Instructions to use SearchingMan/Z-Image-Turbo-student-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SearchingMan/Z-Image-Turbo-student-adapter with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SearchingMan/Z-Image-Turbo-student-adapter", 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
- Draw Things
- DiffusionBee
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README.md
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@@ -43,7 +43,7 @@ Measured on T4 (22 GB VRAM) with `torch.bfloat16`, `guidance_scale=0.0`, 9 infer
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| Peak VRAM | 21.35 GB | 16.76 GB | **4.59 GB (22%)** |
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| Generation time | 3.9s | 3.5s | — |
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The student+adapter brings peak VRAM from
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## Quick Start
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| Peak VRAM | 21.35 GB | 16.76 GB | **4.59 GB (22%)** |
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| Generation time | 3.9s | 3.5s | — |
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The student+adapter brings peak VRAM from 21.4 GB down to 16.8 GB fitting comfortably on a 22 GB T4 where the original barely fits. The DiT transformer and VAE are unchanged (12 GB total); all savings come from the text encoder.
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## Quick Start
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