├── .gitignore ├── LICENSE ├── README.md ├── briarmbg.py ├── db_examples.py ├── gradio_demo.py ├── gradio_demo_bg.py ├── imgs ├── alter │ ├── i1.jpeg │ ├── i2.png │ ├── i3.png │ ├── i4.png │ ├── i5.png │ ├── i6.webp │ ├── o1.png │ ├── o2.png │ ├── o3.png │ ├── o4.png │ └── o5.png ├── bgs │ ├── 1.webp │ ├── 10.webp │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 2.webp │ ├── 3.webp │ ├── 4.webp │ ├── 5.webp │ ├── 6.webp │ ├── 7.webp │ ├── 8.webp │ └── 9.webp ├── i1.webp ├── i10.png ├── i11.png ├── i13.png ├── i14.png ├── i15.png ├── i16.png ├── i3.png ├── i5.png ├── i6.jpg ├── i7.jpg ├── i8.webp ├── i9.png ├── o1.png ├── o10.png ├── o11.png ├── o12.png ├── o13.png ├── o14.png ├── o15.png ├── o16.png ├── o2.png ├── o3.png ├── o4.png ├── o5.png ├── o6.png ├── o7.png ├── o8.png └── o9.png ├── models └── model_download_here └── requirements.txt /.gitignore: -------------------------------------------------------------------------------- 1 | *.safetensors 2 | 3 | # Byte-compiled / optimized / DLL files 4 | __pycache__/ 5 | *.py[cod] 6 | *$py.class 7 | 8 | # C extensions 9 | *.so 10 | 11 | # Distribution / packaging 12 | .Python 13 | build/ 14 | develop-eggs/ 15 | dist/ 16 | downloads/ 17 | eggs/ 18 | .eggs/ 19 | lib/ 20 | lib64/ 21 | parts/ 22 | sdist/ 23 | var/ 24 | wheels/ 25 | share/python-wheels/ 26 | *.egg-info/ 27 | .installed.cfg 28 | *.egg 29 | MANIFEST 30 | 31 | # PyInstaller 32 | # Usually these files are written by a python script from a template 33 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 34 | *.manifest 35 | *.spec 36 | 37 | # Installer logs 38 | pip-log.txt 39 | pip-delete-this-directory.txt 40 | 41 | # Unit test / coverage reports 42 | htmlcov/ 43 | .tox/ 44 | .nox/ 45 | .coverage 46 | .coverage.* 47 | .cache 48 | nosetests.xml 49 | coverage.xml 50 | *.cover 51 | *.py,cover 52 | .hypothesis/ 53 | .pytest_cache/ 54 | cover/ 55 | 56 | # Translations 57 | *.mo 58 | *.pot 59 | 60 | # Django stuff: 61 | *.log 62 | local_settings.py 63 | db.sqlite3 64 | db.sqlite3-journal 65 | 66 | # Flask stuff: 67 | instance/ 68 | .webassets-cache 69 | 70 | # Scrapy stuff: 71 | .scrapy 72 | 73 | # Sphinx documentation 74 | docs/_build/ 75 | 76 | # PyBuilder 77 | .pybuilder/ 78 | target/ 79 | 80 | # Jupyter Notebook 81 | .ipynb_checkpoints 82 | 83 | # IPython 84 | profile_default/ 85 | ipython_config.py 86 | 87 | # pyenv 88 | # For a library or package, you might want to ignore these files since the code is 89 | # intended to run in multiple environments; otherwise, check them in: 90 | # .python-version 91 | 92 | # pipenv 93 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 94 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 95 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 96 | # install all needed dependencies. 97 | #Pipfile.lock 98 | 99 | # poetry 100 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 101 | # This is especially recommended for binary packages to ensure reproducibility, and is more 102 | # commonly ignored for libraries. 103 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 104 | #poetry.lock 105 | 106 | # pdm 107 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 108 | #pdm.lock 109 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 110 | # in version control. 111 | # https://pdm.fming.dev/#use-with-ide 112 | .pdm.toml 113 | 114 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 115 | __pypackages__/ 116 | 117 | # Celery stuff 118 | celerybeat-schedule 119 | celerybeat.pid 120 | 121 | # SageMath parsed files 122 | *.sage.py 123 | 124 | # Environments 125 | .env 126 | .venv 127 | env/ 128 | venv/ 129 | ENV/ 130 | env.bak/ 131 | venv.bak/ 132 | 133 | # Spyder project settings 134 | .spyderproject 135 | .spyproject 136 | 137 | # Rope project settings 138 | .ropeproject 139 | 140 | # mkdocs documentation 141 | /site 142 | 143 | # mypy 144 | .mypy_cache/ 145 | .dmypy.json 146 | dmypy.json 147 | 148 | # Pyre type checker 149 | .pyre/ 150 | 151 | # pytype static type analyzer 152 | .pytype/ 153 | 154 | # Cython debug symbols 155 | cython_debug/ 156 | 157 | # PyCharm 158 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 159 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 160 | # and can be added to the global gitignore or merged into this file. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # IC-Light 2 | 3 | IC-Light is a project to manipulate the illumination of images. 4 | 5 | The name "IC-Light" stands for **"Imposing Consistent Light"** (we will briefly describe this at the end of this page). 6 | 7 | Currently, we release two types of models: text-conditioned relighting model and background-conditioned model. Both types take foreground images as inputs. 8 | 9 | **Note that "iclightai dot com" is a scam website. They have no relationship with us. Do not give scam websites money! This GitHub repo is the only official IC-Light.** 10 | 11 | # News 12 | 13 | [Alternative model](https://github.com/lllyasviel/IC-Light/discussions/109) for stronger illumination modifications. 14 | 15 | Some news about flux is [here](https://github.com/lllyasviel/IC-Light/discussions/98). (A fix [update](https://github.com/lllyasviel/IC-Light/discussions/98#discussioncomment-11370266) is added at Nov 25, more demos will be uploaded soon.) 16 | 17 | # Get Started 18 | 19 | Below script will run the text-conditioned relighting model: 20 | 21 | git clone https://github.com/lllyasviel/IC-Light.git 22 | cd IC-Light 23 | conda create -n iclight python=3.10 24 | conda activate iclight 25 | pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121 26 | pip install -r requirements.txt 27 | python gradio_demo.py 28 | 29 | Or, to use background-conditioned demo: 30 | 31 | python gradio_demo_bg.py 32 | 33 | Model downloading is automatic. 34 | 35 | Note that the "gradio_demo.py" has an official [huggingFace Space here](https://huggingface.co/spaces/lllyasviel/IC-Light). 36 | 37 | # Screenshot 38 | 39 | ### Text-Conditioned Model 40 | 41 | (Note that the "Lighting Preference" are just initial latents - eg., if the Lighting Preference is "Left" then initial latent is left white right black.) 42 | 43 | --- 44 | 45 | **Prompt: beautiful woman, detailed face, warm atmosphere, at home, bedroom** 46 | 47 | Lighting Preference: Left 48 | 49 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/87265483-aa26-4d2e-897d-b58892f5fdd7) 50 | 51 | --- 52 | 53 | **Prompt: beautiful woman, detailed face, sunshine from window** 54 | 55 | Lighting Preference: Left 56 | 57 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/148c4a6d-82e7-4e3a-bf44-5c9a24538afc) 58 | 59 | --- 60 | 61 | **beautiful woman, detailed face, neon, Wong Kar-wai, warm** 62 | 63 | Lighting Preference: Left 64 | 65 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/f53c9de2-534a-42f4-8272-6d16a021fc01) 66 | 67 | --- 68 | 69 | **Prompt: beautiful woman, detailed face, sunshine, outdoor, warm atmosphere** 70 | 71 | Lighting Preference: Right 72 | 73 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/25d6ea24-a736-4a0b-b42d-700fe8b2101e) 74 | 75 | --- 76 | 77 | **Prompt: beautiful woman, detailed face, sunshine, outdoor, warm atmosphere** 78 | 79 | Lighting Preference: Left 80 | 81 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/dd30387b-0490-46ee-b688-2191fb752e68) 82 | 83 | --- 84 | 85 | **Prompt: beautiful woman, detailed face, sunshine from window** 86 | 87 | Lighting Preference: Right 88 | 89 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/6c9511ca-f97f-401a-85f3-92b4442000e3) 90 | 91 | --- 92 | 93 | **Prompt: beautiful woman, detailed face, shadow from window** 94 | 95 | Lighting Preference: Left 96 | 97 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/e73701d5-890e-4b15-91ee-97f16ea3c450) 98 | 99 | --- 100 | 101 | **Prompt: beautiful woman, detailed face, sunset over sea** 102 | 103 | Lighting Preference: Right 104 | 105 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/ff26ac3d-1b12-4447-b51f-73f7a5122a05) 106 | 107 | --- 108 | 109 | **Prompt: handsome boy, detailed face, neon light, city** 110 | 111 | Lighting Preference: Left 112 | 113 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/d7795e02-46f7-444f-93e7-4d6460840437) 114 | 115 | --- 116 | 117 | **Prompt: beautiful woman, detailed face, light and shadow** 118 | 119 | Lighting Preference: Left 120 | 121 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/706f70a8-d1a0-4e0b-b3ac-804e8e231c0f) 122 | 123 | (beautiful woman, detailed face, soft studio lighting) 124 | 125 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/fe0a72df-69d4-4e11-b661-fb8b84d0274d) 126 | 127 | --- 128 | 129 | **Prompt: Buddha, detailed