Instructions to use duja1/tjmo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use duja1/tjmo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("duja1/tjmo", 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
- Draw Things
- DiffusionBee
Upload the model tjmo
Browse files- README.md +30 -0
- model.ckpt +3 -0
- token_identifier.txt +1 -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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widget:
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- text: t123ove
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---
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### tjmo Dreambooth model trained by duja1 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
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You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
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Sample pictures of:
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t123ove (use that on your prompt)
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model.ckpt
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version https://git-lfs.github.com/spec/v1
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oid sha256:9de3ed6fe231b164a1b9b383233761c03bbc3ef202fa66a14ec9af1b672eece0
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size 2132856622
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token_identifier.txt
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t123ove
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