Instructions to use mozci/nonstablediff with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mozci/nonstablediff with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("mozci/nonstablediff") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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license: creativeml-openrail-m
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base_model: CompVis/stable-diffusion-v1-4
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- diffusers
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- lora
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inference: true
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# LoRA text2image fine-tuning - mozci/nonstablediff
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These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the mozci/tinysketch dataset. You can find some example images in the following.
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license: creativeml-openrail-m
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base_model: CompVis/stable-diffusion-v1-4
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- diffusers
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- lora
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inference: true
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datasets:
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- mozci/tinysketch
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library_name: diffusers
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pipeline_tag: text-to-image
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# LoRA text2image fine-tuning - mozci/nonstablediff
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Stable diffusion fine-tuning to achieve simple sketches as the outputs. These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the mozci/tinysketch dataset. You can find some example images in the following.
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