Instructions to use PonyMeng/TPSO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use PonyMeng/TPSO with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("PonyMeng/TPSO", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
TPSO Unconditional Contexts
Precomputed unconditional contexts for TPSO v0.1.0. This repository does not contain diffusion-model weights.
| Model | File |
|---|---|
| Stable Diffusion 1.5 | sd15_kappa0.8_lambda1.pt |
| Stable Diffusion 2.1 | sd21_kappa0.8_lambda1.pt |
| Stable Diffusion 3.5 Medium | sd35_kappa0.8_lambda0.pt |
TPSO downloads and verifies the matching file automatically:
tpso-generate \
--model sd15 \
--prompt "A photograph of a red panda in a bamboo forest" \
--num-images 4
Each file contains optimized CLIP representations and metadata. The TPSO
loader uses torch.load(..., weights_only=True) and verifies its SHA-256 digest.
TPSO code is Apache-2.0. Upstream model licenses and access terms still apply.
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