Instructions to use tcherbert/deana1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tcherbert/deana1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("tcherbert/deana1") prompt = "deana" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 879 Bytes
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license: other
license_name: stabilityai-ai-community
license_link: https://huggingface.co/stabilityai/stable-diffusion-3.5-large/blob/main/LICENSE.md
language:
- en
tags:
- sd3.5-large
- diffusers
- lora
- replicate
base_model: stabilityai/stable-diffusion-3.5-large
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: deana
---
# Deana1
<Gallery />
Trained on Replicate using:
https://replicate.com/lucataco/sd3.5-fine-tuner/train
## Trigger words
You should use `deana` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
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