Upload folder using huggingface_hub

.gitattributes

@@ -51,3 +51,4 @@ nunchaku/assets/lora.jpg filter=lfs diff=lfs merge=lfs -text
 nunchaku/assets/teaser.jpg filter=lfs diff=lfs merge=lfs -text
 nunchaku/assets/visual.jpg filter=lfs diff=lfs merge=lfs -text
 nunchaku/assets/wechat.jpg filter=lfs diff=lfs merge=lfs -text
+assets/comfyui.jpg filter=lfs diff=lfs merge=lfs -text

.github/ISSUE_TEMPLATE/1-bug-report.yml

@@ -0,0 +1,41 @@
+# modified from https://github.com/sgl-project/sglang/blob/main/.github/ISSUE_TEMPLATE/1-bug-report.yml
+name: 🐞 Bug report
+description: Create a report to help us reproduce and fix the bug
+title: "[Bug] "
+labels: ['Bug']
+
+body:
+- type: checkboxes
+  attributes:
+    label: Checklist
+    options:
+    - label: 1. I have searched for related issues and FAQs (https://github.com/mit-han-lab/nunchaku/blob/main/docs/faq.md) but was unable to find a solution.
+    - label: 2. The issue persists in the latest version.
+    - label: 3. Please note that without environment information and a minimal reproducible example, it will be difficult for us to reproduce and address the issue, which may delay our response.
+    - label: 4. If your report is a question rather than a bug, please submit it as a discussion at https://github.com/mit-han-lab/ComfyUI-nunchaku/discussions/new/choose. Otherwise, this issue will be closed.
+    - label: 5. I will do my best to describe the issue in English.
+- type: textarea
+  attributes:
+    label: Describe the Bug
+    description: Provide a clear and concise explanation of the bug you encountered.
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: Environment
+    description: |
+      Please include relevant environment details such as your system specifications, Python version, PyTorch version, and CUDA version.
+    placeholder: "Example: Ubuntu 24.04, Python 3.11, PyTorch 2.6, CUDA 12.4"
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: Reproduction Steps
+    description: |
+      Please provide clear, step-by-step instructions to help us reproduce the issue. Be as specific as possible. Include the following:
+      - The ComfyUI workflow file (`.json`) you used, along with a screenshot of your workflow
+      - Links to the models, LoRAs, and control components involved
+      - The exact prompt(s) used
+      - Any input images required by the workflow
+  validations:
+    required: true

.github/ISSUE_TEMPLATE/2-feature-request.yml

@@ -0,0 +1,24 @@
+# modified from https://github.com/sgl-project/sglang/blob/main/.github/ISSUE_TEMPLATE/2-feature-request.yml
+name: 🚀 Feature request
+description: Suggest an idea for this project
+title: "[Feature] "
+
+body:
+- type: checkboxes
+  attributes:
+    label: Checklist
+    options:
+    - label: 1. If the issue you raised is not a feature but a question, please raise a discussion at https://github.com/mit-han-lab/ComfyUI-nunchaku/discussions/new/choose. Otherwise, it will be closed.
+    - label: 2. I will do my best to describe the issue in English.
+- type: textarea
+  attributes:
+    label: Motivation
+    description: |
+      A clear and concise description of the motivation of the feature.
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: Related resources
+    description: |
+      If there is an official code release or third-party implementations, please also provide the information here, which would be very helpful.

.github/workflows/close_inactive_issues.yaml

@@ -0,0 +1,97 @@
+# Borrowed from https://github.com/sgl-project/sglang/blob/main/.github/workflows/close-inactive-issues.yml
+name: Close Inactive Issues
+
+on:
+  schedule:
+    - cron: '0 0 * * *'
+  workflow_dispatch:
+
+permissions:
+  issues: write
+  contents: read
+
+jobs:
+  close-inactive-issues:
+    if: github.repository == 'mit-han-lab/ComfyUI-nunchaku'
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check and close inactive issues
+        uses: actions/github-script@v6
+        with:
+          github-token: ${{secrets.GITHUB_TOKEN}}
+          script: |
+            const sixtyDaysAgo = new Date(Date.now() - 60 * 24 * 60 * 60 * 1000);
+
+            const [owner, repo] = process.env.GITHUB_REPOSITORY.split('/');
+            console.log(`Owner: ${owner}, Repo: ${repo}`);
+
+            async function fetchIssues(page = 1) {
+              console.log(`Fetching issues for ${owner}/${repo}, page ${page}`);
+              return await github.rest.issues.listForRepo({
+                owner,
+                repo,
+                state: 'open',
+                sort: 'updated',
+                direction: 'asc',
+                per_page: 100,
+                page: page
+              });
+            }
+
+            async function processIssues() {
+              console.log('Starting to process issues');
+              console.log(`Repository: ${owner}/${repo}`);
+
+              let page = 1;
+              let hasMoreIssues = true;
+              while (hasMoreIssues) {
+                try {
+                  const issues = await fetchIssues(page);
+                  console.log(`Fetched ${issues.data.length} issues on page ${page}`);
+
+                  if (issues.data.length === 0) {
+                    hasMoreIssues = false;
+                    break;
+                  }
+
+                  for (const issue of issues.data) {
+                    // Skip if the issue has 'good first issue' label
+                    if (issue.labels.some(label => label.name === 'good first issue')) {
+                      console.log(`Skipping issue #${issue.number} as it's marked as 'good first issue'`);
+                      continue;
+                    }
+                    if (new Date(issue.updated_at) < sixtyDaysAgo) {
+                      try {
+                        await github.rest.issues.update({
+                          owner,
+                          repo,
+                          issue_number: issue.number,
+                          state: 'closed',
+                          labels: [...issue.labels.map(l => l.name), 'inactive']
+                        });
+                        await github.rest.issues.createComment({
+                          owner,
+                          repo,
+                          issue_number: issue.number,
+                          body: 'This issue has been automatically closed due to 60-day inactivity. Please feel free to reopen it if needed.'
+                        });
+                        console.log(`Closed issue #${issue.number} due to inactivity.`);
+                      } catch (error) {
+                        console.error(`Failed to close issue #${issue.number}: ${error.message}`);
+                      }
+                    } else {
+                      console.log(`Issue #${issue.number} is still active. Stopping processing.`);
+                      hasMoreIssues = false;
+                      break;
+                    }
+                  }
+                  page += 1;
+                } catch (error) {
+                  console.error(`Error fetching issues on page ${page}: ${error.message}`);
+                  hasMoreIssues = false;
+                }
+              }
+              console.log('Finished processing issues');
+            }
+
+            await processIssues();

.github/workflows/lint.yaml

@@ -0,0 +1,23 @@
+name: Lint
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+      - name: Install dependencies
+        run: pip install ruff
+      - name: Run ruff check
+        run: ruff check nodes --output-format github

.gitignore

@@ -1 +1,205 @@
+# Prerequisites
+*.d
+
+# Compiled Object files
+*.slo
+*.lo
+*.o
+*.obj
+
+# Precompiled Headers
+*.gch
+*.pch
+
+# Compiled Dynamic libraries
+*.so
+*.dylib
+*.dll
+
+# Fortran module files
+*.mod
+*.smod
+
+# Compiled Static libraries
+*.lai
+*.la
+*.a
+*.lib
+
+# Executables
+*.exe
+*.out
+*.app
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
+
+# VS Code
+.vscode/
+!.vscode/settings.json
+
+.gradio/
+
 .DS_Store
+*.log
+*.pt
+*.nsys-rep
+*.ncu-rep

.pre-commit-config.yaml

@@ -0,0 +1,6 @@
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.3.2
+    hooks:
+      - id: ruff
+        args: []

.ruff_cache/.gitignore

@@ -0,0 +1,2 @@
+# Automatically created by ruff.
+*

.ruff_cache/CACHEDIR.TAG

@@ -0,0 +1 @@
+Signature: 8a477f597d28d172789f06886806bc55

LICENCE.txt

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+      any Contribution intentionally submitted for inclusion in the Work
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+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
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+   Copyright [2024] [MIT HAN Lab]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
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+
+       http://www.apache.org/licenses/LICENSE-2.0
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README.md

