Instructions to use scrapware/amanivae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use scrapware/amanivae with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lykon/AnyLoRA", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("scrapware/amanivae") prompt = " amanivae " image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 1,113 Bytes
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tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: ' amanivae '
parameters:
negative_prompt: >-
amqnivae.pt (Bluewish)
output:
url: images/realametta_v2012HQ_amanivae.png
- text: ' amenivae '
parameters:
negative_prompt: >-
amenivae (Neutral Colored)
output:
url: images/realametta_v2012HQ_amenivae.png
- text: ' animevae '
parameters:
negative_prompt: >-
Sample of animevae.pt original.
output:
url: images/realametta_v2012HQ_animevae.png
base_model: Lykon/AnyLoRA
instance_prompt: null
license: openrail
---
# amanivae
<Gallery />
## Model description
Neutral colored animevae only animevae(100%) on feat latest floating tensor genetic shift technology. half size pruned to only state_dict(rest keep loss_logver).
<b>Amanivae:</b> bluewish</ br>
<b>Amenivae:</b> Neutral Colored<b>
<b>(!) Adjust with some rate(0.1-0.9) from animevae.pt original.</b>
## Download model
Weights for this model are available in Safetensors format.
[Download](/scrapware/amanivae/tree/main) them in the Files & versions tab.
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