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Support NoticeI 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.
| File | Format | Size | Use Case |
|---|---|---|---|
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 |
ComfyUI/models/diffusion_models/ (or unet) folder and select it in the appropriate loader node.1from diffusers import FluxPipeline
2import torch
3
4pipe = FluxPipeline.from_pretrained(
5 "Granddyser/biglove-klein2-fp8",
6 torch_dtype=torch.bfloat16
7)
8pipe.to("cuda")
9
10image = pipe(
11 prompt="your prompt here",
12 num_inference_steps=4,
13 guidance_scale=0.0,
14).images[0]
15
16image.save("output.png")