Instructions to use ms2stationthis/impresstation-zimage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ms2stationthis/impresstation-zimage with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Tongyi-MAI/Z-Image", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ms2stationthis/impresstation-zimage") prompt = "stationthis, stationthis, low poly playstation screenshot style, A determined young woman with fiery red pigtails tied…" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
impresstation-zimage
zimage LoRA "impresstation-zimage" — trigger "stationthis".
Trigger word: stationthis
Sample Outputs
Usage (Diffusers)
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained(
"Tongyi-MAI/Z-Image-Turbo", torch_dtype=torch.bfloat16
).to("cuda")
pipe.load_lora_weights("ms2stationthis/impresstation-zimage")
image = pipe("stationthis, a character portrait", guidance_scale=4.0, num_inference_steps=25).images[0]
image.save("out.png")
Recommended Settings
| LoRA strength | Guidance | Steps | Resolution |
|---|---|---|---|
| 0.8–1.0 | 4.0 | 25 | 1024×1024 |
Training Details
- Base: Tongyi-MAI/Z-Image
- Steps: 3000 · Network: LoRA rank 32 / alpha 32
- Optimizer: adamw8bit, lr 1e-4 · Scheduler: flowmatch
- Resolution: 512, 768, 1024 (multi-res bucketed) · Precision: bf16 train / fp16 save
Reproduction
This repo includes the full dataset/ (33 image-caption pairs) and the exact config.yaml so the LoRA can be retrained as-is.
About
Trained on StationThis — an AI creative platform powered by $MS2. Train your own LoRAs via @stationthisbot on Telegram.
Published via noema.
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