---
library_name: diffusers
base_model:
- black-forest-labs/flux-3-image-klein-comfy
---

This is a randomly initialized tiny model based on [black-forest-labs/flux-3-image-klein-comfy](https://huggingface.co/black-forest-labs/flux-3-image-klein-comfy) for testing and debugging.

| Feature | Source (BF16 component set) | This repo |
|---|---|---|
| Checkpoint tensor elements | 19.40B BF16, 84.05M F32, 1 I64 | 10.07M BF16, 1.09M F32, 2 I64 |
| Safetensors weight files | 39.13 GB (4 files) | 24.61 MB (6 files) |
| Image transformer width / heads / head dimension | 3072 / 24 / 128 | 128 / 4 / 32 |
| Per-mode / joint blocks | 5 / 28 | 2 / 2 |
| Image / reference latent channels | 128 / 128 | Same |
| Text encoder layers / width / output taps | 36 / 2560 / 8 | 8 / 8 / 8 |
| Text condition width | 20480 | 64 |
| Prompt upsampler layers / width | 32 / 4096 | 8 / 8; two attention cycles |
| Text encoder / upsampler vocabulary rows | 151936 / 248320 | Same |
| VAE stages / residual blocks / precision | 4 / 2 / FP32 | Same |
| VAE stage widths | 128 / 256 / 512 / 512 | 32 / 32 / 32 / 32 |
| Denoiser / text encoders precision | BF16 | Same |
| Inputs / output | Text, up to 10 reference images, bounding boxes → image | Same |

| File path | Size |
|------|------|
| model.safetensors | 4.4MB |
| prompt_upsampler/model.safetensors | 8.4MB |
| text_encoder/model.safetensors | 3.0MB |
| transformer/diffusion_pytorch_model.safetensors | 4.4MB |
| vae/diffusion_pytorch_model.safetensors | 2.2MB |
| vae/flux2-vae.safetensors | 2.2MB |

### Example usage

#### SGLang (✅ verified)

Install the FLUX configuration plugin included in `tiny-random/flux-3-image-klein-comfy`. It reads text/VAE dimensions through official diffusion hooks; the installed SGLang files are unchanged. CUDA Graph warmup covers 64×64 text-to-image; reference-image signatures use SDPA outside captured graphs.

```bash
hf download tiny-random/flux-3-image-klein-comfy --local-dir ./flux-3-image-klein-comfy
python -m pip install --no-deps ./flux-3-image-klein-comfy/runtime
```

```bash
sglang serve \
  --model-path tiny-random/flux-3-image-klein-comfy \
  --model-type diffusion \
  --num-gpus 4 \
  --tp-size 4 \
  --ulysses-degree 1 \
  --ring-degree 1 \
  --attention-backend torch_sdpa \
  --enable-breakable-cuda-graph \
  --warmup-resolutions 64x64
```

#### Diffusers (✅ verified)

Use Diffusers from commit aaf57d03a3edfbd9de457d2872417a92f149a91d or a release containing FLUX 3. The repo includes the native Diffusers component layout for standard pretrained loading.

```python
def run_diffusers(model_id: str):
    import torch
    from PIL import Image
    from diffusers import ModularPipeline

    pipe = ModularPipeline.from_pretrained(model_id)
    pipe.load_components(
        pretrained_model_name_or_path=model_id, dtype={"default": torch.bfloat16, "vae": torch.float32}
    )
    pipe.to("cuda")
    image = pipe(
        prompt="Combine these scenes into a red and green illustration.",
        image=[Image.new("RGB", (64, 64), "blue"), Image.new("RGB", (64, 64), "green")],
        height=64,
        width=64,
        num_inference_steps=2,
        max_new_tokens=10,
        text_encoder_out_layers=tuple(range(1, 9)),
        generator=torch.Generator(device="cuda").manual_seed(42),
        output="images",
    )[0]
    image.save("flux3-output.png")
    print(pipe.transformer)
    print(image.size)


if __name__ == "__main__":
    run_diffusers(model_id="tiny-random/flux-3-image-klein-comfy")
```

### Creating this repo

- Code: [.meta/create_model.py](.meta/create_model.py)
- Model structure: [.meta/model.log](.meta/model.log)
- Environment: [.meta/env.json](.meta/env.json)

### Change log

- 2026-10-26: Created a tiny BF16 FLUX 3 Image Klein with its text encoder, prompt upsampler and FP32 VAE.

*This repo is generated by GPT-6.1-Sol.*