@@ -0,0 +1,186 @@
+<div align="center" id="nunchaku_logo">
+  <img src="https://raw.githubusercontent.com/mit-han-lab/nunchaku/96615bd93a1f0d2cf98039fddecfec43ce34cc96/assets/nunchaku.svg" alt="logo" width="220"></img>
+</div>
+<h3 align="center">
+<a href="http://arxiv.org/abs/2411.05007"><b>Paper</b></a> | <a href="https://hanlab.mit.edu/projects/svdquant"><b>Website</b></a> | <a href="https://hanlab.mit.edu/blog/svdquant"><b>Blog</b></a> | <a href="https://svdquant.mit.edu"><b>Demo</b></a> | <a href="https://huggingface.co/collections/mit-han-lab/svdquant-67493c2c2e62a1fc6e93f45c"><b>HuggingFace</b></a> | <a href="https://modelscope.cn/collections/svdquant-468e8f780c2641"><b>ModelScope</b></a>
+</h3>
+
+<h3 align="center">
+<a href="README.md"><b>English</b></a> | <a href="README_ZH.md"><b>中文</b></a>
+</h3>
+
+This repository provides the ComfyUI node for [**Nunchaku**](https://github.com/mit-han-lab/nunchaku), an efficient inference engine for 4-bit neural networks quantized with [SVDQuant](http://arxiv.org/abs/2411.05007). For the quantization library, check out [DeepCompressor](https://github.com/mit-han-lab/deepcompressor).
+
+Join our user groups on [**Slack**](https://join.slack.com/t/nunchaku/shared_invite/zt-3170agzoz-NgZzWaTrEj~n2KEV3Hpl5Q), [**Discord**](https://discord.gg/Wk6PnwX9Sm) and [**WeChat**](https://github.com/mit-han-lab/nunchaku/blob/main/assets/wechat.jpg?raw=true) for discussions—details [here](https://github.com/mit-han-lab/nunchaku/issues/149). If you have any questions, run into issues, or are interested in contributing, feel free to share your thoughts with us!
+
+# Nunchaku ComfyUI Node
+
+![comfyui](assets/comfyui.jpg)
+## News
+
+- **[2025-04-16]** 🎥 Released tutorial videos in both [**English**](https://youtu.be/YHAVe-oM7U8?si=cM9zaby_aEHiFXk0) and [**Chinese**](https://www.bilibili.com/video/BV1BTocYjEk5/?share_source=copy_web&vd_source=8926212fef622f25cc95380515ac74ee) to assist installation and usage.
+- **[2025-04-09]** 📢 Published the [April roadmap](https://github.com/mit-han-lab/nunchaku/issues/266) and an [FAQ](https://github.com/mit-han-lab/nunchaku/discussions/262) to help the community get started and stay up to date with Nunchaku’s development.
+- **[2025-04-05]** 🚀 **Release v0.2.0!** This release introduces [**multi-LoRA**](workflows/nunchaku-flux.1-dev.json) and [**ControlNet**](workflows/nunchaku-flux.1-dev-controlnet-union-pro.json) support, with enhanced performance using FP16 attention and First-Block Cache. We've also added [**20-series GPU**](examples/flux.1-dev-turing.py) compatibility and official workflows for [FLUX.1-redux](workflows/nunchaku-flux.1-redux-dev.json)!
+
+## Installation
+
+We provide tutorial videos to help you install and use Nunchaku on Windows, available in both [**English**](https://youtu.be/YHAVe-oM7U8?si=cM9zaby_aEHiFXk0) and [**Chinese**](https://www.bilibili.com/video/BV1BTocYjEk5/?share_source=copy_web&vd_source=8926212fef622f25cc95380515ac74ee). You can also follow the corresponding step-by-step text guide at [`docs/setup_windows.md`](docs/setup_windows.md). If you run into issues, these resources are a good place to start.
+
+### Prerequisites
+
+Please first install `nunchaku` following the instructions in [README.md](https://github.com/mit-han-lab/nunchaku?tab=readme-ov-file#installation). 
+
+### Comfy-CLI
+
+You can easily use [`comfy-cli`](https://github.com/Comfy-Org/comfy-cli) to run ComfyUI with Nunchaku:
+
+```shell
+pip install comfy-cli  # Install ComfyUI CLI  
+comfy install          # Install ComfyUI  
+comfy node registry-install ComfyUI-nunchaku  # Install Nunchaku  
+```
+
+### ComfyUI-Manager
+
+1. Install [ComfyUI](https://github.com/comfyanonymous/ComfyUI/tree/master) with 
+
+   ```shell
+   git clone https://github.com/comfyanonymous/ComfyUI.git
+   cd ComfyUI
+   pip install -r requirements.txt
+   ```
+
+2. Install [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager) with the following commands:
+
+   ```shell
+   cd custom_nodes
+   git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager
+   ```
+
+3. Launch ComfyUI
+
+   ```shell
+   cd ..  # Return to the ComfyUI root directory  
+   python main.py
+   ```
+
+4. Open the Manager, search `ComfyUI-nunchaku` in the Custom Nodes Manager and then install it.
+
+
+### Manual Installation
+1. Set up [ComfyUI](https://github.com/comfyanonymous/ComfyUI/tree/master) with the following commands:
+
+   ```shell
+   git clone https://github.com/comfyanonymous/ComfyUI.git
+   cd ComfyUI
+   pip install -r requirements.txt
+   ```
+
+2. Clone this repository into the `custom_nodes` directory inside ComfyUI:
+
+   ```shell
+   cd custom_nodes
+   git clone https://github.com/mit-han-lab/ComfyUI-nunchaku nunchaku_nodes
+   ```
+
+## Usage
+
+1. **Set Up ComfyUI and Nunchaku**:
+
+     * Nunchaku workflows can be found at [`workflows`](./workflows). To use them, copy the files to `user/default/workflows` in the ComfyUI root directory:
+
+       ```shell
+       cd ComfyUI
+       
+       # Create the workflows directory if it doesn't exist
+       mkdir -p user/default/workflows
+       
+       # Copy workflow configurations
+       cp custom_nodes/nunchaku_nodes/workflows/* user/default/workflows/
+       ```
+
+     * Install any missing nodes (e.g., `comfyui-inpainteasy`) by following [this tutorial](https://github.com/ltdrdata/ComfyUI-Manager?tab=readme-ov-file#support-of-missing-nodes-installation).
+
+2. **Download Required Models**: Follow [this tutorial](https://comfyanonymous.github.io/ComfyUI_examples/flux/) to download the necessary models into the appropriate directories. Alternatively, use the following commands:
+
+   ```shell
+   huggingface-cli download comfyanonymous/flux_text_encoders clip_l.safetensors --local-dir models/text_encoders
+   huggingface-cli download comfyanonymous/flux_text_encoders t5xxl_fp16.safetensors --local-dir models/text_encoders
+   huggingface-cli download black-forest-labs/FLUX.1-schnell ae.safetensors --local-dir models/vae
+   ```
+
+3. **Run ComfyUI**: To start ComfyUI, navigate to its root directory and run `python main.py`. If you are using `comfy-cli`, simply run `comfy launch`.
+
+4. **Select the Nunchaku Workflow**: Choose one of the Nunchaku workflows (workflows that start with `nunchaku-`) to get started. For the `flux.1-fill` workflow, you can use the built-in **MaskEditor** tool to apply a mask over an image.
+
+5. All the 4-bit models are available at our [HuggingFace](https://huggingface.co/collections/mit-han-lab/svdquant-67493c2c2e62a1fc6e93f45c) or [ModelScope](https://modelscope.cn/collections/svdquant-468e8f780c2641) collection. Except [`svdq-flux.1-t5`](https://huggingface.co/mit-han-lab/svdq-flux.1-t5), please download the **entire model folder** to `models/diffusion_models`.
+
+## Nunchaku Nodes
+
+**Note: We've renamed our nodes from 'SVDQuant XXX Loader' to 'Nunchaku XXX Loader'. Please update your workflows accordingly.**
+
+* **Nunchaku Flux DiT Loader**: A node for loading the FLUX diffusion model. 
+
+  * `model_path`: Specifies the model's location. You need to manually download the model folder from our [Hugging Face](https://huggingface.co/collections/mit-han-lab/svdquant-67493c2c2e62a1fc6e93f45c) or [ModelScope](https://modelscope.cn/collections/svdquant-468e8f780c2641) collection. For example, run
+
+    ```shell
+    huggingface-cli download mit-han-lab/svdq-int4-flux.1-dev --local-dir models/diffusion_models/svdq-int4-flux.1-dev
+    ```
+
+    After downloading, set `model_path` to the corresponding folder name. 
+
+    **Note: If you rename the model folder, ensure that `comfy_config.json` is present in the folder. You can find this file in our corresponding repositories on [Hugging Face](https://huggingface.co/collections/mit-han-lab/svdquant-67493c2c2e62a1fc6e93f45c) or [ModelScope](https://modelscope.cn/collections/svdquant-468e8f780c2641).**
+
+  * `cache_threshold`: Controls the [First-Block Cache](https://github.com/chengzeyi/ParaAttention?tab=readme-ov-file#first-block-cache-our-dynamic-caching) tolerance, similar to `residual_diff_threshold` in [WaveSpeed](https://github.com/chengzeyi/Comfy-WaveSpeed). Increasing this value improves speed but may reduce quality. A typical value is 0.12. Setting it to 0 disables the effect.
+    
+  * `attention`: Defines the attention implementation method. You can choose between `flash-attention2` or `nunchaku-fp16`. Our `nunchaku-fp16` is approximately 1.2× faster than `flash-attention2` without compromising precision. For Turing GPUs (20-series), where `flash-attention2` is unsupported, you must use `nunchaku-fp16`.
+    
+  * `cpu_offload`: Enables CPU offloading for the transformer model. While this reduces GPU memory usage, it may slow down inference.
+
+    - When set to `auto`, it will automatically detect your available GPU memory. If your GPU has more than 14GiB of memory, offloading will be disabled. Otherwise, it will be enabled.
+    - **Memory usage will be further optimized in node later.**
+
+  * `device_id`: Indicates the GPU ID for running the model.
+
+  * `data_type`: Defines the data type for the dequantized tensors. Turing GPUs (20-series) do not support `bfloat16` and can only use `float16`.
+
+  * `i2f_mode`: For Turing (20-series) GPUs, this option controls the GEMM implementation mode. `enabled` and `always` modes exhibit minor differences. This option is ignored on other GPU architectures.
+
+* **Nunchaku FLUX LoRA Loader**: A node for loading LoRA modules for SVDQuant FLUX models.
+
+  * Place your LoRA checkpoints in the `models/loras` directory. These will appear as selectable options under `lora_name`.
+  * `lora_strength`: Controls the strength of the LoRA module.
+  * You can connect **multiple LoRA nodes** together.
+  * **Note**: Starting from version 0.2.0, there is no need to convert LoRAs. Simply provide the **original LoRA files** to the loader.
+
+* **Nunchaku Text Encoder Loader**: A node for loading the text encoders.
+
+  * For FLUX, use the following files:
+
+    - `text_encoder1`: `t5xxl_fp16.safetensors` (or FP8/GGUF versions of T5 encoders).
+    - `text_encoder2`: `clip_l.safetensors`
+
+  * `t5_min_length`: Sets the minimum sequence length for T5 text embeddings. The default in `DualCLIPLoader` is hardcoded to 256, but for better image quality, use 512 here.
+
+  * `use_4bit_t5`: Specifies whether you need to use our quantized 4-bit T5 to save GPU memory.
+
+  * `int4_model`: Specifies the INT4 T5 location. This option is only used when `use_4bit_t5` is enabled. You can download our INT4 T5 model folder to `models/text_encoders` from [HuggingFace](https://huggingface.co/mit-han-lab/svdq-flux.1-t5) or [ModelScope](https://modelscope.cn/models/Lmxyy1999/svdq-flux.1-t5). For example, you can run the following command:
+
+       ```shell
+       huggingface-cli download mit-han-lab/svdq-flux.1-t5 --local-dir models/text_encoders/svdq-flux.1-t5
+       ```
+
+       After downloading, specify the corresponding folder name as the `int4_model`.
+
+
+    * **Note**: Currently, loading the **4-bit T5 model** consumes excessive memory. **We will optimize this later.**
+
+
+
+* **FLUX.1 Depth Preprocessor (deprecated)** : A legacy node for loading a depth estimation model and producing a corresponding depth map. The `model_path` parameter specifies the location of the model checkpoint. You can manually download the model repository from [Hugging Face](https://huggingface.co/LiheYoung/depth-anything-large-hf) and place it under the `models/checkpoints` directory. Alternatively, use the following CLI command:
+
+  ```shell
+  huggingface-cli download LiheYoung/depth-anything-large-hf --local-dir models/checkpoints/depth-anything-large-hf
+  ```
+
+  **Note**: This node is deprecated and will be removed in a future release. Please use the updated **"Depth Anything"** node with the `depth_anything_vitl14.pth` model file instead.

