nodes/lldacing-ComfyUI_StableDelight_ll
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

init

Browse Source
This commit is contained in:
刘雪峰
2024-11-26 17:38:26 +08:00
commit b1cee89eb4
12 changed files with 1404 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
.github/workflows/publish.yml
+23
View File
@@ -0,0 +1,23 @@
name: Publish to Comfy registry
on:
workflow_dispatch:
push:
branches:
- main
paths:
- "pyproject.toml"
jobs:
publish-node:
name: Publish Custom Node to registry
runs-on: ubuntu-latest
# if this is a forked repository. Skipping the workflow.
if: github.event.repository.fork == false
steps:
- name: Check out code
uses: actions/checkout@v4
- name: Publish Custom Node
uses: Comfy-Org/publish-node-action@main
with:
## Add your own personal access token to your Github Repository secrets and reference it here.
personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
.gitignore
+162
View File
@@ -0,0 +1,162 @@
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
.pdm.toml
.pdm-python
.pdm-build/
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
LICENSE
+201
View File
@@ -0,0 +1,201 @@
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
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.
README.md
+42
View File
@@ -0,0 +1,42 @@
[中文文档](README.md)
StableDelight 是一种从纹理表面去除镜面反射的尖端解决方案。
注：实测下来，物体变化有点大，特别是半透明的物体，如样例中的灯泡、玻璃。期待原项目的后期改进。
## 预览
![save api extended](example/workflow_base.png)
## 安装
- 手动安装
```shell
cd custom_nodes
git clone https://github.com/lldacing/ComfyUI_StableDelight_ll.git
cd ComfyUI_StableDelight_ll
# 重启comfyUI
```
## 模型
从[HuggingFace](https://huggingface.co/Stable-X/yoso-delight-v0-4-base/tree/main)下载所有文件放到目录`ComfyUI/models/diffusers/Stable-X--yoso-delight-v0-4-base`
建议使用huggingface-cli下载
```
# 设置代理，按需设置，也可开全局代理
set https_proxy=http://127.0.0.1:7890
# 在ComfyUI/models/diffusers/目录下启动命令行执行下面的命令，如果找不到huggingface-cli，huggingface-cli在${python_home}/Scripts目录下，使用全路径
huggingface-cli download Stable-X/yoso-delight-v0-4-base --local-dir Stable-X--yoso-delight-v0-4-base
```
目录结构如下：
```
ComfyUI
└─models
└─diffusers
└─Stable-X--yoso-delight-v0-4-base
```
## 感谢
原项目 [Stable-X/StableDelight](https://github.com/Stable-X/StableDelight)
README_EN.md
+42
View File
@@ -0,0 +1,42 @@
[English](README_EN.md)
StableDelight is a cutting-edge solution for specular reflection removal from textured surfaces.
Note: According to my tests, the objects will change a lot, especially semi transparent objects such as light bulbs and glass in the sample. Looking forward to future improvements.
## Preview
![save api extended](example/workflow_base.png)
## Install
- Manual
```shell
cd custom_nodes
git clone https://github.com/lldacing/ComfyUI_StableDelight_ll.git
cd ComfyUI_StableDelight_ll
# restart ComfyUI
```
## Model
From [HuggingFace](https://huggingface.co/Stable-X/yoso-delight-v0-4-base/tree/main) download all files to `ComfyUI/models/diffusers/Stable-X--yoso-delight-v0-4-base` directory.
