Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
Instructions to use CompVis/stable-diffusion-v1-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CompVis/stable-diffusion-v1-4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", dtype=torch.bfloat16, device_map="cuda") prompt = "A high tech solarpunk utopia in the Amazon rainforest" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Train an unconditional LDM on different classes, is it ok?
#222
by Vincent171 - opened
Hi, I see that all pre-trained unconditional LDMs are trained on just one class such as "flowers", "landscape",... Therefore, they can only generate images related to one specific class (e.g., generating different flowers, but still flowers, not a car or a dog).
If we train an unconditional LDM on images of different datasets that include various classes, could the model generate something cool? Depending on the random seed, maybe sometimes it generate a flower, but sometimes a dog?