Instructions to use Romecr/merryx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Romecr/merryx with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Romecr/merryx", 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
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Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (#2)
- Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (b920f029e816ba44a5c8b284c082684194c0ec0f)0fa55a3
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Upload sample_images with huggingface_hub
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Upload model_index.json with huggingface_hub
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Upload vae with huggingface_hub
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Upload the concept merryx embeds and token
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initial commit
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