Instructions to use apurik-parv/abstract-nature-pattern-v1-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use apurik-parv/abstract-nature-pattern-v1-2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("apurik-parv/abstract-nature-pattern-v1-2", dtype=torch.bfloat16, device_map="cuda") 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
- DiffusionBee
abstract_nature_pattern_v1.2 Dreambooth model trained by apurik-parv with TheLastBen's fast-DreamBooth notebook
This model is trained with more data but unfortunately it overfits the image. I am not aware of the extent of overfitting some images are good some are really bad. Please leave suggestions in comment.
Inference Prompt: abnapa
The model is trained on 1024x1024 images for a total step of 3000.
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