies where each submodule should go. It doesn’t need to be defined for each
parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
same device.
Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
more information about each option see [designing a device
map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
max_memory (`Dict`, *optional*):
A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
each GPU and the available CPU RAM if unset.
offload_folder (`str` or `os.PathLike`, *optional*):
The path to offload weights if device_map contains the value `"disk"`.
offload_state_dict (`bool`, *optional*):
If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
when there is some disk offload.
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.
use_safetensors (`bool`, *optional*, defaults to `None`):
If set to `None`, the safetensors weights are downloaded if they're available **and** if the
safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
weights. If set to `False`, safetensors weights are not loaded.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
class). The overwritten components are passed directly to the pipelines `__init__` method. See example
below for more information.
variant (`str`, *optional*):
Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
loading `from_flax`.
<Tip>
To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
`huggingface-cli login`.
</Tip>
Examples:
```py
>>> from diffusers import AutoPipelineForTextToImage
>>> pipeline = AutoPipelineForImageToImage.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> print(pipeline.__class__)
```
"""
config = cls.load_config(pretrained_model_or_path)
orig_class_name = config["_class_name"]
if "controlnet" in kwargs:
orig_class_name = config["_class_name"].replace("Pipeline", "ControlNetPipeline")
image_2_image_cls = _get_task_class(AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, orig_class_name)
return image_2_image_cls.from_pretrained(pretrained_model_or_path, **kwargs)
@classmethod
def from_pipe(cls, pipeline, **kwargs):
r"""
Instantiates a image-to-image Pytorch diffusion pipeline from another instantiated diffusion pipeline class.
The from_pipe() method takes care of returning the correct pipeline class instance by finding the
image-to-image pipeline linked to the pipeline class using pattern matching on pipeline class name.
All the modules the pipeline contains will be used to initialize the new pipeline without reallocating
additional memoery.
The pipeline is set in evaluation mode (`model.eval()`) by default.
Parameters:
pipeline (`DiffusionPipeline`):
an instantiated `DiffusionPipeline` object
Examples:
```py
>>> from diffusers import AutoPipelineForTextToImage, AutoPipelineForImageToImage
>>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(
... "runwayml/stable-diffusion-v1-5", requires_safety_checker=False
... )
>>> pipe_i2i = AutoPipelineForImageToImage.from_pipe(pipe_t2i)
```
"""
original_config = dict(pipeline.config)
original_cls_name = pipeline.__class__.__name__
# derive the pipeline class to instantiate
image_2_image_cls = _get_task_class(AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, original_cls_name)
# define expected module and optional kwargs given the pipeline signature
expected_modules, optional_kwargs = _get_signature_keys(image_2_image_cls)
pretrained_model_name_or_path = original_config.pop("_name_or_path", None)
# allow users pass modules in `kwargs` to override the original pipeline's components
passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
original_class_obj = {
k: pipeline.components[k]
for k, v in pipeline.components.items()
if k in expected_modules and k not in passed_class_obj
}
# allow users pass optional kwargs to override the original pipelines config attribute
passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
original_pipe_kwargs = {
k: original_config[k]
for k, v in original_config.items()
if k in optional_kwargs and k not in passed_pipe_kwargs
}
# config attribute that were not expected by original pipeline is stored as its private attribute
# we will pass them as optional arguments if they can be accepted by the pipeline
additional_pipe_kwargs = [
k[1:]
for k in original_config.keys()
if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
]
for k in additional_pipe_kwargs:
original_pipe_kwargs[k] = original_config.pop(f"_{k}")
image_2_image_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}
# store unused config as private attribute
unused_original_config = {
f"{'' if k.startswith('_') else '_'}{k}": original_config[k]
for k, v in original_config.items()
if k not in image_2_image_kwargs
}
missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(image_2_image_kwargs.keys())
if len(missing_modules) > 0:
raise ValueError(
f"Pipeline {image_2_image_cls} expected {expected_modules}, but only {set(passed_class_obj.keys()) + set(original_class_obj.keys())} were passed"
)
model = image_2_image_cls(**image_2_image_kwargs)
model.register_to_config(_name_or_path=pretrained_model_name_or_path)
model.register_to_config(**unused_original_config)
return model
class AutoPipelineForInpainting(ConfigMixin):
r"""
AutoPipeline for inpainting generation.
