---
license: apache-2.0
base_model: black-forest-labs/FLUX.2-klein-base-4B
library_name: diffusers
pipeline_tag: text-to-image
tags:
- flux2
- reinforcement-learning
---

# Version Base v0.1 Seasonal 39-Family, Step 600 EMA

Complete native BF16 `diffusers.Flux2KleinPipeline`. Trained EMA LoRA weights
(rank 32, alpha 64) are merged into the transformer. No FAR or PEFT runtime
is needed for inference.

This is the 39-family Version Base v0.1 Seasonal curriculum experiment, not the static
AlphaGRPO baseline. Training: 512px, 20 rollout steps, CFG 4, 16 prompts per
iteration, group size 14. `100pct` denotes a target, not a verified
100% online-authored fraction.

Source run: `vb-v01-isolated-lifecycle-20260913`. W&B run: `fb24f2db64306fd0bf7dc90c08823fa6`.
Exact experiment, source checkpoint hash, source revision, configuration hash
and the complete family list are recorded in `provenance.json` and
`export_manifest.json`.

`verification.json` records strict offline reloading, nonzero parameter
differences from Base before and after serialization, and same-seed
512px/20-step image differences from Base. These checks validate the export;
they are not benchmark results or evidence of quality improvement.

```python
import torch
from diffusers import Flux2KleinPipeline

pipe = Flux2KleinPipeline.from_pretrained(
    "kimi000/cedar-cloud-83", torch_dtype=torch.bfloat16
).to("cuda")
image = pipe(
    prompt="A red ceramic teapot beside two blue cups on a wooden table.",
    height=512, width=512, num_inference_steps=20, guidance_scale=4.0,
    generator=torch.Generator("cuda").manual_seed(0),
).images[0]
```