README_ZH.md

@@ -0,0 +1,182 @@
+<div align="center" id="nunchaku_logo">
+  <img src="https://raw.githubusercontent.com/mit-han-lab/nunchaku/96615bd93a1f0d2cf98039fddecfec43ce34cc96/assets/nunchaku.svg" alt="logo" width="220"></img>
+</div>
+<h3 align="center">
+<a href="http://arxiv.org/abs/2411.05007"><b>论文</b></a> | <a href="https://hanlab.mit.edu/projects/svdquant"><b>网站</b></a> | <a href="https://hanlab.mit.edu/blog/svdquant"><b>博客</b></a> | <a href="https://svdquant.mit.edu"><b>演示</b></a> | <a href="https://huggingface.co/collections/mit-han-lab/svdquant-67493c2c2e62a1fc6e93f45c"><b>HuggingFace</b></a> | <a href="https://modelscope.cn/collections/svdquant-468e8f780c2641"><b>ModelScope</b></a>
+</h3>
+
+<h3 align="center">
+<a href="README.md"><b>English</b></a> | <a href="README_ZH.md"><b>中文</b></a>
+</h3>
+
+该储存库为[**Nunchaku**](https://github.com/mit-han-lab/nunchaku)提供了ComfyUI节点，这是一个用于使用[SVDQuant](http://arxiv.org/abs/2411.05007)量化的 4 位神经网络的高效推理引擎。有关量化库，请查看 [DeepCompressor](https://github.com/mit-han-lab/deepcompressor).
+
+加入我们，在[**Slack**](https://join.slack.com/t/nunchaku/shared_invite/zt-3170agzoz-NgZzWaTrEj~n2KEV3Hpl5Q), [**Discord**](https://discord.gg/Wk6PnwX9Sm) 和 [**微信**](https://github.com/mit-han-lab/nunchaku/blob/main/assets/wechat.jpg?raw=true) 上的社区群组进行讨论———详情点击[这里](https://github.com/mit-han-lab/nunchaku/issues/149). 如果您有任何问题、遇到问题或有兴趣做出贡献，请随时与我们分享您的想法！
+
+# Nunchaku ComfyUI节点
+
+![comfyui](assets/comfyui.jpg)
+## 最新消息
+
+- **[2025-04-09]** 🎥 发布了[**英文**](https://youtu.be/YHAVe-oM7U8?si=cM9zaby_aEHiFXk0)和[**中文**](https://www.bilibili.com/video/BV1BTocYjEk5/?share_source=copy_web&vd_source=8926212fef622f25cc95380515ac74ee)教程视频，协助安装和使用Nunchaku。
+- **[2025-04-09]** 📢 发布了 [4月更新计划](https://github.com/mit-han-lab/nunchaku/issues/266)和[常见问题解答](https://github.com/mit-han-lab/nunchaku/discussions/262)来帮助社区朋友快速入门并及时了解Nunchaku的发展情况。
+- **[2025-04-05]** 🚀 **v0.2.0发布!** 这个版本支持了[**多LoRA**](workflows/nunchaku-flux.1-dev.json)和[**ControlNet**](workflows/nunchaku-flux.1-dev-controlnet-union-pro.json)，并且使用FP16 attention和First-Block Cache来增强性能. 我们添加了对[**Invidia20系显卡**](examples/flux.1-dev-turing.py)的支持，并制作了[FLUX.1-redux](workflows/nunchaku-flux.1-redux-dev.json)的官方工作流。
+
+## 安装方法
+
+我们提供了在 Windows 上安装和使用 Nunchaku 的教学视频，支持[**英文**](https://youtu.be/YHAVe-oM7U8?si=cM9zaby_aEHiFXk0)和[**中文**](https://www.bilibili.com/video/BV1BTocYjEk5/?share_source=copy_web&vd_source=8926212fef622f25cc95380515ac74ee)两个版本。同时，你也可以参考对应的图文教程 [`docs/setup_windows.md`](docs/setup_windows.md)。如果在安装过程中遇到问题，建议优先查阅这些资源。
+
+### 前置工作
+
+请先参阅[README.md](https://github.com/mit-han-lab/nunchaku?tab=readme-ov-file#installation)来安装 `nunchaku`。
+
+### Comfy-CLI：ComfyUI的命令工具
+
+您可以使用[`comfy-cli`](https://github.com/Comfy-Org/comfy-cli)在ComfyUI中运行Nunchaku：
+
+```shell
+pip install comfy-cli  # Install ComfyUI CLI  
+comfy install          # Install ComfyUI  
+comfy node registry-install ComfyUI-nunchaku  # Install Nunchaku  
+```
+
+### ComfyUI-Manager
+
+1. 首先使用以下指令安装[ComfyUI](https://github.com/comfyanonymous/ComfyUI/tree/master)
+
+   ```shell
+   git clone https://github.com/comfyanonymous/ComfyUI.git
+   cd ComfyUI
+   pip install -r requirements.txt
+   ```
+
+2. 使用以下命令安装[ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager)（这是一个节点管理插件）
+
+   ```shell
+   cd custom_nodes
+   git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager
+   ```
+
+3. 启动ComfyUI
+
+   ```shell
+   cd ..  # Return to the ComfyUI root directory  
+   python main.py
+   ```
+
+4. 打开Manager后, 在Custom Nodes Manager中搜索`ComfyUI-nunchaku`节点并且下载它then install it.
+
+
+### 手动安装
+1. 使用以下命令设置[ComfyUI](https://github.com/comfyanonymous/ComfyUI/tree/master)
+
+   ```shell
+   git clone https://github.com/comfyanonymous/ComfyUI.git
+   cd ComfyUI
+   pip install -r requirements.txt
+   ```
+
+2. 将此仓库克隆到 ComfyUI 中的目录中：`custom_nodes`
+
+   ```shell
+   cd custom_nodes
+   git clone https://github.com/mit-han-lab/ComfyUI-nunchaku nunchaku_nodes
+   ```
+
+## 使用说明
+
+1. **设置ComfyUI和Nunchaku**:
+
+     * Nunchaku的工作流可以在[`workflows`](./workflows)找到。想要找到它们，请将文件复制到ComfyUI的根目录中： `user/default/workflows`
+       ```shell
+       cd ComfyUI
+       
+       # Create the workflows directory if it doesn't exist
+       mkdir -p user/default/workflows
+       
+       # Copy workflow configurations
+       cp custom_nodes/nunchaku_nodes/workflows/* user/default/workflows/
+       ```
+
+     * 按照[本教程](https://github.com/ltdrdata/ComfyUI-Manager?tab=readme-ov-file#support-of-missing-nodes-installation).安装所有缺失节点 (例如 `comfyui-inpainteasy`) 
+
+2. **下载必要模型**: 按照[本教程](https://comfyanonymous.github.io/ComfyUI_examples/flux/)把必要的模型下载到对应的目录中。或者使用以下命令：
+
+   ```shell
+   huggingface-cli download comfyanonymous/flux_text_encoders clip_l.safetensors --local-dir models/text_encoders
+   huggingface-cli download comfyanonymous/flux_text_encoders t5xxl_fp16.safetensors --local-dir models/text_encoders
+   huggingface-cli download black-forest-labs/FLUX.1-schnell ae.safetensors --local-dir models/vae
+   ```
+
+3. **运行ComfyUI**： 要启动 ComfyUI,请导航到其根目录并运行：`python main.py`。如果您使用的是 `comfy-cli`, 请运行 `comfy launch`.
+
+4. **选择Nunchaku工作流**：选择一个Nunchaku工作流开始使用(文件名以`nunchaku-`为开头的工作流)。在使用`flux.1-fill`的工作流时, 可以使用ComfyUI内置的**MaskEditor**工具来涂抹遮罩。
+
+5.所有四位模型都可以在[HuggingFace](https://huggingface.co/collections/mit-han-lab/svdquant-67493c2c2e62a1fc6e93f45c)或者[ModelScope](https://modelscope.cn/collections/svdquant-468e8f780c2641)中找到。除了[`svdq-flux.1-t5`](https://huggingface.co/mit-han-lab/svdq-flux.1-t5)，请将**整个模型文件夹**下载并放入到`models/diffusion_models`文件夹中。
+
+## Nunchaku节点
+
+**注:我们已将“SVDQuant XXX Loader”节点重命名为“Nunchaku XXX Loader”，请更新工作流。**
+
+* **Nunchaku Flux DiT Loader节点**：用于加载Flux扩散模型的节点
+
+  * `model_path`：指定模型的位置。您需要从我们的[Hugging Face](https://huggingface.co/collections/mit-han-lab/svdquant-67493c2c2e62a1fc6e93f45c)或者[ModelScope](https://modelscope.cn/collections/svdquant-468e8f780c2641)中手动下载模型文件夹。例如：运行
+
+    ```shell
+    huggingface-cli download mit-han-lab/svdq-int4-flux.1-dev --local-dir models/diffusion_models/svdq-int4-flux.1-dev
+    ```
+
+    下载完成后, 把`model_path`设置为对应的文件夹名称。
+
+    **注：如果重命名模型文件夹，确保文件夹中包含`comfy_config.json`.您可以在[Hugging Face](https://huggingface.co/collections/mit-han-lab/svdquant-67493c2c2e62a1fc6e93f45c)或者[ModelScope](https://modelscope.cn/collections/svdquant-468e8f780c2641)上的相应存储库中找到此文件。**
+
+  * `cache_threshold`：控制[First-Block Cache](https://github.com/chengzeyi/ParaAttention?tab=readme-ov-file#first-block-cache-our-dynamic-caching)的容差，类似于[WaveSpeed](https://github.com/chengzeyi/Comfy-WaveSpeed)中的`residual_diff_threshold`。增加此值可以提高速度，但可能会降低质量。典型值为 0.12。将其设置为 0 将禁用该效果。
+    
+  * `attention`：定义 attention 的实现方法. 您可以在`flash-attention2`或`nunchaku-fp16`之间进行选择。我们的`nunchaku-fp16`在不影响精度的情况下大约比`flash-attention2`快1.2x倍。对于Turing架构的显卡(20系), 如果不支持`flash-attention2`，则必须使用 `nunchaku-fp16`。
+    
+  * `cpu_offload`：为transformer模型启用CPU卸载。虽然这减少了GPU内存的使用，但它可能会减慢推理速度。
+
+    -当设置为`auto`的时候，它将自动检测您的可用 GPU 内存。如果您的GPU内存超过14GiB，则将禁用卸载。否则，它将启用。
+    - **以后将在节点中进一步优化内存使用。**
+
+  * `device_id`：模型运行时使用的GPU ID。
+
+  * `data_type`：定义去量子化张量的数据类型。Turing架构的GPU(20系)不支持`bfloat16`，只能只用`float16`.
+
+  * `i2f_mode`：对于Turing架构的GPU(20系)，此选项控制GEMM的实现模式。`enabled`和`always`模式的差异细微。在其他架构的GPU上可以忽略这个选项。
+
+* **Nunchaku FLUX LoRA Loader**：用于加载SVDQuant FLUX模型的LoRA模型的节点
+
+  * 将LoRA Checkpints文件放在`models/loras`目录中。这些LoRA模型将在`lora_name`下显示为可选选项。
+  * `lora_strength`：控制LoRA模型的强度。
+  * 您可以将多个**multiple LoRA nodes**模型连接使用
+  * **注**：从0.2.0版本开始，不需要转换LoRA了。可以在加载器中加载原始的LoRA文件
+
+* **Nunchaku Text Encoder Loader**：用于加载文本编码器的节点。
+
+  * 对于FLUX，请使用以下文件：
+
+    - `text_encoder1`: `t5xxl_fp16.safetensors`（或 T5 编码器的 FP8/GGUF 版本）。
+    - `text_encoder2`: `clip_l.safetensors`
+
+  * `t5_min_length`：设置 T5 文本嵌入的最小序列长度。在`DualCLIPLoader`中的默认硬编码为256，但为了获得更好的图像质量，请在此处使用 512。
+
+  * `use_4bit_t5`：指定您是否需要使用我们的量化4位T5来节省GPU内存
+
+  * `int4_model`：指定INT4 T5的位置。这个选项仅在`use_4bit_t5`启用时使用。您可以从[HuggingFace](https://huggingface.co/mit-han-lab/svdq-flux.1-t5)或[ModelScope](https://modelscope.cn/models/Lmxyy1999/svdq-flux.1-t5)下载模型到`models/text_encoders`文件夹。例如，您可以使用以下命令：
+
+       ```shell
+       huggingface-cli download mit-han-lab/svdq-flux.1-t5 --local-dir models/text_encoders/svdq-flux.1-t5
+       ```
+
+       After downloading, specify the corresponding folder name as the `int4_model`.
+  
+  **注意**：目前，加载**4-bit T5 model**会消耗过多内存. **我们将在以后对其进行优化**
+
+* **FLUX.1 Depth Preprocessor (已弃用)**：一个用于加载depth模型并生成相应深度图的旧节点。`model_path`参数指定checkpoint模型的位置。您可以从[Hugging Face](https://huggingface.co/LiheYoung/depth-anything-large-hf) 下载模型并放在`models/checkpoints`目录中。或者，使用以下CLI命令：
+
+  ```shell
+  huggingface-cli download LiheYoung/depth-anything-large-hf --local-dir models/checkpoints/depth-anything-large-hf
+  ```
+
+  **注意**：此节点已弃用，并将在未来发行版中删除。请改用更新后的**"Depth Anything"**节点来替代加载`depth_anything_vitl14.pth`。