Suggest using huggingface-cli to download
```
# Start the command line in the ComfyUI/models/diffusers directory and execute the following command
huggingface-cli download Stable-X/yoso-delight-v0-4-base --local-dir Stable-X--yoso-delight-v0-4-base
```
目录结构如下：
```
ComfyUI
└─models
└─diffusers
└─Stable-X--yoso-delight-v0-4-base
```
## Thanks
Original Project [Stable-X/StableDelight](https://github.com/Stable-X/StableDelight)
__init__.py
+13
View File
@@ -0,0 +1,13 @@
import os
import sys
# 获取当前目录的父目录
parent_dir = os.path.dirname(os.path.abspath(__file__))
# 添加父目录到系统路径
sys.path.insert(0, parent_dir)
from .nodes import BaseNode
NODE_CLASS_MAPPINGS = {**BaseNode.NODE_CLASS_MAPPINGS}
NODE_DISPLAY_NAME_MAPPINGS = {**BaseNode.NODE_DISPLAY_NAME_MAPPINGS}
example/workflow_base.png
BIN
View File
Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 831 KiB

nodes/BaseNode.py
+131
View File
@@ -0,0 +1,131 @@
import os
import torch
from comfy import model_management
import folder_paths
from .libs.YosoDelightPipe import YosoDelightPipeline
deviceType = model_management.get_torch_device().type
class LoadStableDelightModel:
@classmethod
def INPUT_TYPES(cls):
paths = []
for search_path in folder_paths.get_folder_paths("diffusers"):
if os.path.exists(search_path):
for root, subdir, files in os.walk(search_path, followlinks=True):
if "model_index.json" in files:
paths.append(os.path.relpath(root, start=search_path))
return {
"required": {
"model": (paths,),
"device": (["AUTO", "CPU"], )
}
}
RETURN_TYPES = ("DelightMODEL",)
RETURN_NAMES = ("model",)
FUNCTION = "load_model"
CATEGORY = "stableX/delight"
DESCRIPTION = "Load BEN model from folder models/rembg/ben or the path of ben configured in the extra YAML file"
def load_model(self, model, device):
for search_path in folder_paths.get_folder_paths("diffusers"):
if os.path.exists(search_path):
path = os.path.join(search_path, model)
if os.path.exists(path):
model = path
break
if device == "AUTO":
device_type = deviceType
else:
device_type = "cpu"
# Stable-X/yoso-delight-v0-4-base
pipe = YosoDelightPipeline.from_pretrained(model,
cache_dir=folder_paths.get_folder_paths("diffusers")[0],
safety_checker=None,
variant="fp16",
torch_dtype=torch.float16 if device_type == "cuda" else None,
t_start=0,
local_files_only=True).to(device_type)
# 启用 xformers
if model_management.XFORMERS_IS_AVAILABLE and device_type == "cuda":
pipe.enable_xformers_memory_efficient_attention()
return (pipe, )
class ApplyStableDelight:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": ("DelightMODEL",),
"images": ("IMAGE",),
"strength": ("FLOAT",
{
"default": 1,
"min": 0.01,
"max": 5.0,
"step": 0.1
}),
"resolution": ("INT",
{
"default": 1024,
"step": 8,
"min": 0,
"max": 4096,
"tooltip": "Preprocess the resolution of the image, if eq 0, using image original size"
}),
"upscale_method": (["nearest-exact", "bilinear", "area", "bicubic", "lanczos"], {"default": "bilinear"}),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "delight"
CATEGORY = "stableX/delight"
def delight(self, model, images, strength, resolution, upscale_method):
_images = []
_masks = []
for image in images:
h, w, c = image.shape
# h, w, c -> c, h, w
im_tensor = image.permute(2, 0, 1)
with torch.no_grad():
result_image = model(im_tensor,
controlnet_conditioning_scale=strength,
skip_preprocess=resolution == 0,
processing_resolution=resolution,
resample_methodinput=upscale_method,
resample_method_output=upscale_method,
output_type="pt"
).prediction.cpu()
# 归一化
result_image = (result_image.clamp(-1, 1) + 1) / 2
# b, c, h, w -> b, h, w, c
result_image = result_image.permute(0, 2, 3, 1)
_images.append(result_image)
out_images = torch.cat(_images, dim=0)
return out_images,
NODE_CLASS_MAPPINGS = {
"LoadStableDelightModel": LoadStableDelightModel,
"ApplyStableDelight": ApplyStableDelight,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LoadStableDelightModel": "LoadStableDelightModel",
