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[major] update the lora conversion instructions; add customized lora comfyui... 

[major] update the lora conversion instructions; add customized lora comfyui nodes; upgrade the version
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README.md
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...@@ -4,7 +4,8 @@ Nunchaku is an inference engine designed for 4-bit diffusion models, as demonstr ...@@ -4,7 +4,8 @@ Nunchaku is an inference engine designed for 4-bit diffusion models, as demonstr
### [Paper](http://arxiv.org/abs/2411.05007) | [Project](https://hanlab.mit.edu/projects/svdquant) | [Blog](https://hanlab.mit.edu/blog/svdquant) | [Demo](https://svdquant.mit.edu) ### [Paper](http://arxiv.org/abs/2411.05007) | [Project](https://hanlab.mit.edu/projects/svdquant) | [Blog](https://hanlab.mit.edu/blog/svdquant) | [Demo](https://svdquant.mit.edu)
- **[2025-02-14]** 🔥 [LoRA conversion script](nunchaku/convert_lora.py) is now available! - **[2025-02-18]** 🔥 [**Customized LoRA conversion**](#Customized LoRA) and [**model quantization**](#Customized Model Quantization) instructions are now available! **[ComfyUI](./comfyui)** workflows now support **customized LoRA**, along with **FLUX.1-Tools**!
- **[2025-02-14]** 🔥 **[LoRA conversion script](nunchaku/convert_lora.py)** is now available! [ComfyUI FLUX.1-tools workflows](./comfyui) is released!
- **[2025-02-11]** 🎉 **[SVDQuant](http://arxiv.org/abs/2411.05007) has been selected as a ICLR 2025 Spotlight! FLUX.1-tools Gradio demos are now available!** Check [here](#gradio-demos) for the usage details! Our new [depth-to-image demo](https://svdquant.mit.edu/flux1-depth-dev/) is also online—try it out! - **[2025-02-11]** 🎉 **[SVDQuant](http://arxiv.org/abs/2411.05007) has been selected as a ICLR 2025 Spotlight! FLUX.1-tools Gradio demos are now available!** Check [here](#gradio-demos) for the usage details! Our new [depth-to-image demo](https://svdquant.mit.edu/flux1-depth-dev/) is also online—try it out!
- **[2025-02-04]** **🚀 4-bit [FLUX.1-tools](https://blackforestlabs.ai/flux-1-tools/) is here!** Enjoy a **2-3× speedup** over the original models. Check out the [examples](./examples) for usage. **ComfyUI integration is coming soon!** - **[2025-02-04]** **🚀 4-bit [FLUX.1-tools](https://blackforestlabs.ai/flux-1-tools/) is here!** Enjoy a **2-3× speedup** over the original models. Check out the [examples](./examples) for usage. **ComfyUI integration is coming soon!**
- **[2025-01-23]** 🚀 **4-bit [SANA](https://nvlabs.github.io/Sana/) support is here!** Experience a 2-3× speedup compared to the 16-bit model. Check out the [usage example](./examples/sana_1600m_pag.py) and the [deployment guide](app/sana/t2i) for more details. Explore our live demo at [svdquant.mit.edu](https://svdquant.mit.edu)! - **[2025-01-23]** 🚀 **4-bit [SANA](https://nvlabs.github.io/Sana/) support is here!** Experience a 2-3× speedup compared to the 16-bit model. Check out the [usage example](./examples/sana_1600m_pag.py) and the [deployment guide](app/sana/t2i) for more details. Explore our live demo at [svdquant.mit.edu](https://svdquant.mit.edu)!
...@@ -54,8 +55,8 @@ SVDQuant is a post-training quantization technique for 4-bit weights and activat ...@@ -54,8 +55,8 @@ SVDQuant is a post-training quantization technique for 4-bit weights and activat
conda create -n nunchaku python=3.11 conda create -n nunchaku python=3.11
conda activate nunchaku conda activate nunchaku
pip install torch torchvision torchaudio pip install torch torchvision torchaudio
pip install diffusers ninja wheel transformers accelerate sentencepiece protobuf pip install ninja wheel diffusers transformers accelerate sentencepiece protobuf huggingface_hub
pip install huggingface_hub peft opencv-python einops gradio spaces GPUtil pip install peft opencv-python gradio spaces GPUtil # For gradio demos
``` ```
2. Install `nunchaku` package: 2. Install `nunchaku` package:
...@@ -74,6 +75,8 @@ SVDQuant is a post-training quantization technique for 4-bit weights and activat ...@@ -74,6 +75,8 @@ SVDQuant is a post-training quantization technique for 4-bit weights and activat
pip install -e . --no-build-isolation pip install -e . --no-build-isolation
``` ```
[Optional] You can verify your installation by running `python -m nunchaku.test`. This will execute our 4-bit FLUX.1-schnell model, which may take some time to download.
## Usage Example ## Usage Example
In [examples](examples), we provide minimal scripts for running INT4 [FLUX.1](https://github.com/black-forest-labs/flux) and [SANA](https://github.com/NVlabs/Sana) models with Nunchaku. For example, the [script](examples/flux.1-dev.py) for [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) is as follows: In [examples](examples), we provide minimal scripts for running INT4 [FLUX.1](https://github.com/black-forest-labs/flux) and [SANA](https://github.com/NVlabs/Sana) models with Nunchaku. For example, the [script](examples/flux.1-dev.py) for [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) is as follows:
...@@ -94,6 +97,73 @@ image.save("flux.1-dev.png") ...@@ -94,6 +97,73 @@ image.save("flux.1-dev.png")
Specifically, `nunchaku` shares the same APIs as [diffusers](https://github.com/huggingface/diffusers) and can be used in a similar way. Specifically, `nunchaku` shares the same APIs as [diffusers](https://github.com/huggingface/diffusers) and can be used in a similar way.
## Customized LoRA
![lora](./assets/lora.jpg)
[SVDQuant](http://arxiv.org/abs/2411.05007) seamlessly integrates with off-the-shelf LoRAs without requiring requantization. To convert your LoRA safetensors to our format, use the following command:
```shell
python -m nunchaku.lora.flux.convert \
--quant-path mit-han-lab/svdq-int4-flux.1-dev/transformer_blocks.safetensors \
--lora-path aleksa-codes/flux-ghibsky-illustration/lora.safetensors \
--lora-format diffusers \
--output-root ./nunchaku_loras \
--lora-name svdq-int4-flux.1-dev-ghibsky
```
Argument Details:
- `--quant-path`: The path to the quantized base model. It can be a local path or a remote Hugging Face model. For example, you can use [`mit-han-lab/svdq-int4-flux.1-dev/transformer_blocks.safetensors`](https://huggingface.co/mit-han-lab/svdq-int4-flux.1-dev/blob/main/transformer_blocks.safetensors) for FLUX.1-dev.
- `--lora-path`: The path to your LoRA safetensors, which can also be a local or remote Hugging Face model.
- `--lora-format`: Specifies the LoRA format. Supported formats include:
- `diffusers` (e.g., [aleksa-codes/flux-ghibsky-illustration](https://huggingface.co/aleksa-codes/flux-ghibsky-illustration))
- `comfyui` (e.g., [Shakker-Labs/FLUX.1-dev-LoRA-Children-Simple-Sketch](https://huggingface.co/Shakker-Labs/FLUX.1-dev-LoRA-Children-Simple-Sketch))
- `xlab` (e.g., [XLabs-AI/flux-RealismLora](https://huggingface.co/XLabs-AI/flux-RealismLora))
- `--output-root`: Specifies the output directory for the converted LoRA.
- `--lora-name`: Sets the name of the converted LoRA file (without `.safetensors` extension).
