Commit 000fbec3 authored by wuxk1's avatar wuxk1
Browse files

optim for test1,2,5

parent 57b0ad8e
...@@ -5,7 +5,8 @@ __pycache__/ ...@@ -5,7 +5,8 @@ __pycache__/
!/input/example.png !/input/example.png
/models/ /models/
/temp/ /temp/
/custom_nodes/ /custom_nodes/**/*.png
/custom_nodes/*.png
!custom_nodes/example_node.py.example !custom_nodes/example_node.py.example
extra_model_paths.yaml extra_model_paths.yaml
/.vs /.vs
......
<div align="center"> ### 拉取代码
git clone http://developer.sourcefind.cn/codes/wuxk1/dcu-comui.git -b dev
# ComfyUI
**The most powerful and modular visual AI engine and application.** ### 替换权重
准备对应`custom_nodes/comfyui_controlnet_aux/ckpts`下模型权重并放置对应位置
[![Website][website-shield]][website-url] `custom_nodes/comfyui_controlnet_aux/ckpts/depth-anything/Depth-Anything-V2-Large/depth_anything_v2_vitl.pth`
[![Dynamic JSON Badge][discord-shield]][discord-url] `custom_nodes/comfyui_controlnet_aux/ckpts/hr16/DWPose-TorchScript-BatchSize5/dw-ll_ucoco_384_bs5.torchscript.pt`
[![Twitter][twitter-shield]][twitter-url] `custom_nodes/comfyui_controlnet_aux/ckpts/hr16/yolox-onnx/yolox_l.torchscript.pt`
[![Matrix][matrix-shield]][matrix-url] `custom_nodes/comfyui_controlnet_aux/ckpts/yzd-v/DWPose/dw-ll_ucoco_384.onnx`
<br> `custom_nodes/comfyui_controlnet_aux/ckpts/yzd-v/DWPose/yolox_l.onnx`
[![][github-release-shield]][github-release-link]
[![][github-release-date-shield]][github-release-link] ### 运行
[![][github-downloads-shield]][github-downloads-link] `python dcu-comui/main.py --disable-cuda-malloc --port 8195 --listen 0.0.0.0 --use-pytorch-cross-attention`
[![][github-downloads-latest-shield]][github-downloads-link] ### 测试
`python dcu-comui/workflow_test/test.py --ip 0.0.0.0 --port 8195`
[matrix-shield]: https://img.shields.io/badge/Matrix-000000?style=flat&logo=matrix&logoColor=white
[matrix-url]: https://app.element.io/#/room/%23comfyui_space%3Amatrix.org
[website-shield]: https://img.shields.io/badge/ComfyOrg-4285F4?style=flat
[website-url]: https://www.comfy.org/
<!-- Workaround to display total user from https://github.com/badges/shields/issues/4500#issuecomment-2060079995 -->
[discord-shield]: https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2Fcomfyorg%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&logo=discord&logoColor=white&label=Discord&color=green&suffix=%20total
[discord-url]: https://www.comfy.org/discord
[twitter-shield]: https://img.shields.io/twitter/follow/ComfyUI
[twitter-url]: https://x.com/ComfyUI
[github-release-shield]: https://img.shields.io/github/v/release/comfyanonymous/ComfyUI?style=flat&sort=semver
[github-release-link]: https://github.com/comfyanonymous/ComfyUI/releases
[github-release-date-shield]: https://img.shields.io/github/release-date/comfyanonymous/ComfyUI?style=flat
[github-downloads-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/total?style=flat
[github-downloads-latest-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/latest/total?style=flat&label=downloads%40latest
[github-downloads-link]: https://github.com/comfyanonymous/ComfyUI/releases
![ComfyUI Screenshot](https://github.com/user-attachments/assets/7ccaf2c1-9b72-41ae-9a89-5688c94b7abe)
</div>
ComfyUI lets you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. Available on Windows, Linux, and macOS.
## Get Started
#### [Desktop Application](https://www.comfy.org/download)
- The easiest way to get started.
- Available on Windows & macOS.
#### [Windows Portable Package](#installing)
- Get the latest commits and completely portable.
- Available on Windows.
#### [Manual Install](#manual-install-windows-linux)
Supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend).
## [Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
See what ComfyUI can do with the [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/).
## Features
- Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
- Image Models
- SD1.x, SD2.x ([unCLIP](https://comfyanonymous.github.io/ComfyUI_examples/unclip/))
- [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
- [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/)
- [SD3 and SD3.5](https://comfyanonymous.github.io/ComfyUI_examples/sd3/)
- Pixart Alpha and Sigma
- [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/)
- [HunyuanDiT](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_dit/)
- [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
- [Lumina Image 2.0](https://comfyanonymous.github.io/ComfyUI_examples/lumina2/)
- [HiDream](https://comfyanonymous.github.io/ComfyUI_examples/hidream/)
- [Qwen Image](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/)
- Image Editing Models
- [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/)
- [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model)
- [HiDream E1.1](https://comfyanonymous.github.io/ComfyUI_examples/hidream/#hidream-e11)
- [Qwen Image Edit](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/#edit-model)
- Video Models
- [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/)
- [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
- [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/)
- [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)
- [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/)
- [Wan 2.2](https://comfyanonymous.github.io/ComfyUI_examples/wan22/)
- Audio Models
- [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
- [ACE Step](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
- 3D Models
- [Hunyuan3D 2.0](https://docs.comfy.org/tutorials/3d/hunyuan3D-2)
- Asynchronous Queue system
- Many optimizations: Only re-executes the parts of the workflow that changes between executions.
- Smart memory management: can automatically run large models on GPUs with as low as 1GB vram with smart offloading.
- Works even if you don't have a GPU with: ```--cpu``` (slow)
- Can load ckpt and safetensors: All in one checkpoints or standalone diffusion models, VAEs and CLIP models.
- Safe loading of ckpt, pt, pth, etc.. files.
- Embeddings/Textual inversion
- [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
- [Hypernetworks](https://comfyanonymous.github.io/ComfyUI_examples/hypernetworks/)
- Loading full workflows (with seeds) from generated PNG, WebP and FLAC files.
- Saving/Loading workflows as Json files.
- Nodes interface can be used to create complex workflows like one for [Hires fix](https://comfyanonymous.github.io/ComfyUI_examples/2_pass_txt2img/) or much more advanced ones.
- [Area Composition](https://comfyanonymous.github.io/ComfyUI_examples/area_composition/)
- [Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/) with both regular and inpainting models.
- [ControlNet and T2I-Adapter](https://comfyanonymous.github.io/ComfyUI_examples/controlnet/)
- [Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)](https://comfyanonymous.github.io/ComfyUI_examples/upscale_models/)
- [GLIGEN](https://comfyanonymous.github.io/ComfyUI_examples/gligen/)
- [Model Merging](https://comfyanonymous.github.io/ComfyUI_examples/model_merging/)
- [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)
- Latent previews with [TAESD](#how-to-show-high-quality-previews)
- Works fully offline: core will never download anything unless you want to.
- Optional API nodes to use paid models from external providers through the online [Comfy API](https://docs.comfy.org/tutorials/api-nodes/overview).
- [Config file](extra_model_paths.yaml.example) to set the search paths for models.
Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/)
## Release Process
ComfyUI follows a weekly release cycle targeting Friday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories:
1. **[ComfyUI Core](https://github.com/comfyanonymous/ComfyUI)**
- Releases a new stable version (e.g., v0.7.0)
- Serves as the foundation for the desktop release
2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)**
- Builds a new release using the latest stable core version
3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)**
- Weekly frontend updates are merged into the core repository
- Features are frozen for the upcoming core release
- Development continues for the next release cycle
## Shortcuts
| Keybind | Explanation |
|------------------------------------|--------------------------------------------------------------------------------------------------------------------|
| `Ctrl` + `Enter` | Queue up current graph for generation |
| `Ctrl` + `Shift` + `Enter` | Queue up current graph as first for generation |
| `Ctrl` + `Alt` + `Enter` | Cancel current generation |
| `Ctrl` + `Z`/`Ctrl` + `Y` | Undo/Redo |
| `Ctrl` + `S` | Save workflow |
| `Ctrl` + `O` | Load workflow |
| `Ctrl` + `A` | Select all nodes |
| `Alt `+ `C` | Collapse/uncollapse selected nodes |
| `Ctrl` + `M` | Mute/unmute selected nodes |
| `Ctrl` + `B` | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) |
| `Delete`/`Backspace` | Delete selected nodes |
| `Ctrl` + `Backspace` | Delete the current graph |
| `Space` | Move the canvas around when held and moving the cursor |
| `Ctrl`/`Shift` + `Click` | Add clicked node to selection |
| `Ctrl` + `C`/`Ctrl` + `V` | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes) |
| `Ctrl` + `C`/`Ctrl` + `Shift` + `V` | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |
| `Shift` + `Drag` | Move multiple selected nodes at the same time |
| `Ctrl` + `D` | Load default graph |
| `Alt` + `+` | Canvas Zoom in |
| `Alt` + `-` | Canvas Zoom out |
| `Ctrl` + `Shift` + LMB + Vertical drag | Canvas Zoom in/out |
| `P` | Pin/Unpin selected nodes |
| `Ctrl` + `G` | Group selected nodes |
| `Q` | Toggle visibility of the queue |
| `H` | Toggle visibility of history |
| `R` | Refresh graph |
| `F` | Show/Hide menu |
| `.` | Fit view to selection (Whole graph when nothing is selected) |
| Double-Click LMB | Open node quick search palette |
| `Shift` + Drag | Move multiple wires at once |
| `Ctrl` + `Alt` + LMB | Disconnect all wires from clicked slot |
`Ctrl` can also be replaced with `Cmd` instead for macOS users
# Installing
## Windows Portable
There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the [releases page](https://github.com/comfyanonymous/ComfyUI/releases).
### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z)
Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
If you have trouble extracting it, right click the file -> properties -> unblock
#### How do I share models between another UI and ComfyUI?
See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor.
## [comfy-cli](https://docs.comfy.org/comfy-cli/getting-started)
You can install and start ComfyUI using comfy-cli:
```bash
pip install comfy-cli
comfy install
```
## Manual Install (Windows, Linux)
Python 3.13 is very well supported. If you have trouble with some custom node dependencies you can try 3.12
Git clone this repo.
Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
Put your VAE in: models/vae
### AMD GPUs (Linux only)
AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.4```
This is the command to install the nightly with ROCm 6.4 which might have some performance improvements:
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.4```
### Intel GPUs (Windows and Linux)
(Option 1) Intel Arc GPU users can install native PyTorch with torch.xpu support using pip. More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html)
1. To install PyTorch xpu, use the following command:
```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/xpu```
This is the command to install the Pytorch xpu nightly which might have some performance improvements:
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu```
(Option 2) Alternatively, Intel GPUs supported by Intel Extension for PyTorch (IPEX) can leverage IPEX for improved performance.
1. visit [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) for more information.
### NVIDIA
Nvidia users should install stable pytorch using this command:
```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu129```
This is the command to install pytorch nightly instead which might have performance improvements.
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu129```
#### Troubleshooting
If you get the "Torch not compiled with CUDA enabled" error, uninstall torch with:
```pip uninstall torch```
And install it again with the command above.
### Dependencies
Install the dependencies by opening your terminal inside the ComfyUI folder and:
```pip install -r requirements.txt```
After this you should have everything installed and can proceed to running ComfyUI.
### Others:
#### Apple Mac silicon
You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version.
1. Install pytorch nightly. For instructions, read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide (make sure to install the latest pytorch nightly).
1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux.
1. Install the ComfyUI [dependencies](#dependencies). If you have another Stable Diffusion UI [you might be able to reuse the dependencies](#i-already-have-another-ui-for-stable-diffusion-installed-do-i-really-have-to-install-all-of-these-dependencies).
1. Launch ComfyUI by running `python main.py`
> **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux).
#### DirectML (AMD Cards on Windows)
This is very badly supported and is not recommended. There are some unofficial builds of pytorch ROCm on windows that exist that will give you a much better experience than this. This readme will be updated once official pytorch ROCm builds for windows come out.
```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
#### Ascend NPUs
For models compatible with Ascend Extension for PyTorch (torch_npu). To get started, ensure your environment meets the prerequisites outlined on the [installation](https://ascend.github.io/docs/sources/ascend/quick_install.html) page. Here's a step-by-step guide tailored to your platform and installation method:
1. Begin by installing the recommended or newer kernel version for Linux as specified in the Installation page of torch-npu, if necessary.
2. Proceed with the installation of Ascend Basekit, which includes the driver, firmware, and CANN, following the instructions provided for your specific platform.
3. Next, install the necessary packages for torch-npu by adhering to the platform-specific instructions on the [Installation](https://ascend.github.io/docs/sources/pytorch/install.html#pytorch) page.
4. Finally, adhere to the [ComfyUI manual installation](#manual-install-windows-linux) guide for Linux. Once all components are installed, you can run ComfyUI as described earlier.
#### Cambricon MLUs
For models compatible with Cambricon Extension for PyTorch (torch_mlu). Here's a step-by-step guide tailored to your platform and installation method:
1. Install the Cambricon CNToolkit by adhering to the platform-specific instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cntoolkit_3.7.2/cntoolkit_install_3.7.2/index.html)
2. Next, install the PyTorch(torch_mlu) following the instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cambricon_pytorch_1.17.0/user_guide_1.9/index.html)
3. Launch ComfyUI by running `python main.py`
#### Iluvatar Corex
For models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step guide tailored to your platform and installation method:
1. Install the Iluvatar Corex Toolkit by adhering to the platform-specific instructions on the [Installation](https://support.iluvatar.com/#/DocumentCentre?id=1&nameCenter=2&productId=520117912052801536)
2. Launch ComfyUI by running `python main.py`
# Running
```python main.py```
### For AMD cards not officially supported by ROCm
Try running it with this command if you have issues:
For 6700, 6600 and maybe other RDNA2 or older: ```HSA_OVERRIDE_GFX_VERSION=10.3.0 python main.py```
For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py```
### AMD ROCm Tips
You can enable experimental memory efficient attention on recent pytorch in ComfyUI on some AMD GPUs using this command, it should already be enabled by default on RDNA3. If this improves speed for you on latest pytorch on your GPU please report it so that I can enable it by default.
```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention```
You can also try setting this env variable `PYTORCH_TUNABLEOP_ENABLED=1` which might speed things up at the cost of a very slow initial run.
# Notes
Only parts of the graph that have an output with all the correct inputs will be executed.
Only parts of the graph that change from each execution to the next will be executed, if you submit the same graph twice only the first will be executed. If you change the last part of the graph only the part you changed and the part that depends on it will be executed.
Dragging a generated png on the webpage or loading one will give you the full workflow including seeds that were used to create it.
You can use () to change emphasis of a word or phrase like: (good code:1.2) or (bad code:0.8). The default emphasis for () is 1.1. To use () characters in your actual prompt escape them like \\( or \\).
You can use {day|night}, for wildcard/dynamic prompts. With this syntax "{wild|card|test}" will be randomly replaced by either "wild", "card" or "test" by the frontend every time you queue the prompt. To use {} characters in your actual prompt escape them like: \\{ or \\}.
Dynamic prompts also support C-style comments, like `// comment` or `/* comment */`.
To use a textual inversion concepts/embeddings in a text prompt put them in the models/embeddings directory and use them in the CLIPTextEncode node like this (you can omit the .pt extension):
```embedding:embedding_filename.pt```
## How to show high-quality previews?
Use ```--preview-method auto``` to enable previews.
