Instructions to use GaumlessGraham/Inner1730_10Real with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use GaumlessGraham/Inner1730_10Real with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("GaumlessGraham/Inner1730_10Real", 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 inner.py
Browse files
inner.py
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@@ -264,7 +264,7 @@ def evaluate(config, epoch, pipeline):
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import sys
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# Sample some images from random noise (this is the backward diffusion process).
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# The default pipeline output type is `List[PIL.Image]`
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for k in range(1,
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images = pipeline(
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batch_size=config.eval_batch_size,
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import sys
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# Sample some images from random noise (this is the backward diffusion process).
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# The default pipeline output type is `List[PIL.Image]`
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for k in range(1, 20):
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images = pipeline(
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batch_size=config.eval_batch_size,
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