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Commit 218d333e authored by Samuel Tesfai's avatar Samuel Tesfai
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Merge https://github.com/mit-han-lab/nunchaku into migrate_tinychat

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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
### [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!
- **[2025-02-04]** **🚀 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)!
......@@ -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 activate nunchaku
pip install torch torchvision torchaudio
pip install diffusers ninja wheel transformers accelerate sentencepiece protobuf
pip install huggingface_hub peft opencv-python einops gradio spaces GPUtil
pip install ninja wheel diffusers transformers accelerate sentencepiece protobuf huggingface_hub
pip install peft opencv-python gradio spaces GPUtil # For gradio demos
```
2. Install `nunchaku` package:
......@@ -74,6 +75,8 @@ SVDQuant is a post-training quantization technique for 4-bit weights and activat
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
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")
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
Please refer to [comfyui/README.md](comfyui/README.md) for the usage in [ComfyUI](https://github.com/comfyanonymous/ComfyUI).
......@@ -109,6 +179,10 @@ Please refer to [comfyui/README.md](comfyui/README.md) for the usage in [ComfyUI
* SANA:
* 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
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.
......@@ -118,7 +192,7 @@ Please refer to [app/flux/t2i/README.md](app/flux/t2i/README.md) for instruction
- [ ] Easy installation
- [x] Comfy UI node
- [x] Customized LoRA conversion instructions
- [ ] Customized model quantization instructions
- [x] Customized model quantization instructions
- [x] FLUX.1 tools support
- [ ] Modularization
- [ ] IP-Adapter integration
......
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comfyui/README.md
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......@@ -3,53 +3,76 @@
![comfyui](../assets/comfyui.jpg)
## Installation
1. Install `nunchaku` following [README.md](https://github.com/mit-han-lab/nunchaku?tab=readme-ov-file#installation).
2. Install dependencies needed to run custom ComfyUI nodes:
```shell
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:
Please first install `nunchaku` following the instructions in [README.md](https://github.com/mit-han-lab/nunchaku?tab=readme-ov-file#installation).
### ComfyUI-CLI
```shell
cd ComfyUI/custom_nodes
git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager
comfy node registry-install svdquant
```
### 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
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`.
* Place the SVDQuant workflow configurations from [`workflows`](./workflows) into `user/default/workflows`.
* For example
* 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:
```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:
```shell
huggingface-cli download comfyanonymous/flux_text_encoders clip_l.safetensors --local-dir models/clip
huggingface-cli download comfyanonymous/flux_text_encoders t5xxl_fp16.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/text_encoders
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
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
......@@ -75,10 +98,18 @@ git clone https://github.com/ltdrdata/ComfyUI-Manager comfyui-manager
* `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.
* **Note**: Currently, only **one LoRA** can be loaded at a time.
* `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.
* **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
- `text_encoder1`: `t5xxl_fp16.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_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
git clone https://github.com/mit-han-lab/deepcompressor
cd deepcompressor
......@@ -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
huggingface-cli download mit-han-lab/svdq-flux.1-t5 --local-dir models/text_encoders/svdq-flux.1-t5
```
```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`.
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 @ 218d333e
# 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()}
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
from .nodes.lora import SVDQuantFluxLoraLoader
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 @ 218d333e
from .flux import SVDQuantFluxLoraLoader
comfyui/nodes/lora/flux.py 0 → 100644
View file @ 218d333e
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 @ 218d333e
from .flux import SVDQuantFluxDiTLoader
from .text_encoder import SVDQuantTextEncoderLoader
comfyui/nodes.py → comfyui/nodes/models/flux.py
View file @ 218d333e
import os
import types
import comfy.model_base
import comfy.model_patcher
import comfy.sd
import folder_paths
import GPUtil
import torch
import numpy as np
from comfy.ldm.common_dit import pad_to_patch_size
from comfy.supported_models import Flux, FluxSchnell
from diffusers import FluxTransformer2DModel
from einops import rearrange, repeat
from torch import nn
from transformers import T5EncoderModel
from image_gen_aux import DepthPreprocessor
from nunchaku.models.transformer_flux import NunchakuFluxTransformer2dModel
class ComfyUIFluxForwardWrapper(nn.Module):
def __init__(self, model: NunchakuFluxTransformer2dModel, config):
super(ComfyUIFluxForwardWrapper, self).__init__()
......@@ -41,9 +36,7 @@ class ComfyUIFluxForwardWrapper(nn.Module):
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
)
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
......@@ -67,15 +60,14 @@ class ComfyUIFluxForwardWrapper(nn.Module):
guidance=guidance if self.config["guidance_embed"] else None,
).sample
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 = 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]
return out
class SVDQuantFluxDiTLoader:
@classmethod
def INPUT_TYPES(s):
# folder_paths.get_filename_list("loras"),
model_paths = [
"mit-han-lab/svdq-int4-flux.1-schnell",
"mit-han-lab/svdq-int4-flux.1-dev",
......@@ -83,31 +75,23 @@ class SVDQuantFluxDiTLoader:
"mit-han-lab/svdq-int4-flux.1-depth-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 = sorted(
[
folder
for folder in local_folders
if not folder.startswith(".")