face, sci-fi RGB glowing, cyberpunk** 130 | 131 | Lighting Preference: Left 132 | 133 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/68d60c68-ce23-4902-939e-11629ccaf39a) 134 | 135 | --- 136 | 137 | **Prompt: Buddha, detailed face, natural lighting** 138 | 139 | Lighting Preference: Left 140 | 141 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/1841d23d-0a0d-420b-a5ab-302da9c47c17) 142 | 143 | --- 144 | 145 | **Prompt: toy, detailed face, shadow from window** 146 | 147 | Lighting Preference: Bottom 148 | 149 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/dcb97439-ea6b-483e-8e68-cf5d320368c7) 150 | 151 | --- 152 | 153 | **Prompt: toy, detailed face, sunset over sea** 154 | 155 | Lighting Preference: Right 156 | 157 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/4f78b897-621d-4527-afa7-78d62c576100) 158 | 159 | --- 160 | 161 | **Prompt: dog, magic lit, sci-fi RGB glowing, studio lighting** 162 | 163 | Lighting Preference: Bottom 164 | 165 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/1db9cac9-8d3f-4f40-82e2-e3b0cafd8613) 166 | 167 | --- 168 | 169 | **Prompt: mysteriou human, warm atmosphere, warm atmosphere, at home, bedroom** 170 | 171 | Lighting Preference: Right 172 | 173 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/5d5aa7e5-8cbd-4e1f-9f27-2ecc3c30563a) 174 | 175 | --- 176 | 177 | ### Background-Conditioned Model 178 | 179 | The background conditioned model does not require careful prompting. One can just use simple prompts like "handsome man, cinematic lighting". 180 | 181 | --- 182 | 183 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/0b2a889f-682b-4393-b1ec-2cabaa182010) 184 | 185 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/477ca348-bd47-46ff-81e6-0ffc3d05feb2) 186 | 187 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/5bc9d8d9-02cd-442e-a75c-193f115f2ad8) 188 | 189 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/a35e4c57-e199-40e2-893b-cb1c549612a9) 190 | 191 | --- 192 | 193 | A more structured visualization: 194 | 195 | ![r1](https://github.com/lllyasviel/IC-Light/assets/19834515/c1daafb5-ac8b-461c-bff2-899e4c671ba3) 196 | 197 | # Imposing Consistent Light 198 | 199 | In HDR space, illumination has a property that all light transports are independent. 200 | 201 | As a result, the blending of appearances of different light sources is equivalent to the appearance with mixed light sources: 202 | 203 | ![cons](https://github.com/lllyasviel/IC-Light/assets/19834515/27c67787-998e-469f-862f-047344e100cd) 204 | 205 | Using the above [light stage](https://www.pauldebevec.com/Research/LS/) as an example, the two images from the "appearance mixture" and "light source mixture" are consistent (mathematically equivalent in HDR space, ideally). 206 | 207 | We imposed such consistency (using MLPs in latent space) when training the relighting models. 208 | 209 | As a result, the model is able to produce highly consistent relight - **so** consistent that different relightings can even be merged as normal maps! Despite the fact that the models are latent diffusion. 210 | 211 | ![r2](https://github.com/lllyasviel/IC-Light/assets/19834515/25068f6a-f945-4929-a3d6-e8a152472223) 212 | 213 | From left to right are inputs, model outputs relighting, devided shadow image, and merged normal maps. Note that the model is not trained with any normal map data. This normal estimation comes from the consistency of relighting. 214 | 215 | You can reproduce this experiment using this button (it is 4x slower because it relight image 4 times) 216 | 217 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/d9c37bf7-2136-446c-a9a5-5a341e4906de) 218 | 219 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/fcf5dd55-0309-4e8e-9721-d55931ea77f0) 220 | 221 | Below are bigger images (feel free to try yourself to get more results!) 222 | 223 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/12335218-186b-4c61-b43a-79aea9df8b21) 224 | 225 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/2daab276-fdfa-4b0c-abcb-e591f575598a) 226 | 227 | For reference, [geowizard](https://fuxiao0719.github.io/projects/geowizard/) (geowizard is a really great work!): 228 | 229 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/4ba1a96d-e218-42ab-83ae-a7918d56ee5f) 230 | 231 | And, [switchlight](https://arxiv.org/pdf/2402.18848) (switchlight is another great work!): 232 | 233 | ![image](https://github.com/lllyasviel/IC-Light/assets/19834515/fbdd961f-0b26-45d2-802e-ffd734affab8) 234 | 235 | # Model Notes 236 | 237 | * **iclight_sd15_fc.safetensors** - The default relighting model, conditioned on text and foreground. You can use initial latent to influence the relighting. 238 | 239 | * **iclight_sd15_fcon.safetensors** - Same as "iclight_sd15_fc.safetensors" but trained with offset noise. Note that the default "iclight_sd15_fc.safetensors" outperform this model slightly in a user study. And this is the reason why the default model is the model without offset noise. 240 | 241 | * **iclight_sd15_fbc.safetensors** - Relighting model conditioned with text, foreground, and background. 242 | 243 | Also, note that the original [BRIA RMBG 1.4](https://huggingface.co/briaai/RMBG-1.4) is for non-commercial use. If you use IC-Light in commercial projects, replace it with other background replacer like [BiRefNet](https://github.com/ZhengPeng7/BiRefNet). 244 | 245 | # Cite 246 | 247 | @inproceedings{ 248 | zhang2025scaling, 249 | title={Scaling In-the-Wild Training for Diffusion-based Illumination Harmonization and Editing by Imposing Consistent Light Transport}, 250 | author={Lvmin Zhang and Anyi Rao and Maneesh Agrawala}, 251 | booktitle={The Thirteenth International Conference on Learning Representations}, 252 | year={2025}, 253 | url={https://openreview.net/forum?id=u1cQYxRI1H} 254 | } 255 | 256 | # Related Work 257 | 258 | Also read ... 259 | 260 | [Total Relighting: Learning to Relight Portraits for Background Replacement](https://augmentedperception.github.io/total_relighting/) 261 | 262 | [Relightful Harmonization: Lighting-aware Portrait Background Replacement](https://arxiv.org/abs/2312.06886) 263 | 264 | [SwitchLight: Co-design of Physics-driven Architecture and Pre-training Framework for Human Portrait Relighting](https://arxiv.org/pdf/2402.18848) 265 | -------------------------------------------------------------------------------- /briarmbg.py: -------------------------------------------------------------------------------- 1 | # RMBG1.4 (diffusers implementation) 2 | # Found on huggingface space of several projects 3 | # Not sure which project is the source of this file 4 | 5 | import torch 6 | import torch.nn as nn 7 | import torch.nn.functional as F 8 | from huggingface_hub import PyTorchModelHubMixin 9 | 10 | 11 | class REBNCONV(nn.Module): 12 | def __init__(self, in_ch=3, out_ch=3, dirate=1, stride=1): 13 | super(REBNCONV, self).__init__() 14 | 15 | self.conv_s1 = nn.Conv2d( 16 | in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate, stride=stride 17 | ) 18 | self.bn_s1 = nn.BatchNorm2d(out_ch) 19 | self.relu_s1 = nn.ReLU(inplace=True) 20 | 21 | def forward(self, x): 22 | hx = x 23 | xout = self.relu_s1(self.bn_s1(self.conv_s1(hx))) 24 | 25 | return xout 26 | 27 | 28 | def _upsample_like(src, tar): 29 | src = F.interpolate(src, size=tar.shape[2:], mode="bilinear") 30 | return src 31 | 32 | 33 | ### RSU-7 ### 34 | class RSU7(nn.Module): 35 | def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512): 36 | super(RSU7, self).