[`AutoPipelineForInpainting`] is a generic pipeline class that will be instantiated as one of the inpainting
pipeline class in diffusers.
The pipeline type (for example [`IFInpaintingPipeline`]) is automatically selected when created with the
AutoPipelineForInpainting.from_pretrained(pretrained_model_name_or_path) or
AutoPipelineForInpainting.from_pipe(pipeline) class methods .
This class cannot be instantiated using __init__() (throws an error).
Class attributes:
- **config_name** (`str`) -- The configuration filename that stores the class and module names of all the
diffusion pipeline's components.
"""
config_name = "model_index.json"
def __init__(self, *args, **kwargs):
raise EnvironmentError(
f"{self.__class__.__name__} is designed to be instantiated "
f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
f"`{self.__class__.__name__}.from_pipe(pipeline)` methods."
)
@classmethod
def from_pretrained(cls, pretrained_model_or_path, **kwargs):
r"""
Instantiates a inpainting Pytorch diffusion pipeline from pretrained pipeline weight.
The from_pretrained() method takes care of returning the correct pipeline class instance by:
1. Detect the pipeline class of the pretrained_model_or_path based on the _class_name property of its
config object
2. Find the inpainting pipeline linked to the pipeline class using pattern matching on pipeline class name.
If a `controlnet` argument is passed, it will instantiate a StableDiffusionControlNetInpaintPipeline object.
The pipeline is set in evaluation mode (`model.eval()`) by default.
If you get the error message below, you need to finetune the weights for your downstream task:
```
Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
- conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *repo id* (for example `CompVis/ldm-text2im-large-256`) of a pretrained pipeline
hosted on the Hub.
- A path to a *directory* (for example `./my_pipeline_directory/`) containing pipeline weights
saved using
[`~DiffusionPipeline.save_pretrained`].
torch_dtype (`str` or `torch.dtype`, *optional*):
Override the default `torch.dtype` and load the model with another dtype. If "auto" is passed, the
dtype is automatically derived from the model's weights.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
incompletely downloaded files are deleted.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`):
Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only (`bool`, *optional*, defaults to `False`):
Whether to only load local model weights and configuration files or not. If set to `True`, the model
won't be downloaded from the Hub.
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
`diffusers-cli login` (stored in `~/.huggingface`) is used.
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.
custom_revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id similar to
`revision` when loading a custom pipeline from the Hub. It can be a 🤗 Diffusers version when loading a
custom pipeline from GitHub, otherwise it defaults to `"main"` when loading from the Hub.
mirror (`str`, *optional*):
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.
device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
A map that specifies where each submodule should go. It doesn’t need to be defined for each
parameter/buffer name; once a given module name is inside, every submodule of it will be sent to the
same device.
Set `device_map="auto"` to have 🤗 Accelerate automatically compute the most optimized `device_map`. For
more information about each option see [designing a device
map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
max_memory (`Dict`, *optional*):
A dictionary device identifier for the maximum memory. Will default to the maximum memory available for
each GPU and the available CPU RAM if unset.
offload_folder (`str` or `os.PathLike`, *optional*):
The path to offload weights if device_map contains the value `"disk"`.
offload_state_dict (`bool`, *optional*):
If `True`, temporarily offloads the CPU state dict to the hard drive to avoid running out of CPU RAM if
the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to `True`
when there is some disk offload.
