Instructions to use mihirpd/alignprop-trl-aesthetics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mihirpd/alignprop-trl-aesthetics with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("mihirpd/alignprop-trl-aesthetics") prompt = "Squirrel" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
AlignProp Aesthetics TRL

- Prompt
- Squirrel

- Prompt
- Lion
Model description
This is a text to image model finetuned on top of Stable Diffusion v1.5 using aesthetic reward function on animals dataset, via reward backpropagation as per AlignProp (https://align-prop.github.io/). The model was trained using the code in TRL repository.
Trigger words
You should use alignprop to trigger the image generation.
You should use reward backpropagation to trigger the image generation.
You should use rlhf to trigger the image generation.
You should use diffusion to trigger the image generation.
You should use reward gradients to trigger the image generation.
Download model
Weights for this model are available in Safetensors,PyTorch format.
Download them in the Files & versions tab.
- Downloads last month
- 9
Model tree for mihirpd/alignprop-trl-aesthetics
Base model
runwayml/stable-diffusion-v1-5