__init__() 37 | 38 | self.in_ch = in_ch 39 | self.mid_ch = mid_ch 40 | self.out_ch = out_ch 41 | 42 | self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) ## 1 -> 1/2 43 | 44 | self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) 45 | self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 46 | 47 | self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) 48 | self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 49 | 50 | self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) 51 | self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 52 | 53 | self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) 54 | self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 55 | 56 | self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1) 57 | self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 58 | 59 | self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1) 60 | 61 | self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2) 62 | 63 | self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) 64 | self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) 65 | self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) 66 | self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) 67 | self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) 68 | self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) 69 | 70 | def forward(self, x): 71 | b, c, h, w = x.shape 72 | 73 | hx = x 74 | hxin = self.rebnconvin(hx) 75 | 76 | hx1 = self.rebnconv1(hxin) 77 | hx = self.pool1(hx1) 78 | 79 | hx2 = self.rebnconv2(hx) 80 | hx = self.pool2(hx2) 81 | 82 | hx3 = self.rebnconv3(hx) 83 | hx = self.pool3(hx3) 84 | 85 | hx4 = self.rebnconv4(hx) 86 | hx = self.pool4(hx4) 87 | 88 | hx5 = self.rebnconv5(hx) 89 | hx = self.pool5(hx5) 90 | 91 | hx6 = self.rebnconv6(hx) 92 | 93 | hx7 = self.rebnconv7(hx6) 94 | 95 | hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1)) 96 | hx6dup = _upsample_like(hx6d, hx5) 97 | 98 | hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1)) 99 | hx5dup = _upsample_like(hx5d, hx4) 100 | 101 | hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) 102 | hx4dup = _upsample_like(hx4d, hx3) 103 | 104 | hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) 105 | hx3dup = _upsample_like(hx3d, hx2) 106 | 107 | hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) 108 | hx2dup = _upsample_like(hx2d, hx1) 109 | 110 | hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) 111 | 112 | return hx1d + hxin 113 | 114 | 115 | ### RSU-6 ### 116 | class RSU6(nn.Module): 117 | def __init__(self, in_ch=3, mid_ch=12, out_ch=3): 118 | super(RSU6, self).__init__() 119 | 120 | self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) 121 | 122 | self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) 123 | self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 124 | 125 | self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) 126 | self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 127 | 128 | self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) 129 | self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 130 | 131 | self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) 132 | self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 133 | 134 | self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1) 135 | 136 | self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2) 137 | 138 | self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) 139 | self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) 140 | self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) 141 | self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) 142 | self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) 143 | 144 | def forward(self, x): 145 | hx = x 146 | 147 | hxin = self.rebnconvin(hx) 148 | 149 | hx1 = self.rebnconv1(hxin) 150 | hx = self.pool1(hx1) 151 | 152 | hx2 = self.rebnconv2(hx) 153 | hx = self.pool2(hx2) 154 | 155 | hx3 = self.rebnconv3(hx) 156 | hx = self.pool3(hx3) 157 | 158 | hx4 = self.rebnconv4(hx) 159 | hx = self.pool4(hx4) 160 | 161 | hx5 = self.rebnconv5(hx) 162 | 163 | hx6 = self.rebnconv6(hx5) 164 | 165 | hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1)) 166 | hx5dup = _upsample_like(hx5d, hx4) 167 | 168 | hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) 169 | hx4dup = _upsample_like(hx4d, hx3) 170 | 171 | hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) 172 | hx3dup = _upsample_like(hx3d, hx2) 173 | 174 | hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) 175 | hx2dup = _upsample_like(hx2d, hx1) 176 | 177 | hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) 178 | 179 | return hx1d + hxin 180 | 181 | 182 | ### RSU-5 ### 183 | class RSU5(nn.Module): 184 | def __init__(self, in_ch=3, mid_ch=12, out_ch=3): 185 | super(RSU5, self).__init__() 186 | 187 | self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) 188 | 189 | self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) 190 | self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 191 | 192 | self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) 193 | self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 194 | 195 | self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) 196 | self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 197 | 198 | self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) 199 | 200 | self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2) 201 | 202 | self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) 203 | self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) 204 | self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) 205 | self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) 206 | 207 | def forward(self, x): 208 | hx = x 209 | 210 | hxin = self.rebnconvin(hx) 211 | 212 | hx1 = self.rebnconv1(hxin) 213 | hx = self.pool1(hx1) 214 | 215 | hx2 = self.rebnconv2(hx) 216 | hx = self.pool2(hx2) 217 | 218 | hx3 = self.rebnconv3(hx) 219 | hx = self.pool3(hx3) 220 | 221 | hx4 = self.rebnconv4(hx) 222 | 223 | hx5 = self.rebnconv5(hx4) 224 | 225 | hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1)) 226 | hx4dup = _upsample_like(hx4d, hx3) 227 | 228 | hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) 229 | hx3dup = _upsample_like(hx3d, hx2) 230 | 231 | hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) 232 | hx2dup = _upsample_like(hx2d, hx1) 233 | 234 | hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) 235 | 236 | return hx1d + hxin 237 | 238 | 239 | ### RSU-4 ### 240 | class RSU4(nn.Module): 241 | def __init__(self, in_ch=3, mid_ch=12, out_ch=3): 242 | super(RSU4, self).__init__() 243 | 244 | self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) 245 | 246 | self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) 247 | self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 248 | 249 | self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) 250 | self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 251 | 252 | self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) 253 | 254 | self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2) 255 | 256 | self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) 257 | self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) 258 | self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) 259 | 260 | def forward(self, x): 261 | hx = x 262 | 263 | hxin = self.rebnconvin(hx) 264 | 265 | hx1 = self.rebnconv1(hxin) 266 | hx = self.pool1(hx1) 267 | 268 | hx2 = self.rebnconv2(hx) 269 | hx = self.pool2(hx2) 270 | 271 | hx3 = self.rebnconv3(hx) 272 | 273 | hx4 = self.rebnconv4(hx3) 274 | 275 | hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1)) 276 | hx3dup = _upsample_like(hx3d, hx2) 277 | 278 | hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) 279 | hx2dup = _upsample_like(hx2d, hx1) 280 | 281 | hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) 282 | 283 | return hx1d + hxin 284 | 285 | 286 | ### RSU-4F ### 287 | class RSU4F(nn.Module): 288 | def __init__(self, in_ch=3, mid_ch=12, out_ch=3): 289 | super(RSU4F, self).__init__() 290 | 291 | self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) 292 | 293 | self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) 294 | self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2) 295 | self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4) 296 | 297 | self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8) 298 | 299 | self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4) 300 | self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2) 301 | self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) 302 | 303 | def forward(self, x): 304 | hx = x 305 | 306 | hxin = self.rebnconvin(hx) 307 | 308 | hx1 = self.rebnconv1(hxin) 309 | hx2 = self.rebnconv2(hx1) 310 | hx3 = self.rebnconv3(hx2) 311 | 312 | hx4 = self.rebnconv4(hx3) 313 | 314 | hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1)) 315 | hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1)) 316 | hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1)) 317 | 318 | return hx1d + hxin 319 | 320 | 321 | class myrebnconv(nn.Module): 322 | def __init__( 323 | self, 324 | in_ch=3, 325 | out_ch=1, 326 | kernel_size=3, 327 | stride=1, 328 | padding=1, 329 | dilation=1, 330 | groups=1, 331 | ): 332 | super(myrebnconv, self).__init__() 333 | 334 | self.conv = nn.Conv2d( 335 | in_ch, 336 | out_ch, 337 | kernel_size=kernel_size, 338 | stride=stride, 339 | padding=padding, 340 | dilation=dilation, 341 | groups=groups, 342 | ) 343 | self.bn = nn.BatchNorm2d(out_ch) 344 | self.rl = nn.ReLU(inplace=True) 345 | 346 | def forward(self, x): 347 | return self.rl(self.bn(self.conv(x))) 348 | 349 | 350 | class BriaRMBG(nn.Module, PyTorchModelHubMixin): 351 | def __init__(self, config: dict = {"in_ch": 3, "out_ch": 1}): 352 | super(BriaRMBG, self).