Text-to-Image
Diffusers
TensorBoard
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
dreambooth
Instructions to use omersen/omer_2.1_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use omersen/omer_2.1_trained with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("omersen/omer_2.1_trained", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of person omer" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from omersen/omer_2.1_trained: direct link, hf CLI and curl.
- Browser
- Download file 1.36 GB
-
https://huggingface.co/omersen/omer_2.1_trained/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://omersen/omer_2.1_trained/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/omersen/omer_2.1_trained/resolve/main/text_encoder/pytorch_model.bin
1.36 GB
- Xet hash:
- 507cc9290e3d5d36c0687f7fe94ff070fbf760c00ac389bbfd9c7d11b52d8117
- Size of remote file:
- 1.36 GB
- SHA256:
- 011e594b95e5c3fa2d317d6e8958102cd3f0283dccc10ecf0b705ce57cd6ecb0
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