After converting your LoRA, you can use your converted weight with:
```python
transformer.update_lora_params(path_to_your_converted_lora)
transformer.set_lora_strength(lora_strength)
```
`path_to_your_lora` can also be a remote HuggingFace path. In [examples/flux.1-dev-lora.py](examples/flux.1-dev-lora.py), we provide a minimal example script for running [Ghibsky](https://huggingface.co/aleksa-codes/flux-ghibsky-illustration) LoRA with SVDQuant's INT4 FLUX.1-dev:
```python
import torch
from diffusers import FluxPipeline
from nunchaku.models.transformer_flux import NunchakuFluxTransformer2dModel
transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-int4-flux.1-dev")
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
### LoRA Related Code ###
transformer.update_lora_params(
"mit-han-lab/svdquant-lora-collection/svdq-int4-flux.1-dev-ghibsky.safetensors"
) # Path to your converted LoRA safetensors, can also be a remote HuggingFace path
transformer.set_lora_strength(1) # Your LoRA strength here
### End of LoRA Related Code ###
image = pipeline(
"GHIBSKY style, cozy mountain cabin covered in snow, with smoke curling from the chimney and a warm, inviting light spilling through the windows",
num_inference_steps=25,
guidance_scale=3.5,
).images[0]
image.save("flux.1-dev-ghibsky.png")
```
**For ComfyUI users, we have implemented a node to convert the LoRA weights on the fly. All you need to do is specify the correct LoRA format. Please refer to Please refer to [comfyui/README.md](comfyui/README.md) for more details.**
## ComfyUI ## ComfyUI
Please refer to [comfyui/README.md](comfyui/README.md) for the usage in [ComfyUI](https://github.com/comfyanonymous/ComfyUI). Please refer to [comfyui/README.md](comfyui/README.md) for the usage in [ComfyUI](https://github.com/comfyanonymous/ComfyUI).
...@@ -109,16 +179,20 @@ Please refer to [comfyui/README.md](comfyui/README.md) for the usage in [ComfyUI ...@@ -109,16 +179,20 @@ Please refer to [comfyui/README.md](comfyui/README.md) for the usage in [ComfyUI
* SANA: * SANA:
* Text-to-image: see [`app/sana/t2i`](app/sana/t2i). * Text-to-image: see [`app/sana/t2i`](app/sana/t2i).
## Customized Model Quantization
Please refer to [mit-han-lab/deepcompressor](https://github.com/mit-han-lab/deepcompressor/tree/main/examples/diffusion).
## Benchmark ## Benchmark
Please refer to [app/flux/t2i/README.md](app/flux/t2i/README.md) for instructions on reproducing our paper's quality results and benchmarking inference latency on FLUX.1 models. Please refer to [app/flux/t2i/README.md](app/flux/t2i/README.md) for instructions on reproducing our paper's quality results and benchmarking inference latency on FLUX.1 models.
## Roadmap ## Roadmap
- [ ] Easy installation - [x] Easy installation
- [x] Comfy UI node - [x] Comfy UI node
- [x] Customized LoRA conversion instructions - [x] Customized LoRA conversion instructions
- [ ] Customized model quantization instructions - [x] Customized model quantization instructions
- [x] FLUX.1 tools support - [x] FLUX.1 tools support
- [ ] Modularization - [ ] Modularization
- [ ] IP-Adapter integration - [ ] IP-Adapter integration
......
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comfyui/README.md
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...@@ -3,53 +3,76 @@ ...@@ -3,53 +3,76 @@
![comfyui](../assets/comfyui.jpg) ![comfyui](../assets/comfyui.jpg)
## Installation ## Installation
1. Install `nunchaku` following [README.md](https://github.com/mit-han-lab/nunchaku?tab=readme-ov-file#installation). Please first install `nunchaku` following the instructions in [README.md](https://github.com/mit-han-lab/nunchaku?tab=readme-ov-file#installation).
2. Install dependencies needed to run custom ComfyUI nodes:
```shell ### ComfyUI-CLI
pip install git+https://github.com/asomoza/image_gen_aux.git
```
3. Set up the dependencies for [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
```
4. Install [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager) with the following commands then restart ComfyUI:
```shell ```shell
cd ComfyUI/custom_nodes comfy node registry-install svdquant
git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager
``` ```
### ComfyUI-Manager (Experimental)
1. Install [ComfyUI-Manager](https://github.com/ltdrdata/ComfyUI-Manager) with the following commands then restart ComfyUI:
```shell
cd ComfyUI/custom_nodes
git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager
```
2. Open the Manager, search `svdquant` in the Custom Nodes Manager and then install it.
### Manual Installation
1. Install dependencies needed to run custom ComfyUI nodes:
```shell
pip install git+https://github.com/asomoza/image_gen_aux.git
```
2. Set up the dependencies for [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
```
3. Navigate to the root directory of ComfyUI and link (or copy) the [`nunchaku/comfyui`](./) folder to `custom_nodes/svdquant`. For example:
```shell
# Clone repositories (skip if already cloned)
git clone https://github.com/comfyanonymous/ComfyUI.git
git clone https://github.com/mit-han-lab/nunchaku.git
cd ComfyUI
# Add SVDQuant nodes
cd custom_nodes
ln -s ../../nunchaku/comfyui svdquant
```
## Usage ## Usage
1. **Set Up ComfyUI and SVDQuant**: 1. **Set Up ComfyUI and SVDQuant**:
* Navigate to the root directory of ComfyUI and link (or copy) the [`nunchaku/comfyui`](./) folder to `custom_nodes/svdquant`. * SVDQuant workflows can be found at [`workflows`](./workflows). You can place them in `user/default/workflows` in ComfyUI root directory to load them. For example:
* Place the SVDQuant workflow configurations from [`workflows`](./workflows) into `user/default/workflows`.
* For example ```shell
cd ComfyUI
# Copy workflow configurations
mkdir -p user/default/workflows
cp ../nunchaku/comfyui/workflows/* user/default/workflows/
```
* Install missing nodes (e.g., comfyui-inpainteasy) following [this tutorial](https://github.com/ltdrdata/ComfyUI-Manager?tab=readme-ov-file#support-of-missing-nodes-installation).
```shell
# Clone repositories (skip if already cloned)
git clone https://github.com/comfyanonymous/ComfyUI.git
git clone https://github.com/mit-han-lab/nunchaku.git
cd ComfyUI
# Copy workflow configurations
mkdir -p user/default/workflows
cp ../nunchaku/comfyui/workflows/* user/default/workflows/
# Add SVDQuant nodes
cd custom_nodes
ln -s ../../nunchaku/comfyui svdquant
```
* Install missing nodes (e.g., comfyui-inpainteasy) 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/) and download the required models into the appropriate directories using the commands below: 2. **Download Required Models**: Follow [this tutorial](https://comfyanonymous.github.io/ComfyUI_examples/flux/) and download the required models into the appropriate directories using the commands below:
```shell ```shell
huggingface-cli download comfyanonymous/flux_text_encoders clip_l.safetensors --local-dir models/clip 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/clip 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 huggingface-cli download black-forest-labs/FLUX.1-schnell ae.safetensors --local-dir models/vae
``` ```
...@@ -59,7 +82,7 @@ git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager ...@@ -59,7 +82,7 @@ git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager
python main.py python main.py
``` ```
4. **Select the SVDQuant Workflow**: Choose one of the SVDQuant workflows (`flux.1-dev-svdquant.json`, `flux.1-schnell-svdquant.json`, `flux.1-depth-svdquant.json`, `flux.1-canny-svdquant.json` or `flux.1-fill-svdquant.json`) to get started. For the flux.1 fill workflow, you can use the built-in MaskEditor tool to add mask on top of an image. 4. **Select the SVDQuant Workflow**: Choose one of the SVDQuant workflows (workflows that start with `svdq-`) to get started. For the FLUX.1-Fill workflow, you can use the built-in MaskEditor tool to add mask on top of an image.
## SVDQuant Nodes ## SVDQuant Nodes
...@@ -75,10 +98,18 @@ git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager ...@@ -75,10 +98,18 @@ git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager
* `device_id`: Indicates the GPU ID for running the model. * `device_id`: Indicates the GPU ID for running the model.
