Image-to-Image
Diffusers
Safetensors
English
self-conditioning
visual-reasoning
flux
qwen-image
lora
Instructions to use alfalfa-w/self-conditioning-deep-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use alfalfa-w/self-conditioning-deep-reasoning with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("alfalfa-w/self-conditioning-deep-reasoning") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Add metadata, link to paper and code
#1
by nielsr HF Staff - opened
This PR improves the model card by:
- Adding the missing
library_name(diffusers) andpipeline_tag(image-to-image) metadata for better discoverability on the Hub. - Adding direct links to the paper Self-Conditioning Enables Deep Sequential Reasoning in Flow Matching and the GitHub repository https://github.com/imzzy1201/self-conditioning-deep-reasoning for easy reference.
alfalfa-w changed pull request status to merged