Instructions to use pcuenq/marigold-coco-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pcuenq/marigold-coco-segmentation with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("pcuenq/marigold-coco-segmentation", 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
Link to post (#3)
Browse files- Link to post (2d0af359a1f6a84e32cbf8b99054fe149d2629ff)
README.md
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The model was trained on the [COCO dataset](https://huggingface.co/datasets/ariG23498/coco2017) using [this library](https://github.com/pcuenca/cocogold) to extract random crops and segmentation masks.
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For more details, please refer to the post
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## Checkpoints
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The model was trained on the [COCO dataset](https://huggingface.co/datasets/ariG23498/coco2017) using [this library](https://github.com/pcuenca/cocogold) to extract random crops and segmentation masks.
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For more details, please refer to [the post](https://huggingface.co/blog/pcuenq/cocogold).
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## Checkpoints
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