__init__() 353 | in_ch = config["in_ch"] 354 | out_ch = config["out_ch"] 355 | self.conv_in = nn.Conv2d(in_ch, 64, 3, stride=2, padding=1) 356 | self.pool_in = nn.MaxPool2d(2, stride=2, ceil_mode=True) 357 | 358 | self.stage1 = RSU7(64, 32, 64) 359 | self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 360 | 361 | self.stage2 = RSU6(64, 32, 128) 362 | self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 363 | 364 | self.stage3 = RSU5(128, 64, 256) 365 | self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 366 | 367 | self.stage4 = RSU4(256, 128, 512) 368 | self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 369 | 370 | self.stage5 = RSU4F(512, 256, 512) 371 | self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True) 372 | 373 | self.stage6 = RSU4F(512, 256, 512) 374 | 375 | # decoder 376 | self.stage5d = RSU4F(1024, 256, 512) 377 | self.stage4d = RSU4(1024, 128, 256) 378 | self.stage3d = RSU5(512, 64, 128) 379 | self.stage2d = RSU6(256, 32, 64) 380 | self.stage1d = RSU7(128, 16, 64) 381 | 382 | self.side1 = nn.Conv2d(64, out_ch, 3, padding=1) 383 | self.side2 = nn.Conv2d(64, out_ch, 3, padding=1) 384 | self.side3 = nn.Conv2d(128, out_ch, 3, padding=1) 385 | self.side4 = nn.Conv2d(256, out_ch, 3, padding=1) 386 | self.side5 = nn.Conv2d(512, out_ch, 3, padding=1) 387 | self.side6 = nn.Conv2d(512, out_ch, 3, padding=1) 388 | 389 | # self.outconv = nn.Conv2d(6*out_ch,out_ch,1) 390 | 391 | def forward(self, x): 392 | hx = x 393 | 394 | hxin = self.conv_in(hx) 395 | # hx = self.pool_in(hxin) 396 | 397 | # stage 1 398 | hx1 = self.stage1(hxin) 399 | hx = self.pool12(hx1) 400 | 401 | # stage 2 402 | hx2 = self.stage2(hx) 403 | hx = self.pool23(hx2) 404 | 405 | # stage 3 406 | hx3 = self.stage3(hx) 407 | hx = self.pool34(hx3) 408 | 409 | # stage 4 410 | hx4 = self.stage4(hx) 411 | hx = self.pool45(hx4) 412 | 413 | # stage 5 414 | hx5 = self.stage5(hx) 415 | hx = self.pool56(hx5) 416 | 417 | # stage 6 418 | hx6 = self.stage6(hx) 419 | hx6up = _upsample_like(hx6, hx5) 420 | 421 | # -------------------- decoder -------------------- 422 | hx5d = self.stage5d(torch.cat((hx6up, hx5), 1)) 423 | hx5dup = _upsample_like(hx5d, hx4) 424 | 425 | hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1)) 426 | hx4dup = _upsample_like(hx4d, hx3) 427 | 428 | hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1)) 429 | hx3dup = _upsample_like(hx3d, hx2) 430 | 431 | hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1)) 432 | hx2dup = _upsample_like(hx2d, hx1) 433 | 434 | hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1)) 435 | 436 | # side output 437 | d1 = self.side1(hx1d) 438 | d1 = _upsample_like(d1, x) 439 | 440 | d2 = self.side2(hx2d) 441 | d2 = _upsample_like(d2, x) 442 | 443 | d3 = self.side3(hx3d) 444 | d3 = _upsample_like(d3, x) 445 | 446 | d4 = self.side4(hx4d) 447 | d4 = _upsample_like(d4, x) 448 | 449 | d5 = self.side5(hx5d) 450 | d5 = _upsample_like(d5, x) 451 | 452 | d6 = self.side6(hx6) 453 | d6 = _upsample_like(d6, x) 454 | 455 | return [ 456 | F.sigmoid(d1), 457 | F.sigmoid(d2), 458 | F.sigmoid(d3), 459 | F.sigmoid(d4), 460 | F.sigmoid(d5), 461 | F.sigmoid(d6), 462 | ], [hx1d, hx2d, hx3d, hx4d, hx5d, hx6] 463 | -------------------------------------------------------------------------------- /db_examples.py: -------------------------------------------------------------------------------- 1 | foreground_conditioned_examples = [ 2 | [ 3 | "imgs/i1.webp", 4 | "beautiful woman, detailed face, sunshine, outdoor, warm atmosphere", 5 | "Right Light", 6 | 512, 7 | 960, 8 | 12345, 9 | "imgs/o1.png", 10 | ], 11 | [ 12 | "imgs/i1.webp", 13 | "beautiful woman, detailed face, sunshine, outdoor, warm atmosphere", 14 | "Left Light", 15 | 512, 16 | 960, 17 | 50, 18 | "imgs/o2.png", 19 | ], 20 | [ 21 | "imgs/i3.png", 22 | "beautiful woman, detailed face, neon, Wong Kar-wai, warm", 23 | "Left Light", 24 | 512, 25 | 768, 26 | 12345, 27 | "imgs/o3.png", 28 | ], 29 | [ 30 | "imgs/i3.png", 31 | "beautiful woman, detailed face, sunshine from window", 32 | "Left Light", 33 | 512, 34 | 768, 35 | 12345, 36 | "imgs/o4.png", 37 | ], 38 | [ 39 | "imgs/i5.png", 40 | "beautiful woman, detailed face, warm atmosphere, at home, bedroom", 41 | "Left Light", 42 | 512, 43 | 768, 44 | 123, 45 | "imgs/o5.png", 46 | ], 47 | [ 48 | "imgs/i6.jpg", 49 | "beautiful woman, detailed face, sunshine from window", 50 | "Right Light", 51 | 512, 52 | 768, 53 | 42, 54 | "imgs/o6.png", 55 | ], 56 | [ 57 | "imgs/i7.jpg", 58 | "beautiful woman, detailed face, shadow from window", 59 | "Left Light", 60 | 512, 61 | 768, 62 | 8888, 63 | "imgs/o7.png", 64 | ], 65 | [ 66 | "imgs/i8.webp", 67 | "beautiful woman, detailed face, sunset over sea", 68 | "Right Light", 69 | 512, 70 | 640, 71 | 42, 72 | "imgs/o8.png", 73 | ], 74 | [ 75 | "imgs/i9.png", 76 | "handsome boy, detailed face, neon light, city", 77 | "Left Light", 78 | 512, 79 | 640, 80 | 12345, 81 | "imgs/o9.png", 82 | ], 83 | [ 84 | "imgs/i10.png", 85 | "beautiful woman, detailed face, light and shadow", 86 | "Left Light", 87 | 512, 88 | 960, 89 | 8888, 90 | "imgs/o10.png", 91 | ], 92 | [ 93 | "imgs/i11.png", 94 | "Buddha, detailed face, sci-fi RGB glowing, cyberpunk", 95 | "Left Light", 96 | 512, 97 | 768, 98 | 8888, 99 | "imgs/o11.png", 100 | ], 101 | [ 102 | "imgs/i11.png", 103 | "Buddha, detailed face, natural lighting", 104 | "Left Light", 105 | 512, 106 | 768, 107 | 12345, 108 | "imgs/o12.png", 109 | ], 110 | [ 111 | "imgs/i13.png", 112 | "toy, detailed face, shadow from window", 113 | "Bottom Light", 114 | 512, 115 | 704, 116 | 12345, 117 | "imgs/o13.png", 118 | ], 119 | [ 120 | "imgs/i14.png", 121 | "toy, detailed face, sunset over sea", 122 | "Right Light", 123 | 512, 124 | 704, 125 | 100, 126 | "imgs/o14.png", 127 | ], 128 | [ 129 | "imgs/i15.png", 130 | "dog, magic lit, sci-fi RGB glowing, studio lighting", 131 | "Bottom Light", 132 | 512, 133 | 768, 134 | 12345, 135 | "imgs/o15.png", 136 | ], 137 | [ 138 | "imgs/i16.png", 139 | "mysteriou human, warm atmosphere, warm atmosphere, at home, bedroom", 140 | "Right Light", 141 | 512, 142 | 768, 143 | 100, 144 | "imgs/o16.png", 145 | ], 146 | ] 147 | 148 | bg_samples = [ 149 | 'imgs/bgs/1.webp', 150 | 'imgs/bgs/2.webp', 151 | 'imgs/bgs/3.webp', 152 | 'imgs/bgs/4.webp', 153 | 'imgs/bgs/5.webp', 154 | 'imgs/bgs/6.webp', 155 | 'imgs/bgs/7.webp', 156 | 'imgs/bgs/8.webp', 157 | 'imgs/bgs/9.webp', 158 | 'imgs/bgs/10.webp', 159 | 'imgs/bgs/11.png', 160 | 'imgs/bgs/12.png', 161 | 'imgs/bgs/13.png', 162 | 'imgs/bgs/14.png', 163 | 'imgs/bgs/15.png', 164 | ] 165 | 166 | background_conditioned_examples = [ 167 | [ 168 | "imgs/alter/i3.png", 169 | "imgs/bgs/7.webp", 170 | "beautiful woman, cinematic lighting", 171 | "Use Background Image", 172 | 512, 173 | 768, 174 | 12345, 175 | "imgs/alter/o1.png", 176 | ], 177 | [ 178 | "imgs/alter/i2.png", 179 | "imgs/bgs/11.png", 180 | "statue of an angel, natural lighting", 181 | "Use Flipped Background Image", 182 | 512, 183 | 768, 184 | 12345, 185 | "imgs/alter/o2.png", 186 | ], 187 | [ 188 | "imgs/alter/i1.jpeg", 189 | "imgs/bgs/2.webp", 190 | "beautiful woman, cinematic lighting", 191 | "Use Background Image", 192 | 512, 193 | 768, 194 | 12345, 195 | "imgs/alter/o3.png", 196 | ], 197 | [ 198 | "imgs/alter/i1.jpeg", 199 | "imgs/bgs/3.webp", 200 | "beautiful woman, cinematic lighting", 201 | "Use Background Image", 202 | 512, 203 | 768, 204 | 12345, 205 | "imgs/alter/o4.png", 206 | ], 207 | [ 208 | "imgs/alter/i6.webp", 209 | "imgs/bgs/15.png", 210 | "handsome man, cinematic lighting", 211 | "Use Background Image", 212 | 512, 213 | 768, 214 | 12345, 215 | "imgs/alter/o5.png", 216 | ], 217 | ] 218 | -------------------------------------------------------------------------------- /gradio_demo.py: -------------------------------------------------------------------------------- 1 | import os 2 | import math 3 | import gradio as gr 4 | import numpy as np 5 | import torch 6 | import safetensors.torch as sf 7 | import db_examples 8 | 9 | from PIL import Image 10 | from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline 11 | from diffusers import AutoencoderKL, UNet2DConditionModel, DDIMScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler 12 | from diffusers.models.attention_processor import AttnProcessor2_0 13 | from transformers import CLIPTextModel, CLIPTokenizer 14 | from briarmbg import BriaRMBG 15 | from enum import Enum 16 | from torch.hub import download_url_to_file 17 | 18 | 19 | # 'stablediffusionapi/realistic-vision-v51' 20 | # 'runwayml/stable-diffusion-v1-5' 21 | sd15_name = 'stablediffusionapi/realistic-vision-v51' 