* **SVDQuant LoRA Loader**: A node for loading LoRA modules for SVDQuant diffusion models. * **SVDQuant 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`. Meanwhile, the [example Ghibsky LoRA](https://huggingface.co/aleksa-codes/flux-ghibsky-illustration) is included and will automatically download from our Hugging Face repository when used.
* `lora_format` specifies the LoRA format. Supported formats include:
- `diffusers` (e.g., [aleksa-codes/flux-ghibsky-illustration](https://huggingface.co/aleksa-codes/flux-ghibsky-illustration))
- `comfyui` (e.g., [Shakker-Labs/FLUX.1-dev-LoRA-Children-Simple-Sketch](https://huggingface.co/Shakker-Labs/FLUX.1-dev-LoRA-Children-Simple-Sketch))
- `xlab` (e.g., [XLabs-AI/flux-RealismLora](https://huggingface.co/XLabs-AI/flux-RealismLora))
- `svdquant` (e.g., [mit-han-lab/svdquant-lora-collection](https://huggingface.co/mit-han-lab/svdquant-lora-collection)).
* Place your LoRA checkpoints in the `models/loras` directory. These will appear as selectable options under `lora_name`. **Ensure your LoRA checkpoints conform to the SVDQuant format. **A LoRA conversion script will be released soon. Meanwhile, example LoRAs are included and will automatically download from our Hugging Face repository when used. * `base_model_name` specifies the path to the quantized base model. If `lora_format` is already set to `svdquant`, this option has no use. You can set it to the same value as `model_path` in the above **SVDQuant Flux DiT Loader**.
* **Note**: Currently, only **one LoRA** can be loaded at a time. * **Note**: Currently, **only one LoRA** can be loaded at a time.
* **SVDQuant Text Encoder Loader**: A node for loading the text encoders. * **SVDQuant Text Encoder Loader**: A node for loading the text encoders.
...@@ -86,11 +117,11 @@ git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager ...@@ -86,11 +117,11 @@ git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager
- `text_encoder1`: `t5xxl_fp16.safetensors` - `text_encoder1`: `t5xxl_fp16.safetensors`
- `text_encoder2`: `clip_l.safetensors` - `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 in SVDQuant, use 512 here. * `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 in SVDQuant, use 512 here.
* `t5_precision`: Specifies the precision of the T5 text encoder. Choose `INT4` to use the INT4 text encoder, which reduces GPU memory usage by approximately 15GB. Please install [`deepcompressor`](https://github.com/mit-han-lab/deepcompressor) when using it: * `t5_precision`: Specifies the precision of the T5 text encoder. Choose `INT4` to use the INT4 text encoder, which reduces GPU memory usage by approximately 15GB. Please install [`deepcompressor`](https://github.com/mit-han-lab/deepcompressor) when using it:
```shell ```shell
git clone https://github.com/mit-han-lab/deepcompressor git clone https://github.com/mit-han-lab/deepcompressor
cd deepcompressor cd deepcompressor
...@@ -99,10 +130,21 @@ git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager ...@@ -99,10 +130,21 @@ git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager
``` ```
* `int4_model`: Specifies the INT4 model location. This option is only used when `t5_precision` is set to `INT4`. By default, the path is `mit-han-lab/svdq-flux.1-t5`, and the model will automatically download from our Hugging Face repository. Alternatively, you can manually download the model directory by running the following command: * `int4_model`: Specifies the INT4 model location. This option is only used when `t5_precision` is set to `INT4`. By default, the path is `mit-han-lab/svdq-flux.1-t5`, and the model will automatically download from our Hugging Face repository. Alternatively, you can manually download the model directory by running the following command:
```shell ```shell
huggingface-cli download mit-han-lab/svdq-flux.1-t5 --local-dir models/text_encoders/svdq-flux.1-t5 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`.
After downloading, specify the corresponding folder name as the `int4_model`.
* **FLUX.1 Depth Preprocessor**: A node for loading the depth estimation model and output the depth map. `model_path` specifies the model location. If set to [`LiheYoung/depth-anything-large-hf`](https://huggingface.co/LiheYoung/depth-anything-large-hf), the model will be automatically downloaded from the Hugging Face repository. Alternatively, you can manually download the repository at `models/checkpoints` by running the following command example:
```shell
huggingface-cli download LiheYoung/depth-anything-large-hf --local-dir models/checkpoints/depth-anything-large-hf
```
comfyui/__init__.py
View file @ 7b8d221f
# only import if running as a custom node # only import if running as a custom node
try:
import comfy.utils
except ImportError:
pass
else:
from .nodes import NODE_CLASS_MAPPINGS
NODE_DISPLAY_NAME_MAPPINGS = {k: v.TITLE for k, v in NODE_CLASS_MAPPINGS.items()} from .nodes.lora import SVDQuantFluxLoraLoader
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] from .nodes.models import SVDQuantFluxDiTLoader, SVDQuantTextEncoderLoader
from .nodes.preprocessors import FluxDepthPreprocessor
NODE_CLASS_MAPPINGS = {
"SVDQuantFluxDiTLoader": SVDQuantFluxDiTLoader,
"SVDQuantTextEncoderLoader": SVDQuantTextEncoderLoader,
"SVDQuantFluxLoraLoader": SVDQuantFluxLoraLoader,
"SVDQuantDepthPreprocessor": FluxDepthPreprocessor,
}
NODE_DISPLAY_NAME_MAPPINGS = {k: v.TITLE for k, v in NODE_CLASS_MAPPINGS.items()}
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
comfyui/nodes/lora/__init__.py 0 → 100644
View file @ 7b8d221f
from .flux import SVDQuantFluxLoraLoader
comfyui/nodes/lora/flux.py 0 → 100644
View file @ 7b8d221f
import os
import tempfile
import folder_paths
from safetensors.torch import save_file
from nunchaku.lora.flux.comfyui_converter import comfyui2diffusers
from nunchaku.lora.flux.diffusers_converter import convert_to_nunchaku_flux_lowrank_dict
from nunchaku.lora.flux.xlab_converter import xlab2diffusers
class SVDQuantFluxLoraLoader:
def __init__(self):
self.cur_lora_name = "None"
@classmethod
def INPUT_TYPES(s):
lora_name_list = [
"None",
*folder_paths.get_filename_list("loras"),
"aleksa-codes/flux-ghibsky-illustration/lora.safetensors",
]
base_model_paths = [
"mit-han-lab/svdq-int4-flux.1-dev",
"mit-han-lab/svdq-int4-flux.1-schnell",
"mit-han-lab/svdq-int4-flux.1-canny-dev",
"mit-han-lab/svdq-int4-flux.1-depth-dev",
"mit-han-lab/svdq-int4-flux.1-fill-dev",
]
prefix = os.path.join(folder_paths.models_dir, "diffusion_models")
local_base_model_folders = os.listdir(prefix)
local_base_model_folders = sorted(
[
folder
for folder in local_base_model_folders
if not folder.startswith(".") and os.path.isdir(os.path.join(prefix, folder))
]
)
base_model_paths = local_base_model_folders + base_model_paths
return {
"required": {
"model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
"lora_name": (lora_name_list, {"tooltip": "The name of the LoRA."}),
"lora_format": (["comfyui", "diffusers", "svdquant", "xlab"], {"tooltip": "The format of the LoRA."}),
"base_model_name": (
base_model_paths,
{
"tooltip": "If the lora format is SVDQuant, this field has no use. Otherwise, the base model's state dictionary is required for converting the LoRA weights to SVDQuant."
},
),
"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 = "SVDQuant FLUX.1 LoRA Loader"
CATEGORY = "SVDQuant"
DESCRIPTION = (
"LoRAs are used to modify the diffusion model, "
"altering the way in which latents are denoised such as applying styles. "
"Currently, only one LoRA nodes can be applied."
