Instructions to use fassabilf/sd15-dpo-uk-real-b2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fassabilf/sd15-dpo-uk-real-b2000 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fassabilf/sd15-dpo-uk-real-b2000", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
SD 1.5 Diffusion-DPO foto asli UK โ run pembanding beta=2000, setelan lr paper
Run diagnostik, bukan arm tabel utama. Setelan paper Diffusion-DPO (lr 1e-8 x --scale_lr
= 3,2e-7 efektif, warmup 25 % = 236 step, 15 epoch / 945 step, --beta_dpo 2000), dilatih atas
pasangan preferensi dpo/ di dataset fassabilf/ift-train-uk-real (2.000 train / 600 val,
20 okupasi UK), base stable-diffusion-v1-5/stable-diffusion-v1-5, seed 0, 63 step/epoch.
Pembandingnya: fassabilf/sd15-dpo-uk-real (beta=5000, lr 1e-5 setelan proyek).
unet_ep{3..15} = checkpoint-{189..945}/unet; ep3โ14 fp16 hasil compact, ep15 fp32 utuh.
logs/ berisi tensorboard run ini. Analisis dan pembacaannya ada di repo kode
(analysis/dpo_uk_b2000/COMPARE.md): loss DPO turun monoton 0,693 -> 0,615 sementara MAE_real
test ep15 memburuk +0,0195 (CI [+0,0033, +0,0386]) vs base SD 1.5 UK.
Diunggah belakangan (2026-09-23) semata sebagai cadangan sebelum checkpoint dihapus dari pod.
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