and os.path.isdir(os.path.join(prefix, folder))
if not folder.startswith(".") and os.path.isdir(os.path.join(prefix, folder))
]
)
model_paths.extend(local_folders)
model_paths = local_folders + model_paths
ngpus = len(GPUtil.getGPUs())
return {
"required": {
"model_path": (model_paths,),
"device_id": (
"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:
CATEGORY = "SVDQuant"
TITLE = "SVDQuant Flux DiT Loader"
def load_model(
self, model_path: str, device_id: int, **kwargs
) -> tuple[FluxTransformer2DModel]:
def load_model(self, model_path: str, device_id: int, **kwargs) -> tuple[FluxTransformer2DModel]:
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)):
model_path = os.path.join(prefix, model_path)
else:
model_path = model_path
transformer = NunchakuFluxTransformer2dModel.from_pretrained(model_path).to(
device
)
transformer = NunchakuFluxTransformer2dModel.from_pretrained(model_path).to(device)
dit_config = {
"image_model": "flux",
"patch_size": 2,
......@@ -158,9 +138,6 @@ class SVDQuantFluxDiTLoader:
dit_config["in_channels"] = 64
model_config = Flux(dit_config)
else:
assert (
model_path == "mit-han-lab/svdq-int4-flux.1-dev"
), f"model {model_path} not supported"
dit_config["in_channels"] = 16
model_config = Flux(dit_config)
......@@ -168,239 +145,6 @@ class SVDQuantFluxDiTLoader:
model_config.custom_operations = None
model = model_config.get_model({})
model.diffusion_model = ComfyUIFluxForwardWrapper(
transformer, config=dit_config
)
model.diffusion_model = ComfyUIFluxForwardWrapper(transformer, config=dit_config)
model = comfy.model_patcher.ModelPatcher(model, device, device_id)
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 @ 218d333e
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 @ 218d333e
from .depth import FluxDepthPreprocessor
comfyui/nodes/preprocessors/depth.py 0 → 100644
View file @ 218d333e
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 @ 218d333e
[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 @ 218d333e
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 @ 218d333e
......@@ -57,7 +57,7 @@
"Node name for S&R": "KSampler"
},
"widgets_values": [
646386750200194,
896617285614695,
"randomize",
20,
1,
......@@ -323,51 +323,6 @@
"default"
]
},
{
"id": 7,
"type": "CLIPTextEncode",
"pos": [
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],
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},
"order": 5,
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{
"name": "clip",
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{
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"type": "CONDITIONING",
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],
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"title": "CLIP Text Encode (Negative Prompt)",
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},
"widgets_values": [
""
],
"color": "#322",
"bgcolor": "#533"
},
{
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"type": "CLIPTextEncode",
......@@ -483,36 +438,44 @@
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},
{
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"id": 38,
"type": "ImageScale",
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{
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],
"outputs": [
{
"name": "MODEL",
"type": "MODEL",
"name": "IMAGE",
"type": "IMAGE",
"links": [
71
76
],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "SVDQuantFluxDiTLoader"
"Node name for S&R": "ImageScale"
},
"widgets_values": [
"mit-han-lab/svdq-int4-flux.1-canny-dev",
0
"nearest-exact",
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1024,
"center"
]
},
{
......@@ -527,7 +490,7 @@
314.0000305175781
],
"flags": {},
"order": 3,
"order": 2,
"mode": 0,
"inputs": [],
"outputs": [
......@@ -558,44 +521,81 @@
]
},
{
"id": 38,
"type": "ImageScale",
"id": 7,
"type": "CLIPTextEncode",
"pos": [
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323.8695068359375,
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"order": 6,
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},
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"name": "image",
"type": "IMAGE",
"link": 75
"name": "clip",
"type": "CLIP",
"link": 63,
"label": "clip"
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"name": "CONDITIONING",
"type": "CONDITIONING",
"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": [
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-126.4416275024414
],
"size": [
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],
"flags": {},
"order": 3,
"mode": 0,
"inputs": [],
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{
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"type": "MODEL",
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],
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "ImageScale"
"Node name for S&R": "SVDQuantFluxDiTLoader"
},
"widgets_values": [
"nearest-exact",
1024,
1024,
"center"
"mit-han-lab/svdq-int4-flux.1-canny-dev",
0
]
}
],
......@@ -743,9 +743,12 @@
"ds": {
"scale": 0.895430243255241,
"offset": [
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215.36536277004458
838.4305404853558,
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]
},
"node_versions": {
"comfy-core": "0.3.14"
}
},
"version": 0.4
......