"ApplyStableDelight": "ApplyStableDelight",
}
nodes/__init__.py
+15
View File
@@ -0,0 +1,15 @@
# import os
#
# import folder_paths
#
# models_dir_key = "ben"
# models_dir_default = os.path.join(folder_paths.models_dir, "rembg/ben")
#
# if not os.path.exists(models_dir_default):
# os.makedirs(models_dir_default, exist_ok=True)
#
# if models_dir_key not in folder_paths.folder_names_and_paths:
# folder_paths.folder_names_and_paths[models_dir_key] = (
# [models_dir_default], folder_paths.supported_pt_extensions)
# else:
# folder_paths.add_model_folder_path(models_dir_key, models_dir_default)
nodes/libs/YosoDelightPipe.py
+760
View File
@@ -0,0 +1,760 @@
# Copyright 2024 Marigold authors, PRS ETH Zurich. All rights reserved.
# Copyright 2024 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.
# --------------------------------------------------------------------------
# More information and citation instructions are available on the
# --------------------------------------------------------------------------
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
from diffusers.image_processor import PipelineImageInput
from diffusers.models import (
AutoencoderKL,
UNet2DConditionModel,
ControlNetModel,
)
from diffusers.schedulers import (
DDIMScheduler
)
from diffusers.utils import (
BaseOutput,
logging,
replace_example_docstring,
)
from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.controlnet import StableDiffusionControlNetPipeline
from diffusers.pipelines.marigold.marigold_image_processing import MarigoldImageProcessor
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
import pdb
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import diffusers
>>> import torch
>>> pipe = diffusers.MarigoldNormalsPipeline.from_pretrained(
... "prs-eth/marigold-normals-lcm-v0-1", variant="fp16", torch_dtype=torch.float16
... ).to("cuda")
>>> image = diffusers.utils.load_image("https://marigoldmonodepth.github.io/images/einstein.jpg")
>>> normals = pipe(image)
>>> vis = pipe.image_processor.visualize_normals(normals.prediction)
>>> vis[0].save("einstein_normals.png")
```
"""
@dataclass
class YosoDelightOutput(BaseOutput):
"""
Output class for Marigold monocular normals prediction pipeline.
Args:
prediction (`np.ndarray`, `torch.Tensor`):
Predicted normals with values in the range [-1, 1]. The shape is always $numimages \times 3 \times height
\times width$, regardless of whether the images were passed as a 4D array or a list.
uncertainty (`None`, `np.ndarray`, `torch.Tensor`):
Uncertainty maps computed from the ensemble, with values in the range [0, 1]. The shape is $numimages
\times 1 \times height \times width$.
latent (`None`, `torch.Tensor`):
Latent features corresponding to the predictions, compatible with the `latents` argument of the pipeline.
The shape is $numimages * numensemble \times 4 \times latentheight \times latentwidth$.
"""
prediction: Union[np.ndarray, torch.Tensor]
latent: Union[None, torch.Tensor]
gaus_noise: Union[None, torch.Tensor]
class YosoDelightPipeline(StableDiffusionControlNetPipeline):
""" Pipeline for monocular normals estimation using the Marigold method: https://marigoldmonodepth.github.io.
Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
Provides additional conditioning to the `unet` during the denoising process. If you set multiple
ControlNets as a list, the outputs from each ControlNet are added together to create one combined
additional conditioning.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
"""
model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
_exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel]],
scheduler: Union[DDIMScheduler],
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
image_encoder: CLIPVisionModelWithProjection = None,
requires_safety_checker: bool = True,
default_denoising_steps: Optional[int] = 1,
default_processing_resolution: Optional[int] = 768,
prompt="",
empty_text_embedding=None,
t_start: Optional[int] = 401,
):
super().__init__(
vae,
text_encoder,
tokenizer,
unet,
controlnet,
scheduler,
safety_checker,
feature_extractor,
image_encoder,
requires_safety_checker,
)
# TODO yoso ImageProcessor
self.image_processor = MarigoldImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.control_image_processor = MarigoldImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.default_denoising_steps = default_denoising_steps
self.default_processing_resolution = default_processing_resolution
self.prompt = prompt
self.prompt_embeds = None
self.empty_text_embedding = empty_text_embedding
self.t_start= t_start # target_out latents
def check_inputs(
self,
image: PipelineImageInput,
num_inference_steps: int,
ensemble_size: int,
processing_resolution: int,
resample_method_input: str,
resample_method_output: str,
batch_size: int,
ensembling_kwargs: Optional[Dict[str, Any]],
latents: Optional[torch.Tensor],
generator: Optional[Union[torch.Generator, List[torch.Generator]]],
output_type: str,
output_uncertainty: bool,
) -> int:
if num_inference_steps is None:
raise ValueError("`num_inference_steps` is not specified and could not be resolved from the model config.")
if num_inference_steps < 1:
raise ValueError("`num_inference_steps` must be positive.")
if ensemble_size < 1:
raise ValueError("`ensemble_size` must be positive.")
if ensemble_size == 2:
logger.warning(
"`ensemble_size` == 2 results are similar to no ensembling (1); "
"consider increasing the value to at least 3."