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.
use_safetensors (`bool`, *optional*, defaults to `None`):
If set to `None`, the safetensors weights are downloaded if they're available **and** if the
safetensors library is installed. If set to `True`, the model is forcibly loaded from safetensors
weights. If set to `False`, safetensors weights are not loaded.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to overwrite load and saveable variables (the pipeline components of the specific pipeline
class). The overwritten components are passed directly to the pipelines `__init__` method. See example
below for more information.
variant (`str`, *optional*):
Load weights from a specified variant filename such as `"fp16"` or `"ema"`. This is ignored when
loading `from_flax`.
<Tip>
To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with
`huggingface-cli login`.
</Tip>
Examples:
```py
>>> from diffusers import AutoPipelineForTextToImage
>>> pipeline = AutoPipelineForImageToImage.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> print(pipeline.__class__)
```
"""
config = cls.load_config(pretrained_model_or_path)
orig_class_name = config["_class_name"]
if "controlnet" in kwargs:
orig_class_name = config["_class_name"].replace("Pipeline", "ControlNetPipeline")
inpainting_cls = _get_task_class(AUTO_INPAINT_PIPELINES_MAPPING, orig_class_name)
return inpainting_cls.from_pretrained(pretrained_model_or_path, **kwargs)
@classmethod
def from_pipe(cls, pipeline, **kwargs):
r"""
Instantiates a inpainting Pytorch diffusion pipeline from another instantiated diffusion pipeline class.
The from_pipe() method takes care of returning the correct pipeline class instance by finding the inpainting
pipeline linked to the pipeline class using pattern matching on pipeline class name.
All the modules the pipeline class contain will be used to initialize the new pipeline without reallocating
additional memoery.
The pipeline is set in evaluation mode (`model.eval()`) by default.
Parameters:
pipeline (`DiffusionPipeline`):
an instantiated `DiffusionPipeline` object
Examples:
```py
>>> from diffusers import AutoPipelineForTextToImage, AutoPipelineForInpainting
>>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(
... "DeepFloyd/IF-I-XL-v1.0", requires_safety_checker=False
... )
>>> pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_t2i)
```
"""
original_config = dict(pipeline.config)
original_cls_name = pipeline.__class__.__name__
# derive the pipeline class to instantiate
inpainting_cls = _get_task_class(AUTO_INPAINT_PIPELINES_MAPPING, original_cls_name)
# define expected module and optional kwargs given the pipeline signature
expected_modules, optional_kwargs = _get_signature_keys(inpainting_cls)
pretrained_model_name_or_path = original_config.pop("_name_or_path", None)
# allow users pass modules in `kwargs` to override the original pipeline's components
passed_class_obj = {k: kwargs.pop(k) for k in expected_modules if k in kwargs}
original_class_obj = {
k: pipeline.components[k]
for k, v in pipeline.components.items()
if k in expected_modules and k not in passed_class_obj
}
# allow users pass optional kwargs to override the original pipelines config attribute
passed_pipe_kwargs = {k: kwargs.pop(k) for k in optional_kwargs if k in kwargs}
original_pipe_kwargs = {
k: original_config[k]
for k, v in original_config.items()
if k in optional_kwargs and k not in passed_pipe_kwargs
}
# config that were not expected by original pipeline is stored as private attribute
# we will pass them as optional arguments if they can be accepted by the pipeline
additional_pipe_kwargs = [
k[1:]
for k in original_config.keys()
if k.startswith("_") and k[1:] in optional_kwargs and k[1:] not in passed_pipe_kwargs
]
for k in additional_pipe_kwargs:
original_pipe_kwargs[k] = original_config.pop(f"_{k}")
inpainting_kwargs = {**passed_class_obj, **original_class_obj, **passed_pipe_kwargs, **original_pipe_kwargs}
# store unused config as private attribute
unused_original_config = {
f"{'' if k.startswith('_') else '_'}{k}": original_config[k]
for k, v in original_config.items()
if k not in inpainting_kwargs
}
missing_modules = set(expected_modules) - set(pipeline._optional_components) - set(inpainting_kwargs.keys())
if len(missing_modules) > 0:
raise ValueError(