)
def load_lora(self, model, lora_name: str, lora_format: str, base_model_name: str, lora_strength: float):
if self.cur_lora_name == lora_name:
if self.cur_lora_name == "None":
pass # Do nothing since the lora is None
else:
model.model.diffusion_model.model.set_lora_strength(lora_strength)
else:
if lora_name == "None":
model.model.diffusion_model.model.set_lora_strength(0)
else:
try:
lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
except FileNotFoundError:
lora_path = lora_name
if lora_format != "svdquant":
if lora_format == "comfyui":
input_lora = comfyui2diffusers(lora_path)
elif lora_format == "xlab":
input_lora = xlab2diffusers(lora_path)
elif lora_format == "diffusers":
input_lora = lora_path
else:
raise ValueError(f"Invalid LoRA format {lora_format}.")
prefix = os.path.join(folder_paths.models_dir, "diffusion_models")
base_model_path = os.path.join(prefix, base_model_name, "transformer_blocks.safetensors")
if not os.path.exists(base_model_path):
# download from huggingface
base_model_path = os.path.join(base_model_name, "transformer_blocks.safetensors")
state_dict = convert_to_nunchaku_flux_lowrank_dict(base_model_path, input_lora)
with tempfile.NamedTemporaryFile(suffix=".safetensors", delete=True) as tmp_file:
save_file(state_dict, tmp_file.name)
model.model.diffusion_model.model.update_lora_params(tmp_file.name)
else:
model.model.diffusion_model.model.update_lora_params(lora_path)
model.model.diffusion_model.model.set_lora_strength(lora_strength)
self.cur_lora_name = lora_name
return (model,)
comfyui/nodes/models/__init__.py 0 → 100644
View file @ 7b8d221f
from .flux import SVDQuantFluxDiTLoader
from .text_encoder import SVDQuantTextEncoderLoader
comfyui/nodes.py → comfyui/nodes/models/flux.py
View file @ 7b8d221f
import os import os
import types
import comfy.model_base
import comfy.model_patcher import comfy.model_patcher
import comfy.sd
import folder_paths import folder_paths
import GPUtil import GPUtil
import torch import torch
import numpy as np
from comfy.ldm.common_dit import pad_to_patch_size from comfy.ldm.common_dit import pad_to_patch_size
from comfy.supported_models import Flux, FluxSchnell from comfy.supported_models import Flux, FluxSchnell
from diffusers import FluxTransformer2DModel from diffusers import FluxTransformer2DModel
from einops import rearrange, repeat from einops import rearrange, repeat
from torch import nn from torch import nn
from transformers import T5EncoderModel
from image_gen_aux import DepthPreprocessor
from nunchaku.models.transformer_flux import NunchakuFluxTransformer2dModel from nunchaku.models.transformer_flux import NunchakuFluxTransformer2dModel
class ComfyUIFluxForwardWrapper(nn.Module): class ComfyUIFluxForwardWrapper(nn.Module):
def __init__(self, model: NunchakuFluxTransformer2dModel, config): def __init__(self, model: NunchakuFluxTransformer2dModel, config):
super(ComfyUIFluxForwardWrapper, self).__init__() super(ComfyUIFluxForwardWrapper, self).__init__()
...@@ -41,9 +36,7 @@ class ComfyUIFluxForwardWrapper(nn.Module): ...@@ -41,9 +36,7 @@ class ComfyUIFluxForwardWrapper(nn.Module):
patch_size = self.config["patch_size"] patch_size = self.config["patch_size"]
x = pad_to_patch_size(x, (patch_size, patch_size)) x = pad_to_patch_size(x, (patch_size, patch_size))
img = rearrange( img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
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 h_len = (h + (patch_size // 2)) // patch_size
w_len = (w + (patch_size // 2)) // patch_size w_len = (w + (patch_size // 2)) // patch_size
...@@ -67,15 +60,14 @@ class ComfyUIFluxForwardWrapper(nn.Module): ...@@ -67,15 +60,14 @@ class ComfyUIFluxForwardWrapper(nn.Module):
guidance=guidance if self.config["guidance_embed"] else None, guidance=guidance if self.config["guidance_embed"] else None,
).sample ).sample
out = rearrange( out = rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:, :, :h, :w]
out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2
)[:, :, :h, :w]
return out return out
class SVDQuantFluxDiTLoader: class SVDQuantFluxDiTLoader:
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
# folder_paths.get_filename_list("loras"),
model_paths = [ model_paths = [
"mit-han-lab/svdq-int4-flux.1-schnell", "mit-han-lab/svdq-int4-flux.1-schnell",
"mit-han-lab/svdq-int4-flux.1-dev", "mit-han-lab/svdq-int4-flux.1-dev",
...@@ -83,31 +75,23 @@ class SVDQuantFluxDiTLoader: ...@@ -83,31 +75,23 @@ class SVDQuantFluxDiTLoader:
"mit-han-lab/svdq-int4-flux.1-depth-dev", "mit-han-lab/svdq-int4-flux.1-depth-dev",
"mit-han-lab/svdq-int4-flux.1-fill-dev", "mit-han-lab/svdq-int4-flux.1-fill-dev",
] ]
prefix = "models/diffusion_models" prefix = os.path.join(folder_paths.models_dir, "diffusion_models")
local_folders = os.listdir(prefix) local_folders = os.listdir(prefix)
local_folders = sorted( local_folders = sorted(
[ [
folder folder
for folder in local_folders for folder in local_folders
if not folder.startswith(".") if not folder.startswith(".") and os.path.isdir(os.path.join(prefix, folder))
and os.path.isdir(os.path.join(prefix, folder))
] ]
) )
model_paths.extend(local_folders) model_paths = local_folders + model_paths
ngpus = len(GPUtil.getGPUs()) ngpus = len(GPUtil.getGPUs())
return { return {
"required": { "required": {
"model_path": (model_paths,), "model_path": (model_paths,),
"device_id": ( "device_id": (
"INT", "INT",
{ {"default": 0, "min": 0, "max": ngpus, "step": 1, "display": "number", "lazy": True},
"default": 0,
"min": 0,
"max": ngpus,
"step": 1,
"display": "number",
"lazy": True,
},
), ),
} }
} }
...@@ -117,18 +101,14 @@ class SVDQuantFluxDiTLoader: ...@@ -117,18 +101,14 @@ class SVDQuantFluxDiTLoader:
CATEGORY = "SVDQuant" CATEGORY = "SVDQuant"
TITLE = "SVDQuant Flux DiT Loader" TITLE = "SVDQuant Flux DiT Loader"
def load_model( def load_model(self, model_path: str, device_id: int, **kwargs) -> tuple[FluxTransformer2DModel]:
self, model_path: str, device_id: int, **kwargs
) -> tuple[FluxTransformer2DModel]:
device = f"cuda:{device_id}" device = f"cuda:{device_id}"
prefix = "models/diffusion_models" prefix = os.path.join(folder_paths.models_dir, "diffusion_models")
if os.path.exists(os.path.join(prefix, model_path)): if os.path.exists(os.path.join(prefix, model_path)):
model_path = os.path.join(prefix, model_path) model_path = os.path.join(prefix, model_path)
else: else:
model_path = model_path model_path = model_path
transformer = NunchakuFluxTransformer2dModel.from_pretrained(model_path).to( transformer = NunchakuFluxTransformer2dModel.from_pretrained(model_path).to(device)
device
)
dit_config = { dit_config = {
"image_model": "flux", "image_model": "flux",
"patch_size": 2, "patch_size": 2,
...@@ -158,9 +138,6 @@ class SVDQuantFluxDiTLoader: ...@@ -158,9 +138,6 @@ class SVDQuantFluxDiTLoader:
dit_config["in_channels"] = 64 dit_config["in_channels"] = 64
model_config = Flux(dit_config) model_config = Flux(dit_config)
else: else:
assert (
model_path == "mit-han-lab/svdq-int4-flux.1-dev"
), f"model {model_path} not supported"
dit_config["in_channels"] = 16 dit_config["in_channels"] = 16
model_config = Flux(dit_config) model_config = Flux(dit_config)
...@@ -168,239 +145,6 @@ class SVDQuantFluxDiTLoader: ...@@ -168,239 +145,6 @@ class SVDQuantFluxDiTLoader:
model_config.custom_operations = None model_config.custom_operations = None
model = model_config.get_model({}) model = model_config.get_model({})