__init__.py

@@ -0,0 +1,13 @@
+# only import if running as a custom node
+from .nodes.lora import NunchakuFluxLoraLoader
+from .nodes.models import NunchakuFluxDiTLoader, NunchakuTextEncoderLoader
+from .nodes.preprocessors import FluxDepthPreprocessor
+
+NODE_CLASS_MAPPINGS = {
+    "NunchakuFluxDiTLoader": NunchakuFluxDiTLoader,
+    "NunchakuTextEncoderLoader": NunchakuTextEncoderLoader,
+    "NunchakuFluxLoraLoader": NunchakuFluxLoraLoader,
+    "NunchakuDepthPreprocessor": FluxDepthPreprocessor,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {k: v.TITLE for k, v in NODE_CLASS_MAPPINGS.items()}
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]

nodes/lora/__init__.py

@@ -0,0 +1 @@
+from .flux import NunchakuFluxLoraLoader

nodes/lora/flux.py

@@ -0,0 +1,77 @@
+import copy
+import logging
+
+import folder_paths
+from nunchaku.lora.flux import to_diffusers
+from ..models.flux import ComfyFluxWrapper
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("NunchakuFluxLoraLoader")
+
+
+class NunchakuFluxLoraLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": (
+                    "MODEL",
+                    {"tooltip": "The diffusion model the LoRA will be applied to."},
+                ),
+                "lora_name": (
+                    folder_paths.get_filename_list("loras"),
+                    {"tooltip": "The name of the LoRA."},
+                ),
+                "lora_strength": (
+                    "FLOAT",
+                    {
+                        "default": 1.0,
+                        "min": -100.0,
+                        "max": 100.0,
+                        "step": 0.01,
+                        "tooltip": "How strongly to modify the diffusion model. This value can be negative.",
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    OUTPUT_TOOLTIPS = ("The modified diffusion model.",)
+    FUNCTION = "load_lora"
+    TITLE = "Nunchaku FLUX.1 LoRA Loader"
+
+    CATEGORY = "Nunchaku"
+    DESCRIPTION = (
+        "LoRAs are used to modify the diffusion model, "
+        "altering the way in which latents are denoised such as applying styles. "
+        "You can link multiple LoRA nodes."
+    )
+
+    def load_lora(self, model, lora_name: str, lora_strength: float):
+        model_wrapper = model.model.diffusion_model
+        assert isinstance(model_wrapper, ComfyFluxWrapper)
+
+        transformer = model_wrapper.model
+        model_wrapper.model = None
+        ret_model = copy.deepcopy(model)  # copy everything except the model
+        ret_model_wrapper = ret_model.model.diffusion_model
+        assert isinstance(ret_model_wrapper, ComfyFluxWrapper)
+
+        model_wrapper.model = transformer
+        ret_model_wrapper.model = transformer
+
+        lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
+        ret_model_wrapper.loras.append((lora_path, lora_strength))
+
+        sd = to_diffusers(lora_path)
+        if "transformer.x_embedder.lora_A.weight" in sd:
+            new_in_channels = sd["transformer.x_embedder.lora_A.weight"].shape[1]
+            assert new_in_channels % 4 == 0
+            new_in_channels = new_in_channels // 4
+
+            old_in_channels = ret_model.model.model_config.unet_config["in_channels"]
+            assert old_in_channels <= new_in_channels
+            if old_in_channels < new_in_channels:
+                ret_model.model.model_config.unet_config["in_channels"] = new_in_channels
+
+        return (ret_model,)