f"Pipeline {inpainting_cls} expected {expected_modules}, but only {set(passed_class_obj.keys()) + set(original_class_obj.keys())} were passed"
)
model = inpainting_cls(**inpainting_kwargs)
model.register_to_config(_name_or_path=pretrained_model_name_or_path)
model.register_to_config(**unused_original_config)
return model

3
src/diffusers/pipelines/kandinsky/pipeline_kandinsky.py
View File

@@ -20,8 +20,6 @@ from transformers import (
)
from ...models import UNet2DConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
is_accelerate_available,
@@ -30,6 +28,7 @@ from ...utils import (
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from .text_encoder import MultilingualCLIP

3
src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py
View File

@@ -23,8 +23,6 @@ from transformers import (
)
from ...models import UNet2DConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler
from ...utils import (
is_accelerate_available,
@@ -33,6 +31,7 @@ from ...utils import (
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from .text_encoder import MultilingualCLIP

3
src/diffusers/pipelines/kandinsky/pipeline_kandinsky_inpaint.py
View File

@@ -25,8 +25,6 @@ from transformers import (
)
from ...models import UNet2DConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler
from ...utils import (
is_accelerate_available,
@@ -35,6 +33,7 @@ from ...utils import (
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from .text_encoder import MultilingualCLIP

2
src/diffusers/pipelines/kandinsky/pipeline_kandinsky_prior.py
View File

@@ -21,7 +21,6 @@ import torch
from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import UnCLIPScheduler
from ...utils import (
BaseOutput,
@@ -29,6 +28,7 @@ from ...utils import (
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
logger = logging.get_logger(__name__) # pylint: disable=invalid-name

3
src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2.py
View File

@@ -17,8 +17,6 @@ from typing import List, Optional, Union
import torch
from ...models import UNet2DConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
@@ -27,6 +25,7 @@ from ...utils import (
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name

3
src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet.py
View File

@@ -17,8 +17,6 @@ from typing import List, Optional, Union
import torch
from ...models import UNet2DConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
@@ -27,6 +25,7 @@ from ...utils import (
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name

3
src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py
View File

@@ -20,8 +20,6 @@ import torch
from PIL import Image
from ...models import UNet2DConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
@@ -30,6 +28,7 @@ from ...utils import (
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name

3
src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py
View File

@@ -20,8 +20,6 @@ import torch
from PIL import Image
from ...models import UNet2DConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
@@ -30,6 +28,7 @@ from ...utils import (
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name

3
src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_inpainting.py
View File

@@ -22,8 +22,6 @@ import torch.nn.functional as F
from PIL import Image
from ...models import UNet2DConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
@@ -32,6 +30,7 @@ from ...utils import (
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name

2
src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py
View File

@@ -5,7 +5,6 @@ import torch
from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import UnCLIPScheduler
from ...utils import (
logging,
@@ -13,6 +12,7 @@ from ...utils import (
replace_example_docstring,
)
from ..kandinsky import KandinskyPriorPipelineOutput
from ..pipeline_utils import DiffusionPipeline
logger = logging.get_logger(__name__) # pylint: disable=invalid-name

2
src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior_emb2emb.py
View File

@@ -5,7 +5,6 @@ import torch
from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import UnCLIPScheduler
from ...utils import (
logging,
@@ -13,6 +12,7 @@ from ...utils import (
replace_example_docstring,
)
from ..kandinsky import KandinskyPriorPipelineOutput
from ..pipeline_utils import DiffusionPipeline