)
if ensemble_size == 1 and output_uncertainty:
raise ValueError(
"Computing uncertainty by setting `output_uncertainty=True` also requires setting `ensemble_size` "
"greater than 1."
)
if processing_resolution is None:
raise ValueError(
"`processing_resolution` is not specified and could not be resolved from the model config."
)
if processing_resolution < 0:
raise ValueError(
"`processing_resolution` must be non-negative: 0 for native resolution, or any positive value for "
"downsampled processing."
)
if processing_resolution % self.vae_scale_factor != 0:
raise ValueError(f"`processing_resolution` must be a multiple of {self.vae_scale_factor}.")
if resample_method_input not in ("nearest", "nearest-exact", "bilinear", "bicubic", "area"):
raise ValueError(
"`resample_method_input` takes string values compatible with PIL library: "
"nearest, nearest-exact, bilinear, bicubic, area."
)
if resample_method_output not in ("nearest", "nearest-exact", "bilinear", "bicubic", "area"):
raise ValueError(
"`resample_method_output` takes string values compatible with PIL library: "
"nearest, nearest-exact, bilinear, bicubic, area."
)
if batch_size < 1:
raise ValueError("`batch_size` must be positive.")
if output_type not in ["pt", "np"]:
raise ValueError("`output_type` must be one of `pt` or `np`.")
if latents is not None and generator is not None:
raise ValueError("`latents` and `generator` cannot be used together.")
if ensembling_kwargs is not None:
if not isinstance(ensembling_kwargs, dict):
raise ValueError("`ensembling_kwargs` must be a dictionary.")
if "reduction" in ensembling_kwargs and ensembling_kwargs["reduction"] not in ("closest", "mean"):
raise ValueError("`ensembling_kwargs['reduction']` can be either `'closest'` or `'mean'`.")
# image checks
num_images = 0
W, H = None, None
if not isinstance(image, list):
image = [image]
for i, img in enumerate(image):
if isinstance(img, np.ndarray) or torch.is_tensor(img):
if img.ndim not in (2, 3, 4):
raise ValueError(f"`image[{i}]` has unsupported dimensions or shape: {img.shape}.")
H_i, W_i = img.shape[-2:]
N_i = 1
if img.ndim == 4:
N_i = img.shape[0]
elif isinstance(img, Image.Image):
W_i, H_i = img.size
N_i = 1
else:
raise ValueError(f"Unsupported `image[{i}]` type: {type(img)}.")
if W is None:
W, H = W_i, H_i
elif (W, H) != (W_i, H_i):
raise ValueError(
f"Input `image[{i}]` has incompatible dimensions {(W_i, H_i)} with the previous images {(W, H)}"
)
num_images += N_i
# latents checks
if latents is not None:
if not torch.is_tensor(latents):
raise ValueError("`latents` must be a torch.Tensor.")
if latents.dim() != 4:
raise ValueError(f"`latents` has unsupported dimensions or shape: {latents.shape}.")
if processing_resolution > 0:
max_orig = max(H, W)
new_H = H * processing_resolution // max_orig
new_W = W * processing_resolution // max_orig
if new_H == 0 or new_W == 0:
raise ValueError(f"Extreme aspect ratio of the input image: [{W} x {H}]")
W, H = new_W, new_H
w = (W + self.vae_scale_factor - 1) // self.vae_scale_factor
h = (H + self.vae_scale_factor - 1) // self.vae_scale_factor
shape_expected = (num_images * ensemble_size, self.vae.config.latent_channels, h, w)
if latents.shape != shape_expected:
raise ValueError(f"`latents` has unexpected shape={latents.shape} expected={shape_expected}.")
# generator checks
if generator is not None:
if isinstance(generator, list):
if len(generator) != num_images * ensemble_size:
raise ValueError(
"The number of generators must match the total number of ensemble members for all input images."