nodes/models/__init__.py

@@ -0,0 +1,2 @@
+from .flux import NunchakuFluxDiTLoader
+from .text_encoder import NunchakuTextEncoderLoader

nodes/models/configs/flux.1-canny-dev.json

@@ -0,0 +1,25 @@
+{
+  "model_class": "Flux",
+  "model_config": {
+    "axes_dim": [
+      16,
+      56,
+      56
+    ],
+    "context_in_dim": 4096,
+    "depth": 19,
+    "depth_single_blocks": 38,
+    "disable_unet_model_creation": true,
+    "guidance_embed": true,
+    "hidden_size": 3072,
+    "image_model": "flux",
+    "in_channels": 32,
+    "mlp_ratio": 4.0,
+    "num_heads": 24,
+    "out_channels": 16,
+    "patch_size": 2,
+    "qkv_bias": true,
+    "theta": 10000,
+    "vec_in_dim": 768
+  }
+}

nodes/models/configs/flux.1-depth-dev.json

@@ -0,0 +1,25 @@
+{
+  "model_class": "Flux",
+  "model_config": {
+    "axes_dim": [
+      16,
+      56,
+      56
+    ],
+    "context_in_dim": 4096,
+    "depth": 19,
+    "depth_single_blocks": 38,
+    "disable_unet_model_creation": true,
+    "guidance_embed": true,
+    "hidden_size": 3072,
+    "image_model": "flux",
+    "in_channels": 32,
+    "mlp_ratio": 4.0,
+    "num_heads": 24,
+    "out_channels": 16,
+    "patch_size": 2,
+    "qkv_bias": true,
+    "theta": 10000,
+    "vec_in_dim": 768
+  }
+}

nodes/models/configs/flux.1-dev.json

@@ -0,0 +1,25 @@
+{
+  "model_class": "Flux",
+  "model_config": {
+    "axes_dim": [
+      16,
+      56,
+      56
+    ],
+    "context_in_dim": 4096,
+    "depth": 19,
+    "depth_single_blocks": 38,
+    "disable_unet_model_creation": true,
+    "guidance_embed": true,
+    "hidden_size": 3072,
+    "image_model": "flux",
+    "in_channels": 16,
+    "mlp_ratio": 4.0,
+    "num_heads": 24,
+    "out_channels": 16,
+    "patch_size": 2,
+    "qkv_bias": true,
+    "theta": 10000,
+    "vec_in_dim": 768
+  }
+}

nodes/models/configs/flux.1-fill-dev.json

@@ -0,0 +1,25 @@
+{
+  "model_class": "Flux",
+  "model_config": {
+    "axes_dim": [
+      16,
+      56,
+      56
+    ],
+    "context_in_dim": 4096,
+    "depth": 19,
+    "depth_single_blocks": 38,
+    "disable_unet_model_creation": true,
+    "guidance_embed": true,
+    "hidden_size": 3072,
+    "image_model": "flux",
+    "in_channels": 64,
+    "mlp_ratio": 4.0,
+    "num_heads": 24,
+    "out_channels": 16,
+    "patch_size": 2,
+    "qkv_bias": true,
+    "theta": 10000,
+    "vec_in_dim": 768
+  }
+}

nodes/models/configs/flux.1-schnell.json

@@ -0,0 +1,25 @@
+{
+  "model_class": "FluxSchnell",
+  "model_config": {
+    "axes_dim": [
+      16,
+      56,
+      56
+    ],
+    "context_in_dim": 4096,
+    "depth": 19,
+    "depth_single_blocks": 38,
+    "disable_unet_model_creation": true,
+    "guidance_embed": false,
+    "hidden_size": 3072,
+    "image_model": "flux",
+    "in_channels": 16,
+    "mlp_ratio": 4.0,
+    "num_heads": 24,
+    "out_channels": 16,
+    "patch_size": 2,
+    "qkv_bias": true,
+    "theta": 10000,
+    "vec_in_dim": 768
+  }
+}

nodes/models/configs/shuttle-jaguar.json

@@ -0,0 +1,24 @@
+{
+  "model_class": "FluxSchnell",
+  "model_config": {
+    "axes_dim": [
+      16,
+      56,
+      56
+    ],
+    "context_in_dim": 4096,
+    "depth": 19,
+    "depth_single_blocks": 38,
+    "guidance_embed": false,
+    "hidden_size": 3072,
+    "image_model": "flux",
+    "in_channels": 16,
+    "mlp_ratio": 4.0,
+    "num_heads": 24,
+    "out_channels": 16,
+    "patch_size": 2,
+    "qkv_bias": true,
+    "theta": 10000,
+    "vec_in_dim": 768
+  }
+}

