Instructions to use mske11/9othy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mske11/9othy 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-Turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("mske11/9othy") prompt = "9othy, ultra realistic close-up portrait of a young woman, long flowing dark brown hair with subtle natural highlights and loose strands framing her face, flawless smooth skin with faint pores and soft glow, gentle soft smile with relaxed lips, expressive hazel eyes looking directly at the camera, warm studio lighting creating soft shadows across cheekbones and jawline, shallow depth of field, bokeh background, cinematic 85mm lens photography, ultra detailed texture on hair, skin, and eyes, photorealistic rendering with realistic light reflections" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
9othy

- Prompt
- 9othy, ultra realistic close-up portrait of a young woman, long flowing dark brown hair with subtle natural highlights and loose strands framing her face, flawless smooth skin with faint pores and soft glow, gentle soft smile with relaxed lips, expressive hazel eyes looking directly at the camera, warm studio lighting creating soft shadows across cheekbones and jawline, shallow depth of field, bokeh background, cinematic 85mm lens photography, ultra detailed texture on hair, skin, and eyes, photorealistic rendering with realistic light reflections
Trigger words
You should use 9othy to trigger the image generation.
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Model tree for mske11/9othy
Base model
Tongyi-MAI/Z-Image-Turbo