The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth, taesdxl_decoder.pth, taesd3_decoder.pth and taef1_decoder.pth](https://github.com/madebyollin/taesd/) and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI and launch it with `--preview-method taesd` to enable high-quality previews.
## How to use TLS/SSL?
Generate a self-signed certificate (not appropriate for shared/production use) and key by running the command: `openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj "/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname"`
Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app will now be accessible with `https://...` instead of `http://...`.
> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above.
<br/><br/>If you use a container, note that the volume mount `-v` can be a relative path so `... -v ".\:/openssl-certs" ...` would create the key & cert files in the current directory of your command prompt or powershell terminal.
## Support and dev channel
[Discord](https://comfy.org/discord): Try the #help or #feedback channels.
[Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source).
See also: [https://www.comfy.org/](https://www.comfy.org/)
## Frontend Development
As of August 15, 2024, we have transitioned to a new frontend, which is now hosted in a separate repository: [ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend). This repository now hosts the compiled JS (from TS/Vue) under the `web/` directory.
### Reporting Issues and Requesting Features
For any bugs, issues, or feature requests related to the frontend, please use the [ComfyUI Frontend repository](https://github.com/Comfy-Org/ComfyUI_frontend). This will help us manage and address frontend-specific concerns more efficiently.
### Using the Latest Frontend
The new frontend is now the default for ComfyUI. However, please note:
1. The frontend in the main ComfyUI repository is updated fortnightly.
2. Daily releases are available in the separate frontend repository.
To use the most up-to-date frontend version:
1. For the latest daily release, launch ComfyUI with this command line argument:
```
--front-end-version Comfy-Org/ComfyUI_frontend@latest
```
2. For a specific version, replace `latest` with the desired version number:
```
--front-end-version Comfy-Org/ComfyUI_frontend@1.2.2
```
This approach allows you to easily switch between the stable fortnightly release and the cutting-edge daily updates, or even specific versions for testing purposes.
### Accessing the Legacy Frontend
If you need to use the legacy frontend for any reason, you can access it using the following command line argument:
```
--front-end-version Comfy-Org/ComfyUI_legacy_frontend@latest
```
This will use a snapshot of the legacy frontend preserved in the [ComfyUI Legacy Frontend repository](https://github.com/Comfy-Org/ComfyUI_legacy_frontend).
# QA
### Which GPU should I buy for this?
[See this page for some recommendations](https://github.com/comfyanonymous/ComfyUI/wiki/Which-GPU-should-I-buy-for-ComfyUI)
<div align="center">
# ComfyUI
**The most powerful and modular visual AI engine and application.**
[![Website][website-shield]][website-url]
[![Dynamic JSON Badge][discord-shield]][discord-url]
[![Twitter][twitter-shield]][twitter-url]
[![Matrix][matrix-shield]][matrix-url]
<br>
[![][github-release-shield]][github-release-link]
[![][github-release-date-shield]][github-release-link]
[![][github-downloads-shield]][github-downloads-link]
[![][github-downloads-latest-shield]][github-downloads-link]
[matrix-shield]: https://img.shields.io/badge/Matrix-000000?style=flat&logo=matrix&logoColor=white
[matrix-url]: https://app.element.io/#/room/%23comfyui_space%3Amatrix.org
[website-shield]: https://img.shields.io/badge/ComfyOrg-4285F4?style=flat
[website-url]: https://www.comfy.org/
<!-- Workaround to display total user from https://github.com/badges/shields/issues/4500#issuecomment-2060079995 -->
[discord-shield]: https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2Fcomfyorg%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&logo=discord&logoColor=white&label=Discord&color=green&suffix=%20total
[discord-url]: https://www.comfy.org/discord
[twitter-shield]: https://img.shields.io/twitter/follow/ComfyUI
[twitter-url]: https://x.com/ComfyUI
[github-release-shield]: https://img.shields.io/github/v/release/comfyanonymous/ComfyUI?style=flat&sort=semver
[github-release-link]: https://github.com/comfyanonymous/ComfyUI/releases
[github-release-date-shield]: https://img.shields.io/github/release-date/comfyanonymous/ComfyUI?style=flat
[github-downloads-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/total?style=flat
[github-downloads-latest-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/latest/total?style=flat&label=downloads%40latest
[github-downloads-link]: https://github.com/comfyanonymous/ComfyUI/releases
![ComfyUI Screenshot](https://github.com/user-attachments/assets/7ccaf2c1-9b72-41ae-9a89-5688c94b7abe)
</div>
ComfyUI lets you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. Available on Windows, Linux, and macOS.
## Get Started
#### [Desktop Application](https://www.comfy.org/download)
- The easiest way to get started.
- Available on Windows & macOS.
#### [Windows Portable Package](#installing)
- Get the latest commits and completely portable.
- Available on Windows.
#### [Manual Install](#manual-install-windows-linux)
Supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend).
## [Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
See what ComfyUI can do with the [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/).
## Features
- Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
- Image Models
- SD1.x, SD2.x ([unCLIP](https://comfyanonymous.github.io/ComfyUI_examples/unclip/))
- [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
- [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/)
- [SD3 and SD3.5](https://comfyanonymous.github.io/ComfyUI_examples/sd3/)
- Pixart Alpha and Sigma
- [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/)
- [HunyuanDiT](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_dit/)
- [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
- [Lumina Image 2.0](https://comfyanonymous.github.io/ComfyUI_examples/lumina2/)
- [HiDream](https://comfyanonymous.github.io/ComfyUI_examples/hidream/)
- [Qwen Image](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/)
- Image Editing Models
- [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/)
- [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model)
- [HiDream E1.1](https://comfyanonymous.github.io/ComfyUI_examples/hidream/#hidream-e11)
- [Qwen Image Edit](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/#edit-model)
- Video Models
- [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/)
- [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
- [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/)
- [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)
- [Wan 2.1](https://comfyanonymous.github.io/ComfyUI_examples/wan/)
- [Wan 2.2](https://comfyanonymous.github.io/ComfyUI_examples/wan22/)
- Audio Models
- [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
- [ACE Step](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
- 3D Models
- [Hunyuan3D 2.0](https://docs.comfy.org/tutorials/3d/hunyuan3D-2)
- Asynchronous Queue system
- Many optimizations: Only re-executes the parts of the workflow that changes between executions.
- Smart memory management: can automatically run large models on GPUs with as low as 1GB vram with smart offloading.
- Works even if you don't have a GPU with: ```--cpu``` (slow)
- Can load ckpt and safetensors: All in one checkpoints or standalone diffusion models, VAEs and CLIP models.
- Safe loading of ckpt, pt, pth, etc.. files.
- Embeddings/Textual inversion
- [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
- [Hypernetworks](https://comfyanonymous.github.io/ComfyUI_examples/hypernetworks/)
- Loading full workflows (with seeds) from generated PNG, WebP and FLAC files.
- Saving/Loading workflows as Json files.
- Nodes interface can be used to create complex workflows like one for [Hires fix](https://comfyanonymous.github.io/ComfyUI_examples/2_pass_txt2img/) or much more advanced ones.
- [Area Composition](https://comfyanonymous.github.io/ComfyUI_examples/area_composition/)
- [Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/) with both regular and inpainting models.
- [ControlNet and T2I-Adapter](https://comfyanonymous.github.io/ComfyUI_examples/controlnet/)
- [Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)](https://comfyanonymous.github.io/ComfyUI_examples/upscale_models/)
- [GLIGEN](https://comfyanonymous.github.io/ComfyUI_examples/gligen/)
- [Model Merging](https://comfyanonymous.github.io/ComfyUI_examples/model_merging/)
- [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)
- Latent previews with [TAESD](#how-to-show-high-quality-previews)
- Works fully offline: core will never download anything unless you want to.