model.diffusion_model = ComfyUIFluxForwardWrapper( model.diffusion_model = ComfyUIFluxForwardWrapper(transformer, config=dit_config)
transformer, config=dit_config
)
model = comfy.model_patcher.ModelPatcher(model, device, device_id) model = comfy.model_patcher.ModelPatcher(model, device, device_id)
return (model,) return (model,)
def svdquant_t5_forward(
self: T5EncoderModel,
input_ids: torch.LongTensor,
attention_mask,
intermediate_output=None,
final_layer_norm_intermediate=True,
dtype: str | torch.dtype = torch.bfloat16,
):
assert attention_mask is None
assert intermediate_output is None
assert final_layer_norm_intermediate
outputs = self.encoder(input_ids, attention_mask=attention_mask)
hidden_states = outputs["last_hidden_state"]
hidden_states = hidden_states.to(dtype=dtype)
return hidden_states, None
class SVDQuantTextEncoderLoader:
@classmethod
def INPUT_TYPES(s):
model_paths = ["mit-han-lab/svdq-flux.1-t5"]
prefix = "models/text_encoders"
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.extend(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,
},
),
"t5_precision": (["BF16", "INT4"],),
"int4_model": (model_paths, {"tooltip": "The name of the INT4 model."}),
}
}
RETURN_TYPES = ("CLIP",)
FUNCTION = "load_text_encoder"
CATEGORY = "SVDQuant"
TITLE = "SVDQuant Text Encoder Loader"
def load_text_encoder(
self,
model_type: str,
text_encoder1: str,
text_encoder2: str,
t5_min_length: int,
t5_precision: 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 t5_precision == "INT4":
from nunchaku.models.text_encoder import NunchakuT5EncoderModel
transformer = clip.cond_stage_model.t5xxl.transformer
param = next(transformer.parameters())
dtype = param.dtype
device = param.device
prefix = "models/text_encoders"
if os.path.exists(os.path.join(prefix, int4_model)):
model_path = os.path.join(prefix, int4_model)
else:
model_path = int4_model
transformer = NunchakuT5EncoderModel.from_pretrained(model_path)
transformer.forward = types.MethodType(svdquant_t5_forward, transformer)
clip.cond_stage_model.t5xxl.transformer = (
transformer.to(device=device, dtype=dtype)
if device.type == "cuda"
else transformer
)
return (clip,)
class SVDQuantLoraLoader:
def __init__(self):
self.cur_lora_name = "None"
@classmethod
def INPUT_TYPES(s):
hf_lora_names = ["anime", "ghibsky", "realism", "yarn", "sketch"]
lora_name_list = [
"None",
*folder_paths.get_filename_list("loras"),
*[
f"mit-han-lab/svdquant-models/svdq-flux.1-dev-lora-{n}.safetensors"
for n in hf_lora_names
],
]
return {
"required": {
"model": (
"MODEL",
{"tooltip": "The diffusion model the LoRA will be applied to."},
),
"lora_name": (lora_name_list, {"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 = "SVDQuant LoRA Loader"
CATEGORY = "SVDQuant"
DESCRIPTION = (
"LoRAs are used to modify the diffusion model, "
"altering the way in which latents are denoised such as applying styles. "
"Currently, only one LoRA nodes can be applied."
)
def load_lora(self, model, lora_name: str, lora_strength: float):
if self.cur_lora_name == lora_name:
if self.cur_lora_name == "None":
pass # Do nothing since the lora is None
else:
model.model.diffusion_model.model.set_lora_strength(lora_strength)
else:
if lora_name == "None":
model.model.diffusion_model.model.set_lora_strength(0)
else:
try:
lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
except FileNotFoundError:
lora_path = lora_name
model.model.diffusion_model.model.update_lora_params(lora_path)
model.model.diffusion_model.model.set_lora_strength(lora_strength)
self.cur_lora_name = lora_name
return (model,)
class DepthPreprocesser:
@classmethod
def INPUT_TYPES(s):
model_paths = ["LiheYoung/depth-anything-large-hf"]
prefix = "models/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.extend(local_folders)
return {
"required": {
"image": ("IMAGE", {}),
"model_path": (
model_paths,
{"tooltip": "Name of the depth preprocesser model."},
),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "depth_preprocess"
CATEGORY = "Flux.1"
TITLE = "Flux.1 Depth Preprocessor"
def depth_preprocess(self, image, model_path):
prefix = "models/checkpoints"
if os.path.exists(os.path.join(prefix, model_path)):
model_path = os.path.join(prefix, model_path)
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,)
NODE_CLASS_MAPPINGS = {
"SVDQuantFluxDiTLoader": SVDQuantFluxDiTLoader,
"SVDQuantTextEncoderLoader": SVDQuantTextEncoderLoader,
"SVDQuantLoRALoader": SVDQuantLoraLoader,
"DepthPreprocesser": DepthPreprocesser
}
comfyui/nodes/models/text_encoder.py 0 → 100644
View file @ 7b8d221f
import os
import types
import comfy.sd
import folder_paths
import torch
from transformers import T5EncoderModel
def svdquant_t5_forward(
self: T5EncoderModel,
input_ids: torch.LongTensor,
attention_mask,
intermediate_output=None,
final_layer_norm_intermediate=True,
dtype: str | torch.dtype = torch.bfloat16,
):
assert attention_mask is None
assert intermediate_output is None
assert final_layer_norm_intermediate
outputs = self.encoder(input_ids, attention_mask=attention_mask)
hidden_states = outputs["last_hidden_state"]
hidden_states = hidden_states.to(dtype=dtype)
return hidden_states, None
class SVDQuantTextEncoderLoader:
@classmethod
def INPUT_TYPES(s):
model_paths = ["mit-han-lab/svdq-flux.1-t5"]
prefix = os.path.join(folder_paths.models_dir, "text_encoders")
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.extend(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,
},
),
"t5_precision": (["BF16", "INT4"],),
"int4_model": (model_paths, {"tooltip": "The name of the INT4 model."}),
}
}
RETURN_TYPES = ("CLIP",)
FUNCTION = "load_text_encoder"
CATEGORY = "SVDQuant"
TITLE = "SVDQuant Text Encoder Loader"
def load_text_encoder(
self,
model_type: str,
text_encoder1: str,
text_encoder2: str,
t5_min_length: int,
t5_precision: 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 t5_precision == "INT4":
from nunchaku.models.text_encoder import NunchakuT5EncoderModel
transformer = clip.cond_stage_model.t5xxl.transformer
param = next(transformer.parameters())
dtype = param.dtype
device = param.device
prefix = "models/text_encoders"
if os.path.exists(os.path.join(prefix, int4_model)):
model_path = os.path.join(prefix, int4_model)
else:
model_path = int4_model
transformer = NunchakuT5EncoderModel.from_pretrained(model_path)
transformer.forward = types.MethodType(svdquant_t5_forward, transformer)
clip.cond_stage_model.t5xxl.transformer = (
transformer.to(device=device, dtype=dtype) if device.type == "cuda" else transformer
)
return (clip,)
comfyui/nodes/preprocessors/__init__.py 0 → 100644
View file @ 7b8d221f
from .depth import FluxDepthPreprocessor
comfyui/nodes/preprocessors/depth.py 0 → 100644
View file @ 7b8d221f
import os
import folder_paths
import numpy as np
import torch
from image_gen_aux import DepthPreprocessor
class FluxDepthPreprocessor:
@classmethod
def INPUT_TYPES(s):
model_paths = ["LiheYoung/depth-anything-large-hf"]
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 = "SVDQuant"
TITLE = "FLUX.1 Depth Preprocessor"
def depth_preprocess(self, image, model_path):
prefix = os.path.join(folder_paths.models_dir, "checkpoints")
if os.path.exists(os.path.join(prefix, model_path)):
model_path = os.path.join(prefix, model_path)
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,)
comfyui/pyproject.toml 0 → 100644
View file @ 7b8d221f
[project]
name = "svdquant"
description = "SVDQuant ComfyUI Node. 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."
