tiny-random/minimax-h3-image

This is a randomly initialized tiny model based on MiniMaxAI/MiniMax-H3-Image for testing and debugging.

Feature	Source	This repo
Checkpoint tensor elements	66.46B BF16, 604.20M F32	3.55M BF16, 1.13M F32
Safetensors weight files	135.34 GB (28 files)	11.71 MB (3 files)
Diffusion transformer layers / refiner layers	50 / 2	4 / 2
Transformer width / heads / head dimension	5,376 / 56 / 128	64 / 4 / 32
Transformer FFN width	14,336	128
Per-step output heads (PDD)	6	Same
Image transformer image / text width	24 / 5,120	8 / 8
Text encoder layers / width / head dimension	64 / 5,120 / 128	51 / 8 / 32
Text encoder vision tower layers / width	27 / 1,152	2 / 64
Tokenizer / processor vocabulary rows	151,936	Same
Image VAE stages / residual blocks	6 / 2	Same
Image VAE stage widths	128 / 256 / 256 / 512 / 512 / 1024	32 at every stage
Image VAE latent channels / compression	24 / 16x	8 / 16x
ViT decoder layers / heads / head dimension	24 / 16 / 64	4 / 2 / 32
Transformer precision	BF16 blocks, FP32 patch/timestep/output heads	Same
Image VAE precision	FP32	Same
Inputs / output	Text, optional reference images -> image	Same

The DiT conditions on hidden_states[50] of its Qwen3-VL conditioner, so the tiny text encoder keeps 51 decoder layers and shrinks only its width. Six VAE stages keep the source's 16x spatial compression, and the transformer keeps the full 2x2 latent patch and six-step PDD schedule that replaces the base output layer. The text condition width is reduced to 8 columns.

File path	Size
text_encoder/model.safetensors	6.2MB
transformer/diffusion_pytorch_model.safetensors	1.1MB
vae/diffusion_pytorch_model.safetensors	4.5MB

Example usage

Diffusers (✅ verified)

Requires Diffusers 0.43.0 or newer, which ships the MiniMax-H3-Image integration, and Torchvision for the Qwen3-VL video processor.

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

    pipe = ModularPipeline.from_pretrained(model_id)
    # The index stores the components relative to the repo root (`.`) so the same
    # checkpoint loads from a local directory and from the Hub.
    pipe.load_components(
        pretrained_model_name_or_path=model_id, dtype={"default": torch.bfloat16, "vae": torch.float32}
    )
    device = "cuda" if torch.cuda.is_available() else "cpu"
    pipe.to(device)

    image = pipe(
        prompt="A red fox trotting through a snowy pine forest.",
        height=64,
        width=64,
        min_generation_area=1024,
        generator=torch.Generator(device=device).manual_seed(42),
        output="images",
    )[0]
    image.save("minimax-h3-image-t2i.png")
    print("text-to-image", image.size)

    reference = Image.new("RGB", (64, 64), "blue")
    image = pipe(
        prompt="Keep everything in <Picture 1> as it is, but turn the square green.",
        image=reference,
        height=64,
        width=64,
        min_generation_area=1024,
        generator=torch.Generator(device=device).manual_seed(1234),
        output="images",
    )[0]
    image.save("minimax-h3-image-edit.png")
    print("image editing", image.size)


if __name__ == "__main__":
    run_diffusers(model_id="scratch/minimax-h3-image")

Creating this repo

• Code: .meta/create_model.py
• Model structure: .meta/model.log
• Environment: .meta/env.json

Change log

• 2026-11-12: Initial tiny BF16 MiniMax-H3-Image with text-to-image and reference-image editing.

This repo is generated by DeepSeek-V4.1-Flash.