- Optional API nodes to use paid models from external providers through the online [Comfy API](https://docs.comfy.org/tutorials/api-nodes/overview).
- [Config file](extra_model_paths.yaml.example) to set the search paths for models.
Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/)
## Release Process
ComfyUI follows a weekly release cycle targeting Friday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories:
1. **[ComfyUI Core](https://github.com/comfyanonymous/ComfyUI)**
- Releases a new stable version (e.g., v0.7.0)
- Serves as the foundation for the desktop release
2. **[ComfyUI Desktop](https://github.com/Comfy-Org/desktop)**
- Builds a new release using the latest stable core version
3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)**
- Weekly frontend updates are merged into the core repository
- Features are frozen for the upcoming core release
- Development continues for the next release cycle
## Shortcuts
| Keybind | Explanation |
|------------------------------------|--------------------------------------------------------------------------------------------------------------------|
| `Ctrl` + `Enter` | Queue up current graph for generation |
| `Ctrl` + `Shift` + `Enter` | Queue up current graph as first for generation |
| `Ctrl` + `Alt` + `Enter` | Cancel current generation |
| `Ctrl` + `Z`/`Ctrl` + `Y` | Undo/Redo |
| `Ctrl` + `S` | Save workflow |
| `Ctrl` + `O` | Load workflow |
| `Ctrl` + `A` | Select all nodes |
| `Alt `+ `C` | Collapse/uncollapse selected nodes |
| `Ctrl` + `M` | Mute/unmute selected nodes |
| `Ctrl` + `B` | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) |
| `Delete`/`Backspace` | Delete selected nodes |
| `Ctrl` + `Backspace` | Delete the current graph |
| `Space` | Move the canvas around when held and moving the cursor |
| `Ctrl`/`Shift` + `Click` | Add clicked node to selection |
| `Ctrl` + `C`/`Ctrl` + `V` | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes) |
| `Ctrl` + `C`/`Ctrl` + `Shift` + `V` | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |
| `Shift` + `Drag` | Move multiple selected nodes at the same time |
| `Ctrl` + `D` | Load default graph |
| `Alt` + `+` | Canvas Zoom in |
| `Alt` + `-` | Canvas Zoom out |
| `Ctrl` + `Shift` + LMB + Vertical drag | Canvas Zoom in/out |
| `P` | Pin/Unpin selected nodes |
| `Ctrl` + `G` | Group selected nodes |
| `Q` | Toggle visibility of the queue |
| `H` | Toggle visibility of history |
| `R` | Refresh graph |
| `F` | Show/Hide menu |
| `.` | Fit view to selection (Whole graph when nothing is selected) |
| Double-Click LMB | Open node quick search palette |
| `Shift` + Drag | Move multiple wires at once |
| `Ctrl` + `Alt` + LMB | Disconnect all wires from clicked slot |
`Ctrl` can also be replaced with `Cmd` instead for macOS users
# Installing
## Windows Portable
There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the [releases page](https://github.com/comfyanonymous/ComfyUI/releases).
### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z)
Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
If you have trouble extracting it, right click the file -> properties -> unblock
#### How do I share models between another UI and ComfyUI?
See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor.
## [comfy-cli](https://docs.comfy.org/comfy-cli/getting-started)
You can install and start ComfyUI using comfy-cli:
```bash
pip install comfy-cli
comfy install
```
## Manual Install (Windows, Linux)
Python 3.13 is very well supported. If you have trouble with some custom node dependencies you can try 3.12
Git clone this repo.
Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
Put your VAE in: models/vae
### AMD GPUs (Linux only)
AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.4```
This is the command to install the nightly with ROCm 6.4 which might have some performance improvements:
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.4```
### Intel GPUs (Windows and Linux)
(Option 1) Intel Arc GPU users can install native PyTorch with torch.xpu support using pip. More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html)
1. To install PyTorch xpu, use the following command:
```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/xpu```
This is the command to install the Pytorch xpu nightly which might have some performance improvements:
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu```
(Option 2) Alternatively, Intel GPUs supported by Intel Extension for PyTorch (IPEX) can leverage IPEX for improved performance.
1. visit [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) for more information.
### NVIDIA
Nvidia users should install stable pytorch using this command:
```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu129```
This is the command to install pytorch nightly instead which might have performance improvements.
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu129```
#### Troubleshooting
If you get the "Torch not compiled with CUDA enabled" error, uninstall torch with:
```pip uninstall torch```
And install it again with the command above.
### Dependencies
Install the dependencies by opening your terminal inside the ComfyUI folder and:
```pip install -r requirements.txt```
After this you should have everything installed and can proceed to running ComfyUI.
### Others:
#### Apple Mac silicon
You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version.
1. Install pytorch nightly. For instructions, read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide (make sure to install the latest pytorch nightly).
1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux.
1. Install the ComfyUI [dependencies](#dependencies). If you have another Stable Diffusion UI [you might be able to reuse the dependencies](#i-already-have-another-ui-for-stable-diffusion-installed-do-i-really-have-to-install-all-of-these-dependencies).
1. Launch ComfyUI by running `python main.py`
> **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux).
#### DirectML (AMD Cards on Windows)
This is very badly supported and is not recommended. There are some unofficial builds of pytorch ROCm on windows that exist that will give you a much better experience than this. This readme will be updated once official pytorch ROCm builds for windows come out.
```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
#### Ascend NPUs
For models compatible with Ascend Extension for PyTorch (torch_npu). To get started, ensure your environment meets the prerequisites outlined on the [installation](https://ascend.github.io/docs/sources/ascend/quick_install.html) page. Here's a step-by-step guide tailored to your platform and installation method:
1. Begin by installing the recommended or newer kernel version for Linux as specified in the Installation page of torch-npu, if necessary.
2. Proceed with the installation of Ascend Basekit, which includes the driver, firmware, and CANN, following the instructions provided for your specific platform.
3. Next, install the necessary packages for torch-npu by adhering to the platform-specific instructions on the [Installation](https://ascend.github.io/docs/sources/pytorch/install.html#pytorch) page.
4. Finally, adhere to the [ComfyUI manual installation](#manual-install-windows-linux) guide for Linux. Once all components are installed, you can run ComfyUI as described earlier.
#### Cambricon MLUs
For models compatible with Cambricon Extension for PyTorch (torch_mlu). Here's a step-by-step guide tailored to your platform and installation method:
1. Install the Cambricon CNToolkit by adhering to the platform-specific instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cntoolkit_3.7.2/cntoolkit_install_3.7.2/index.html)
2. Next, install the PyTorch(torch_mlu) following the instructions on the [Installation](https://www.cambricon.com/docs/sdk_1.15.0/cambricon_pytorch_1.17.0/user_guide_1.9/index.html)
3. Launch ComfyUI by running `python main.py`
#### Iluvatar Corex
For models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step guide tailored to your platform and installation method:
1. Install the Iluvatar Corex Toolkit by adhering to the platform-specific instructions on the [Installation](https://support.iluvatar.com/#/DocumentCentre?id=1&nameCenter=2&productId=520117912052801536)
2. Launch ComfyUI by running `python main.py`
# Running
```python main.py```
### For AMD cards not officially supported by ROCm
Try running it with this command if you have issues:
For 6700, 6600 and maybe other RDNA2 or older: ```HSA_OVERRIDE_GFX_VERSION=10.3.0 python main.py```
For AMD 7600 and maybe other RDNA3 cards: ```HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py```
### AMD ROCm Tips
You can enable experimental memory efficient attention on recent pytorch in ComfyUI on some AMD GPUs using this command, it should already be enabled by default on RDNA3. If this improves speed for you on latest pytorch on your GPU please report it so that I can enable it by default.
```TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention```
You can also try setting this env variable `PYTORCH_TUNABLEOP_ENABLED=1` which might speed things up at the cost of a very slow initial run.
