> ## Documentation Index > Fetch the complete documentation index at: https://miles.radixark.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # FLUX 3 Image Klein > FLUX 3 Image Klein DiffusionNFT with PickScore (LoRA) and full-finetune Flow-GRPO with HPSv2.1, both fully colocated on 8 GPUs. ## 1. Model introduction FLUX 3 Image Klein is a flow-matching DiT for text-to-image generation and image editing: per-stream mode blocks for the image and text tokens, then joint single-stream blocks over `[image | text]`, with 4-axis RoPE and per-token AdaLN modulation. Miles supports RL on the text-to-image task only (`--diffusion-task t2i`); image-conditioned requests are not supported. Rollouts run on sglang-diffusion's native FLUX 3 Image Klein pipeline (`Flux3ImagePipeline`) at 1024×1024 in bf16; the trainer runs the `MilesModelBackend` package `miles/backends/fsdp_utils/models/flux3_image/`, which builds only the text-to-image path. ## 2. Supported variants | Variant | Checkpoint | Status | | - | - | - | | Klein Base (CFG, not step-distilled) | [black-forest-labs/flux-3-image-klein-base](https://huggingface.co/black-forest-labs/flux-3-image-klein-base) (gated: needs `HF_TOKEN`) | Recipes below | The step-distilled Klein variant samples without CFG through a different denoise loop and is not supported. The launchers' `--model-path` option also takes a local package directory. ## 3. Family config From `miles/backends/fsdp_utils/configs/flux3_image.py`: | Property | Value | Why | | - | - | - | | Rollout microgroup | 1 (validated) | The engine's rollout path accepts one batch with one output per request | | Micro-batch | 1 (validated) | Text length varies per prompt and the DiT takes no padding mask | | Attention | `--fsdp-attention-backend _flash_3` on Hopper (SM9x), `_flash_4` on SM10x Blackwell; the trainer rejects other GPUs | The engine's default flash kernel on each GPU; its backward is deterministic under `--deterministic-mode` (the Flow-GRPO recipe sets it, the DiffusionNFT recipe does not) | | CFG off | `--diffusion-guidance-scale 1` requires `--rollout-patch-group flux3_image` | Unpatched, the engine runs the whole CFG pair even at a scale of 1, packed into one forward over both branches: not bitwise the conditional velocity | | CFG combine | `neg + scale · (pos − neg)`, two batch-1 forwards; `true_cfg_scale` wins when set | Same formula as the engine | | Full-weight sync | Weight bridge renames `*.{query,key}_norm.scale` to `.weight` | The engine's tensor-update path skips names its module lacks | The `flux3_image` rollout patch group (`miles/backends/sglang_diffusion_utils/monkey_patches/patch_flux3_image_cfg_off.py`) runs a conditional-only denoise loop when the CFG scale is 1 or less: one DiT forward per step, the rollout transition hook intact, CFG-on requests unchanged. ## 4. Launch Both recipes put training, rollout and the reward model on the same 8 GPUs (`--colocate` plus `---reward-colocate`, one reward worker per engine) and decode rollout responses in parallel parser actors (`--sglang-server-concurrency 4 --rollout-fetch-in-parser --rollout-parser-num-workers 32`). The recipes target Hopper; on SM10x Blackwell, append `--extra-args "--fsdp-attention-backend _flash_4"`. ### 4.1 DiffusionNFT + PickScore, LoRA, 8 GPUs colocated `scripts/run_diffusion_nft_flux3_image_pickscore_8gpu.py` — LoRA rank 32 / alpha 64 synced over CUDA IPC in `dynamic` merge mode (the engine keeps the adapter unmerged, like the trainer), an EMA rollout policy and deterministic ODE rollouts at 16 steps without CFG: NFT trains the unguided velocity, so the rollout samples it too. 8 prompts × 8 samples per rollout, lr 3e-4, gradient checkpointing. **Status:** [📈 V — Verified](/docs/diffusion/user-guide/recipe-verification.md#v) ```bash theme={null} python3 scripts/run_diffusion_nft_flux3_image_pickscore_8gpu.py ``` ### 4.2 Flow-GRPO + HPSv2.1, full finetune, 8 GPUs colocated `scripts/run_diffusion_grpo_flux3_image_hps_8gpu.py` — full finetune (no LoRA) with fp32 master and reduce, bf16 forward, lr 1e-5; full weights sync over CUDA IPC. 10 sampling steps with CFG 3.5 on both the rollout and the trainer, Flow-SDE noise level 0.7 on one SDE step per rollout drawn from steps 1-2, clip 1e-4, no KL. Old log-probs come from the rollout: with this noise level the train-rollout drift leaves the ratio at 1 (`approx_kl` at most \~1e-12, `clipfrac` 0). 