Verified: 9 months ago
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The FLUX.1 [dev] Model is licensed by Black Forest Labs. Inc. under the FLUX.1 [dev] Non-Commercial License. Copyright Black Forest Labs. Inc.
IN NO EVENT SHALL BLACK FOREST LABS, INC. BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH USE OF THIS MODEL.
For custom_nodes - https://github.com/bananasss00/ComfyUI_bitsandbytes_NF4-Lora
Direct link https://github.com/bananasss00/ComfyUI_bitsandbytes_NF4-Lora/archive/refs/heads/master.zip
Key Features of the Nodes
On-the-fly Conversion: The nodes allow conversion of FP8 models into NF4 format in real time. Generation speed does not drop with LoRA use.
Model Loading Optimizations: Improved model loading times by allowing users to specify the data type (load_dtype) of the model, avoiding unnecessary re-conversions.
Post-Generation Model Unloading Fixes: Ensures proper model partially unloading after generation, addressing previous issues that could affect memory management.
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Comparison of NF4-FP8-GGUF_Q4_0 generations with two LoRA: HyperFlux+realism_lora at 8 steps:
https://imgsli.com/MzA0Nzgz https://imgsli.com/MzA0Nzg0 https://imgsli.com/MzA0Nzg2 https://imgsli.com/MzA0Nzg3
Tips for Best Quality
Use FP8 models: Although NF4 models are supported, the quality of applied LoRA is significantly higher if FP8 models are used.
Adjust the LoRA weight for NF4 models: When using NF4 models as inputs, you may need to increase the LoRA weight, otherwise the LoRA effect may not be noticeable. Also, in the Advanced Nodes section, try setting the rounding_format parameter to a preset of 2,1,7. Keep in mind, however, that using these settings may cause artifacts - experimenting with custom values may yield better results. Solutions I don't yet know how to effectively apply LoRA to nf4 models.
The node uses a modified rounding function from ComfyUI, which can be found https://github.com/comfyanonymous/ComfyUI/blob/203942c8b29dfbf59a7976dcee29e8ab44a1b32d/comfy/float.py#L14.
When the preset is set to 2, 1, 7, or custom, these three values determine the EXPONENT_BITS, MANTISSA_BITS, and EXPONENT_BIAS within the function, which control the precision of floating-point calculations.
Feel free to check out the project and contribute at the GitHub repository.
https://github.com/bananasss00/ComfyUI_bitsandbytes_NF4-Lora
Author: https://civitai.com/user/egordubrovskiy9112843