22 | tokenizer = CLIPTokenizer.from_pretrained(sd15_name, subfolder="tokenizer") 23 | text_encoder = CLIPTextModel.from_pretrained(sd15_name, subfolder="text_encoder") 24 | vae = AutoencoderKL.from_pretrained(sd15_name, subfolder="vae") 25 | unet = UNet2DConditionModel.from_pretrained(sd15_name, subfolder="unet") 26 | rmbg = BriaRMBG.from_pretrained("briaai/RMBG-1.4") 27 | 28 | # Change UNet 29 | 30 | with torch.no_grad(): 31 | new_conv_in = torch.nn.Conv2d(8, unet.conv_in.out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding) 32 | new_conv_in.weight.zero_() 33 | new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight) 34 | new_conv_in.bias = unet.conv_in.bias 35 | unet.conv_in = new_conv_in 36 | 37 | unet_original_forward = unet.forward 38 | 39 | 40 | def hooked_unet_forward(sample, timestep, encoder_hidden_states, **kwargs): 41 | c_concat = kwargs['cross_attention_kwargs']['concat_conds'].to(sample) 42 | c_concat = torch.cat([c_concat] * (sample.shape[0] // c_concat.shape[0]), dim=0) 43 | new_sample = torch.cat([sample, c_concat], dim=1) 44 | kwargs['cross_attention_kwargs'] = {} 45 | return unet_original_forward(new_sample, timestep, encoder_hidden_states, **kwargs) 46 | 47 | 48 | unet.forward = hooked_unet_forward 49 | 50 | # Load 51 | 52 | model_path = './models/iclight_sd15_fc.safetensors' 53 | 54 | if not os.path.exists(model_path): 55 | download_url_to_file(url='https://huggingface.co/lllyasviel/ic-light/resolve/main/iclight_sd15_fc.safetensors', dst=model_path) 56 | 57 | sd_offset = sf.load_file(model_path) 58 | sd_origin = unet.state_dict() 59 | keys = sd_origin.keys() 60 | sd_merged = {k: sd_origin[k] + sd_offset[k] for k in sd_origin.keys()} 61 | unet.load_state_dict(sd_merged, strict=True) 62 | del sd_offset, sd_origin, sd_merged, keys 63 | 64 | # Device 65 | 66 | device = torch.device('cuda') 67 | text_encoder = text_encoder.to(device=device, dtype=torch.float16) 68 | vae = vae.to(device=device, dtype=torch.bfloat16) 69 | unet = unet.to(device=device, dtype=torch.float16) 70 | rmbg = rmbg.to(device=device, dtype=torch.float32) 71 | 72 | # SDP 73 | 74 | unet.set_attn_processor(AttnProcessor2_0()) 75 | vae.set_attn_processor(AttnProcessor2_0()) 76 | 77 | # Samplers 78 | 79 | ddim_scheduler = DDIMScheduler( 80 | num_train_timesteps=1000, 81 | beta_start=0.00085, 82 | beta_end=0.012, 83 | beta_schedule="scaled_linear", 84 | clip_sample=False, 85 | set_alpha_to_one=False, 86 | steps_offset=1, 87 | ) 88 | 89 | euler_a_scheduler = EulerAncestralDiscreteScheduler( 90 | num_train_timesteps=1000, 91 | beta_start=0.00085, 92 | beta_end=0.012, 93 | steps_offset=1 94 | ) 95 | 96 | dpmpp_2m_sde_karras_scheduler = DPMSolverMultistepScheduler( 97 | num_train_timesteps=1000, 98 | beta_start=0.00085, 99 | beta_end=0.012, 100 | algorithm_type="sde-dpmsolver++", 101 | use_karras_sigmas=True, 102 | steps_offset=1 103 | ) 104 | 105 | # Pipelines 106 | 107 | t2i_pipe = StableDiffusionPipeline( 108 | vae=vae, 109 | text_encoder=text_encoder, 110 | tokenizer=tokenizer, 111 | unet=unet, 112 | scheduler=dpmpp_2m_sde_karras_scheduler, 113 | safety_checker=None, 114 | requires_safety_checker=False, 115 | feature_extractor=None, 116 | image_encoder=None 117 | ) 118 | 119 | i2i_pipe = StableDiffusionImg2ImgPipeline( 120 | vae=vae, 121 | text_encoder=text_encoder, 122 | tokenizer=tokenizer, 123 | unet=unet, 124 | scheduler=dpmpp_2m_sde_karras_scheduler, 125 | safety_checker=None, 126 | requires_safety_checker=False, 127 | feature_extractor=None, 128 | image_encoder=None 129 | ) 130 | 131 | 132 | @torch.inference_mode() 133 | def encode_prompt_inner(txt: str): 134 | max_length = tokenizer.model_max_length 135 | chunk_length = tokenizer.model_max_length - 2 136 | id_start = tokenizer.bos_token_id 137 | id_end = tokenizer.eos_token_id 138 | id_pad = id_end 139 | 140 | def pad(x, p, i): 141 | return x[:i] if len(x) >= i else x + [p] * (i - len(x)) 142 | 143 | tokens = tokenizer(txt, truncation=False, add_special_tokens=False)["input_ids"] 144 | chunks = [[id_start] + tokens[i: i + chunk_length] + [id_end] for i in range(0, len(tokens), chunk_length)] 145 | chunks = [pad(ck, id_pad, max_length) for ck in chunks] 146 | 147 | token_ids = torch.tensor(chunks).to(device=device, dtype=torch.int64) 148 | conds = text_encoder(token_ids).last_hidden_state 149 | 150 | return conds 151 | 152 | 153 | @torch.inference_mode() 154 | def encode_prompt_pair(positive_prompt, negative_prompt): 155 | c = encode_prompt_inner(positive_prompt) 156 | uc = encode_prompt_inner(negative_prompt) 157 | 158 | c_len = float(len(c)) 159 | uc_len = float(len(uc)) 160 | max_count = max(c_len, uc_len) 161 | c_repeat = int(math.ceil(max_count / c_len)) 162 | uc_repeat = int(math.ceil(max_count / uc_len)) 163 | max_chunk = max(len(c), len(uc)) 164 | 165 | c = torch.cat([c] * c_repeat, dim=0)[:max_chunk] 166 | uc = torch.cat([uc] * uc_repeat, dim=0)[:max_chunk] 167 | 168 | c = torch.cat([p[None, ...] for p in c], dim=1) 169 | uc = torch.cat([p[None, ...] for p in uc], dim=1) 170 | 171 | return c, uc 172 | 173 | 174 | @torch.inference_mode() 175 | def pytorch2numpy(imgs, quant=True): 176 | results = [] 177 | for x in imgs: 178 | y = x.movedim(0, -1) 179 | 180 | if quant: 181 | y = y * 127.5 + 127.5 182 | y = y.detach().float().cpu().numpy().clip(0, 255).astype(np.uint8) 183 | else: 184 | y = y * 0.5 + 0.5 185 | y = y.detach().float().cpu().numpy().clip(0, 1).astype(np.float32) 186 | 187 | results.append(y) 188 | return results 189 | 190 | 191 | @torch.inference_mode() 192 | def numpy2pytorch(imgs): 193 | h = torch.from_numpy(np.stack(imgs, axis=0)).float() / 127.0 - 1.0 # so that 127 must be strictly 0.0 194 | h = h.movedim(-1, 1) 195 | return h 196 | 197 | 198 | def resize_and_center_crop(image, target_width, target_height): 199 | pil_image = Image.fromarray(image) 200 | original_width, original_height = pil_image.size 201 | scale_factor = max(target_width / original_width, target_height / original_height) 202 | resized_width = int(round(original_width * scale_factor)) 203 | resized_height = int(round(original_height * scale_factor)) 204 | resized_image = pil_image.resize((resized_width, resized_height), Image.LANCZOS) 205 | left = (resized_width - target_width) / 2 206 | top = (resized_height - target_height) / 2 207 | right = (resized_width + target_width) / 2 208 | bottom = (resized_height + target_height) / 2 209 | cropped_image = resized_image.crop((left, top, right, bottom)) 210 | return np.array(cropped_image) 211 | 212 | 213 | def resize_without_crop(image, target_width, target_height): 214 | pil_image = Image.fromarray(image) 215 | resized_image = pil_image.resize((target_width, target_height), Image.LANCZOS) 216 | return np.array(resized_image) 217 | 218 | 219 | @torch.inference_mode() 220 | def run_rmbg(img, sigma=0.0): 221 | H, W, C = img.shape 222 | assert C == 3 223 | k = (256.0 / float(H * W)) ** 0.5 224 | feed = resize_without_crop(img, int(64 * round(W * k)), int(64 * round(H * k))) 225 | feed = numpy2pytorch([feed]).to(device=device, dtype=torch.float32) 226 | alpha = rmbg(feed)[0][0] 227 | alpha = torch.nn.functional.interpolate(alpha, size=(H, W), mode="bilinear") 228 | alpha = alpha.movedim(1, -1)[0] 229 | alpha = alpha.detach().float().cpu().numpy().clip(0, 1) 230 | result = 127 + (img.astype(np.float32) - 127 + sigma) * alpha 231 | return result.clip(0, 255).astype(np.uint8), alpha 232 | 233 | 234 | @torch.inference_mode() 235 | def process(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source): 236 | bg_source = BGSource(bg_source) 237 | input_bg = None 238 | 239 | if bg_source == BGSource.NONE: 240 | pass 241 | elif bg_source == BGSource.LEFT: 242 | gradient = np.linspace(255, 0, image_width) 243 | image = np.tile(gradient, (image_height, 1)) 244 | input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8) 245 | elif bg_source == BGSource.RIGHT: 246 | gradient = np.linspace(0, 255, image_width) 247 | image = np.tile(gradient, (image_height, 1)) 248 | input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8) 249 | elif bg_source == BGSource.TOP: 250 | gradient = np.linspace(255, 0, image_height)[:, None] 251 | image = np.tile(gradient, (1, image_width)) 252 | input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8) 253 | elif bg_source == BGSource.BOTTOM: 254 | gradient = np.linspace(0, 255, image_height)[:, None] 255 | image = np.tile(gradient, (1, image_width)) 256 | input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8) 257 | else: 258 | raise 'Wrong initial latent!' 