version = "0.1.1"
license = { file = "LICENSE.txt" }
dependencies = []
requires-python = ">=3.11, <3.13"
[project.urls]
Repository = "https://github.com/mit-han-lab/nunchaku"
# Used by Comfy Registry https://comfyregistry.org
[tool.comfy]
PublisherId = "lmxyy1999"
DisplayName = "svdquant"
Icon = "https://raw.githubusercontent.com/mit-han-lab/nunchaku/bfd9aa3ae84f51a26414f1600d725a0098472820/assets/logo.svg"
comfyui/requirements.txt 0 → 100644
View file @ 7b8d221f
GPUtil
diffusers>=0.32.2
accelerate
sentencepiece
protobuf
huggingface_hub
git+https://github.com/asomoza/image_gen_aux.git
\ No newline at end of file
comfyui/workflows/flux.1-canny-svdquant.json → comfyui/workflows/svdq-flux.1-canny.json
View file @ 7b8d221f
...@@ -57,7 +57,7 @@ ...@@ -57,7 +57,7 @@
"Node name for S&R": "KSampler" "Node name for S&R": "KSampler"
}, },
"widgets_values": [ "widgets_values": [
646386750200194, 896617285614695,
"randomize", "randomize",
20, 20,
1, 1,
...@@ -323,51 +323,6 @@ ...@@ -323,51 +323,6 @@
"default" "default"
] ]
}, },
{
"id": 7,
"type": "CLIPTextEncode",
"pos": [
307,
282
],
"size": [
425.27801513671875,
180.6060791015625
],
"flags": {
"collapsed": true
},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "clip",
"type": "CLIP",
"link": 63,
"label": "clip"
}
],
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [
68
],
"slot_index": 0,
"label": "CONDITIONING"
}
],
"title": "CLIP Text Encode (Negative Prompt)",
"properties": {
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
""
],
"color": "#322",
"bgcolor": "#533"
},
{ {
"id": 23, "id": 23,
"type": "CLIPTextEncode", "type": "CLIPTextEncode",
...@@ -483,36 +438,44 @@ ...@@ -483,36 +438,44 @@
] ]
}, },
{ {
"id": 36, "id": 38,
"type": "SVDQuantFluxDiTLoader", "type": "ImageScale",
"pos": [ "pos": [
865.4989624023438, 379.69903564453125,
-95.86973571777344 565.2651977539062
], ],
"size": [ "size": [
315, 315,
82 130
], ],
"flags": {}, "flags": {},
"order": 2, "order": 6,
"mode": 0, "mode": 0,
"inputs": [], "inputs": [
{
"name": "image",
"type": "IMAGE",
"link": 75
}
],
"outputs": [ "outputs": [
{ {
"name": "MODEL", "name": "IMAGE",
"type": "MODEL", "type": "IMAGE",
"links": [ "links": [
71 76
], ],
"slot_index": 0 "slot_index": 0
} }
], ],
"properties": { "properties": {
"Node name for S&R": "SVDQuantFluxDiTLoader" "Node name for S&R": "ImageScale"
}, },
"widgets_values": [ "widgets_values": [
"mit-han-lab/svdq-int4-flux.1-canny-dev", "nearest-exact",
0 1024,
1024,
"center"
] ]
}, },
{ {
...@@ -527,7 +490,7 @@ ...@@ -527,7 +490,7 @@
314.0000305175781 314.0000305175781
], ],
"flags": {}, "flags": {},
"order": 3, "order": 2,
"mode": 0, "mode": 0,
"inputs": [], "inputs": [],
"outputs": [ "outputs": [
...@@ -558,44 +521,81 @@ ...@@ -558,44 +521,81 @@
] ]
}, },
{ {
"id": 38, "id": 7,
"type": "ImageScale", "type": "CLIPTextEncode",
"pos": [ "pos": [
379.69903564453125, 323.8695068359375,
565.2651977539062 387.9589538574219
], ],
"size": [ "size": [
315, 425.27801513671875,
130 180.6060791015625
], ],
"flags": {}, "flags": {
"order": 6, "collapsed": true
},
"order": 5,
"mode": 0, "mode": 0,
"inputs": [ "inputs": [
{ {
"name": "image", "name": "clip",
"type": "IMAGE", "type": "CLIP",
"link": 75 "link": 63,
"label": "clip"
} }
], ],
"outputs": [ "outputs": [
{ {
"name": "IMAGE", "name": "CONDITIONING",
"type": "IMAGE", "type": "CONDITIONING",
"links": [ "links": [
76 68
],
"slot_index": 0,
"label": "CONDITIONING"
}
],
"title": "CLIP Text Encode (Negative Prompt)",
"properties": {
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
""
],
"color": "#322",
"bgcolor": "#533"
},
{
"id": 36,
"type": "SVDQuantFluxDiTLoader",
"pos": [
823.9686279296875,
-126.4416275024414
],
"size": [
395.6002197265625,
105.77959442138672
],
"flags": {},
"order": 3,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "MODEL",
"type": "MODEL",
"links": [
71
], ],
"slot_index": 0 "slot_index": 0
} }
], ],
"properties": { "properties": {
"Node name for S&R": "ImageScale" "Node name for S&R": "SVDQuantFluxDiTLoader"
}, },
"widgets_values": [ "widgets_values": [
"nearest-exact", "mit-han-lab/svdq-int4-flux.1-canny-dev",
1024, 0
1024,
"center"
] ]
} }
], ],
...@@ -743,9 +743,12 @@ ...@@ -743,9 +743,12 @@
"ds": { "ds": {
"scale": 0.895430243255241, "scale": 0.895430243255241,
"offset": [ "offset": [
203.26101803057463, 838.4305404853558,
215.36536277004458 332.05158795287764
] ]
},
"node_versions": {
"comfy-core": "0.3.14"
} }
}, },
"version": 0.4 "version": 0.4
......
