Instructions to use vititpun/baine3e-face-h-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vititpun/baine3e-face-h-v1 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("vititpun/baine3e-face-h-v1") 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
baine3e FACE_H v1 โ Z-Image Turbo identity LoRA
Private identity LoRA for the AI-generated persona @baine3e (FACE_H).
Trigger: ohwx woman
Training
- Trainer: WaveSpeed z-image-lora-trainer (targets Z-Image Turbo)
- Steps: 3000
- Learning rate: 0.0001
- LoRA rank: 8
- Dataset: 28 curated images + captions
- Cost: $3.75
Inference
Load on any Z-Image Turbo + LoRA endpoint (fal.ai, WaveSpeed, local ComfyUI).
Example (fal.ai):
result = fal_client.subscribe(
"fal-ai/z-image/turbo/lora",
arguments={
"prompt": "ohwx woman, a candid phone photo of ohwx woman, amateur photo, ...",
"loras": [{"path": "https://huggingface.co/vititpun/baine3e-face-h-v1/resolve/main/face_h_v1.safetensors", "scale": 1.0}],
},
)
Submitted
- Task ID:
11ad805789fc4a8aa4931e65aeb88751 - Submitted: 2026-04-21T13:56:23.940235+00:00
- Mirrored: 2026-04-21T14:08:59.526226+00:00
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Model tree for vititpun/baine3e-face-h-v1
Base model
Tongyi-MAI/Z-Image-Turbo