Instructions to use JiaxinGe/Diffusers-BAGEL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JiaxinGe/Diffusers-BAGEL with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JiaxinGe/Diffusers-BAGEL", 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
pipeline update
Browse files- pipeline.py +1 -1
pipeline.py
CHANGED
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@@ -7041,7 +7041,7 @@ class BagelPipeline(DiffusionPipeline):
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return out
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def to(self, device):
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-
super().to(device)
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if hasattr(self, "_inferencer"):
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self._inferencer.to(device)
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return self
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return out
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def to(self, device):
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+
super().to(device)
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if hasattr(self, "_inferencer"):
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self._inferencer.to(device)
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return self
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