Instructions to use schrum2/MarioDiffusion-MLM-regular0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use schrum2/MarioDiffusion-MLM-regular0 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("schrum2/MarioDiffusion-MLM-regular0", 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
more debug
Browse files
models/pipeline_loader.py
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@@ -19,7 +19,10 @@ def get_pipeline(model_path):
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# Assume it's a Hugging Face Hub model ID
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# Try to load config to determine if it's text-conditional
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print(model_path)
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has_text_encoder = "text_encoder" in config
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if has_text_encoder:
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# Assume it's a Hugging Face Hub model ID
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# Try to load config to determine if it's text-conditional
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print(model_path)
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print("\n\n\n\n")
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config = DiffusionPipeline.load_config(model_path)
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print(config)
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print("\n\n\n\n")
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has_text_encoder = "text_encoder" in config
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if has_text_encoder:
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