Instructions to use silverside/dyslv_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use silverside/dyslv_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("undefined", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("silverside/dyslv_2") 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
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Download README.md from silverside/dyslv_2: direct link, hf CLI and curl.
- Browser
- Download file 558 Bytes
-
https://huggingface.co/silverside/dyslv_2/resolve/main/README.md
- Command line
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hf download hf://silverside/dyslv_2/README.md
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curl -L -o README.md https://huggingface.co/silverside/dyslv_2/resolve/main/README.md
558 Bytes
metadata
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: undefined
instance_prompt: null
license: other
dyslv_2
Model description
steps 1000 learning_rate 0.00007
Trigger words
You should use `` to trigger the image generation.
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
Training at fal.ai
Training was done using fal.ai/models/fal-ai/flux-2-trainer.