comfyui/workflows/flux.1-depth-svdquant.json → comfyui/workflows/svdq-flux.1-depth.json
View file @ 218d333e
{
"last_node_id": 44,
"last_link_id": 85,
"last_node_id": 45,
"last_link_id": 88,
"nodes": [
{
"id": 7,
......@@ -124,40 +124,6 @@
10
]
},
{
"id": 32,
"type": "VAELoader",
"pos": [
656,
165
],
"size": [
315,
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],
"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,
"type": "KSampler",
......@@ -213,7 +179,7 @@
"Node name for S&R": "KSampler"
},
"widgets_values": [
718322679777603,
704308966490490,
"randomize",
20,
1,
......@@ -223,118 +189,144 @@
]
},
{
"id": 35,
"type": "InstructPixToPixConditioning",
"id": 39,
"type": "SVDQuantFluxDiTLoader",
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"properties": {
"Node name for S&R": "SVDQuantFluxDiTLoader"
},
"widgets_values": [
"mit-han-lab/svdq-int4-flux.1-depth-dev",
0
]
},
{
"id": 43,
"type": "PreviewImage",
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],
"size": [
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],
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"mode": 0,
"inputs": [
{
"name": "positive",
"type": "CONDITIONING",
"link": 67,
"label": "positive"
},
"name": "images",
"type": "IMAGE",
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"label": "images"
}
],
"outputs": [],
"properties": {
"Node name for S&R": "PreviewImage"
},
"widgets_values": []
},
{
"id": 8,
"type": "VAEDecode",
"pos": [
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],
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"flags": {},
"order": 12,
"mode": 0,
"inputs": [
{
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"label": "negative"
"name": "samples",
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"label": "samples"
},
{
"name": "vae",
"type": "VAE",
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"link": 60,
"label": "vae"
},
{
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"label": "pixels"
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],
"outputs": [
{
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"type": "CONDITIONING",
"name": "IMAGE",
"type": "IMAGE",
"links": [
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85
],
"slot_index": 0,
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{
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"label": "negative"
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{
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"label": "latent"
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],
"properties": {
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{
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-172.0343017578125
1912.7984619140625,
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"order": 13,
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......@@ -355,7 +347,7 @@
"name": "IMAGE",
"type": "IMAGE",
"links": [
83
86
],
"slot_index": 0
}
......@@ -370,48 +362,6 @@
"center"
]
},
{
"id": 17,
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},
"widgets_values": [
"robot.png",
"image"
]
},
{
"id": 23,
"type": "CLIPTextEncode",
......@@ -450,104 +400,138 @@
"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."