)
if not all(g.device.type == generator[0].device.type for g in generator):
raise ValueError("`generator` device placement is not consistent in the list.")
elif not isinstance(generator, torch.Generator):
raise ValueError(f"Unsupported generator type: {type(generator)}.")
return num_images
def progress_bar(self, iterable=None, total=None, desc=None, leave=True):
if not hasattr(self, "_progress_bar_config"):
self._progress_bar_config = {}
elif not isinstance(self._progress_bar_config, dict):
raise ValueError(
f"`self._progress_bar_config` should be of type `dict`, but is {type(self._progress_bar_config)}."
)
progress_bar_config = dict(**self._progress_bar_config)
progress_bar_config["desc"] = progress_bar_config.get("desc", desc)
progress_bar_config["leave"] = progress_bar_config.get("leave", leave)
if iterable is not None:
return tqdm(iterable, **progress_bar_config)
elif total is not None:
return tqdm(total=total, **progress_bar_config)
else:
raise ValueError("Either `total` or `iterable` has to be defined.")
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image: PipelineImageInput,
prompt: Union[str, List[str]] = None,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_inference_steps: Optional[int] = None,
ensemble_size: int = 1,
processing_resolution: Optional[int] = None,
match_input_resolution: bool = True,
resample_method_input: str = "bilinear",
resample_method_output: str = "bilinear",
batch_size: int = 1,
ensembling_kwargs: Optional[Dict[str, Any]] = None,
latents: Optional[Union[torch.Tensor, List[torch.Tensor]]] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
num_images_per_prompt: Optional[int] = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
output_type: str = "np",
output_uncertainty: bool = False,
output_latent: bool = False,
skip_preprocess: bool = False,
return_dict: bool = True,
**kwargs,
):
"""
Function invoked when calling the pipeline.
Args:
image (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`),
`List[torch.Tensor]`: An input image or images used as an input for the normals estimation task. For
arrays and tensors, the expected value range is between `[0, 1]`. Passing a batch of images is possible
by providing a four-dimensional array or a tensor. Additionally, a list of images of two- or
three-dimensional arrays or tensors can be passed. In the latter case, all list elements must have the
same width and height.
num_inference_steps (`int`, *optional*, defaults to `None`):
Number of denoising diffusion steps during inference. The default value `None` results in automatic
selection. The number of steps should be at least 10 with the full Marigold models, and between 1 and 4
for Marigold-LCM models.
ensemble_size (`int`, defaults to `1`):
Number of ensemble predictions. Recommended values are 5 and higher for better precision, or 1 for
faster inference.
processing_resolution (`int`, *optional*, defaults to `None`):
Effective processing resolution. When set to `0`, matches the larger input image dimension. This
produces crisper predictions, but may also lead to the overall loss of global context. The default
value `None` resolves to the optimal value from the model config.
match_input_resolution (`bool`, *optional*, defaults to `True`):
When enabled, the output prediction is resized to match the input dimensions. When disabled, the longer
side of the output will equal to `processing_resolution`.
resample_method_input (`str`, *optional*, defaults to `"bilinear"`):
Resampling method used to resize input images to `processing_resolution`. The accepted values are:
`"nearest"`, `"nearest-exact"`, `"bilinear"`, `"bicubic"`, or `"area"`.
resample_method_output (`str`, *optional*, defaults to `"bilinear"`):
Resampling method used to resize output predictions to match the input resolution. The accepted values
are `"nearest"`, `"nearest-exact"`, `"bilinear"`, `"bicubic"`, or `"area"`.
batch_size (`int`, *optional*, defaults to `1`):
Batch size; only matters when setting `ensemble_size` or passing a tensor of images.
ensembling_kwargs (`dict`, *optional*, defaults to `None`)
Extra dictionary with arguments for precise ensembling control. The following options are available:
- reduction (`str`, *optional*, defaults to `"closest"`): Defines the ensembling function applied in
every pixel location, can be either `"closest"` or `"mean"`.
latents (`torch.Tensor`, *optional*, defaults to `None`):
Latent noise tensors to replace the random initialization. These can be taken from the previous
function call's output.
generator (`torch.Generator`, or `List[torch.Generator]`, *optional*, defaults to `None`):
Random number generator object to ensure reproducibility.
output_type (`str`, *optional*, defaults to `"np"`):
Preferred format of the output's `prediction` and the optional `uncertainty` fields. The accepted
values are: `"np"` (numpy array) or `"pt"` (torch tensor).
output_uncertainty (`bool`, *optional*, defaults to `False`):
When enabled, the output's `uncertainty` field contains the predictive uncertainty map, provided that
the `ensemble_size` argument is set to a value above 2.
output_latent (`bool`, *optional*, defaults to `False`):
When enabled, the output's `latent` field contains the latent codes corresponding to the predictions
within the ensemble. These codes can be saved, modified, and used for subsequent calls with the
`latents` argument.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.marigold.MarigoldDepthOutput`] instead of a plain tuple.
