Instructions to use GaumlessGraham/7inchInnerRace1730 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use GaumlessGraham/7inchInnerRace1730 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/7inchInnerRace1730", 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 innerRace.py
Browse files- innerRace.py +4 -4
innerRace.py
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@@ -56,15 +56,15 @@ from dataclasses import dataclass
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@dataclass
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class TrainingConfig:
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image_size =
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train_batch_size = 10
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eval_batch_size =
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num_epochs = 250
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gradient_accumulation_steps = 1
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learning_rate = 1e-4
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lr_warmup_steps = 500
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save_image_epochs =
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save_model_epochs =
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mixed_precision = "fp16" # `no` for float32, `fp16` for automatic mixed precision
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output_dir = "7inchInnerRace1730" # the model name locally and on the HF Hub
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@dataclass
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class TrainingConfig:
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image_size = 256 # the generated image resolution
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train_batch_size = 10
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eval_batch_size = 16 # how many images to sample during evaluation
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num_epochs = 250
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gradient_accumulation_steps = 1
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learning_rate = 1e-4
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lr_warmup_steps = 500
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save_image_epochs = 100
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save_model_epochs = 100
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mixed_precision = "fp16" # `no` for float32, `fp16` for automatic mixed precision
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output_dir = "7inchInnerRace1730" # the model name locally and on the HF Hub
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