KiwiMate 1.0
Collection
7 items • Updated
How to use KiwiMate/KiwiMate-Image-Mini-1.0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.2-klein-4B", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("KiwiMate/KiwiMate-Image-Mini-1.0")
prompt = "Turn this cat into a dog"
input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
image = pipe(image=input_image, prompt=prompt).images[0]KiwiMate-Image-Mini-1.0 is a LoRA fine-tune of black-forest-labs/FLUX.2-klein-4B, a compact 4B-parameter text-to-image diffusion model. It delivers fast, high-quality image generation while staying lightweight enough to run on consumer GPUs.
| File | Description |
|---|---|
KiwiMate-Image-Mini-1.0.safetensors |
Final LoRA weights (92 MB) |
KiwiMate-Image-Mini-1.0_000000250 – _000002750.safetensors |
Training step checkpoints |
KiwiMate-Image-Mini-1.0_merged.safetensors |
Full merged model (7.75 GB), no base model required |
Load the LoRA on top of the base FLUX.2-klein-4B model:
from diffusers import FluxKleinPipeline
import torch
pipe = FluxKleinPipeline.from_pretrained(
"black-forest-labs/FLUX.2-klein-4B",
torch_dtype=torch.bfloat16,
).to("cuda")
pipe.load_lora_weights("KiwiMate/KiwiMate-Image-Mini-1.0", weight_name="KiwiMate-Image-Mini-1.0.safetensors")
image = pipe("your prompt here").images[0]
For a standalone setup without the base model, use the merged checkpoint:
pipe = FluxKleinPipeline.from_pretrained(
"KiwiMate/KiwiMate-Image-Mini-1.0",
torch_dtype=torch.bfloat16,
).to("cuda")
Apache 2.0
Base model
black-forest-labs/FLUX.2-klein-4B