# Notes
Only parts of the graph that have an output with all the correct inputs will be executed.
Only parts of the graph that change from each execution to the next will be executed, if you submit the same graph twice only the first will be executed. If you change the last part of the graph only the part you changed and the part that depends on it will be executed.
Dragging a generated png on the webpage or loading one will give you the full workflow including seeds that were used to create it.
You can use () to change emphasis of a word or phrase like: (good code:1.2) or (bad code:0.8). The default emphasis for () is 1.1. To use () characters in your actual prompt escape them like \\( or \\).
You can use {day|night}, for wildcard/dynamic prompts. With this syntax "{wild|card|test}" will be randomly replaced by either "wild", "card" or "test" by the frontend every time you queue the prompt. To use {} characters in your actual prompt escape them like: \\{ or \\}.
Dynamic prompts also support C-style comments, like `// comment` or `/* comment */`.
To use a textual inversion concepts/embeddings in a text prompt put them in the models/embeddings directory and use them in the CLIPTextEncode node like this (you can omit the .pt extension):
```embedding:embedding_filename.pt```
## How to show high-quality previews?
Use ```--preview-method auto``` to enable previews.
The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth, taesdxl_decoder.pth, taesd3_decoder.pth and taef1_decoder.pth](https://github.com/madebyollin/taesd/) and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI and launch it with `--preview-method taesd` to enable high-quality previews.
## How to use TLS/SSL?
Generate a self-signed certificate (not appropriate for shared/production use) and key by running the command: `openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj "/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname"`
Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app will now be accessible with `https://...` instead of `http://...`.
> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above.
<br/><br/>If you use a container, note that the volume mount `-v` can be a relative path so `... -v ".\:/openssl-certs" ...` would create the key & cert files in the current directory of your command prompt or powershell terminal.
## Support and dev channel
[Discord](https://comfy.org/discord): Try the #help or #feedback channels.
[Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source).
See also: [https://www.comfy.org/](https://www.comfy.org/)
## Frontend Development
As of August 15, 2024, we have transitioned to a new frontend, which is now hosted in a separate repository: [ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend). This repository now hosts the compiled JS (from TS/Vue) under the `web/` directory.
### Reporting Issues and Requesting Features
For any bugs, issues, or feature requests related to the frontend, please use the [ComfyUI Frontend repository](https://github.com/Comfy-Org/ComfyUI_frontend). This will help us manage and address frontend-specific concerns more efficiently.
### Using the Latest Frontend
The new frontend is now the default for ComfyUI. However, please note:
1. The frontend in the main ComfyUI repository is updated fortnightly.
2. Daily releases are available in the separate frontend repository.
To use the most up-to-date frontend version:
1. For the latest daily release, launch ComfyUI with this command line argument:
```
--front-end-version Comfy-Org/ComfyUI_frontend@latest
```
2. For a specific version, replace `latest` with the desired version number:
```
--front-end-version Comfy-Org/ComfyUI_frontend@1.2.2
```
This approach allows you to easily switch between the stable fortnightly release and the cutting-edge daily updates, or even specific versions for testing purposes.
### Accessing the Legacy Frontend
If you need to use the legacy frontend for any reason, you can access it using the following command line argument:
```
--front-end-version Comfy-Org/ComfyUI_legacy_frontend@latest
```
This will use a snapshot of the legacy frontend preserved in the [ComfyUI Legacy Frontend repository](https://github.com/Comfy-Org/ComfyUI_legacy_frontend).
# QA
### Which GPU should I buy for this?
[See this page for some recommendations](https://github.com/comfyanonymous/ComfyUI/wiki/Which-GPU-should-I-buy-for-ComfyUI)
MIT License
Copyright (c) 2024 Liu Sida
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
# config.py
cfg_mnet = {
'name': 'mobilenet0.25',
'min_sizes': [[16, 32], [64, 128], [256, 512]],
'steps': [8, 16, 32],
'variance': [0.1, 0.2],
'clip': False,
'loc_weight': 2.0,
'gpu_train': True,
'batch_size': 32,
'ngpu': 1,
'epoch': 250,
'decay1': 190,
'decay2': 220,
'image_size': 640,
'pretrain': True,
'return_layers': {'stage1': 1, 'stage2': 2, 'stage3': 3},
'in_channel': 32,
'out_channel': 64
}
cfg_re50 = {
'name': 'Resnet50',
'min_sizes': [[16, 32], [64, 128], [256, 512]],
'steps': [8, 16, 32],
'variance': [0.1, 0.2],
'clip': False,
'loc_weight': 2.0,
'gpu_train': True,
'batch_size': 24,
'ngpu': 4,
'epoch': 100,
'decay1': 70,
'decay2': 90,
'image_size': 840,
'pretrain': True,
'return_layers': {'layer2': 1, 'layer3': 2, 'layer4': 3},
'in_channel': 256,
'out_channel': 256
}
import torch
from itertools import product as product
import numpy as np
from math import ceil
class PriorBox(object):
def __init__(self, cfg, image_size=None, phase='train'):
super(PriorBox, self).__init__()
self.min_sizes = cfg['min_sizes']
self.steps = cfg['steps']
self.clip = cfg['clip']
self.image_size = image_size
self.feature_maps = [[ceil(self.image_size[0]/step), ceil(self.image_size[1]/step)] for step in self.steps]
self.name = "s"
def forward(self):
anchors = []
for k, f in enumerate(self.feature_maps):
min_sizes = self.min_sizes[k]
for i, j in product(range(f[0]), range(f[1])):
for min_size in min_sizes:
s_kx = min_size / self.image_size[1]
s_ky = min_size / self.image_size[0]
dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]]
dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]]
for cy, cx in product(dense_cy, dense_cx):
anchors += [cx, cy, s_kx, s_ky]
# back to torch land
output = torch.Tensor(anchors).view(-1, 4)
if self.clip:
output.clamp_(max=1, min=0)
return output
import time
import torch
import torch.nn as nn
import torchvision.models._utils as _utils
import torchvision.models as models
import torch.nn.functional as F
from torch.autograd import Variable
def conv_bn(inp, oup, stride = 1, leaky = 0):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
nn.LeakyReLU(negative_slope=leaky, inplace=True)
)
def conv_bn_no_relu(inp, oup, stride):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
nn.BatchNorm2d(oup),
)
def conv_bn1X1(inp, oup, stride, leaky=0):
return nn.Sequential(
nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False),
nn.BatchNorm2d(oup),
nn.LeakyReLU(negative_slope=leaky, inplace=True)
)
def conv_dw(inp, oup, stride, leaky=0.1):
return nn.Sequential(
nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
nn.BatchNorm2d(inp),
nn.LeakyReLU(negative_slope= leaky,inplace=True),
nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
nn.LeakyReLU(negative_slope= leaky,inplace=True),
)
class SSH(nn.Module):
def __init__(self, in_channel, out_channel):
super(SSH, self).__init__()
assert out_channel % 4 == 0
leaky = 0
if (out_channel <= 64):
leaky = 0.1
self.conv3X3 = conv_bn_no_relu(in_channel, out_channel//2, stride=1)
self.conv5X5_1 = conv_bn(in_channel, out_channel//4, stride=1, leaky = leaky)
self.conv5X5_2 = conv_bn_no_relu(out_channel//4, out_channel//4, stride=1)
self.conv7X7_2 = conv_bn(out_channel//4, out_channel//4, stride=1, leaky = leaky)
self.conv7x7_3 = conv_bn_no_relu(out_channel//4, out_channel//4, stride=1)
def forward(self, input):
conv3X3 = self.conv3X3(input)
conv5X5_1 = self.conv5X5_1(input)
conv5X5 = self.conv5X5_2(conv5X5_1)
conv7X7_2 = self.conv7X7_2(conv5X5_1)
conv7X7 = self.conv7x7_3(conv7X7_2)
out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1)
out = F.relu(out)
return out
class FPN(nn.Module):
def __init__(self,in_channels_list,out_channels):
super(FPN,self).__init__()
leaky = 0
if (out_channels <= 64):
