Instructions to use bytedance-research/RealCustom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bytedance-research/RealCustom with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bytedance-research/RealCustom", 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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Add pipeline tag and library name, add sample usage
Verified signatureThis PR improves the model card by: - Adding the `pipeline_tag: text-to-image` to enhance discoverability on the Hugging Face Hub. - Adding the `library_name: diffusers` to enable the automated code snippet for `diffusers`. - Including a Python sample usage snippet directly from the GitHub README for text-to-image generation.d67482f
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