"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",
"bgcolor": "#353"
},
{
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"type": "PreviewImage",
"id": 32,
"type": "VAELoader",
"pos": [
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],
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315,
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],
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"order": 10,
"order": 2,
"mode": 0,
"inputs": [
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"outputs": [
{
"name": "images",
"type": "IMAGE",
"link": 84,
"label": "images"
"name": "VAE",
"type": "VAE",
"links": [
60,
69
],
"slot_index": 0,
"label": "VAE"
}
],
"outputs": [],
"properties": {
"Node name for S&R": "PreviewImage"
"Node name for S&R": "VAELoader"
},
"widgets_values": []
"widgets_values": [
"ae.safetensors"
]
},
{
"id": 40,
"type": "DepthPreprocesser",
"id": 35,
"type": "InstructPixToPixConditioning",
"pos": [
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1008,
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],
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"order": 10,
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{
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"label": "vae"
},
{
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"link": 88,
"label": "pixels"
}
],
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{
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"type": "IMAGE",
"name": "positive",
"type": "CONDITIONING",
"links": [
80,
84
64
],
"slot_index": 0
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"label": "positive"
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{
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"label": "negative"
},
{
"name": "latent",
"type": "LATENT",
"links": [
73
],
"slot_index": 2,
"label": "latent"
}
],
"properties": {
"Node name for S&R": "DepthPreprocesser"
"Node name for S&R": "InstructPixToPixConditioning"
},
"widgets_values": [
"LiheYoung/depth-anything-large-hf"
]
"widgets_values": []
},
{
"id": 8,
"type": "VAEDecode",
"id": 45,
"type": "SVDQuantDepthPreprocessor",
"pos": [
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{
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"link": 60,
"label": "vae"
"name": "image",
"type": "IMAGE",
"link": 86
}
],
"outputs": [
......@@ -555,43 +539,59 @@
"name": "IMAGE",
"type": "IMAGE",
"links": [
85
87,
88
],
"slot_index": 0,
"label": "IMAGE"
"slot_index": 0
}
],
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"Node name for S&R": "VAEDecode"
"Node name for S&R": "FluxDepthPreprocessor"
},
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"widgets_values": [
"LiheYoung/depth-anything-large-hf"
]
},
{
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"type": "LoadImage",
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-152.99026489257812,
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"order": 13,
"order": 3,
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"inputs": [],
"outputs": [
{
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"name": "IMAGE",
"type": "IMAGE",
"link": 85,
"label": "images"
"links": [
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],
"slot_index": 0,
"shape": 3,
"label": "IMAGE"
},
{
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"links": null,
"shape": 3,
"label": "MASK"
}
],
"outputs": [],
"properties": {},
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"ComfyUI"
"logo_example.png",
"image"
]
}
],
......@@ -693,43 +693,43 @@
"MODEL"
],
[
80,
40,
82,
17,
0,
42,
0,
35,
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"IMAGE"
],
[
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"IMAGE"
],
[
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"IMAGE"
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[
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"IMAGE"
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[
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"IMAGE"
]
],
......@@ -737,11 +737,14 @@
"config": {},
"extra": {
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"scale": 0.8140274938684042,
"offset": [
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1060.3416359459316,
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]
},
"node_versions": {
"comfy-core": "0.3.14"
}
},
"version": 0.4
......
comfyui/workflows/flux.1-fill-svdquant.json → comfyui/workflows/svdq-flux.1-fill.json
View file @ 218d333e
......@@ -167,7 +167,7 @@
"Node name for S&R": "KSampler"
},
"widgets_values": [
1078304127779394,
54184445162233,
"randomize",
20,
1,
......@@ -388,74 +388,6 @@
0
]
},
{
"id": 48,
"type": "Note",
"pos": [
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],
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],
"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": [
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],
"size": [
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],
"flags": {},
"order": 4,
"mode": 0,
"inputs": [],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
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],
"slot_index": 0,
"shape": 3
},
{
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"type": "MASK",
"links": [
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],
"slot_index": 1,
"shape": 3
}
],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"clipspace/clipspace-mask-123191.png [input]",
"image"
]
},
{
"id": 58,
"type": "ImageAndMaskResizeNode",
......@@ -547,10 +479,80 @@
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
"A robot with a closed eye pink face is giving a presentation"
"A wooden basket of a cat."
],
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"bgcolor": "#353"
},
{
"id": 48,
"type": "Note",
"pos": [
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},
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"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,
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"size": [
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"flags": {
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},
"order": 4,
"mode": 0,
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},
{
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],
"properties": {
"Node name for S&R": "LoadImage"
},
"widgets_values": [
"clipspace/clipspace-mask-8389612.599999994.png [input]",
"image"
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}
],
"links": [
......@@ -697,8 +699,8 @@
"ds": {
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"offset": [
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"node_versions": {
......
examples/flux.1-dev-lora.py 0 → 100644
View file @ 218d333e
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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