Instructions to use dada22231/dsafsdaf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dada22231/dsafsdaf with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zenless-lab/sdxl-anything-xl", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("dada22231/dsafsdaf") 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
| base_model: zenless-lab/sdxl-anything-xl | |
| tags: | |
| - stable-diffusion-xl | |
| - sdxl | |
| - lora | |
| - diffusers | |
| - art | |
| - style | |
| license: other | |
| language: | |
| - en | |
| # LoRA Model | |
| This is a LoRA (Low-Rank Adaptation) model trained on SDXL. | |
| ## Model Details | |
| - **Base Model**: zenless-lab/sdxl-anything-xl | |
| - **Training Method**: LoRA | |
| - **LoRA Rank**: 64 | |
| - **Optimizer**: Prodigy | |
| ## Usage | |
| ```python | |
| from diffusers import DiffusionPipeline | |
| import torch | |
| # Load pipeline | |
| pipe = DiffusionPipeline.from_pretrained( | |
| "zenless-lab/sdxl-anything-xl", | |
| torch_dtype=torch.float16 | |
| ).to("cuda") | |
| # Load LoRA weights | |
| pipe.load_lora_weights("dada22231/b426e506-75a2-4d9a-8952-22652d903752") | |
| # Generate image | |
| prompt = "your prompt in lora style" | |
| image = pipe(prompt).images[0] | |
| ``` | |
| ## Training Details | |
| - Trained with AI Toolkit | |
| - Mixed precision: bf16 | |
| - Batch size: 12 | |