Mindscend/mindscend-image-0.2

Mindscend Image 0.2

A small class-conditional image generator trained from scratch by Mindscend. It makes 64×64 pictures of ten subjects and runs in the browser (ONNX Runtime Web, WebGPU) in the Mindscend AI Lab.

Architecture	Rectified-flow U-Net (levels ×1, ×2, ×2, ×2; attention at the 2nd level), class-conditional with classifier-free guidance
Parameters	38.3M
Resolution	64×64 RGB
Classes	flower, bird, insect, boat, train, mountain, building, water, bridge, sculpture (index 10 = no class)
Training data	60,000 images (6,000 per class) picked from PD12M by caption; photos only, no people
Training	80,000 steps, batch 64, AdamW 2e-4, EMA weights, one RTX 3080 Ti
Format	ONNX fp32, opset 17 (browser); Core ML FP16 (Apple)

Quality

FID against 60,000 training images, 30 Euler steps, only 200 generated samples (20 per class). FID on so few samples reads high; compare these only with our own runs measured the same way:

guidance	FID	Inception score
1.0	121.2	5.09
1.5	115.3	5.23
2.0	111.1	5.44
3.0	111.0	5.63

It is a research model: subjects are recognisable, details are not. Many PD12M photos are old, so many results look like old photographs.

Use

mindscend-image-0.2.onnx is one velocity call: inputs x (b×3×64×64, float32), t (b, float32, 1 = noise, 0 = image), y (b, int64 class); output v. Sampling with N Euler steps and guidance g, conditional and unconditional (y = 10) in one batch of 2:

x = randn(3, 64, 64)
for i = N .. 1:  vc, vu = model([x, x], [i/N, i/N], [class, 10]);  x -= (vu + g * (vc - vu)) / N
image = clamp(x * 0.5 + 0.5, 0, 1)

coreml/MindscendImage.mlpackage is the same model as a Core ML ML program (FP16, fixed batch of 2: the class and the no-class row), used by the Mindscend AI Lab app on iPhone, iPad and Mac.

config.json holds the class names and the Lab's defaults (20 steps, guidance 2.0).

License

Weights: Apache-2.0. Training images: PD12M, public domain / CC0 (metadata CDLA-Permissive-2.0).