leaky = 0.1
self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride = 1, leaky = leaky)
self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride = 1, leaky = leaky)
self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride = 1, leaky = leaky)
self.merge1 = conv_bn(out_channels, out_channels, leaky = leaky)
self.merge2 = conv_bn(out_channels, out_channels, leaky = leaky)
def forward(self, input):
# names = list(input.keys())
input = list(input.values())
output1 = self.output1(input[0])
output2 = self.output2(input[1])
output3 = self.output3(input[2])
up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode="nearest")
output2 = output2 + up3
output2 = self.merge2(output2)
up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode="nearest")
output1 = output1 + up2
output1 = self.merge1(output1)
out = [output1, output2, output3]
return out
class MobileNetV1(nn.Module):
def __init__(self):
super(MobileNetV1, self).__init__()
self.stage1 = nn.Sequential(
conv_bn(3, 8, 2, leaky = 0.1), # 3
conv_dw(8, 16, 1), # 7
conv_dw(16, 32, 2), # 11
conv_dw(32, 32, 1), # 19
conv_dw(32, 64, 2), # 27
conv_dw(64, 64, 1), # 43
)
self.stage2 = nn.Sequential(
conv_dw(64, 128, 2), # 43 + 16 = 59
conv_dw(128, 128, 1), # 59 + 32 = 91
conv_dw(128, 128, 1), # 91 + 32 = 123
conv_dw(128, 128, 1), # 123 + 32 = 155
conv_dw(128, 128, 1), # 155 + 32 = 187
conv_dw(128, 128, 1), # 187 + 32 = 219
)
self.stage3 = nn.Sequential(
conv_dw(128, 256, 2), # 219 +3 2 = 241
conv_dw(256, 256, 1), # 241 + 64 = 301
)
self.avg = nn.AdaptiveAvgPool2d((1,1))
self.fc = nn.Linear(256, 1000)
def forward(self, x):
x = self.stage1(x)
x = self.stage2(x)
x = self.stage3(x)
x = self.avg(x)
# x = self.model(x)
x = x.view(-1, 256)
x = self.fc(x)
return x
import os
import torch
import torch.nn as nn
import torchvision.models.detection.backbone_utils as backbone_utils
import torchvision.models._utils as _utils
import torch.nn.functional as F
from collections import OrderedDict
from .net import MobileNetV1 as MobileNetV1
from .net import FPN as FPN
from .net import SSH as SSH
class ClassHead(nn.Module):
def __init__(self,inchannels=512,num_anchors=3):
super(ClassHead,self).__init__()
self.num_anchors = num_anchors
self.conv1x1 = nn.Conv2d(inchannels,self.num_anchors*2,kernel_size=(1,1),stride=1,padding=0)
def forward(self,x):
out = self.conv1x1(x)
out = out.permute(0,2,3,1).contiguous()
return out.view(out.shape[0], -1, 2)
class BboxHead(nn.Module):
def __init__(self,inchannels=512,num_anchors=3):
super(BboxHead,self).__init__()
self.conv1x1 = nn.Conv2d(inchannels,num_anchors*4,kernel_size=(1,1),stride=1,padding=0)
def forward(self,x):
out = self.conv1x1(x)
out = out.permute(0,2,3,1).contiguous()
return out.view(out.shape[0], -1, 4)
class LandmarkHead(nn.Module):
def __init__(self,inchannels=512,num_anchors=3):
super(LandmarkHead,self).__init__()
self.conv1x1 = nn.Conv2d(inchannels,num_anchors*10,kernel_size=(1,1),stride=1,padding=0)
def forward(self,x):
out = self.conv1x1(x)
out = out.permute(0,2,3,1).contiguous()
return out.view(out.shape[0], -1, 10)
class RetinaFace(nn.Module):
def __init__(self, cfg = None, phase = 'train', weights_path='', device="cuda"):
"""
:param cfg: Network related settings.
:param phase: train or test.
"""
weights_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), weights_path)
super(RetinaFace,self).__init__()
self.phase = phase
backbone = None
if cfg['name'] == 'mobilenet0.25':
backbone = MobileNetV1()
if cfg['pretrain']:
checkpoint = torch.load(weights_path, map_location=torch.device(device))
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict'].items():
name = k[7:] # remove module.
new_state_dict[name] = v
# load params
backbone.load_state_dict(new_state_dict)
elif cfg['name'] == 'Resnet50':
import torchvision.models as models
backbone = models.resnet50(pretrained=cfg['pretrain'])
self.body = _utils.IntermediateLayerGetter(backbone, cfg['return_layers'])
in_channels_stage2 = cfg['in_channel']
in_channels_list = [
in_channels_stage2 * 2,
in_channels_stage2 * 4,
in_channels_stage2 * 8,
]
out_channels = cfg['out_channel']
self.fpn = FPN(in_channels_list,out_channels)
self.ssh1 = SSH(out_channels, out_channels)
self.ssh2 = SSH(out_channels, out_channels)
self.ssh3 = SSH(out_channels, out_channels)
self.ClassHead = self._make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
self.BboxHead = self._make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
self.LandmarkHead = self._make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])
def _make_class_head(self,fpn_num=3,inchannels=64,anchor_num=2):
classhead = nn.ModuleList()
for i in range(fpn_num):
classhead.append(ClassHead(inchannels,anchor_num))
return classhead
def _make_bbox_head(self,fpn_num=3,inchannels=64,anchor_num=2):
bboxhead = nn.ModuleList()
for i in range(fpn_num):
bboxhead.append(BboxHead(inchannels,anchor_num))
return bboxhead
def _make_landmark_head(self,fpn_num=3,inchannels=64,anchor_num=2):
landmarkhead = nn.ModuleList()
for i in range(fpn_num):
landmarkhead.append(LandmarkHead(inchannels,anchor_num))
return landmarkhead
def forward(self,inputs):
out = self.body(inputs)
# FPN
fpn = self.fpn(out)
# SSH
feature1 = self.ssh1(fpn[0])
feature2 = self.ssh2(fpn[1])
feature3 = self.ssh3(fpn[2])
features = [feature1, feature2, feature3]
bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1)
classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)],dim=1)
ldm_regressions = torch.cat([self.LandmarkHead[i](feature) for i, feature in enumerate(features)], dim=1)
if self.phase == 'train':
output = (bbox_regressions, classifications, ldm_regressions)
else:
output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)
return output
\ No newline at end of file
import os
import time
import math
import torch
import torch.backends.cudnn as cudnn
import numpy as np
from .data import cfg_mnet, cfg_re50
from .layers.functions.prior_box import PriorBox
from .utils.nms.py_cpu_nms import py_cpu_nms
from .models.retinaface import RetinaFace
from .utils.box_utils import decode, decode_landm
class Pytorch_RetinaFace:
def __init__(self, cfg="mobile0.25", pretrained_path="./weights/mobilenet0.25_Final.pth", weights_path="./weights/mobilenetV1X0.25_pretrain.tar", device="auto", vis_thres=0.6, top_k=5000, keep_top_k=750, nms_threshold=0.4, confidence_threshold=0.02):
self.vis_thres = vis_thres
self.top_k = top_k
self.keep_top_k = keep_top_k
self.nms_threshold = nms_threshold
self.confidence_threshold = confidence_threshold
self.cfg = cfg_mnet if cfg=="mobile0.25" else cfg_re50
if device == 'auto':
# Automatically choose the device
if torch.cuda.is_available():
self.device = torch.device("cuda")
elif torch.backends.mps.is_available():
self.device = torch.device("mps")
else:
self.device = torch.device("cpu")
else:
# Use the specified device
self.device = torch.device(device)
print("Using device:", self.device)
self.net = RetinaFace(cfg=self.cfg, weights_path=weights_path, phase='test', device=self.device).to(self.device)
self.load_model_weights(pretrained_path)
self.net.eval()
def check_keys(self, model, pretrained_state_dict):
ckpt_keys = set(pretrained_state_dict.keys())
model_keys = set(model.state_dict().keys())
used_pretrained_keys = model_keys & ckpt_keys
unused_pretrained_keys = ckpt_keys - model_keys
missing_keys = model_keys - ckpt_keys
print('Missing keys:{}'.format(len(missing_keys)))
print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
print('Used keys:{}'.format(len(used_pretrained_keys)))
assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
return True
def remove_prefix(self, state_dict, prefix):
''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
print('remove prefix \'{}\''.format(prefix))
f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
return {f(key): value for key, value in state_dict.items()}
def load_model_weights(self, pretrained_path):
pretrained_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), pretrained_path)
print('Loading pretrained model from {}'.format(pretrained_path))
pretrained_dict = torch.load(pretrained_path, map_location=self.device)
if "state_dict" in pretrained_dict.keys():
pretrained_dict = self.remove_prefix(pretrained_dict['state_dict'], 'module.')