nodes/models/flux.py

@@ -0,0 +1,357 @@
+import json
+import os
+import gc
+
+import torch
+from diffusers import FluxPipeline, FluxTransformer2DModel
+from einops import rearrange
+from torch import nn
+
+import comfy.model_patcher
+import comfy.model_management
+import folder_paths
+from comfy.ldm.common_dit import pad_to_patch_size
+from comfy.supported_models import Flux, FluxSchnell
+from nunchaku import NunchakuFluxTransformer2dModel
+from nunchaku.caching.diffusers_adapters.flux import apply_cache_on_transformer
+from nunchaku.caching.utils import cache_context, create_cache_context
+from nunchaku.lora.flux.compose import compose_lora
+from nunchaku.utils import is_turing, load_state_dict_in_safetensors
+
+
+class ComfyFluxWrapper(nn.Module):
+    def __init__(self, model: NunchakuFluxTransformer2dModel, config):
+        super(ComfyFluxWrapper, self).__init__()
+        self.model = model
+        self.dtype = next(model.parameters()).dtype
+        self.config = config
+        self.loras = []
+
+        self._prev_timestep = None  # for first-block cache
+        self._cache_context = None
+
+    def forward(
+        self,
+        x,
+        timestep,
+        context,
+        y,
+        guidance,
+        control=None,
+        transformer_options={},
+        **kwargs,
+    ):
+        if isinstance(timestep, torch.Tensor):
+            if timestep.numel() == 1:
+                timestep_float = timestep.item()
+            else:
+                timestep_float = timestep.flatten()[0].item()
+        else:
+            assert isinstance(timestep, float)
+            timestep_float = timestep
+
+        model = self.model
+        assert isinstance(model, NunchakuFluxTransformer2dModel)
+
+        bs, c, h, w = x.shape
+        patch_size = self.config["patch_size"]
+        x = pad_to_patch_size(x, (patch_size, patch_size))
+
+        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+
+        h_len = (h + (patch_size // 2)) // patch_size
+        w_len = (w + (patch_size // 2)) // patch_size
+        img_ids = FluxPipeline._prepare_latent_image_ids(bs, h_len, w_len, x.device, x.dtype)
+        txt_ids = torch.zeros((context.shape[1], 3), device=x.device, dtype=x.dtype)
+
+        # load and compose LoRA
+        if self.loras != model.comfy_lora_meta_list:
+            lora_to_be_composed = []
+            for _ in range(max(0, len(model.comfy_lora_meta_list) - len(self.loras))):
+                model.comfy_lora_meta_list.pop()
+                model.comfy_lora_sd_list.pop()
+            for i in range(len(self.loras)):
+                meta = self.loras[i]
+                if i >= len(model.comfy_lora_meta_list):
+                    sd = load_state_dict_in_safetensors(meta[0])
+                    model.comfy_lora_meta_list.append(meta)
+                    model.comfy_lora_sd_list.append(sd)
+                elif model.comfy_lora_meta_list[i] != meta:
+                    if meta[0] != model.comfy_lora_meta_list[i][0]:
+                        sd = load_state_dict_in_safetensors(meta[0])
+                        model.comfy_lora_sd_list[i] = sd
+                    model.comfy_lora_meta_list[i] = meta
+                lora_to_be_composed.append(({k: v for k, v in model.comfy_lora_sd_list[i].items()}, meta[1]))
+
+            composed_lora = compose_lora(lora_to_be_composed)
+
+            if len(composed_lora) == 0:
+                model.reset_lora()
+            else:
+                if "x_embedder.lora_A.weight" in composed_lora:
+                    new_in_channels = composed_lora["x_embedder.lora_A.weight"].shape[1]
+                    current_in_channels = model.x_embedder.in_features
+                    if new_in_channels < current_in_channels:
+                        model.reset_x_embedder()
+                model.update_lora_params(composed_lora)
+
+        controlnet_block_samples = None if control is None else [y.to(x.dtype) for y in control["input"]]
+        controlnet_single_block_samples = None if control is None else [y.to(x.dtype) for y in control["output"]]
+        if getattr(model, "_is_cached", False):
+            if self._prev_timestep is None or self._prev_timestep < timestep_float:
+                self._cache_context = create_cache_context()
+            with cache_context(self._cache_context):
+                out = model(
+                    hidden_states=img,
+                    encoder_hidden_states=context,
+                    pooled_projections=y,
+                    timestep=timestep,
+                    img_ids=img_ids,
+                    txt_ids=txt_ids,
+                    guidance=guidance if self.config["guidance_embed"] else None,
+                    controlnet_block_samples=controlnet_block_samples,
+                    controlnet_single_block_samples=controlnet_single_block_samples,
+                ).sample
+        else:
+            out = model(
+                hidden_states=img,
+                encoder_hidden_states=context,
+                pooled_projections=y,
+                timestep=timestep,
+                img_ids=img_ids,
+                txt_ids=txt_ids,
+                guidance=guidance if self.config["guidance_embed"] else None,
+                controlnet_block_samples=controlnet_block_samples,
+                controlnet_single_block_samples=controlnet_single_block_samples,
+            ).sample
+
+        out = rearrange(
+            out,
+            "b (h w) (c ph pw) -> b c (h ph) (w pw)",
+            h=h_len,
+            w=w_len,
+            ph=patch_size,
+            pw=patch_size,
+        )
+        out = out[:, :, :h, :w]
+
+        self._prev_timestep = timestep_float
+        return out
+
+
+class NunchakuFluxDiTLoader:
+    def __init__(self):
+        self.transformer = None
+        self.model_path = None
+        self.device = None
+        self.cpu_offload = None
+        self.cache_threshold = None
+        self.data_type = None
+        self.patcher = None
+        self.device = comfy.model_management.get_torch_device()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        prefixes = folder_paths.folder_names_and_paths["diffusion_models"][0]
+        local_folders = set()
+        for prefix in prefixes:
+            if os.path.exists(prefix) and os.path.isdir(prefix):
+                local_folders_ = os.listdir(prefix)
+                local_folders_ = [
+                    folder
+                    for folder in local_folders_
+                    if not folder.startswith(".") and os.path.isdir(os.path.join(prefix, folder))
+                ]
+                local_folders.update(local_folders_)
+        model_paths = sorted(list(local_folders))
+        ngpus = torch.cuda.device_count()
+
+        all_turing = True
+        for i in range(torch.cuda.device_count()):
+            if not is_turing(f"cuda:{i}"):
+                all_turing = False
+
+        if all_turing:
+            attention_options = ["nunchaku-fp16"]  # turing GPUs do not support flashattn2
+            dtype_options = ["float16"]
+        else:
+            attention_options = ["nunchaku-fp16", "flash-attention2"]
+            dtype_options = ["bfloat16", "float16"]
+
+        return {
+            "required": {
+                "model_path": (
+                    model_paths,
+                    {"tooltip": "The SVDQuant quantized FLUX.1 models."},
+                ),
+                "cache_threshold": (
+                    "FLOAT",
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 1,
+                        "step": 0.001,
+                        "tooltip": "Adjusts the caching tolerance like `residual_diff_threshold` in WaveSpeed. "
+                        "Increasing the value enhances speed at the cost of quality. "
+                        "A typical setting is 0.12. Setting it to 0 disables the effect.",
+                    },
+                ),
+                "attention": (
+                    attention_options,
+                    {
+                        "default": attention_options[0],
+                        "tooltip": "Attention implementation. The default implementation is `flash-attention2`. "
+                        "`nunchaku-fp16` use FP16 attention, offering ~1.2× speedup. "
+                        "Note that 20-series GPUs can only use `nunchaku-fp16`.",
+                    },
+                ),
+                "cpu_offload": (
+                    ["auto", "enable", "disable"],
+                    {
+                        "default": "auto",
+                        "tooltip": "Whether to enable CPU offload for the transformer model."
+                                   "auto' will enable it if the GPU memory is less than 14G.",
+                    },
+                ),
+                "device_id": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": ngpus - 1,
+                        "step": 1,
+                        "display": "number",
+                        "lazy": True,
+                        "tooltip": "The GPU device ID to use for the model.",
+                    },
+                ),
+                "data_type": (
+                    dtype_options,
+                    {
+                        "default": dtype_options[0],
+                        "tooltip": "Specifies the model's data type. Default is `bfloat16`. "
+                        "For 20-series GPUs, which do not support `bfloat16`, use `float16` instead.",
+                    },
+                ),
+            },
+            "optional": {
+                "i2f_mode": (
+                    ["enabled", "always"],
+                    {
+                        "default": "enabled",
+                        "tooltip": "The GEMM implementation for 20-series GPUs"
+                                   "— this option is only applicable to these GPUs.",
+                    },
+                )
+            },
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "load_model"
+    CATEGORY = "Nunchaku"
+    TITLE = "Nunchaku FLUX DiT Loader"
+
+    def load_model(
+        self,
+        model_path: str,
+        attention: str,
+        cache_threshold: float,
+        cpu_offload: str,
+        device_id: int,
+        data_type: str,
+        **kwargs,
+    ) -> tuple[FluxTransformer2DModel]:
+        device = f"cuda:{device_id}"
+        prefixes = folder_paths.folder_names_and_paths["diffusion_models"][0]
+        for prefix in prefixes:
+            if os.path.exists(os.path.join(prefix, model_path)):
+                model_path = os.path.join(prefix, model_path)
+                break
+
+        # Check if the device_id is valid
+        if device_id >= torch.cuda.device_count():
+            raise ValueError(f"Invalid device_id: {device_id}. Only {torch.cuda.device_count()} GPUs available.")
+
+        # Get the GPU properties
+        gpu_properties = torch.cuda.get_device_properties(device_id)
+        gpu_memory = gpu_properties.total_memory / (1024**2)  # Convert to MiB
+        gpu_name = gpu_properties.name
+        print(f"GPU {device_id} ({gpu_name}) Memory: {gpu_memory} MiB")
+
+        # Check if CPU offload needs to be enabled
+        if cpu_offload == "auto":
+            if gpu_memory < 14336:  # 14GB threshold
+                cpu_offload_enabled = True
+                print("VRAM < 14GiB，enable CPU offload")
+            else:
+                cpu_offload_enabled = False
+                print("VRAM > 14GiB，disable CPU offload")
+        elif cpu_offload == "enable":
+            cpu_offload_enabled = True
+            print("Enable CPU offload")
+        else:
+            cpu_offload_enabled = False
+            print("Disable CPU offload")
+
+        if (
+            self.model_path != model_path
+            or self.device != device
+            or self.cpu_offload != cpu_offload_enabled
+            or self.cache_threshold != cache_threshold
+            or self.data_type != data_type
+        ):
+            if self.transformer is not None:
+                model_size = comfy.model_management.module_size(self.transformer)
+                transformer = self.transformer
+                self.transformer = None
+                transformer.to('cpu')
+                del transformer
+                gc.collect()
+                comfy.model_management.cleanup_models_gc()
+                comfy.model_management.soft_empty_cache()
+                comfy.model_management.free_memory(model_size, device)
+
+            self.transformer = NunchakuFluxTransformer2dModel.from_pretrained(
+                model_path,
+                offload=cpu_offload_enabled,
+                device=device,
+                torch_dtype=torch.float16 if data_type == "float16" else torch.bfloat16,
+            )
+            self.transformer = apply_cache_on_transformer(
+                transformer=self.transformer, residual_diff_threshold=cache_threshold
+            )
+            self.model_path = model_path
+            self.device = device
+            self.cpu_offload = cpu_offload_enabled
+            self.cache_threshold = cache_threshold
+
+        transformer = self.transformer
+        if attention == "nunchaku-fp16":
+            transformer.set_attention_impl("nunchaku-fp16")
+        else:
+            assert attention == "flash-attention2"
+            transformer.set_attention_impl("flashattn2")
+
+        if os.path.exists(os.path.join(model_path, "comfy_config.json")):
+            config_path = os.path.join(model_path, "comfy_config.json")
+        else:
+            default_config_root = os.path.join(os.path.dirname(__file__), "configs")
+            config_name = os.path.basename(model_path).replace("svdq-int4-", "").replace("svdq-fp4-", "")
+            config_path = os.path.join(default_config_root, f"{config_name}.json")
+            assert os.path.exists(config_path), f"Config file not found: {config_path}"
+
+        print(f"Loading configuration from {config_path}")
+        comfy_config = json.load(open(config_path, "r"))
+        model_class_name = comfy_config["model_class"]
+        if model_class_name == "FluxSchnell":
+            model_class = FluxSchnell
+        else:
+            assert model_class_name == "Flux", f"Unknown model class {model_class_name}."
+            model_class = Flux
+        model_config = model_class(comfy_config["model_config"])
+        model_config.set_inference_dtype(torch.bfloat16, None)
+        model_config.custom_operations = None
+        model = model_config.get_model({})
+        model.diffusion_model = ComfyFluxWrapper(transformer, config=comfy_config["model_config"])
+        model = comfy.model_patcher.ModelPatcher(model, device, device_id)
+        return (model,)

