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
Add results
Browse files
README.md
CHANGED
|
@@ -20,6 +20,14 @@ This project proves that we can reduce VRAM usage by replacing Z-Image-Turbo's o
|
|
| 20 |
|
| 21 |
No other optimizations are applied — the DiT transformer and VAE are unchanged. The VRAM savings come entirely from the smaller text encoder.
|
| 22 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 23 |
## Architecture
|
| 24 |
|
| 25 |
```
|
|
|
|
| 20 |
|
| 21 |
No other optimizations are applied — the DiT transformer and VAE are unchanged. The VRAM savings come entirely from the smaller text encoder.
|
| 22 |
|
| 23 |
+
|
| 24 |
+
## Results
|
| 25 |
+
|
| 26 |
+
Original | Qwen3-1.7B
|
| 27 |
+
:-------------------------:|:-------------------------:
|
| 28 |
+
![]](assets/ref_00.png) | ![]](assets/student_00.png)
|
| 29 |
+
|
| 30 |
+
|
| 31 |
## Architecture
|
| 32 |
|
| 33 |
```
|