Instructions to use hassanblend/evamendes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hassanblend/evamendes with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hassanblend/evamendes", 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
Add `scale_factor` to vae config. (#2) 4ccabd9
Add `clip_sample=False` to scheduler to make model compatible with DDIM. (#1) 7d230d4
Update README.md 52dc28d
Upload sample_images with huggingface_hub 5a685df
Upload model_index.json with huggingface_hub e1e4da7
Upload vae with huggingface_hub a780222
Upload unet with huggingface_hub 6a0b753
Upload tokenizer with huggingface_hub 557b2d0
Upload text_encoder with huggingface_hub d8d817c
Upload scheduler with huggingface_hub 8ad21d1
Upload safety_checker with huggingface_hub f86dfc3
Upload feature_extractor with huggingface_hub 7e94773
Upload the concept EvaMendes embeds and token d117e0c
initial commit eabf0d6
Hassan commited on