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
Access pipeline via other file
Browse files- model_index.json +1 -1
model_index.json
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@@ -1,7 +1,7 @@
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{
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"_class_name": "TextConditionalDDPMPipeline",
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"_diffusers_version": "0.32.2",
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"custom_pipeline": "
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"components": {
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"unet": { "type": "UNet2DConditionModel", "subfolder": "unet" },
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"text_encoder": { "type": "models.text_model.TransformerModel", "subfolder": "text_encoder" },
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{
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"_class_name": "TextConditionalDDPMPipeline",
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"_diffusers_version": "0.32.2",
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"custom_pipeline": "pipeline.py",
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"components": {
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"unet": { "type": "UNet2DConditionModel", "subfolder": "unet" },
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"text_encoder": { "type": "models.text_model.TransformerModel", "subfolder": "text_encoder" },
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