Examples:
Returns:
[`~pipelines.marigold.MarigoldNormalsOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.marigold.MarigoldNormalsOutput`] is returned, otherwise a
`tuple` is returned where the first element is the prediction, the second element is the uncertainty
(or `None`), and the third is the latent (or `None`).
"""
# 0. Resolving variables.
device = self._execution_device
dtype = self.dtype
# Model-specific optimal default values leading to fast and reasonable results.
if num_inference_steps is None:
num_inference_steps = self.default_denoising_steps
if processing_resolution is None:
processing_resolution = self.default_processing_resolution
# 1. Check inputs.
num_images = self.check_inputs(
image,
num_inference_steps,
ensemble_size,
processing_resolution,
resample_method_input,
resample_method_output,
batch_size,
ensembling_kwargs,
latents,
generator,
output_type,
output_uncertainty,
)
# 2. Prepare empty text conditioning.
# Model invocation: self.tokenizer, self.text_encoder.
if self.empty_text_embedding is None:
prompt = ""
text_inputs = self.tokenizer(
prompt,
padding="do_not_pad",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids.to(device)
self.empty_text_embedding = self.text_encoder(text_input_ids)[0] # [1,2,1024]
# 3. prepare prompt
if self.prompt_embeds is None:
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
self.prompt,
device,
num_images_per_prompt,
False,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=None,
lora_scale=None,
clip_skip=None,
)
self.prompt_embeds = prompt_embeds
self.negative_prompt_embeds = negative_prompt_embeds
# 4. Preprocess input images. This function loads input image or images of compatible dimensions `(H, W)`,
# optionally downsamples them to the `processing_resolution` `(PH, PW)`, where
# `max(PH, PW) == processing_resolution`, and pads the dimensions to `(PPH, PPW)` such that these values are
# divisible by the latent space downscaling factor (typically 8 in Stable Diffusion). The default value `None`
# of `processing_resolution` resolves to the optimal value from the model config. It is a recommended mode of
# operation and leads to the most reasonable results. Using the native image resolution or any other processing
# resolution can lead to loss of either fine details or global context in the output predictions.
if not skip_preprocess:
image, padding, original_resolution = self.image_processor.preprocess(
image, processing_resolution, resample_method_input, device, dtype
) # [N,3,PPH,PPW]
else:
padding = (0, 0)
original_resolution = image.shape[2:]
