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 gaussian_diffusion.py
Browse files- gaussian_diffusion.py +1 -1
gaussian_diffusion.py
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@@ -10,7 +10,7 @@ import numpy as np
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import torch as th
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import enum
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from diffusion_utils import discretized_gaussian_log_likelihood, normal_kl
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def mean_flat(tensor):
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import torch as th
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import enum
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from .diffusion_utils import discretized_gaussian_log_likelihood, normal_kl
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def mean_flat(tensor):
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