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
Upload README.md with huggingface_hub
Browse files
README.md
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---
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base_model: zenless-lab/sdxl-anything-xl
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tags:
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- stable-diffusion-xl
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- sdxl
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- lora
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- diffusers
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- art
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- style
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license: other
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language:
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- en
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---
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# LoRA Model
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This is a LoRA (Low-Rank Adaptation) model trained on SDXL.
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## Model Details
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- **Base Model**: zenless-lab/sdxl-anything-xl
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- **Training Method**: LoRA
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- **LoRA Rank**: 64
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- **Optimizer**: Prodigy
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## Usage
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```python
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from diffusers import DiffusionPipeline
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import torch
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# Load pipeline
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pipe = DiffusionPipeline.from_pretrained(
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"zenless-lab/sdxl-anything-xl",
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torch_dtype=torch.float16
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).to("cuda")
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# Load LoRA weights
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pipe.load_lora_weights("dada22231/b426e506-75a2-4d9a-8952-22652d903752")
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# Generate image
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prompt = "your prompt in lora style"
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image = pipe(prompt).images[0]
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```
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## Training Details
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- Trained with AI Toolkit
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- Mixed precision: bf16
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- Batch size: 12
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