nodes/models/text_encoder.py

@@ -0,0 +1,139 @@
+import os
+import types
+
+import torch
+from torch import nn
+from transformers import T5EncoderModel
+
+import comfy.sd
+import folder_paths
+from nunchaku import NunchakuT5EncoderModel
+
+
+def nunchaku_t5_forward(
+    self: T5EncoderModel,
+    input_ids: torch.LongTensor,
+    attention_mask,
+    embeds=None,
+    intermediate_output=None,
+    final_layer_norm_intermediate=True,
+    dtype: str | torch.dtype = torch.bfloat16,
+    **kwargs,
+):
+    assert attention_mask is None
+    assert intermediate_output is None
+    assert final_layer_norm_intermediate
+    outputs = self.encoder(input_ids=input_ids, inputs_embeds=embeds, attention_mask=attention_mask)
+    hidden_states = outputs["last_hidden_state"]
+    hidden_states = hidden_states.to(dtype=dtype)
+    return hidden_states, None
+
+
+class WrappedEmbedding(nn.Module):
+    def __init__(self, embedding: nn.Embedding):
+        super().__init__()
+        self.embedding = embedding
+
+    def forward(self, input: torch.Tensor, out_dtype: torch.dtype | None = None):
+        return self.embedding(input)
+
+    @property
+    def weight(self):
+        return self.embedding.weight
+
+
+class NunchakuTextEncoderLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        prefixes = folder_paths.folder_names_and_paths["text_encoders"][0]
+        local_folders = set()
+        for prefix in prefixes:
+            if os.path.exists(prefix) and os.path.isdir(prefix):
+                local_folders_ = os.listdir(prefix)
+                local_folders_ = [
+                    folder
+                    for folder in local_folders_
+                    if not folder.startswith(".") and os.path.isdir(os.path.join(prefix, folder))
+                ]
+                local_folders.update(local_folders_)
+        model_paths = ["none"] + sorted(list(local_folders))
+        return {
+            "required": {
+                "model_type": (["flux"],),
+                "text_encoder1": (folder_paths.get_filename_list("text_encoders"),),
+                "text_encoder2": (folder_paths.get_filename_list("text_encoders"),),
+                "t5_min_length": (
+                    "INT",
+                    {
+                        "default": 512,
+                        "min": 256,
+                        "max": 1024,
+                        "step": 128,
+                        "display": "number",
+                        "lazy": True,
+                    },
+                ),
+                "use_4bit_t5": (["disable", "enable"],),
+                "int4_model": (
+                    model_paths,
+                    {"tooltip": "The name of the 4-bit T5 model."},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "load_text_encoder"
+
+    CATEGORY = "Nunchaku"
+
+    TITLE = "Nunchaku Text Encoder Loader"
+
+    def load_text_encoder(
+        self,
+        model_type: str,
+        text_encoder1: str,
+        text_encoder2: str,
+        t5_min_length: int,
+        use_4bit_t5: str,
+        int4_model: str,
+    ):
+        text_encoder_path1 = folder_paths.get_full_path_or_raise("text_encoders", text_encoder1)
+        text_encoder_path2 = folder_paths.get_full_path_or_raise("text_encoders", text_encoder2)
+        if model_type == "flux":
+            clip_type = comfy.sd.CLIPType.FLUX
+        else:
+            raise ValueError(f"Unknown type {model_type}")
+
+        clip = comfy.sd.load_clip(
+            ckpt_paths=[text_encoder_path1, text_encoder_path2],
+            embedding_directory=folder_paths.get_folder_paths("embeddings"),
+            clip_type=clip_type,
+        )
+
+        if model_type == "flux":
+            clip.tokenizer.t5xxl.min_length = t5_min_length
+
+        if use_4bit_t5 == "enable":
+            assert int4_model != "none", "Please select a 4-bit T5 model."
+            transformer = clip.cond_stage_model.t5xxl.transformer
+            param = next(transformer.parameters())
+            dtype = param.dtype
+            device = param.device
+
+            prefixes = folder_paths.folder_names_and_paths["text_encoders"][0]
+            model_path = None
+            for prefix in prefixes:
+                if os.path.exists(os.path.join(prefix, int4_model)):
+                    model_path = os.path.join(prefix, int4_model)
+                    break
+            if model_path is None:
+                model_path = int4_model
+            transformer = NunchakuT5EncoderModel.from_pretrained(model_path)
+            transformer.forward = types.MethodType(nunchaku_t5_forward, transformer)
+            transformer.shared = WrappedEmbedding(transformer.shared)
+
+            clip.cond_stage_model.t5xxl.transformer = (
+                transformer.to(device=device, dtype=dtype) if device.type == "cuda" else transformer
+            )
+
+        return (clip,)

nodes/preprocessors/__init__.py

@@ -0,0 +1 @@
+from .depth import FluxDepthPreprocessor

nodes/preprocessors/depth.py

@@ -0,0 +1,49 @@
+import os
+
+import folder_paths
+import numpy as np
+import torch
+
+
+class FluxDepthPreprocessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        model_paths = []
+        prefix = os.path.join(folder_paths.models_dir, "checkpoints")
+        local_folders = os.listdir(prefix)
+        local_folders = sorted(
+            [
+                folder
+                for folder in local_folders
+                if not folder.startswith(".") and os.path.isdir(os.path.join(prefix, folder))
+            ]
+        )
+        model_paths = local_folders + model_paths
+        return {
+            "required": {
+                "image": ("IMAGE", {}),
+                "model_path": (
+                    model_paths,
+                    {"tooltip": "Name of the depth preprocessor model."},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "depth_preprocess"
+    CATEGORY = "Nunchaku"
+    TITLE = "FLUX.1 Depth Preprocessor"
+
+    def depth_preprocess(self, image, model_path):
+        prefixes = folder_paths.folder_names_and_paths["checkpoints"][0]
+        for prefix in prefixes:
+            if os.path.exists(os.path.join(prefix, model_path)):
+                model_path = os.path.join(prefix, model_path)
+                break
+        from image_gen_aux import DepthPreprocessor
+
+        processor = DepthPreprocessor.from_pretrained(model_path)
+        np_image = np.asarray(image)
+        np_result = np.array(processor(np_image)[0].convert("RGB"))
+        out_tensor = torch.from_numpy(np_result.astype(np.float32) / 255.0).unsqueeze(0)
+        return (out_tensor,)

pyproject.toml

@@ -0,0 +1,23 @@
+[project]
+name = "ComfyUI-nunchaku"
+description = "Nunchaku ComfyUI Node. Nunchaku is the inference that supports SVDQuant. SVDQuant is a new post-training training quantization paradigm for diffusion models, which quantize both the weights and activations of FLUX.1 to 4 bits, achieving 3.5× memory and 8.7× latency reduction on a 16GB laptop 4090 GPU. See more details: https://github.com/mit-han-lab/nunchaku"
+version = "0.2.0"
+license = { file = "LICENSE.txt" }
+dependencies = []
+requires-python = ">=3.10, <3.13"
+
+[tool.ruff]
+line-length = 120
+
+[tool.ruff.lint]
+select = ["E", "W", "F"]
+ignore = ["F401"] 
+
+[project.urls]
+Repository = "https://github.com/mit-han-lab/ComfyUI-nunchaku"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "lmxyy1999"
+DisplayName = "ComfyUI-nunchaku"
+Icon = "https://raw.githubusercontent.com/mit-han-lab/nunchaku/96615bd93a1f0d2cf98039fddecfec43ce34cc96/assets/nunchaku.svg"

requirements.txt

@@ -0,0 +1,6 @@
+GPUtil
+diffusers>=0.32.2
+accelerate
+sentencepiece
+protobuf
+huggingface_hub

