Instructions to use LHRuig/ppasc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LHRuig/ppasc with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("LHRuig/ppasc") prompt = "suit" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
File size: 558 Bytes
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tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: suit
output:
url: >-
images/michael-kors-blue-performance-stretch-slim-fit-wedding-suit-coat.webp
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: ppasc
---
# ppasc
<Gallery />
## Model description
p pasc lora
## Trigger words
You should use `ppasc` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/LHRuig/ppasc/tree/main) them in the Files & versions tab.
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