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rockeycoss/
LeapAlign

diffusers

safetensors

2604.15311

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LeapAlign: Post-Training Flow Matching Models at Any Generation Step by Building Two-Step Trajectories

This repository provides FLUX checkpoints fine-tuned with LeapAlign and a minimal inference snippet.

Checkpoints

All checkpoints are trained by applying LeapAlign on top of FLUX.1-dev.

flux_geneval_hpsv2: FLUX + LeapAlign + GenEval prompt set + HPSv2.1 reward
flux_hpdv2_hpsv2: FLUX + LeapAlign + HPDv2 prompt set + HPSv2.1 reward
flux_mjhq30k_hpsv3: FLUX + LeapAlign + MJHQ-30k prompt set + HPSv3 reward

Download

Download checkpoint folders from the Hugging Face Hub:

bash

# Download into the current directory (creates local folders)
huggingface-cli download rockeycoss/LeapAlign --local-dir . --include "flux_geneval_hpsv2/*"
huggingface-cli download rockeycoss/LeapAlign --local-dir . --include "flux_hpdv2_hpsv2/*"
huggingface-cli download rockeycoss/LeapAlign --local-dir . --include "flux_mjhq30k_hpsv3/*"

Inference

python

import torch

from diffusers import FluxPipeline, FluxTransformer2DModel

base_id = "black-forest-labs/FLUX.1-dev"
ckpt_dir = "flux_geneval_hpsv2"  # or "flux_hpdv2_hpsv2" / "flux_mjhq30k_hpsv3"

pipe = FluxPipeline.from_pretrained(
    base_id,
    torch_dtype=torch.bfloat16,
    use_safetensors=True,
)

# Load LeapAlign-aligned transformer weights from local folder
pipe.transformer = FluxTransformer2DModel.from_pretrained(
    ckpt_dir,
    torch_dtype=torch.bfloat16,
)

pipe.to("cuda")

image = pipe(
    "a very cute cat",
    guidance_scale=3.5,
    height=720,
    width=720,
    num_inference_steps=50,
    max_sequence_length=512,
).images[0]

image.save("out.png")