else:
pretrained_dict = self.remove_prefix(pretrained_dict, 'module.')
self.check_keys(self.net, pretrained_dict)
self.net.load_state_dict(pretrained_dict, strict=False)
self.net.to(self.device)
return self.net
def center_and_crop_rescale(self, image, dets, scale_factor=4, shift_factor=0.35, aspect_ratio=1.0):
cropped_imgs = []
bbox_infos = []
for index, bbox in enumerate(dets):
if bbox[4] < self.vis_thres:
continue
x1, y1, x2, y2 = map(int, bbox[:4])
face_width = x2 - x1
face_height = y2 - y1
default_area = face_width * face_height
default_area *= scale_factor
default_side = math.sqrt(default_area)
# New height and width based on aspect_ratio
new_face_width = int(default_side * math.sqrt(aspect_ratio))
new_face_height = int(default_side / math.sqrt(aspect_ratio))
# Center coordinates of the detected face
center_x = x1 + face_width // 2
center_y = y1 + face_height // 2 + int(new_face_height * (0.5 - shift_factor))
original_crop_x1 = center_x - new_face_width // 2
original_crop_x2 = center_x + new_face_width // 2
original_crop_y1 = center_y - new_face_height // 2
original_crop_y2 = center_y + new_face_height // 2
# Crop coordinates, adjusted to the image boundaries
crop_x1 = max(0, original_crop_x1)
crop_x2 = min(image.shape[1], original_crop_x2)
crop_y1 = max(0, original_crop_y1)
crop_y2 = min(image.shape[0], original_crop_y2)
# Crop the region and add padding to form a square
cropped_imgs.append(image[crop_y1:crop_y2, crop_x1:crop_x2])
bbox_infos.append(((original_crop_x2-original_crop_x1, original_crop_y2-original_crop_y1),(original_crop_x1, original_crop_y1, original_crop_x2, original_crop_y2)))
return cropped_imgs, bbox_infos
def detect_faces(self, img):
resize = 1
if len(img.shape) == 4 and img.shape[0] == 1:
img = torch.squeeze(img, 0)
if isinstance(img, np.ndarray):
img = torch.from_numpy(img)
img = img.to(self.device)
im_height, im_width, _ = img.shape
scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
mean_values = torch.tensor([104, 117, 123], dtype=torch.float32).to(self.device)
mean_values = mean_values.view(1, 1, 3)
img -= mean_values
img = img.permute(2, 0, 1)
img = img.unsqueeze(0)
scale = scale.to(self.device)
tic = time.time()
with torch.no_grad():
loc, conf, landms = self.net(img) # forward pass
print('net forward time: {:.4f}'.format(time.time() - tic))
priorbox = PriorBox(self.cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(self.device)
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance'])
scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2]])
scale1 = scale1.to(self.device)
landms = landms * scale1 / resize
landms = landms.cpu().numpy()
# Ignore low scores
inds = np.where(scores > self.confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# Keep top-K before NMS
order = scores.argsort()[::-1][:self.top_k]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# Perform NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
keep = py_cpu_nms(dets, self.nms_threshold)
dets = dets[keep, :]
landms = landms[keep]
# Keep top-K faster NMS
dets = dets[:self.keep_top_k, :]
landms = landms[:self.keep_top_k, :]
dets = np.concatenate((dets, landms), axis=1)
return dets
import torch
import numpy as np
def point_form(boxes):
""" Convert prior_boxes to (xmin, ymin, xmax, ymax)
representation for comparison to point form ground truth data.
Args:
boxes: (tensor) center-size default boxes from priorbox layers.
Return:
boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
"""
return torch.cat((boxes[:, :2] - boxes[:, 2:]/2, # xmin, ymin
boxes[:, :2] + boxes[:, 2:]/2), 1) # xmax, ymax
def center_size(boxes):
""" Convert prior_boxes to (cx, cy, w, h)
representation for comparison to center-size form ground truth data.
Args:
boxes: (tensor) point_form boxes
Return:
boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
"""
return torch.cat((boxes[:, 2:] + boxes[:, :2])/2, # cx, cy
boxes[:, 2:] - boxes[:, :2], 1) # w, h
def intersect(box_a, box_b):
""" We resize both tensors to [A,B,2] without new malloc:
[A,2] -> [A,1,2] -> [A,B,2]
[B,2] -> [1,B,2] -> [A,B,2]
Then we compute the area of intersect between box_a and box_b.
Args:
box_a: (tensor) bounding boxes, Shape: [A,4].
box_b: (tensor) bounding boxes, Shape: [B,4].
Return:
(tensor) intersection area, Shape: [A,B].
"""
A = box_a.size(0)
B = box_b.size(0)
max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2),
box_b[:, 2:].unsqueeze(0).expand(A, B, 2))
min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2),
box_b[:, :2].unsqueeze(0).expand(A, B, 2))
inter = torch.clamp((max_xy - min_xy), min=0)
return inter[:, :, 0] * inter[:, :, 1]
def jaccard(box_a, box_b):
"""Compute the jaccard overlap of two sets of boxes. The jaccard overlap
is simply the intersection over union of two boxes. Here we operate on
ground truth boxes and default boxes.
E.g.:
A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)
Args:
box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4]
box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4]
Return:
jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)]
"""
inter = intersect(box_a, box_b)
area_a = ((box_a[:, 2]-box_a[:, 0]) *
(box_a[:, 3]-box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B]
area_b = ((box_b[:, 2]-box_b[:, 0]) *
(box_b[:, 3]-box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B]
union = area_a + area_b - inter
return inter / union # [A,B]
def matrix_iou(a, b):
"""
return iou of a and b, numpy version for data augenmentation
"""
lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)
return area_i / (area_a[:, np.newaxis] + area_b - area_i)
def matrix_iof(a, b):
"""
return iof of a and b, numpy version for data augenmentation
"""
lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
return area_i / np.maximum(area_a[:, np.newaxis], 1)
def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx):
"""Match each prior box with the ground truth box of the highest jaccard
overlap, encode the bounding boxes, then return the matched indices
corresponding to both confidence and location preds.
Args:
threshold: (float) The overlap threshold used when mathing boxes.
truths: (tensor) Ground truth boxes, Shape: [num_obj, 4].
priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4].
variances: (tensor) Variances corresponding to each prior coord,
Shape: [num_priors, 4].
labels: (tensor) All the class labels for the image, Shape: [num_obj].
landms: (tensor) Ground truth landms, Shape [num_obj, 10].
loc_t: (tensor) Tensor to be filled w/ endcoded location targets.
conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds.
landm_t: (tensor) Tensor to be filled w/ endcoded landm targets.
idx: (int) current batch index
Return:
The matched indices corresponding to 1)location 2)confidence 3)landm preds.