16 prompts × 16 samples per rollout, gradient checkpointing. **Status:** [📈 V — Verified](/docs/diffusion/user-guide/recipe-verification.md#v) ```bash theme={null} python3 scripts/run_diffusion_grpo_flux3_image_hps_8gpu.py ``` ## 5. Reference results Both runs use the default seed and the recipe defaults on one 8×H200 node, except that the Flow-GRPO run stops at 150 rollouts (`--num-rollout 150`; the recipe default is 200). Held-out numbers use the first 32 prompts of each reward's test split, one image per prompt at a fixed seed, at the training step count. ### 5.1 DiffusionNFT + PickScore The PickScore reward (`rollout/reward/raw_mean`, PickScore / 26) rises from 0.769 (mean of the first 10 rollouts) to 0.857 (mean of the last 10) over 100 rollouts: ![FLUX 3 Image Klein DiffusionNFT PickScore reward mean](https://mintcdn.com/radixark/sL-lqXHDLHHpg3Xm/diffusion/assets/images/flux3-image/nft_pickscore_reward_mean.png?fit=max\&auto=format\&n=sL-lqXHDLHHpg3Xm\&q=85\&s=b9b2e9a650231f09a7be8327aa21b47d) On held-out PickScore prompts at 16 steps, the training setting. Klein Base's own default is 50 steps with CFG 3.5, so neither base row shows its default quality: | Model | Sampling | PickScore / 26 | | - | - | - | | Base | CFG 3.5 | 0.833 | | Base | CFG off | 0.775 | | NFT, rollout 100 | CFG off | **0.878** | The trained model without CFG beats the base model without CFG on 32/32 prompts and the base model with CFG 3.5 on 27/32. It also drifts toward PickScore's preferences: brighter, more saturated images, and on some prompts it adds or beautifies people. Case studies (base with CFG 3.5, base without CFG, NFT without CFG): *An impressionist portrait of a shar pei* ![shar pei: base CFG 3.5, base CFG off, NFT](https://mintcdn.com/radixark/sL-lqXHDLHHpg3Xm/diffusion/assets/images/flux3-image/nft_shar_pei.webp?fit=max\&auto=format\&n=sL-lqXHDLHHpg3Xm\&q=85\&s=b15463acd6d1a934cb2a6a0d7421351f) *A digital illustration of a dragon flying over a castle, fantasy style* ![dragon over a castle: base CFG 3.5, base CFG off, NFT](https://mintcdn.com/radixark/sL-lqXHDLHHpg3Xm/diffusion/assets/images/flux3-image/nft_dragon_castle.webp?fit=max\&auto=format\&n=sL-lqXHDLHHpg3Xm\&q=85\&s=3fc94b2f45316a94dcebf17cdfaa2264) *an epic angel dressed in blue with white wings* ![angel: base CFG 3.5, base CFG off, NFT](https://mintcdn.com/radixark/sL-lqXHDLHHpg3Xm/diffusion/assets/images/flux3-image/nft_angel.webp?fit=max\&auto=format\&n=sL-lqXHDLHHpg3Xm\&q=85\&s=80b57cd4b76893bd587a91e81b87677a) *enigmatic black square building on top of a purple hill, smoke stacks* ![black building on a purple hill: base CFG 3.5, base CFG off, NFT](https://mintcdn.com/radixark/sL-lqXHDLHHpg3Xm/diffusion/assets/images/flux3-image/nft_black_building.webp?fit=max\&auto=format\&n=sL-lqXHDLHHpg3Xm\&q=85\&s=726642a0b38a8cee7c74c98d7be4a427) ### 5.2 Flow-GRPO + HPSv2.1, full finetune The HPSv2.1 reward rises from 0.263 (mean of the first 10 rollouts) to 0.278 (mean of the last 10) over 150 rollouts; it is flat for the first \~20 rollouts, as expected of a full finetune at lr 1e-5 with clip 1e-4: ![FLUX 3 Image Klein Flow-GRPO HPSv2.1 reward mean](https://mintcdn.com/radixark/sL-lqXHDLHHpg3Xm/diffusion/assets/images/flux3-image/grpo_hps_reward_mean.png?fit=max\&auto=format\&n=sL-lqXHDLHHpg3Xm\&q=85\&s=f5f32ec328ca4d515e0327990524d96c) On held-out HPDv2 prompts at 10 steps with CFG 3.5 (the training settings, not Klein Base's 50-step default), HPSv2.1 goes from 0.270 (base) to 0.282 (rollout 150), higher on 29/32 prompts. The gain is small but consistent: images keep their composition and gain detail and color. Case studies (base and Flow-GRPO, both with CFG 3.5): *Anthropomorphic alien plant creature with big eyes and leafy limbs depicted in a detailed painting.* ![alien plant creature: base, Flow-GRPO](https://mintcdn.com/radixark/sL-lqXHDLHHpg3Xm/diffusion/assets/images/flux3-image/grpo_alien_plant.webp?fit=max\&auto=format\&n=sL-lqXHDLHHpg3Xm\&q=85\&s=6385eab8dbd9ea53a50f7e784d7deae5) *A massive and brightly colored spacecraft in a deserted landscape, depicted in retro 1960s sci-fi art.* ![retro spacecraft: base, Flow-GRPO](https://mintcdn.com/radixark/sL-lqXHDLHHpg3Xm/diffusion/assets/images/flux3-image/grpo_retro_spacecraft.webp?fit=max\&auto=format\&n=sL-lqXHDLHHpg3Xm\&q=85\&s=e2d7d07dcb50b27755b082626f1105d3) *A cyber girl with demon horns holds a black feather in front of a cybercity with a gloomy expression.* ![cyber girl: base, Flow-GRPO](https://mintcdn.com/radixark/sL-lqXHDLHHpg3Xm/diffusion/assets/images/flux3-image/grpo_cyber_girl.webp?fit=max\&auto=format\&n=sL-lqXHDLHHpg3Xm\&q=85\&s=204f3e61c3c6aa52bca8b79276881ff8) ## 6. Pairs well with * [LoRA Training and Weight Sync](/docs/diffusion/advanced/lora.md) — IPC adapter sync and merge modes. * [Rewards](/docs/diffusion/user-guide/rewards.md) — PickScore and HPSv2.1. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.