comfyui/workflows/flux.1-depth-svdquant.json → comfyui/workflows/svdq-flux.1-depth.json
View file @ 7b8d221f
{ {
"last_node_id": 44, "last_node_id": 45,
"last_link_id": 85, "last_link_id": 88,
"nodes": [ "nodes": [
{ {
"id": 7, "id": 7,
...@@ -124,40 +124,6 @@ ...@@ -124,40 +124,6 @@
10 10
] ]
}, },
{
"id": 32,
"type": "VAELoader",
"pos": [
656,
165
],
"size": [
315,
58
],
"flags": {},
"order": 1,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "VAE",
"type": "VAE",
"links": [
60,
69
],
"slot_index": 0,
"label": "VAE"
}
],
"properties": {
"Node name for S&R": "VAELoader"
},
"widgets_values": [
"ae.safetensors"
]
},
{ {
"id": 3, "id": 3,
"type": "KSampler", "type": "KSampler",
...@@ -213,7 +179,7 @@ ...@@ -213,7 +179,7 @@
"Node name for S&R": "KSampler" "Node name for S&R": "KSampler"
}, },
"widgets_values": [ "widgets_values": [
718322679777603, 704308966490490,
"randomize", "randomize",
20, 20,
1, 1,
...@@ -223,118 +189,144 @@ ...@@ -223,118 +189,144 @@
] ]
}, },
{ {
"id": 35, "id": 39,
"type": "InstructPixToPixConditioning", "type": "SVDQuantFluxDiTLoader",
"pos": [ "pos": [
1008, 707.80908203125,
118 -172.0343017578125
], ],
"size": [ "size": [
235.1999969482422, 315,
86 82
],
"flags": {},
"order": 1,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "MODEL",
"type": "MODEL",
"links": [
78
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "SVDQuantFluxDiTLoader"
},
"widgets_values": [
"mit-han-lab/svdq-int4-flux.1-depth-dev",
0
]
},
{
"id": 43,
"type": "PreviewImage",
"pos": [
1001.3873291015625,
432.09039306640625
],
"size": [
571.5869140625,
625.5296020507812
], ],
"flags": {}, "flags": {},
"order": 9, "order": 9,
"mode": 0, "mode": 0,
"inputs": [ "inputs": [
{ {
"name": "positive", "name": "images",
"type": "CONDITIONING", "type": "IMAGE",
"link": 67, "link": 87,
"label": "positive" "label": "images"
}, }
],
"outputs": [],
"properties": {
"Node name for S&R": "PreviewImage"
},
"widgets_values": []
},
{
"id": 8,
"type": "VAEDecode",
"pos": [
1620,
98
],
"size": [
210,
46
],
"flags": {},
"order": 12,
"mode": 0,
"inputs": [
{ {
"name": "negative", "name": "samples",
"type": "CONDITIONING", "type": "LATENT",
"link": 68, "link": 7,
"label": "negative" "label": "samples"
}, },
{ {
"name": "vae", "name": "vae",
"type": "VAE", "type": "VAE",
"link": 69, "link": 60,
"label": "vae" "label": "vae"
},
{
"name": "pixels",
"type": "IMAGE",
"link": 80,
"label": "pixels"
} }
], ],
"outputs": [ "outputs": [
{ {
"name": "positive", "name": "IMAGE",
"type": "CONDITIONING", "type": "IMAGE",
"links": [ "links": [
64 85
], ],
"slot_index": 0, "slot_index": 0,
"label": "positive" "label": "IMAGE"
},
{
"name": "negative",
"type": "CONDITIONING",
"links": [
65
],
"slot_index": 1,
"label": "negative"
},
{
"name": "latent",
"type": "LATENT",
"links": [
73
],
"slot_index": 2,
"label": "latent"
} }
], ],
"properties": { "properties": {
"Node name for S&R": "InstructPixToPixConditioning" "Node name for S&R": "VAEDecode"
}, },
"widgets_values": [] "widgets_values": []
}, },
{ {
"id": 39, "id": 44,
"type": "SVDQuantFluxDiTLoader", "type": "SaveImage",
"pos": [ "pos": [
707.80908203125, 1912.7984619140625,
-172.0343017578125 109.0069580078125
], ],
"size": [ "size": [
315, 828.9535522460938,
82 893.8475341796875
], ],
"flags": {}, "flags": {},
"order": 2, "order": 13,
"mode": 0, "mode": 0,
"inputs": [], "inputs": [
"outputs": [
{ {
"name": "MODEL", "name": "images",
"type": "MODEL", "type": "IMAGE",
"links": [ "link": 85,
78 "label": "images"
],
"slot_index": 0
} }
], ],
"properties": { "outputs": [],
"Node name for S&R": "SVDQuantFluxDiTLoader" "properties": {},
},
"widgets_values": [ "widgets_values": [
"mit-han-lab/svdq-int4-flux.1-depth-dev", "ComfyUI"
0
] ]
}, },
{ {
"id": 42, "id": 42,
"type": "ImageScale", "type": "ImageScale",
"pos": [ "pos": [
378.3890686035156, 174.98765563964844,
472.7001953125 450.5818786621094
], ],
"size": [ "size": [
315, 315,
...@@ -355,7 +347,7 @@ ...@@ -355,7 +347,7 @@
"name": "IMAGE", "name": "IMAGE",
"type": "IMAGE", "type": "IMAGE",
"links": [ "links": [
83 86
], ],
"slot_index": 0 "slot_index": 0
} }
...@@ -370,48 +362,6 @@ ...@@ -370,48 +362,6 @@
"center" "center"
] ]
}, },
{
"id": 17,
"type": "LoadImage",
"pos": [
30.604948043823242,
419.3930358886719
],
"size": [
315,
314.0000305175781
],
"flags": {},
"order": 3,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
82
],
"slot_index": 0,
"shape": 3,
"label": "IMAGE"
},
{
"name": "MASK",
"type": "MASK",
"links": null,
"shape": 3,
"label": "MASK"
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"robot.png",
"image"
]
},
{ {
"id": 23, "id": 23,
"type": "CLIPTextEncode", "type": "CLIPTextEncode",
...@@ -450,104 +400,138 @@ ...@@ -450,104 +400,138 @@
"Node name for S&R": "CLIPTextEncode" "Node name for S&R": "CLIPTextEncode"
}, },
"widgets_values": [ "widgets_values": [
"A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts." "ethereal fantasy concept art of A logo of 'MIT HAN Lab'. magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy"
], ],
"color": "#232", "color": "#232",
"bgcolor": "#353" "bgcolor": "#353"
}, },
{ {
"id": 43, "id": 32,
"type": "PreviewImage", "type": "VAELoader",
"pos": [ "pos": [
1001.3873291015625, 630.5574951171875,
432.09039306640625 280.441650390625
], ],
"size": [ "size": [
571.5869140625, 315,
625.5296020507812 58
], ],
"flags": {}, "flags": {},
"order": 10, "order": 2,
"mode": 0, "mode": 0,
"inputs": [ "inputs": [],
"outputs": [
{ {
"name": "images", "name": "VAE",
"type": "IMAGE", "type": "VAE",
"link": 84, "links": [
"label": "images" 60,
69
],
"slot_index": 0,
"label": "VAE"
} }
], ],
"outputs": [],
"properties": { "properties": {
"Node name for S&R": "PreviewImage" "Node name for S&R": "VAELoader"
}, },
"widgets_values": [] "widgets_values": [
"ae.safetensors"
]
}, },
{ {
"id": 40, "id": 35,
"type": "DepthPreprocesser", "type": "InstructPixToPixConditioning",
"pos": [ "pos": [
639.0159301757812, 1008,
350.06134033203125 118
], ],
"size": [ "size": [
315, 235.1999969482422,
58 86
], ],
"flags": {}, "flags": {},
"order": 8, "order": 10,
"mode": 0, "mode": 0,
"inputs": [ "inputs": [
{ {
"name": "image", "name": "positive",
"type": "CONDITIONING",
"link": 67,
"label": "positive"
},
{
"name": "negative",
"type": "CONDITIONING",
"link": 68,
"label": "negative"
},
{
"name": "vae",
"type": "VAE",
"link": 69,
"label": "vae"
},
{
"name": "pixels",
"type": "IMAGE", "type": "IMAGE",
"link": 83 "link": 88,
"label": "pixels"
} }
], ],
"outputs": [ "outputs": [
{ {
"name": "IMAGE", "name": "positive",
"type": "IMAGE", "type": "CONDITIONING",
"links": [ "links": [
80, 64
84
], ],
"slot_index": 0 "slot_index": 0,
"label": "positive"
},
{
"name": "negative",
"type": "CONDITIONING",
"links": [