259 | 260 | rng = torch.Generator(device=device).manual_seed(int(seed)) 261 | 262 | fg = resize_and_center_crop(input_fg, image_width, image_height) 263 | 264 | concat_conds = numpy2pytorch([fg]).to(device=vae.device, dtype=vae.dtype) 265 | concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor 266 | 267 | conds, unconds = encode_prompt_pair(positive_prompt=prompt + ', ' + a_prompt, negative_prompt=n_prompt) 268 | 269 | if input_bg is None: 270 | latents = t2i_pipe( 271 | prompt_embeds=conds, 272 | negative_prompt_embeds=unconds, 273 | width=image_width, 274 | height=image_height, 275 | num_inference_steps=steps, 276 | num_images_per_prompt=num_samples, 277 | generator=rng, 278 | output_type='latent', 279 | guidance_scale=cfg, 280 | cross_attention_kwargs={'concat_conds': concat_conds}, 281 | ).images.to(vae.dtype) / vae.config.scaling_factor 282 | else: 283 | bg = resize_and_center_crop(input_bg, image_width, image_height) 284 | bg_latent = numpy2pytorch([bg]).to(device=vae.device, dtype=vae.dtype) 285 | bg_latent = vae.encode(bg_latent).latent_dist.mode() * vae.config.scaling_factor 286 | latents = i2i_pipe( 287 | image=bg_latent, 288 | strength=lowres_denoise, 289 | prompt_embeds=conds, 290 | negative_prompt_embeds=unconds, 291 | width=image_width, 292 | height=image_height, 293 | num_inference_steps=int(round(steps / lowres_denoise)), 294 | num_images_per_prompt=num_samples, 295 | generator=rng, 296 | output_type='latent', 297 | guidance_scale=cfg, 298 | cross_attention_kwargs={'concat_conds': concat_conds}, 299 | ).images.to(vae.dtype) / vae.config.scaling_factor 300 | 301 | pixels = vae.decode(latents).sample 302 | pixels = pytorch2numpy(pixels) 303 | pixels = [resize_without_crop( 304 | image=p, 305 | target_width=int(round(image_width * highres_scale / 64.0) * 64), 306 | target_height=int(round(image_height * highres_scale / 64.0) * 64)) 307 | for p in pixels] 308 | 309 | pixels = numpy2pytorch(pixels).to(device=vae.device, dtype=vae.dtype) 310 | latents = vae.encode(pixels).latent_dist.mode() * vae.config.scaling_factor 311 | latents = latents.to(device=unet.device, dtype=unet.dtype) 312 | 313 | image_height, image_width = latents.shape[2] * 8, latents.shape[3] * 8 314 | 315 | fg = resize_and_center_crop(input_fg, image_width, image_height) 316 | concat_conds = numpy2pytorch([fg]).to(device=vae.device, dtype=vae.dtype) 317 | concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor 318 | 319 | latents = i2i_pipe( 320 | image=latents, 321 | strength=highres_denoise, 322 | prompt_embeds=conds, 323 | negative_prompt_embeds=unconds, 324 | width=image_width, 325 | height=image_height, 326 | num_inference_steps=int(round(steps / highres_denoise)), 327 | num_images_per_prompt=num_samples, 328 | generator=rng, 329 | output_type='latent', 330 | guidance_scale=cfg, 331 | cross_attention_kwargs={'concat_conds': concat_conds}, 332 | ).images.to(vae.dtype) / vae.config.scaling_factor 333 | 334 | pixels = vae.decode(latents).sample 335 | 336 | return pytorch2numpy(pixels) 337 | 338 | 339 | @torch.inference_mode() 340 | def process_relight(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source): 341 | input_fg, matting = run_rmbg(input_fg) 342 | results = process(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source) 343 | return input_fg, results 344 | 345 | 346 | quick_prompts = [ 347 | 'sunshine from window', 348 | 'neon light, city', 349 | 'sunset over sea', 350 | 'golden time', 351 | 'sci-fi RGB glowing, cyberpunk', 352 | 'natural lighting', 353 | 'warm atmosphere, at home, bedroom', 354 | 'magic lit', 355 | 'evil, gothic, Yharnam', 356 | 'light and shadow', 357 | 'shadow from window', 358 | 'soft studio lighting', 359 | 'home atmosphere, cozy bedroom illumination', 360 | 'neon, Wong Kar-wai, warm' 361 | ] 362 | quick_prompts = [[x] for x in quick_prompts] 363 | 364 | 365 | quick_subjects = [ 366 | 'beautiful woman, detailed face', 367 | 'handsome man, detailed face', 368 | ] 369 | quick_subjects = [[x] for x in quick_subjects] 370 | 371 | 372 | class BGSource(Enum): 373 | NONE = "None" 374 | LEFT = "Left Light" 375 | RIGHT = "Right Light" 376 | TOP = "Top Light" 377 | BOTTOM = "Bottom Light" 378 | 379 | 380 | block = gr.Blocks().queue() 381 | with block: 382 | with gr.Row(): 383 | gr.Markdown("## IC-Light (Relighting with Foreground Condition)") 384 | with gr.Row(): 385 | with gr.Column(): 386 | with gr.Row(): 387 | input_fg = gr.Image(source='upload', type="numpy", label="Image", height=480) 388 | output_bg = gr.Image(type="numpy", label="Preprocessed Foreground", height=480) 389 | prompt = gr.Textbox(label="Prompt") 390 | bg_source = gr.Radio(choices=[e.value for e in BGSource], 391 | value=BGSource.NONE.value, 392 | label="Lighting Preference (Initial Latent)", type='value') 393 | example_quick_subjects = gr.Dataset(samples=quick_subjects, label='Subject Quick List', samples_per_page=1000, components=[prompt]) 394 | example_quick_prompts = gr.Dataset(samples=quick_prompts, label='Lighting Quick List', samples_per_page=1000, components=[prompt]) 395 | relight_button = gr.Button(value="Relight") 396 | 397 | with gr.Group(): 398 | with gr.Row(): 399 | num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1) 400 | seed = gr.Number(label="Seed", value=12345, precision=0) 401 | 402 | with gr.Row(): 403 | image_width = gr.Slider(label="Image Width", minimum=256, maximum=1024, value=512, step=64) 404 | image_height = gr.Slider(label="Image Height", minimum=256, maximum=1024, value=640, step=64) 405 | 406 | with gr.Accordion("Advanced options", open=False): 407 | steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=25, step=1) 408 | cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=2, step=0.01) 409 | lowres_denoise = gr.Slider(label="Lowres Denoise (for initial latent)", minimum=0.1, maximum=1.0, value=0.9, step=0.01) 410 | highres_scale = gr.Slider(label="Highres Scale", minimum=1.0, maximum=3.0, value=1.5, step=0.01) 411 | highres_denoise = gr.Slider(label="Highres Denoise", minimum=0.1, maximum=1.0, value=0.5, step=0.01) 412 | a_prompt = gr.Textbox(label="Added Prompt", value='best quality') 413 | n_prompt = gr.Textbox(label="Negative Prompt", value='lowres, bad anatomy, bad hands, cropped, worst quality') 414 | with gr.Column(): 415 | result_gallery = gr.Gallery(height=832, object_fit='contain', label='Outputs') 416 | with gr.Row(): 417 | dummy_image_for_outputs = gr.Image(visible=False, label='Result') 418 | gr.Examples( 419 | fn=lambda *args: ([args[-1]], None), 420 | examples=db_examples.foreground_conditioned_examples, 421 | inputs=[ 422 | input_fg, prompt, bg_source, image_width, image_height, seed, dummy_image_for_outputs 423 | ], 424 | outputs=[result_gallery, output_bg], 425 | run_on_click=True, examples_per_page=1024 426 | ) 427 | ips = [input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source] 428 | relight_button.click(fn=process_relight, inputs=ips, outputs=[output_bg, result_gallery]) 429 | example_quick_prompts.click(lambda x, y: ', '.join(y.split(', ')[:2] + [x[0]]), inputs=[example_quick_prompts, prompt], outputs=prompt, show_progress=False, queue=False) 430 | example_quick_subjects.click(lambda x: x[0], inputs=example_quick_subjects, outputs=prompt, show_progress=False, queue=False) 431 | 432 | 433 | block.launch(server_name='0.0.0.0') 434 | -------------------------------------------------------------------------------- /gradio_demo_bg.py: -------------------------------------------------------------------------------- 1 | import os 2 | import math 3 | import gradio as gr 4 | import numpy as np 5 | import torch 6 | import safetensors.torch as sf 7 | import db_examples 8 | 9 | from PIL import Image 10 | from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline 11 | from diffusers import AutoencoderKL, UNet2DConditionModel, DDIMScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler 12 | from diffusers.models.attention_processor import AttnProcessor2_0 13 | from transformers import CLIPTextModel, CLIPTokenizer 14 | from briarmbg import BriaRMBG 15 | from enum import Enum 16 | from torch.hub import download_url_to_file 17 | 18 | 19 | # 'stablediffusionapi/realistic-vision-v51' 20 | # 'runwayml/stable-diffusion-v1-5' 21 | sd15_name = 'stablediffusionapi/realistic-vision-v51' 22 | tokenizer = CLIPTokenizer.from_pretrained(sd15_name, subfolder="tokenizer") 23 | text_encoder = CLIPTextModel.from_pretrained(sd15_name, subfolder="text_encoder") 24 | vae = AutoencoderKL.from_pretrained(sd15_name, subfolder="vae") 25 | unet = UNet2DConditionModel.from_pretrained(sd15_name, subfolder="unet") 26 | rmbg = BriaRMBG.from_pretrained("briaai/RMBG-1.4") 27 | 28 | # Change UNet 29 | 30 | with torch.no_grad(): 31 | new_conv_in = torch.nn.Conv2d(12, unet.conv_in.out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding) 32 | new_conv_in.weight.zero_() 33 | new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight) 34 | new_conv_in.bias = unet.conv_in.bias 35 | unet.conv_in = new_conv_in 36 | 37 | unet_original_forward = unet.forward 38 | 39 | 40 | def hooked_unet_forward(sample, timestep, encoder_hidden_states, **kwargs): 41 | c_concat = kwargs['cross_attention_kwargs']['concat_conds'].to(sample) 42 | c_concat = torch.cat([c_concat] * (sample.shape[0] // c_concat.shape[0]), dim=0) 43 | new_sample = torch.cat([sample, c_concat], dim=1) 44 | kwargs['cross_attention_kwargs'] = {} 45 | return unet_original_forward(new_sample, timestep, encoder_hidden_states, **kwargs) 46 | 47 | 48 | unet.forward = hooked_unet_forward 49 | 50 | # Load 51 | 52 | model_path = './models/iclight_sd15_fbc.safetensors' 53 | 54 | if not os.path.exists(model_path): 55 | download_url_to_file(url='https://huggingface.co/lllyasviel/ic-light/resolve/main/iclight_sd15_fbc.safetensors', dst=model_path) 56 | 57 | sd_offset = sf.load_file(model_path) 58 | sd_origin = unet.state_dict() 59 | keys = sd_origin.keys() 60 | sd_merged = {k: sd_origin[k] + sd_offset[k] for k in sd_origin.keys()} 61 | unet.load_state_dict(sd_merged, strict=True) 62 | del sd_offset, sd_origin, sd_merged, keys 63 | 64 | # Device 65 | 66 | device = torch.device('cuda') 67 | text_encoder = text_encoder.to(device=device, dtype=torch.float16) 68 | vae = vae.to(device=device, dtype=torch.bfloat16) 69 | unet = unet.to(device=device, dtype=torch.float16) 70 | rmbg = rmbg.to(device=device, dtype=torch.float32) 71 | 72 | # SDP 73 | 74 | unet.set_attn_processor(AttnProcessor2_0()) 75 | vae.set_attn_processor(AttnProcessor2_0()) 76 | 77 | # Samplers 78 | 79 | ddim_scheduler = DDIMScheduler( 80 | num_train_timesteps=1000, 81 | beta_start=0.00085, 82 | beta_end=0.012, 83 | beta_schedule="scaled_linear", 84 | clip_sample=False, 85 | set_alpha_to_one=False, 86 | steps_offset=1, 87 | ) 88 | 89 | euler_a_scheduler = EulerAncestralDiscreteScheduler( 90 | num_train_timesteps=1000, 91 | beta_start=0.00085, 92 | beta_end=0.012, 93 | steps_offset=1 94 | ) 95 | 96 | dpmpp_2m_sde_karras_scheduler = DPMSolverMultistepScheduler( 97 | num_train_timesteps=1000, 98 | beta_start=0.00085, 99 | beta_end=0.012, 100 | algorithm_type="sde-dpmsolver++", 101 | use_karras_sigmas=True, 102 | steps_offset=1 103 | ) 104 | 105 | # Pipelines 106 | 107 | t2i_pipe = StableDiffusionPipeline( 108 | vae=vae, 109 | text_encoder=text_encoder, 110 | tokenizer=tokenizer, 111 | unet=unet, 112 | scheduler=dpmpp_2m_sde_karras_scheduler, 113 | safety_checker=None, 114 | requires_safety_checker=False, 115 | feature_extractor=None, 116 | image_encoder=None 117 | ) 118 | 119 | i2i_pipe = StableDiffusionImg2ImgPipeline( 120 | vae=vae, 121 | text_encoder=text_encoder, 122 | tokenizer=tokenizer, 123 | unet=unet, 124 | scheduler=dpmpp_2m_sde_karras_scheduler, 125 | safety_checker=None, 126 | requires_safety_checker=False, 127 | feature_extractor=None, 128 | image_encoder=None 129 | ) 130 | 131 | 132 | @torch.inference_mode() 133 | def encode_prompt_inner(txt: str): 134 | max_length = tokenizer.model_max_length 135 | chunk_length = tokenizer.model_max_length - 2 136 | id_start = tokenizer.bos_token_id 137 | id_end = tokenizer.eos_token_id 138 | id_pad = id_end 139 | 140 | def pad(x, p, i): 141 | return x[:i] if len(x) >= i else x + [p] * (i - len(x)) 142 | 143 | tokens = tokenizer(txt, truncation=False, add_special_tokens=False)["input_ids"] 144 | chunks = [[id_start] + tokens[i: i + chunk_length] + [id_end] for i in range(0, len(tokens), chunk_length)] 145 | chunks = [pad(ck, id_pad, max_length) for ck in chunks] 146 | 147 | token_ids = torch.tensor(chunks).to(device=device, dtype=torch.int64) 148 | conds = text_encoder(token_ids).last_hidden_state 149 | 150 | return conds 151 | 152 | 153 | @torch.inference_mode() 154 | def encode_prompt_pair(positive_prompt, negative_prompt): 155 | c = encode_prompt_inner(positive_prompt) 156 | uc = encode_prompt_inner(negative_prompt) 157 | 158 | c_len = float(len(c)) 159 | uc_len = float(len(uc)) 160 | max_count = max(c_len, uc_len) 161 | c_repeat = int(math.ceil(max_count / c_len)) 162 | uc_repeat = int(math.ceil(max_count / uc_len)) 163 | max_chunk = max(len(c), len(uc)) 164 | 165 | c = torch.cat([c] * c_repeat, dim=0)[:max_chunk] 166 | uc = torch.cat([uc] * uc_repeat, dim=0)[:max_chunk] 167 | 168 | c = torch.cat([p[None, ...] for p in c], dim=1) 169 | uc = torch.cat([p[None, ...] for p in uc], dim=1) 170 | 171 | return c, uc 172 | 173 | 174 | @torch.inference_mode() 175 | def pytorch2numpy(imgs, quant=True): 176 | results = [] 177 | for x in imgs: 178 | y = x.movedim(0, -1) 179 | 180 | if quant: 181 | y = y * 127.5 + 127.5 182 | y = y.detach().float().cpu().numpy().clip(0, 255).astype(np.uint8) 183 | else: 184 | y = y * 0.5 + 0.5 185 | y = y.detach().float().cpu().numpy().clip(0, 1).astype(np.float32) 186 | 187 | results.append(y) 188 | return results 189 | 190 | 191 | @torch.inference_mode() 192 | def numpy2pytorch(imgs): 193 | h = torch.from_numpy(np.stack(imgs, axis=0)).float() / 127.0 - 1.0 # so that 127 must be strictly 0.0 194 | h = h.movedim(-1, 1) 195 | return h 196 | 197 | 198 | def resize_and_center_crop(image, target_width, target_height): 199 | pil_image = Image.fromarray(image) 200 | original_width, original_height = pil_image.size 201 | scale_factor = max(target_width / original_width, target_height / original_height) 202 | resized_width = int(round(original_width * scale_factor)) 203 | resized_height = int(round(original_height * scale_factor)) 204 | resized_image = pil_image.resize((resized_width, resized_height), Image.LANCZOS) 205 | left = (resized_width - target_width) / 2 206 | top = (resized_height - target_height) / 2 207 | right = (resized_width + target_width) / 2 208 | bottom = (resized_height + target_height) / 2 209 | cropped_image = resized_image.crop((left, top, right, bottom)) 210 | return np.array(cropped_image) 211 | 212 | 213 | def resize_without_crop(image, target_width, target_height): 214 | pil_image = Image.fromarray(image) 215 | resized_image = pil_image.resize((target_width, target_height), Image.LANCZOS) 216 | return np.array(resized_image) 217 | 218 | 219 | @torch.inference_mode() 220 | def run_rmbg(img, sigma=0.0): 221 | H, W, C = img.shape 222 | assert C == 3 223 | k = (256.0 / float(H * W)) ** 0.5 224 | feed = resize_without_crop(img, int(64 * round(W * k)), int(64 * round(H * k))) 225 | feed = numpy2pytorch([feed]).to(device=device, dtype=torch.float32) 226 | alpha = rmbg(feed)[0][0] 227 | alpha = torch.nn.functional.interpolate(alpha, size=(H, W), mode="bilinear") 228 | alpha = alpha.movedim(1, -1)[0] 229 | alpha = alpha.detach().float().cpu().numpy().clip(0, 1) 230 | result = 127 + (img.astype(np.float32) - 127 + sigma) * alpha 231 | return result.clip(0, 255).astype(np.uint8), alpha 232 | 233 | 234 | @torch.inference_mode() 235 | def process(input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source): 236 | bg_source = BGSource(bg_source) 237 | 238 | if bg_source == BGSource.UPLOAD: 239 | pass 240 | elif bg_source == BGSource.UPLOAD_FLIP: 241 | input_bg = np.fliplr(input_bg) 242 | elif bg_source == BGSource.GREY: 243 | input_bg = np.zeros(shape=(image_height, image_width, 3), dtype=np.uint8) + 64 244 | elif bg_source == BGSource.LEFT: 245 | gradient = np.linspace(224, 32, image_width) 246 | image = np.tile(gradient, (image_height, 1)) 247 | input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8) 248 | elif bg_source == BGSource.RIGHT: 249 | gradient = np.linspace(32, 224, image_width) 250 | image = np.tile(gradient, (image_height, 1)) 251 | input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8) 252 | elif bg_source == BGSource.TOP: 253 | gradient = np.linspace(224, 32, image_height)[:, None] 254 | image = np.tile(gradient, (1, image_width)) 255 | input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8) 256 | elif bg_source == BGSource.BOTTOM: 257 | gradient = np.linspace(32, 224, image_height)[:, None] 258 | image = np.tile(gradient, (1, image_width)) 259 | input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8) 260 | else: 261 | raise 'Wrong background source!' 