Instructions to use jadechoghari/mar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jadechoghari/mar with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jadechoghari/mar", 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
Update pipeline.py
Browse files- pipeline.py +1 -1
pipeline.py
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@@ -79,7 +79,7 @@ class MARModel(DiffusionPipeline):
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# download and load the vae
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vae_checkpoint_path = hf_hub_download(
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repo_id=kwargs.get("repo_id", "jadechoghari/mar"),
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-
filename=kwargs.get("vae_filename", "kl16.
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)
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vae_checkpoint_path = kwargs.get("vae_checkpoint_path", vae_checkpoint_path)
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# download and load the vae
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vae_checkpoint_path = hf_hub_download(
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repo_id=kwargs.get("repo_id", "jadechoghari/mar"),
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+
filename=kwargs.get("vae_filename", "kl16.safetensors")
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)
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vae_checkpoint_path = kwargs.get("vae_checkpoint_path", vae_checkpoint_path)
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