lodestones/MOSS-Image-Base-8-steps-dmd

MOSS-Image-Base, 8-step data-space DMD

Work-in-progress distillation of OpenMOSS-Team/MOSS-Image-Base (50 steps, CFG 3) to 8 network evaluations, no CFG. Data-space DMD (arXiv 2610.12102): the generator iterates on its own clean estimate from fixed noise. Trained on compact-JSON scene_graph captions.

Checkpoint: update 12000 (EMA), float32 weights.

Usage (recommended): the LoRA alone, standard sampler

Load lora.safetensors on the stock MOSS-Image-Base transformer (e.g. a ComfyUI LoRA loader, strength 1) and sample with the model's usual flow-matching Euler sampler, shift 5, CFG 1, 4-8 steps. Nothing else from this repo is needed.

Data-space sampler (the training-time form)

transformer/ is a stock MOSSImageTransformer (load it with the release pipeline_moss_image.py); the text encoder, tokenizer and VAE are the base repo's.

Precision: the distillation delta is smaller than bf16's rounding step, so transformer/ must be loaded AND run in fp32 (torch_dtype=torch.float32, no autocast); casting it to bf16 discards most of the distillation. For fast bf16 inference keep the base repo's bf16 transformer and apply the adapter files unmerged:

• lora.safetensors: fp32 LoRA, rank 128 / alpha 128 on every block's proj.{q,k,v,o,ffn_up_gate,ffn_down}, kohya keys diffusion_model.<module>.lora_down.weight / lora_up.weight / alpha; apply as y = W x + (alpha / rank) * up(down(x)).
• time_embedding.safetensors: fp32 replacements for time_embedding.* (the step embedder) AND shared_block_modulation.bias / video_head_modulation.bias; the t=1 time vector is folded into those biases, so the embedder only works together with them. Keep these three modules in fp32.
• generator.safetensors: the same tensors in one file (<module>.lora_A/B keys) plus Q and the step codes.

The sampler differs from the release scheduler:

from safetensors.torch import load_file
ds = load_file("dataspace.safetensors")            # latent_mix.Q [128, 128], step_codes [8]
Q, codes = ds["latent_mix.Q"].cuda(), ds["step_codes"].cuda()
z = torch.randn(1, 128, H // 16, W // 16, device="cuda")   # fp32
x0 = torch.zeros_like(z)
for r in range(8):                            # fewer steps also work (each prefix is trained)
    x = z + torch.einsum("oc,bchw->bohw", Q, x0)
    v = transformer(x, codes[r], prompt_embeds, prompt_mask).sample.float()
    x0 = z - v
# decode x0 with the release VAE (BN denormalize + 2x2 unpatchify)