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Granddyser/flux-klein-9b-Biglove-Collection

sourceHugging Facecc-by-nc-4.0updated 2mo agoView on Hugging Face

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Model Card

BigLove Klein Collection

Support Notice I pay for these models out of my own pocket and share them here so people can access them freely. Please understand that this repository is provided as-is. I’m not able to provide individual support, troubleshooting, custom instructions, or answer repeated questions for every upload. Feel free to download and use the files, but please don’t expect active maintenance or personal support.

Available Files

File	Format	Size	Use Case
`bigLove_klein_krealife1.safetensors`	BF16	~26 GB	Krea-Life variant, contributed by Brooker
`bigLove_klein4_mxfp8.safetensors`	mxfp8	~9 GB	8-bit, smaller file
`BigLoveKlein4_nf4.safetensors`	nv4	~5 GB	4-bit normalized (pruned)
`bigLoveKlein4_nvfp4.safetensors`	nvfp4	~5 GB	4-bit normalized
`bigLove_klein4_Q5_K_M.gguf`	GGUF	~6 GB	5-bit GGUF, balanced (pruned)
`bigLove_klein4_int8_convrot.safetensors`	int8	~9 GB	8-bit, smaller file
`bigLoveKlein4_fp8mixed.safetensors`	fp8	~9 GB	8-bit, smaller file (pruned)
`bigLove_klein4_bf16.safetensors`	BF16	~18 GB	BF16, good balance (pruned)
`bigLove_klein3_Q5_K_M.gguf`	GGUF	6 GB	v.3
`bigLove_klein3.safetensors`	BF16	18 GB	v.3
`bigLove_klein3_fp8.safetensors`	FP8	9 GB	v.3
`klein-biglove-v2.1.safetensors`	sft	na	na
`bigLove_klein2_Bf16.safetensors`	BF16	~18 GB	Full precision, best quality
`bigLove_klein2_bf16_pruned.safetensors`	BF16 (pruned)	~18 GB	Pruned weights, slightly faster
`bigLove_klein2_fp8_pruned.safetensors`	FP8 (pruned)	~9 GB	Good balance of quality & VRAM
`bigLove_klein2_nf4.safetensors`	NF4	~5 GB	Low VRAM, fast inference
`bigLove_klein2.gguf`	GGUF	varies	For GGUF-compatible loaders
`bigLove_klein1_fp8.safetensors`	FP8	~9 GB	First version, FP8 quantized

Usage

ComfyUI

Place the desired model file in your ComfyUI/models/diffusion_models/ (or unet) folder and select it in the appropriate loader node.

Diffusers

python

from diffusers import FluxPipeline
import torch

pipe = FluxPipeline.from_pretrained(
    "Granddyser/biglove-klein2-fp8",
    torch_dtype=torch.bfloat16
)
pipe.to("cuda")

image = pipe(
    prompt="your prompt here",
    num_inference_steps=4,
    guidance_scale=0.0,
).images[0]

image.save("output.png")

Acknowledgments

Special thanks to SubtleShader for the motivation.

License

FLUX.2-klein-base-9B is licensed by Black Forest Labs. Inc. under the FLUX.2-klein-base-9B Non-Commercial License. Copyright Black Forest Labs. Inc.