65
],
"slot_index": 1,
"label": "negative"
},
{
"name": "latent",
"type": "LATENT",
"links": [
73
],
"slot_index": 2,
"label": "latent"
} }
], ],
"properties": { "properties": {
"Node name for S&R": "DepthPreprocesser" "Node name for S&R": "InstructPixToPixConditioning"
}, },
"widgets_values": [ "widgets_values": []
"LiheYoung/depth-anything-large-hf"
]
}, },
{ {
"id": 8, "id": 45,
"type": "VAEDecode", "type": "SVDQuantDepthPreprocessor",
"pos": [ "pos": [
1620, 620.8667602539062,
98 445.8307189941406
], ],
"size": [ "size": [
210, 315,
46 58
], ],
"flags": {}, "flags": {},
"order": 12, "order": 8,
"mode": 0, "mode": 0,
"inputs": [ "inputs": [
{ {
"name": "samples", "name": "image",
"type": "LATENT", "type": "IMAGE",
"link": 7, "link": 86
"label": "samples"
},
{
"name": "vae",
"type": "VAE",
"link": 60,
"label": "vae"
} }
], ],
"outputs": [ "outputs": [
...@@ -555,43 +539,59 @@ ...@@ -555,43 +539,59 @@
"name": "IMAGE", "name": "IMAGE",
"type": "IMAGE", "type": "IMAGE",
"links": [ "links": [
85 87,
88
], ],
"slot_index": 0, "slot_index": 0
"label": "IMAGE"
} }
], ],
"properties": { "properties": {
"Node name for S&R": "VAEDecode" "Node name for S&R": "FluxDepthPreprocessor"
}, },
"widgets_values": [] "widgets_values": [
"LiheYoung/depth-anything-large-hf"
]
}, },
{ {
"id": 44, "id": 17,
"type": "SaveImage", "type": "LoadImage",
"pos": [ "pos": [
1912.7984619140625, -152.99026489257812,
109.0069580078125 409.8635559082031
], ],
"size": [ "size": [
828.9535522460938, 315,
893.8475341796875 314.0000305175781
], ],
"flags": {}, "flags": {},
"order": 13, "order": 3,
"mode": 0, "mode": 0,
"inputs": [ "inputs": [],
"outputs": [
{ {
"name": "images", "name": "IMAGE",
"type": "IMAGE", "type": "IMAGE",
"link": 85, "links": [
"label": "images" 82
],
"slot_index": 0,
"shape": 3,
"label": "IMAGE"
},
{
"name": "MASK",
"type": "MASK",
"links": null,
"shape": 3,
"label": "MASK"
} }
], ],
"outputs": [], "properties": {
"properties": {}, "Node name for S&R": "LoadImage"
},
"widgets_values": [ "widgets_values": [
"ComfyUI" "logo_example.png",
"image"
] ]
} }
], ],
...@@ -693,43 +693,43 @@ ...@@ -693,43 +693,43 @@
"MODEL" "MODEL"
], ],
[ [
80, 82,
40, 17,
0,
42,
0, 0,
35,
3,
"IMAGE" "IMAGE"
], ],
[ [
82, 85,
17, 8,
0, 0,
42, 44,
0, 0,
"IMAGE" "IMAGE"
], ],
[ [
83, 86,
42, 42,
0, 0,
40, 45,
0, 0,
"IMAGE" "IMAGE"
], ],
[ [
84, 87,
40, 45,
0, 0,
43, 43,
0, 0,
"IMAGE" "IMAGE"
], ],
[ [
85, 88,
8, 45,
0,
44,
0, 0,
35,
3,
"IMAGE" "IMAGE"
] ]
], ],
...@@ -737,11 +737,14 @@ ...@@ -737,11 +737,14 @@
"config": {}, "config": {},
"extra": { "extra": {
"ds": { "ds": {
"scale": 0.6115909044841502, "scale": 0.8140274938684042,
"offset": [ "offset": [
724.4911189218763, 1060.3416359459316,
518.3043483917891 529.8567933439979
] ]
},
"node_versions": {
"comfy-core": "0.3.14"
} }
}, },
"version": 0.4 "version": 0.4
......
comfyui/workflows/flux.1-fill-svdquant.json → comfyui/workflows/svdq-flux.1-fill.json
View file @ 7b8d221f
...@@ -167,7 +167,7 @@ ...@@ -167,7 +167,7 @@
"Node name for S&R": "KSampler" "Node name for S&R": "KSampler"
}, },
"widgets_values": [ "widgets_values": [
1078304127779394, 54184445162233,
"randomize", "randomize",
20, 20,
1, 1,
...@@ -388,74 +388,6 @@ ...@@ -388,74 +388,6 @@
0 0
] ]
}, },
{
"id": 48,
"type": "Note",
"pos": [
466.9884033203125,
643.9080810546875
],
"size": [
314.99755859375,
117.98363494873047
],
"flags": {},
"order": 3,
"mode": 0,
"inputs": [],
"outputs": [],
"properties": {
"text": ""
},
"widgets_values": [
"To add mask for fill inpainting, right click on the uploaded image and select \"Open in MaskEditor\". Use the brush tool to add masking and click save to continue."
],
"color": "#432",
"bgcolor": "#653"
},
{
"id": 17,
"type": "LoadImage",
"pos": [
126.66505432128906,
460.53631591796875
],
"size": [
315,
314.0000305175781
],
"flags": {},
"order": 4,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
105
],
"slot_index": 0,
"shape": 3
},
{
"name": "MASK",
"type": "MASK",
"links": [
106
],
"slot_index": 1,
"shape": 3
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"clipspace/clipspace-mask-123191.png [input]",
"image"
]
},
{ {
"id": 58, "id": 58,
"type": "ImageAndMaskResizeNode", "type": "ImageAndMaskResizeNode",
...@@ -547,10 +479,80 @@ ...@@ -547,10 +479,80 @@
"Node name for S&R": "CLIPTextEncode" "Node name for S&R": "CLIPTextEncode"
}, },
"widgets_values": [ "widgets_values": [
"A robot with a closed eye pink face is giving a presentation" "A wooden basket of a cat."
], ],
"color": "#232", "color": "#232",
"bgcolor": "#353" "bgcolor": "#353"
},
{
"id": 48,
"type": "Note",
"pos": [
210.1423797607422,
572.7574462890625
],
"size": [
266.4635925292969,
132.3040771484375
],
"flags": {},
"order": 3,
"mode": 0,
"inputs": [],
"outputs": [],
"properties": {
"text": ""
},
"widgets_values": [
"To add mask for fill inpainting, right click on the uploaded image and select \"Open in MaskEditor\". Use the brush tool to add masking and click save to continue."
],
"color": "#432",
"bgcolor": "#653"
},
{
"id": 17,
"type": "LoadImage",
"pos": [
-225.73123168945312,
316.9361267089844
],
"size": [
423.5578308105469,
437.250732421875
],
"flags": {
"collapsed": false
},
"order": 4,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
105
],
"slot_index": 0,
"shape": 3
},
{
"name": "MASK",
"type": "MASK",
"links": [
106
],
"slot_index": 1,
"shape": 3
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"clipspace/clipspace-mask-8389612.599999994.png [input]",
"image"
]
} }
], ],
"links": [ "links": [
...@@ -697,8 +699,8 @@ ...@@ -697,8 +699,8 @@
"ds": { "ds": {
"scale": 0.8390545288824038, "scale": 0.8390545288824038,
"offset": [ "offset": [
361.8437326514503, 815.2093059315082,
242.3368651567008 185.9955477896796
] ]
}, },
"node_versions": { "node_versions": {
......
examples/flux.1-dev-lora.py 0 → 100644
View file @ 7b8d221f
import torch
from diffusers import FluxPipeline
from nunchaku.models.transformer_flux import NunchakuFluxTransformer2dModel
transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-int4-flux.1-dev")
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
### LoRA Related Code ###
transformer.update_lora_params(
"mit-han-lab/svdquant-lora-collection/svdq-int4-flux.1-dev-ghibsky.safetensors"
) # Path to your converted LoRA safetensors, can also be a remote HuggingFace path
transformer.set_lora_strength(1) # Your LoRA strength here
### End of LoRA Related Code ###
image = pipeline(
"GHIBSKY style, cozy mountain cabin covered in snow, with smoke curling from the chimney and a warm, inviting light spilling through the windows",
num_inference_steps=25,
guidance_scale=3.5,
).images[0]
image.save("flux.1-dev-ghibsky.png")
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