Instructions to use bhaski/yoomyiamges with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bhaski/yoomyiamges with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bhaski/yoomyiamges", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit History
-
Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (#2)
- Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (671b571141ae135e90f4538bff1ed62ff9c0db41)c1a397c
-
Upload sample_images with huggingface_hub
365d2f1 -
Upload model_index.json with huggingface_hub
f861b2c -
Upload vae with huggingface_hub
9462a45 -
Upload unet with huggingface_hub
efa471e -
Upload tokenizer with huggingface_hub
1d06490 -
Upload text_encoder with huggingface_hub
995ec19 -
Upload scheduler with huggingface_hub
250cee0 -
Upload safety_checker with huggingface_hub
a5c47bc -
Upload feature_extractor with huggingface_hub
6649555 -
Upload the concept YooMyIamges embeds and token
3ca8614 -
initial commit
0608097