"""
# jaccard index
overlaps = jaccard(
truths,
point_form(priors)
)
# (Bipartite Matching)
# [1,num_objects] best prior for each ground truth
best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)
# ignore hard gt
valid_gt_idx = best_prior_overlap[:, 0] >= 0.2
best_prior_idx_filter = best_prior_idx[valid_gt_idx, :]
if best_prior_idx_filter.shape[0] <= 0:
loc_t[idx] = 0
conf_t[idx] = 0
return
# [1,num_priors] best ground truth for each prior
best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)
best_truth_idx.squeeze_(0)
best_truth_overlap.squeeze_(0)
best_prior_idx.squeeze_(1)
best_prior_idx_filter.squeeze_(1)
best_prior_overlap.squeeze_(1)
best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2) # ensure best prior
# TODO refactor: index best_prior_idx with long tensor
# ensure every gt matches with its prior of max overlap
for j in range(best_prior_idx.size(0)): # 判别此anchor是预测哪一个boxes
best_truth_idx[best_prior_idx[j]] = j
matches = truths[best_truth_idx] # Shape: [num_priors,4] 此处为每一个anchor对应的bbox取出来
conf = labels[best_truth_idx] # Shape: [num_priors] 此处为每一个anchor对应的label取出来
conf[best_truth_overlap < threshold] = 0 # label as background overlap<0.35的全部作为负样本
loc = encode(matches, priors, variances)
matches_landm = landms[best_truth_idx]
landm = encode_landm(matches_landm, priors, variances)
loc_t[idx] = loc # [num_priors,4] encoded offsets to learn
conf_t[idx] = conf # [num_priors] top class label for each prior
landm_t[idx] = landm
def encode(matched, priors, variances):
"""Encode the variances from the priorbox layers into the ground truth boxes
we have matched (based on jaccard overlap) with the prior boxes.
Args:
matched: (tensor) Coords of ground truth for each prior in point-form
Shape: [num_priors, 4].
priors: (tensor) Prior boxes in center-offset form
Shape: [num_priors,4].
variances: (list[float]) Variances of priorboxes
Return:
encoded boxes (tensor), Shape: [num_priors, 4]
"""
# dist b/t match center and prior's center
g_cxcy = (matched[:, :2] + matched[:, 2:])/2 - priors[:, :2]
# encode variance
g_cxcy /= (variances[0] * priors[:, 2:])
# match wh / prior wh
g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
g_wh = torch.log(g_wh) / variances[1]
# return target for smooth_l1_loss
return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4]
def encode_landm(matched, priors, variances):
"""Encode the variances from the priorbox layers into the ground truth boxes
we have matched (based on jaccard overlap) with the prior boxes.
Args:
matched: (tensor) Coords of ground truth for each prior in point-form
Shape: [num_priors, 10].
priors: (tensor) Prior boxes in center-offset form
Shape: [num_priors,4].
variances: (list[float]) Variances of priorboxes
Return:
encoded landm (tensor), Shape: [num_priors, 10]
"""
# dist b/t match center and prior's center
matched = torch.reshape(matched, (matched.size(0), 5, 2))
priors_cx = priors[:, 0].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
priors_cy = priors[:, 1].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
priors_w = priors[:, 2].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
priors_h = priors[:, 3].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
priors = torch.cat([priors_cx, priors_cy, priors_w, priors_h], dim=2)
g_cxcy = matched[:, :, :2] - priors[:, :, :2]
# encode variance
g_cxcy /= (variances[0] * priors[:, :, 2:])
# g_cxcy /= priors[:, :, 2:]
g_cxcy = g_cxcy.reshape(g_cxcy.size(0), -1)
# return target for smooth_l1_loss
return g_cxcy
# Adapted from https://github.com/Hakuyume/chainer-ssd
def decode(loc, priors, variances):
"""Decode locations from predictions using priors to undo
the encoding we did for offset regression at train time.
Args:
loc (tensor): location predictions for loc layers,
Shape: [num_priors,4]
priors (tensor): Prior boxes in center-offset form.
Shape: [num_priors,4].
variances: (list[float]) Variances of priorboxes
Return:
decoded bounding box predictions
"""
boxes = torch.cat((
priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
boxes[:, :2] -= boxes[:, 2:] / 2
boxes[:, 2:] += boxes[:, :2]
return boxes
def decode_landm(pre, priors, variances):
"""Decode landm from predictions using priors to undo
the encoding we did for offset regression at train time.
Args:
pre (tensor): landm predictions for loc layers,
Shape: [num_priors,10]
priors (tensor): Prior boxes in center-offset form.
Shape: [num_priors,4].
variances: (list[float]) Variances of priorboxes
Return:
decoded landm predictions
"""
landms = torch.cat((priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:],
), dim=1)
return landms
def log_sum_exp(x):
"""Utility function for computing log_sum_exp while determining
This will be used to determine unaveraged confidence loss across
all examples in a batch.
Args:
x (Variable(tensor)): conf_preds from conf layers
"""
x_max = x.data.max()
return torch.log(torch.sum(torch.exp(x-x_max), 1, keepdim=True)) + x_max
# Original author: Francisco Massa:
# https://github.com/fmassa/object-detection.torch
# Ported to PyTorch by Max deGroot (02/01/2017)
def nms(boxes, scores, overlap=0.5, top_k=200):
"""Apply non-maximum suppression at test time to avoid detecting too many
overlapping bounding boxes for a given object.
Args:
boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
scores: (tensor) The class predscores for the img, Shape:[num_priors].
overlap: (float) The overlap thresh for suppressing unnecessary boxes.
top_k: (int) The Maximum number of box preds to consider.
Return:
The indices of the kept boxes with respect to num_priors.
"""
keep = torch.Tensor(scores.size(0)).fill_(0).long()
if boxes.numel() == 0:
return keep
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
area = torch.mul(x2 - x1, y2 - y1)
v, idx = scores.sort(0) # sort in ascending order
# I = I[v >= 0.01]
idx = idx[-top_k:] # indices of the top-k largest vals
xx1 = boxes.new()
yy1 = boxes.new()
xx2 = boxes.new()
yy2 = boxes.new()
w = boxes.new()
h = boxes.new()
# keep = torch.Tensor()
count = 0
while idx.numel() > 0:
i = idx[-1] # index of current largest val
# keep.append(i)
keep[count] = i
count += 1
if idx.size(0) == 1:
break
idx = idx[:-1] # remove kept element from view
# load bboxes of next highest vals
torch.index_select(x1, 0, idx, out=xx1)
torch.index_select(y1, 0, idx, out=yy1)
torch.index_select(x2, 0, idx, out=xx2)
torch.index_select(y2, 0, idx, out=yy2)
# store element-wise max with next highest score
xx1 = torch.clamp(xx1, min=x1[i])
yy1 = torch.clamp(yy1, min=y1[i])
xx2 = torch.clamp(xx2, max=x2[i])
yy2 = torch.clamp(yy2, max=y2[i])
w.resize_as_(xx2)
h.resize_as_(yy2)
w = xx2 - xx1
h = yy2 - yy1
# check sizes of xx1 and xx2.. after each iteration
w = torch.clamp(w, min=0.0)
h = torch.clamp(h, min=0.0)
inter = w*h
# IoU = i / (area(a) + area(b) - i)
rem_areas = torch.index_select(area, 0, idx) # load remaining areas)
union = (rem_areas - inter) + area[i]
IoU = inter/union # store result in iou
# keep only elements with an IoU <= overlap
idx = idx[IoU.le(overlap)]
return keep, count
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
import numpy as np
def py_cpu_nms(dets, thresh):
"""Pure Python NMS baseline."""
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment