Text-to-Image
diffusion
safety
dose-response
sft

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Dose-Response C2 (8M-5%) SFT: Unsafe oversampled to 5%

SFT fine-tuned from the corresponding base on Alchemist (3,350 images) for 20K iterations.

Condition

Label C2 (8M-5%)
Description Unsafe oversampled to p=5%.
Training set size N 8.24M
Unsafe fraction p 5%
Unsafe count U ~412K

Base model

anonym371/dose-response-c2

SFT dataset

Alchemist (3,350 images)

Architecture

Class PRX (rectified-flow DiT)
Hidden size 1792
Depth 16
Heads 28
MLP ratio 3.5
Patch size 32 px
Bottleneck 256
Resolution 512×512

Text encoder

Model google/t5gemma-2b-2b-ul2
Max prompt tokens 256
Dtype bfloat16

Diffusion scheduler

Type x-prediction flow matching
Train timesteps 1000
Timestep shift 3.0

Training

Iterations 20,000
Samples seen ~5.12M
Global batch size 256
Microbatch (per GPU) 32
Hardware 8× NVIDIA H200
Precision bfloat16 (amp_bf16)
Optimizer (transformer blocks) Muon (lr=1e-4, momentum=0.95, nesterov, ns_steps=5, weight_decay=0)
Optimizer (other params) AdamW (lr=1e-4, β=(0.9, 0.95), eps=1e-8, weight_decay=0)
LR schedule 1,000-step linear warmup, constant after
EMA decay 0.999, started at step 0
Random seed 42
Trainer Composer + FSDP

Files

  • denoiser.pt — Consolidated EMA-denoiser checkpoint
  • config.yaml — Full training configuration

Framework

Trained with the PRX framework (Composer + FSDP). The full config.yaml is included for reproducibility.

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