# 5. Encode input image into latent space. At this step, each of the `N` input images is represented with `E`
# ensemble members. Each ensemble member is an independent diffused prediction, just initialized independently.
# Latents of each such predictions across all input images and all ensemble members are represented in the
# `pred_latent` variable. The variable `image_latent` is of the same shape: it contains each input image encoded
# into latent space and replicated `E` times. The latents can be either generated (see `generator` to ensure
# reproducibility), or passed explicitly via the `latents` argument. The latter can be set outside the pipeline
# code. For example, in the Marigold-LCM video processing demo, the latents initialization of a frame is taken
# as a convex combination of the latents output of the pipeline for the previous frame and a newly-sampled
# noise. This behavior can be achieved by setting the `output_latent` argument to `True`. The latent space
# dimensions are `(h, w)`. Encoding into latent space happens in batches of size `batch_size`.
# Model invocation: self.vae.encoder.
image_latent, pred_latent = self.prepare_latents(
image, latents, generator, ensemble_size, batch_size
) # [N*E,4,h,w], [N*E,4,h,w]
gaus_noise = pred_latent.detach().clone()
del image
# 6. obtain control_output
cond_scale =controlnet_conditioning_scale
down_block_res_samples, mid_block_res_sample = self.controlnet(
image_latent.detach(),
self.t_start,
encoder_hidden_states=self.prompt_embeds,
conditioning_scale=cond_scale,
guess_mode=False,
return_dict=False,
)
# 7. YOSO sampling
latent_x_t = self.unet(
pred_latent,
self.t_start,
encoder_hidden_states=self.prompt_embeds,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
return_dict=False,
)[0]
del (
pred_latent,
image_latent,
)
# decoder
prediction = self.decode_prediction(latent_x_t)
prediction = self.image_processor.unpad_image(prediction, padding) # [N*E,3,PH,PW]
prediction = self.image_processor.resize_antialias(
prediction, original_resolution, resample_method_output, is_aa=False
) # [N,3,H,W]
if output_type == "np":
prediction = self.image_processor.pt_to_numpy(prediction) # [N,H,W,3]
# 11. Offload all models
self.maybe_free_model_hooks()
return YosoDelightOutput(
prediction=prediction,
latent=latent_x_t,
gaus_noise=gaus_noise,
)
# Copied from diffusers.pipelines.marigold.pipeline_marigold_depth.MarigoldDepthPipeline.prepare_latents
def prepare_latents(
self,
image: torch.Tensor,
latents: Optional[torch.Tensor],
generator: Optional[torch.Generator],
ensemble_size: int,
batch_size: int,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
准备潜变量。
该函数旨在对给定的图像进行编码，生成相应的潜变量，并根据需要重复这些潜变量以形成一个集合。
同时，它也负责生成对应的预测潜变量。
参数:
- image: 输入图像张量。
- latents: 可选的潜变量张量，如果提供，则直接使用，否则将生成一个新的潜变量张量。
- generator: 可选的随机数生成器，用于生成潜变量。
- ensemble_size: 集合大小，表示潜变量的重复次数。
- batch_size: 批处理大小，用于分批处理输入图像。
返回:
- image_latent: 编码后的图像潜变量。
- pred_latent: 预测潜变量。
"""
def retrieve_latents(encoder_output):
"""
提取潜变量。
该内部函数用于从编码器的输出中提取潜变量。如果输出包含latent_dist属性，则调用其mode方法；
如果输出包含latents属性，则直接返回该属性。如果两者都不包含，则抛出异常。
参数:
- encoder_output: 编码器的输出。
返回:
- 提取到的潜变量。
"""
if hasattr(encoder_output, "latent_dist"):
return encoder_output.latent_dist.mode()
elif hasattr(encoder_output, "latents"):
return encoder_output.latents
else:
raise AttributeError("Could not access latents of provided encoder_output")
# 对输入图像进行分批处理并编码，然后将编码后的潜变量进行拼接
image_latent = torch.cat(
[
retrieve_latents(self.vae.encode(image[i : i + batch_size]))
for i in range(0, image.shape[0], batch_size)
],
dim=0,
) # [N,4,h,w]
# 对编码后的潜变量进行缩放
image_latent = image_latent * self.vae.config.scaling_factor
# 根据集合大小重复潜变量
image_latent = image_latent.repeat_interleave(ensemble_size, dim=0) # [N*E,4,h,w]
# 初始化预测潜变量
pred_latent = torch.zeros_like(image_latent)
# 如果预测潜变量未提供，则生成一个新的随机潜变量
if pred_latent is None:
pred_latent = randn_tensor(
image_latent.shape,
generator=generator,
device=image_latent.device,
dtype=image_latent.dtype,
) # [N*E,4,h,w]
# 返回编码后的图像潜变量和预测潜变量
return image_latent, pred_latent
def decode_prediction(self, pred_latent: torch.Tensor) -> torch.Tensor:
if pred_latent.dim() != 4 or pred_latent.shape[1] != self.vae.config.latent_channels:
raise ValueError(
f"Expecting 4D tensor of shape [B,{self.vae.config.latent_channels},H,W]; got {pred_latent.shape}."
