Instructions to use hodgy/shantae-krea2-turbo-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hodgy/shantae-krea2-turbo-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("krea/Krea-2-Turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("hodgy/shantae-krea2-turbo-v1") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Shantae Krea 2 Turbo LoRA v1
Identity LoRA trained for Krea 2 Turbo with Ostris AI Toolkit.
Usage
- Trigger word:
shantae - Base model:
krea/Krea-2-Turbo - LoRA file:
shantae_krea2_turbo_v1_step3000.safetensors - Recommended starting point: 9 steps, guidance 1, LoRA strength 0.8–1.0
Example prompt:
shantae, close-up portrait, natural window light, detailed skin, shallow depth of field
Sample images
All samples below were generated from the final step-3000 checkpoint at 1024×1024.
Training details
| Setting | Value |
|---|---|
| Training steps | 3,000 |
| LoRA rank / alpha | 32 / 32 |
| Precision | BF16 |
| Optimizer | AdamW8Bit |
| Learning rate | 0.0001 |
| Batch size | 1 |
| Gradient accumulation | 1 |
| Resolution buckets | 512, 768, 1024 |
| Scheduler | FlowMatch |
| Sample steps / guidance | 9 / 1 |
The original AI Toolkit configuration is included as config.yaml.
Notes
Generated images may reproduce limitations or biases present in the training data and base model. Review outputs before use, especially in public or commercial contexts.
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