Instructions to use sd-dreambooth-library/oriblind with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sd-dreambooth-library/oriblind with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("sd-dreambooth-library/oriblind", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit ·
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Parent(s): bfb5871
Upload the concept oriblind embeds and token
Browse files- README.md +13 -0
- oriblind.ckpt +3 -0
README.md
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---
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license: creativeml-openrail-m
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tags:
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- text-to-image
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- stable-diffusion
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---
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### oriblind Dreambooth model trained by apurik-parv with [Shivamshri rao's DreamBooth implementation]
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Instance prompt:**oriblind**
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As the name implies the the model is trained on messy art style sketch /doodle images for 50000 steps.
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Simple prompts can replicate faithfully.
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complicated and contradicting prompts will add elements of noise to the image.
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Feel free to experiment with it.
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oriblind.ckpt
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version https://git-lfs.github.com/spec/v1
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oid sha256:bc4e6a10e4267633ef5bb877d3424e1779555d9dada4cd8558021f853c8825a9
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size 2132856622
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