logger = logging.get_logger(__name__) # pylint: disable=invalid-name

2
src/diffusers/pipelines/shap_e/pipeline_shap_e.py
View File

@@ -22,7 +22,6 @@ import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
@@ -32,6 +31,7 @@ from ...utils import (
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from .renderer import ShapERenderer

2
src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py
View File

@@ -21,7 +21,6 @@ import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
@@ -29,6 +28,7 @@ from ...utils import (
randn_tensor,
replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline
from .renderer import ShapERenderer

2
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py
View File

@@ -23,9 +23,9 @@ from k_diffusion.sampling import BrownianTreeNoiseSampler, get_sigmas_karras
from ...image_processor import VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
from ...pipelines import DiffusionPipeline
from ...schedulers import LMSDiscreteScheduler
from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput

3
src/diffusers/pipelines/unclip/pipeline_unclip.py
View File

@@ -21,10 +21,9 @@ from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput
from ...models import PriorTransformer, UNet2DConditionModel, UNet2DModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import UnCLIPScheduler
from ...utils import logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from .text_proj import UnCLIPTextProjModel

2
src/diffusers/pipelines/unclip/pipeline_unclip_image_variation.py
View File

@@ -26,9 +26,9 @@ from transformers import (
)
from ...models import UNet2DConditionModel, UNet2DModel
from ...pipelines import DiffusionPipeline, ImagePipelineOutput
from ...schedulers import UnCLIPScheduler
from ...utils import logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from .text_proj import UnCLIPTextProjModel

45
src/diffusers/utils/dummy_pt_objects.py
View File

@@ -255,6 +255,51 @@ class AudioPipelineOutput(metaclass=DummyObject):
requires_backends(cls, ["torch"])
class AutoPipelineForImage2Image(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
class AutoPipelineForInpainting(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
class AutoPipelineForText2Image(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
class ConsistencyModelPipeline(metaclass=DummyObject):
_backends = ["torch"]

201
tests/pipelines/test_pipelines_auto.py Normal file
View File

@@ -0,0 +1,201 @@
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# 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 gc
import unittest
from collections import OrderedDict
import torch
from diffusers import (
AutoPipelineForImage2Image,
AutoPipelineForInpainting,
AutoPipelineForText2Image,
ControlNetModel,
)
from diffusers.pipelines.auto_pipeline import (
AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
AUTO_INPAINT_PIPELINES_MAPPING,
AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
)
from diffusers.utils import slow
PRETRAINED_MODEL_REPO_MAPPING = OrderedDict(
[
("stable-diffusion", "runwayml/stable-diffusion-v1-5"),
("if", "DeepFloyd/IF-I-XL-v1.0"),
("kandinsky", "kandinsky-community/kandinsky-2-1"),
("kandinsky22", "kandinsky-community/kandinsky-2-2-decoder"),
]
)
class AutoPipelineFastTest(unittest.TestCase):
def test_from_pipe_consistent(self):
pipe = AutoPipelineForText2Image.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe", requires_safety_checker=False
)
original_config = dict(pipe.config)
pipe = AutoPipelineForImage2Image.from_pipe(pipe)
assert dict(pipe.config) == original_config
pipe = AutoPipelineForText2Image.from_pipe(pipe)
assert dict(pipe.config) == original_config
def test_from_pipe_override(self):
pipe = AutoPipelineForText2Image.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe", requires_safety_checker=False
)
pipe = AutoPipelineForImage2Image.from_pipe(pipe, requires_safety_checker=True)
assert pipe.config.requires_safety_checker is True
pipe = AutoPipelineForText2Image.from_pipe(pipe, requires_safety_checker=True)
assert pipe.config.requires_safety_checker is True
def test_from_pipe_consistent_sdxl(self):
pipe = AutoPipelineForImage2Image.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-xl-pipe",
requires_aesthetics_score=True,
force_zeros_for_empty_prompt=False,
)