)
prediction = self.vae.decode(pred_latent / self.vae.config.scaling_factor, return_dict=False)[0] # [B,3,H,W]
return prediction # [B,3,H,W]
@staticmethod
def normalize_normals(normals: torch.Tensor, eps: float = 1e-6) -> torch.Tensor:
if normals.dim() != 4 or normals.shape[1] != 3:
raise ValueError(f"Expecting 4D tensor of shape [B,3,H,W]; got {normals.shape}.")
norm = torch.norm(normals, dim=1, keepdim=True)
normals /= norm.clamp(min=eps)
return normals
@staticmethod
def ensemble_normals(
normals: torch.Tensor, output_uncertainty: bool, reduction: str = "closest"
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""
Ensembles the normals maps represented by the `normals` tensor with expected shape `(B, 3, H, W)`, where B is
the number of ensemble members for a given prediction of size `(H x W)`.
Args:
normals (`torch.Tensor`):
Input ensemble normals maps.
output_uncertainty (`bool`, *optional*, defaults to `False`):
Whether to output uncertainty map.
reduction (`str`, *optional*, defaults to `"closest"`):
Reduction method used to ensemble aligned predictions. The accepted values are: `"closest"` and
`"mean"`.
Returns:
A tensor of aligned and ensembled normals maps with shape `(1, 3, H, W)` and optionally a tensor of
uncertainties of shape `(1, 1, H, W)`.
"""
if normals.dim() != 4 or normals.shape[1] != 3:
raise ValueError(f"Expecting 4D tensor of shape [B,3,H,W]; got {normals.shape}.")
if reduction not in ("closest", "mean"):
raise ValueError(f"Unrecognized reduction method: {reduction}.")
mean_normals = normals.mean(dim=0, keepdim=True) # [1,3,H,W]
mean_normals = MarigoldNormalsPipeline.normalize_normals(mean_normals) # [1,3,H,W]
sim_cos = (mean_normals * normals).sum(dim=1, keepdim=True) # [E,1,H,W]
sim_cos = sim_cos.clamp(-1, 1) # required to avoid NaN in uncertainty with fp16
uncertainty = None
if output_uncertainty:
uncertainty = sim_cos.arccos() # [E,1,H,W]
uncertainty = uncertainty.mean(dim=0, keepdim=True) / np.pi # [1,1,H,W]
if reduction == "mean":
return mean_normals, uncertainty # [1,3,H,W], [1,1,H,W]
closest_indices = sim_cos.argmax(dim=0, keepdim=True) # [1,1,H,W]
closest_indices = closest_indices.repeat(1, 3, 1, 1) # [1,3,H,W]
closest_normals = torch.gather(normals, 0, closest_indices) # [1,3,H,W]
return closest_normals, uncertainty # [1,3,H,W], [1,1,H,W]
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
def retrieve_timesteps(
scheduler,
num_inference_steps: Optional[int] = None,
device: Optional[Union[str, torch.device]] = None,
timesteps: Optional[List[int]] = None,
sigmas: Optional[List[float]] = None,
**kwargs,
):
"""
Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
Args:
scheduler (`SchedulerMixin`):
The scheduler to get timesteps from.
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
must be `None`.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
timesteps (`List[int]`, *optional*):
Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
`num_inference_steps` and `sigmas` must be `None`.
sigmas (`List[float]`, *optional*):
Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
`num_inference_steps` and `timesteps` must be `None`.
Returns:
`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
second element is the number of inference steps.
"""
if timesteps is not None and sigmas is not None:
raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
if timesteps is not None:
accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
if not accepts_timesteps:
raise ValueError(
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
f" timestep schedules. Please check whether you are using the correct scheduler."
)
scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
elif sigmas is not None:
accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
if not accept_sigmas:
raise ValueError(
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
f" sigmas schedules. Please check whether you are using the correct scheduler."
)
scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
else:
scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
timesteps = scheduler.timesteps
return timesteps, num_inference_steps
nodes/libs/__init__.py
View File
pyproject.toml
+15
View File
@@ -0,0 +1,15 @@
[project]
name = "comfyui_stabledelight_ll"
description = "StableDelight: Revealing Hidden Textures by Removing Specular Reflections(移除纹理表面镜面反射)"
version = "1.0.0"
license = {file = "LICENSE"}
dependencies = ["numpy"]
[project.urls]
Repository = "https://github.com/lldacing/ComfyUI_StableDelight_ll"
# Used by Comfy Registry https://comfyregistry.org
[tool.comfy]
PublisherId = "lldacing"
DisplayName = "ComfyUI_StableDelight_ll"
Icon = ""