DEDUPED Hot AI repos, turned into use cases Get 5 a day

How to train the Z-Image-Turbo backbone at 1024 resolution with Diffusion OPSD

Run Diffusion OPSD on the few-step Z-Image-Turbo regime at 1024 pixels.

  • Research
  • Advanced
  • 1 day
  • From the README
  • Any agent

Who it is forA researcher who wants a few-step, high-resolution diffusion model tuned for a reward and has a Diffusers build with ZImagePipeline.

The problemZ-Image-Turbo needs a specific Diffusers build, and the setup is different from the SD3.5-M path.

The outcomeA Z-Image-Turbo checkpoint trained at 1024 pixels with the Diffusion OPSD recipe, along with the training efficiency numbers to compare with DiffusionNFT.

What you need first

  • A recent Linux machine with CUDA GPUs (eight-GPU profiles are reported in the README)
  • Python 3.10 or 3.11 and conda
  • A Hugging Face account that has accepted the SD3.5-M license, for the gated model
  • Enough disk space for model weights, reward checkpoints, and the Pick-a-Pic prompt manifest

Steps

  1. Complete the base install with pip install -e ".[rewards]" and pip install --no-deps 'image-reward==1.5'.
  2. Check whether ZImagePipeline is available in your Diffusers install. If it is missing, run git clone https://github.com/huggingface/diffusers.git and pip install -e "./diffusers[torch]".
  3. Set export REWARD_CKPT_PATH="$PWD/reward_ckpts" and run bash scripts/download_reward_weights.sh.
  4. Start the Z-Image-Turbo preset by passing the z-image backbone name to bash scripts/train_public.sh and check scripts/train_public.sh for the exact argument. (inferred, check the README)

Prompt for your agent

In the repo worldbench/DiffusionOPSD, confirm ZImagePipeline imports in the installed Diffusers, then start the Z-Image-Turbo training preset for <reward name>. Report the import check result, the launch command, and the first logged seconds per update. Verify that the import check passed before launching training.

Caveat. The README names the 1024 Z-Image-Turbo regime but does not give the exact launch argument for it, so the step is inferred and must be checked in the launcher script.

Share: X LinkedIn Hacker News Email Copy link

Disclaimer

Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked "Seen in the wild", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email [email protected] and we will look at it promptly. Full disclaimer page