262 | 263 | rng = torch.Generator(device=device).manual_seed(seed) 264 | 265 | fg = resize_and_center_crop(input_fg, image_width, image_height) 266 | bg = resize_and_center_crop(input_bg, image_width, image_height) 267 | concat_conds = numpy2pytorch([fg, bg]).to(device=vae.device, dtype=vae.dtype) 268 | concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor 269 | concat_conds = torch.cat([c[None, ...] for c in concat_conds], dim=1) 270 | 271 | conds, unconds = encode_prompt_pair(positive_prompt=prompt + ', ' + a_prompt, negative_prompt=n_prompt) 272 | 273 | latents = t2i_pipe( 274 | prompt_embeds=conds, 275 | negative_prompt_embeds=unconds, 276 | width=image_width, 277 | height=image_height, 278 | num_inference_steps=steps, 279 | num_images_per_prompt=num_samples, 280 | generator=rng, 281 | output_type='latent', 282 | guidance_scale=cfg, 283 | cross_attention_kwargs={'concat_conds': concat_conds}, 284 | ).images.to(vae.dtype) / vae.config.scaling_factor 285 | 286 | pixels = vae.decode(latents).sample 287 | pixels = pytorch2numpy(pixels) 288 | pixels = [resize_without_crop( 289 | image=p, 290 | target_width=int(round(image_width * highres_scale / 64.0) * 64), 291 | target_height=int(round(image_height * highres_scale / 64.0) * 64)) 292 | for p in pixels] 293 | 294 | pixels = numpy2pytorch(pixels).to(device=vae.device, dtype=vae.dtype) 295 | latents = vae.encode(pixels).latent_dist.mode() * vae.config.scaling_factor 296 | latents = latents.to(device=unet.device, dtype=unet.dtype) 297 | 298 | image_height, image_width = latents.shape[2] * 8, latents.shape[3] * 8 299 | fg = resize_and_center_crop(input_fg, image_width, image_height) 300 | bg = resize_and_center_crop(input_bg, image_width, image_height) 301 | concat_conds = numpy2pytorch([fg, bg]).to(device=vae.device, dtype=vae.dtype) 302 | concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor 303 | concat_conds = torch.cat([c[None, ...] for c in concat_conds], dim=1) 304 | 305 | latents = i2i_pipe( 306 | image=latents, 307 | strength=highres_denoise, 308 | prompt_embeds=conds, 309 | negative_prompt_embeds=unconds, 310 | width=image_width, 311 | height=image_height, 312 | num_inference_steps=int(round(steps / highres_denoise)), 313 | num_images_per_prompt=num_samples, 314 | generator=rng, 315 | output_type='latent', 316 | guidance_scale=cfg, 317 | cross_attention_kwargs={'concat_conds': concat_conds}, 318 | ).images.to(vae.dtype) / vae.config.scaling_factor 319 | 320 | pixels = vae.decode(latents).sample 321 | pixels = pytorch2numpy(pixels, quant=False) 322 | 323 | return pixels, [fg, bg] 324 | 325 | 326 | @torch.inference_mode() 327 | def process_relight(input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source): 328 | input_fg, matting = run_rmbg(input_fg) 329 | results, extra_images = process(input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source) 330 | results = [(x * 255.0).clip(0, 255).astype(np.uint8) for x in results] 331 | return results + extra_images 332 | 333 | 334 | @torch.inference_mode() 335 | def process_normal(input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source): 336 | input_fg, matting = run_rmbg(input_fg, sigma=16) 337 | 338 | print('left ...') 339 | left = process(input_fg, input_bg, prompt, image_width, image_height, 1, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, BGSource.LEFT.value)[0][0] 340 | 341 | print('right ...') 342 | right = process(input_fg, input_bg, prompt, image_width, image_height, 1, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, BGSource.RIGHT.value)[0][0] 343 | 344 | print('bottom ...') 345 | bottom = process(input_fg, input_bg, prompt, image_width, image_height, 1, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, BGSource.BOTTOM.value)[0][0] 346 | 347 | print('top ...') 348 | top = process(input_fg, input_bg, prompt, image_width, image_height, 1, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, BGSource.TOP.value)[0][0] 349 | 350 | inner_results = [left * 2.0 - 1.0, right * 2.0 - 1.0, bottom * 2.0 - 1.0, top * 2.0 - 1.0] 351 | 352 | ambient = (left + right + bottom + top) / 4.0 353 | h, w, _ = ambient.shape 354 | matting = resize_and_center_crop((matting[..., 0] * 255.0).clip(0, 255).astype(np.uint8), w, h).astype(np.float32)[..., None] / 255.0 355 | 356 | def safa_divide(a, b): 357 | e = 1e-5 358 | return ((a + e) / (b + e)) - 1.0 359 | 360 | left = safa_divide(left, ambient) 361 | right = safa_divide(right, ambient) 362 | bottom = safa_divide(bottom, ambient) 363 | top = safa_divide(top, ambient) 364 | 365 | u = (right - left) * 0.5 366 | v = (top - bottom) * 0.5 367 | 368 | sigma = 10.0 369 | u = np.mean(u, axis=2) 370 | v = np.mean(v, axis=2) 371 | h = (1.0 - u ** 2.0 - v ** 2.0).clip(0, 1e5) ** (0.5 * sigma) 372 | z = np.zeros_like(h) 373 | 374 | normal = np.stack([u, v, h], axis=2) 375 | normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5 376 | normal = normal * matting + np.stack([z, z, 1 - z], axis=2) * (1 - matting) 377 | 378 | results = [normal, left, right, bottom, top] + inner_results 379 | results = [(x * 127.5 + 127.5).clip(0, 255).astype(np.uint8) for x in results] 380 | return results 381 | 382 | 383 | quick_prompts = [ 384 | 'beautiful woman', 385 | 'handsome man', 386 | 'beautiful woman, cinematic lighting', 387 | 'handsome man, cinematic lighting', 388 | 'beautiful woman, natural lighting', 389 | 'handsome man, natural lighting', 390 | 'beautiful woman, neo punk lighting, cyberpunk', 391 | 'handsome man, neo punk lighting, cyberpunk', 392 | ] 393 | quick_prompts = [[x] for x in quick_prompts] 394 | 395 | 396 | class BGSource(Enum): 397 | UPLOAD = "Use Background Image" 398 | UPLOAD_FLIP = "Use Flipped Background Image" 399 | LEFT = "Left Light" 400 | RIGHT = "Right Light" 401 | TOP = "Top Light" 402 | BOTTOM = "Bottom Light" 403 | GREY = "Ambient" 404 | 405 | 406 | block = gr.Blocks().queue() 407 | with block: 408 | with gr.Row(): 409 | gr.Markdown("## IC-Light (Relighting with Foreground and Background Condition)") 410 | with gr.Row(): 411 | with gr.Column(): 412 | with gr.Row(): 413 | input_fg = gr.Image(source='upload', type="numpy", label="Foreground", height=480) 414 | input_bg = gr.Image(source='upload', type="numpy", label="Background", height=480) 415 | prompt = gr.Textbox(label="Prompt") 416 | bg_source = gr.Radio(choices=[e.value for e in BGSource], 417 | value=BGSource.UPLOAD.value, 418 | label="Background Source", type='value') 419 | 420 | example_prompts = gr.Dataset(samples=quick_prompts, label='Prompt Quick List', components=[prompt]) 421 | bg_gallery = gr.Gallery(height=450, object_fit='contain', label='Background Quick List', value=db_examples.bg_samples, columns=5, allow_preview=False) 422 | relight_button = gr.Button(value="Relight") 423 | 424 | with gr.Group(): 425 | with gr.Row(): 426 | num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1) 427 | seed = gr.Number(label="Seed", value=12345, precision=0) 428 | with gr.Row(): 429 | image_width = gr.Slider(label="Image Width", minimum=256, maximum=1024, value=512, step=64) 430 | image_height = gr.Slider(label="Image Height", minimum=256, maximum=1024, value=640, step=64) 431 | 432 | with gr.Accordion("Advanced options", open=False): 433 | steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1) 434 | cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=7.0, step=0.01) 435 | highres_scale = gr.Slider(label="Highres Scale", minimum=1.0, maximum=3.0, value=1.5, step=0.01) 436 | highres_denoise = gr.Slider(label="Highres Denoise", minimum=0.1, maximum=0.9, value=0.5, step=0.01) 437 | a_prompt = gr.Textbox(label="Added Prompt", value='best quality') 438 | n_prompt = gr.Textbox(label="Negative Prompt", 439 | value='lowres, bad anatomy, bad hands, cropped, worst quality') 440 | normal_button = gr.Button(value="Compute Normal (4x Slower)") 441 | with gr.Column(): 442 | result_gallery = gr.Gallery(height=832, object_fit='contain', label='Outputs') 443 | with gr.Row(): 444 | dummy_image_for_outputs = gr.Image(visible=False, label='Result') 445 | gr.Examples( 446 | fn=lambda *args: [args[-1]], 447 | examples=db_examples.background_conditioned_examples, 448 | inputs=[ 449 | input_fg, input_bg, prompt, bg_source, image_width, image_height, seed, dummy_image_for_outputs 450 | ], 451 | outputs=[result_gallery], 452 | run_on_click=True, examples_per_page=1024 453 | ) 454 | ips = [input_fg, input_bg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source] 455 | relight_button.click(fn=process_relight, inputs=ips, outputs=[result_gallery]) 456 | normal_button.click(fn=process_normal, inputs=ips, outputs=[result_gallery]) 457 | example_prompts.click(lambda x: x[0], inputs=example_prompts, 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-------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | diffusers==0.27.2 2 | transformers==4.36.2 3 | opencv-python 4 | safetensors 5 | pillow==10.2.0 6 | einops 7 | torch 8 | peft 9 | gradio==3.41.2 10 | protobuf==3.20 11 | --------------------------------------------------------------------------------