Instructions to use timbrooks/instruct-pix2pix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use timbrooks/instruct-pix2pix 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("timbrooks/instruct-pix2pix", dtype=torch.bfloat16, device_map="cuda") 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
What training settings did you use?
#19
by WuyangLuo - opened
I want to reproduce the training with the resolution of 512 and based on the pre-trained model sd-v1.5. We train the model 10 epochs. However, our model may generate many poor results.
What could be the cause? Resolution? The number of training epochs?
And what training settings did you use? (resolution, based model, and training epochs)?
Thx!
Can you provide a complete training setting?