workflows/nunchaku-flux.1-canny-lora.json

@@ -0,0 +1,880 @@
+{
+  "id": "b2ac78a1-9f70-4787-baf7-9703dc66e15e",
+  "revision": 0,
+  "last_node_id": 43,
+  "last_link_id": 84,
+  "nodes": [
+    {
+      "id": 38,
+      "type": "ImageScale",
+      "pos": [
+        379.69903564453125,
+        565.2651977539062
+      ],
+      "size": [
+        315,
+        130
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "localized_name": "image",
+          "name": "image",
+          "type": "IMAGE",
+          "link": 75
+        }
+      ],
+      "outputs": [
+        {
+          "localized_name": "IMAGE",
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "slot_index": 0,
+          "links": [
+            76
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.24",
+        "Node name for S&R": "ImageScale"
+      },
+      "widgets_values": [
+        "nearest-exact",
+        1024,
+        1024,
+        "center"
+      ]
+    },
+    {
+      "id": 35,
+      "type": "InstructPixToPixConditioning",
+      "pos": [
+        1031.3619384765625,
+        153.57142639160156
+      ],
+      "size": [
+        235.1999969482422,
+        86
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "positive",
+          "localized_name": "positive",
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 67
+        },
+        {
+          "label": "negative",
+          "localized_name": "negative",
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 68
+        },
+        {
+          "label": "vae",
+          "localized_name": "vae",
+          "name": "vae",
+          "type": "VAE",
+          "link": 69
+        },
+        {
+          "label": "pixels",
+          "localized_name": "pixels",
+          "name": "pixels",
+          "type": "IMAGE",
+          "link": 70
+        }
+      ],
+      "outputs": [
+        {
+          "label": "positive",
+          "localized_name": "positive",
+          "name": "positive",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            64
+          ]
+        },
+        {
+          "label": "negative",
+          "localized_name": "negative",
+          "name": "negative",
+          "type": "CONDITIONING",
+          "slot_index": 1,
+          "links": [
+            65
+          ]
+        },
+        {
+          "label": "latent",
+          "localized_name": "latent",
+          "name": "latent",
+          "type": "LATENT",
+          "slot_index": 2,
+          "links": [
+            66
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.24",
+        "Node name for S&R": "InstructPixToPixConditioning"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 18,
+      "type": "Canny",
+      "pos": [
+        744.2684936523438,
+        566.853515625
+      ],
+      "size": [
+        315,
+        82
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "image",
+          "localized_name": "image",
+          "name": "image",
+          "type": "IMAGE",
+          "link": 76
+        }
+      ],
+      "outputs": [
+        {
+          "label": "IMAGE",
+          "localized_name": "IMAGE",
+          "name": "IMAGE",
+          "shape": 3,
+          "type": "IMAGE",
+          "slot_index": 0,
+          "links": [
+            26,
+            70
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.24",
+        "Node name for S&R": "Canny"
+      },
+      "widgets_values": [
+        0.15,
+        0.3
+      ]
+    },
+    {
+      "id": 32,
+      "type": "VAELoader",
+      "pos": [
+        1071.1641845703125,
+        376.27001953125
+      ],
+      "size": [
+        315,
+        58
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "label": "VAE",
+          "localized_name": "VAE",
+          "name": "VAE",
+          "type": "VAE",
+          "slot_index": 0,
+          "links": [
+            60,
+            69
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.24",
+        "Node name for S&R": "VAELoader"
+      },
+      "widgets_values": [
+        "ae.safetensors"
+      ]
+    },
+    {
+      "id": 8,
+      "type": "VAEDecode",
+      "pos": [
+        1498.102294921875,
+        368.63604736328125
+      ],
+      "size": [
+        210,
+        46
+      ],
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "samples",
+          "localized_name": "samples",
+          "name": "samples",
+          "type": "LATENT",
+          "link": 7
+        },
+        {
+          "label": "vae",
+          "localized_name": "vae",
+          "name": "vae",
+          "type": "VAE",
+          "link": 60
+        }
+      ],
+      "outputs": [
+        {
+          "label": "IMAGE",
+          "localized_name": "IMAGE",
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "slot_index": 0,
+          "links": [
+            9
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.24",
+        "Node name for S&R": "VAEDecode"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 9,
+      "type": "SaveImage",
+      "pos": [
+        1738.742919921875,
+        57.38672637939453
+      ],
+      "size": [
+        828.9535522460938,
+        893.8475341796875
+      ],
+      "flags": {},
+      "order": 14,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "images",
+          "localized_name": "images",
+          "name": "images",
+          "type": "IMAGE",
+          "link": 9
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.24"
+      },
+      "widgets_values": [
+        "ComfyUI",
+        ""
+      ]
+    },
+    {
+      "id": 26,
+      "type": "FluxGuidance",
+      "pos": [
+        899.2306518554688,
+        8.470590591430664
+      ],
+      "size": [
+        317.4000244140625,
+        58
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "conditioning",
+          "localized_name": "conditioning",
+          "name": "conditioning",
+          "type": "CONDITIONING",
+          "link": 41
+        }
+      ],
+      "outputs": [
+        {
+          "label": "CONDITIONING",
+          "localized_name": "CONDITIONING",
+          "name": "CONDITIONING",
+          "shape": 3,
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            67
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.24",
+        "Node name for S&R": "FluxGuidance"
+      },
+      "widgets_values": [
+        30
+      ]
+    },
+    {
+      "id": 7,
+      "type": "CLIPTextEncode",
+      "pos": [
+        531.693359375,
+        458.5068054199219
+      ],
+      "size": [
+        425.27801513671875,
+        180.6060791015625
+      ],
+      "flags": {
+        "collapsed": true
+      },
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "clip",
+          "localized_name": "clip",
+          "name": "clip",
+          "type": "CLIP",
+          "link": 63
+        }
+      ],
+      "outputs": [
+        {
+          "label": "CONDITIONING",
+          "localized_name": "CONDITIONING",
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            68
+          ]
+        }
+      ],
+      "title": "CLIP Text Encode (Negative Prompt)",
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.24",
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        ""
+      ],
+      "color": "#322",
+      "bgcolor": "#533"
+    },
+    {
+      "id": 34,
+      "type": "DualCLIPLoader",
+      "pos": [
+        490.4931640625,
+        251.2148895263672
+      ],
+      "size": [
+        315,
+        122
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "label": "CLIP",
+          "localized_name": "CLIP",
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            62,
+            63
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.24",
+        "Node name for S&R": "DualCLIPLoader"
+      },
+      "widgets_values": [
+        "clip_l.safetensors",
+        "t5xxl_fp16.safetensors",
+        "flux",
+        "default"
+      ]
+    },
+    {
+      "id": 3,
+      "type": "KSampler",
+      "pos": [
+        1340.538818359375,
+        -73.13742065429688
+      ],
+      "size": [
+        315,
+        262
+      ],
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "model",
+          "localized_name": "model",
+          "name": "model",
+          "type": "MODEL",
+          "link": 84
+        },
+        {
+          "label": "positive",
+          "localized_name": "positive",
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 64
+        },
+        {
+          "label": "negative",
+          "localized_name": "negative",
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 65
+        },
+        {
+          "label": "latent_image",
+          "localized_name": "latent_image",
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 66
+        }
+      ],
+      "outputs": [
+        {
+          "label": "LATENT",
+          "localized_name": "LATENT",
+          "name": "LATENT",
+          "type": "LATENT",
+          "slot_index": 0,
+          "links": [
+            7
+          ]
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.24",
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        107906103108925,
+        "randomize",
+        20,
+        1,
+        "euler",
+        "normal",
+        1
+      ]
+    },
+    {
+      "id": 17,
+      "type": "LoadImage",
+      "pos": [
+        6.694743633270264,
+        562.3865966796875
+      ],
+      "size": [
+        315,
+        314.0000305175781
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "label": "IMAGE",
+          "localized_name": "IMAGE",
+          "name": "IMAGE",
+          "shape": 3,
+          "type": "IMAGE",
+          "slot_index": 0,
+          "links": [
+            75
+          ]
+        },
+        {
+          "label": "MASK",
+          "localized_name": "MASK",
+          "name": "MASK",
+          "shape": 3,
+          "type": "MASK",
+          "links": null
+        }
+      ],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.24",
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "robot.png",
+        "image",
+        ""
+      ]
+    },
+    {
+      "id": 19,
+      "type": "PreviewImage",
+      "pos": [
+        1149.2786865234375,
+        565.11669921875
+      ],
+      "size": [
+        439.3782653808594,
+        481.0416259765625
+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "images",
+          "localized_name": "images",
+          "name": "images",
+          "type": "IMAGE",
+          "link": 26
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.24",
+        "Node name for S&R": "PreviewImage"
+      },
+      "widgets_values": [
+        ""
+      ]
+    },
+    {
+      "id": 23,
+      "type": "CLIPTextEncode",
+      "pos": [
+        442.4932861328125,
+        14.832307815551758
+      ],
+      "size": [
+        422.84503173828125,
+        164.31304931640625
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "label": "clip",
+          "localized_name": "clip",
+          "name": "clip",
+          "type": "CLIP",
+          "link": 62
+        }
+      ],
+      "outputs": [
+        {
+          "label": "CONDITIONING",
+          "localized_name": "CONDITIONING",
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
+            41
+          ]
+        }
+      ],
+      "title": "CLIP Text Encode (Positive Prompt)",
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.3.24",
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts, yarn art style"
+      ],
+      "color": "#232",
+      "bgcolor": "#353"
+    },
+    {
+      "id": 42,
+      "type": "NunchakuFluxDiTLoader",
+      "pos": [
+        78.86559295654297,
+        -122.51927185058594
+      ],
+      "size": [
+        315,
+        202
+      ],
+      "flags": {},
+      "order": 3,
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workflows/nunchaku-flux.1-depth.json

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workflows/nunchaku-flux.1-dev.json

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+          "localized_name": "latent_image",
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+            "name": "height"
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+      "outputs": [],
+      "properties": {
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+      },
+      "widgets_values": [
+        "The reference sampling implementation auto adjusts the shift value based on the resolution, if you don't want this you can just bypass (CTRL-B) this ModelSamplingFlux node.\n"
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
+    {
+      "id": 28,
+      "type": "Note",
+      "pos": [
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+      "flags": {},
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+      "inputs": [],
+      "outputs": [],
+      "properties": {
+        "text": ""
+      },
+      "widgets_values": [
+        "If you get an error in any of the nodes above make sure the files are in the correct directories.\n\nSee the top of the examples page for the links : https://comfyanonymous.github.io/ComfyUI_examples/flux/\n\nflux1-dev.safetensors goes in: ComfyUI/models/unet/\n\nt5xxl_fp16.safetensors and clip_l.safetensors go in: ComfyUI/models/clip/\n\nae.safetensors goes in: ComfyUI/models/vae/\n\n\nTip: You can set the weight_dtype above to one of the fp8 types if you have memory issues."
+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
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+          "type": "CLIP_VISION_OUTPUT",
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+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "slot_index": 0,
+          "links": [
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+          "type": "GUIDER",
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+      ],
+      "color": "#432",
+      "bgcolor": "#653"
+    },
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+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
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+          "links": [
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+        "ver": "0.3.27",
+        "Node name for S&R": "CLIPTextEncode"
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