original_config = dict(pipe.config)
pipe = AutoPipelineForText2Image.from_pipe(pipe)
pipe = AutoPipelineForImage2Image.from_pipe(pipe)
assert dict(pipe.config) == original_config
@slow
class AutoPipelineIntegrationTest(unittest.TestCase):
def test_pipe_auto(self):
for model_name, model_repo in PRETRAINED_MODEL_REPO_MAPPING.items():
# test txt2img
pipe_txt2img = AutoPipelineForText2Image.from_pretrained(
model_repo, variant="fp16", torch_dtype=torch.float16
)
self.assertIsInstance(pipe_txt2img, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForText2Image.from_pipe(pipe_txt2img)
self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_txt2img)
self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])
if "kandinsky" not in model_name:
pipe_to = AutoPipelineForInpainting.from_pipe(pipe_txt2img)
self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name])
del pipe_txt2img, pipe_to
gc.collect()
# test img2img
pipe_img2img = AutoPipelineForImage2Image.from_pretrained(
model_repo, variant="fp16", torch_dtype=torch.float16
)
self.assertIsInstance(pipe_img2img, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForText2Image.from_pipe(pipe_img2img)
self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_img2img)
self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])
if "kandinsky" not in model_name:
pipe_to = AutoPipelineForInpainting.from_pipe(pipe_img2img)
self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name])
del pipe_img2img, pipe_to
gc.collect()
# test inpaint
if "kandinsky" not in model_name:
pipe_inpaint = AutoPipelineForInpainting.from_pretrained(
model_repo, variant="fp16", torch_dtype=torch.float16
)
self.assertIsInstance(pipe_inpaint, AUTO_INPAINT_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForText2Image.from_pipe(pipe_inpaint)
self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_inpaint)
self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForInpainting.from_pipe(pipe_inpaint)
self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name])
del pipe_inpaint, pipe_to
gc.collect()
def test_from_pipe_consistent(self):
for model_name, model_repo in PRETRAINED_MODEL_REPO_MAPPING.items():
if model_name in ["kandinsky", "kandinsky22"]:
auto_pipes = [AutoPipelineForText2Image, AutoPipelineForImage2Image]
else:
auto_pipes = [AutoPipelineForText2Image, AutoPipelineForImage2Image, AutoPipelineForInpainting]
# test from_pretrained
for pipe_from_class in auto_pipes:
pipe_from = pipe_from_class.from_pretrained(model_repo, variant="fp16", torch_dtype=torch.float16)
pipe_from_config = dict(pipe_from.config)
for pipe_to_class in auto_pipes:
pipe_to = pipe_to_class.from_pipe(pipe_from)
self.assertEqual(dict(pipe_to.config), pipe_from_config)
del pipe_from, pipe_to
gc.collect()
def test_controlnet(self):
# test from_pretrained
model_repo = "runwayml/stable-diffusion-v1-5"
controlnet_repo = "lllyasviel/sd-controlnet-canny"
controlnet = ControlNetModel.from_pretrained(controlnet_repo, torch_dtype=torch.float16)
pipe_txt2img = AutoPipelineForText2Image.from_pretrained(
model_repo, controlnet=controlnet, torch_dtype=torch.float16
)
self.assertIsInstance(pipe_txt2img, AUTO_TEXT2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"])
pipe_img2img = AutoPipelineForImage2Image.from_pretrained(
model_repo, controlnet=controlnet, torch_dtype=torch.float16
)
self.assertIsInstance(pipe_img2img, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"])
pipe_inpaint = AutoPipelineForInpainting.from_pretrained(
model_repo, controlnet=controlnet, torch_dtype=torch.float16
)
self.assertIsInstance(pipe_inpaint, AUTO_INPAINT_PIPELINES_MAPPING["stable-diffusion-controlnet"])
# test from_pipe
for pipe_from in [pipe_txt2img, pipe_img2img, pipe_inpaint]:
pipe_to = AutoPipelineForText2Image.from_pipe(pipe_from)
self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"])
self.assertEqual(dict(pipe_to.config), dict(pipe_txt2img.config))
pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_from)
self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"])
self.assertEqual(dict(pipe_to.config), dict(pipe_img2img.config))
pipe_to = AutoPipelineForInpainting.from_pipe(pipe_from)
self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING["stable-diffusion-controlnet"])
self.assertEqual(dict(pipe_to.config), dict(pipe_inpaint.config))