Instructions to use CIawevy/Flux.1-dev-TextPecker-SQPA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use CIawevy/Flux.1-dev-TextPecker-SQPA with PEFT:
Task type is invalid.
- Diffusers
How to use CIawevy/Flux.1-dev-TextPecker-SQPA 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("CIawevy/Flux.1-dev-TextPecker-SQPA") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
Improve model card: add metadata, license, and link to paper
#1
by nielsr HF Staff - opened
Hi there! I'm Niels from the Hugging Face community science team.
I've updated the model card for this repository to include relevant metadata such as the pipeline tag and license. I've also added a link to the original paper and the project's GitHub repository to improve documentation and discoverability. Specifically:
- Added
pipeline_tag: text-to-image. - Added
license: apache-2.0. - Included a link to the research paper.
- Maintained the existing sample usage and citation.
CIawevy changed pull request status to merged