Instructions to use VecToRoTceV/model_wireframe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use VecToRoTceV/model_wireframe with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("VecToRoTceV/model_wireframe") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| license: creativeml-openrail-m | |
| base_model: runwayml/stable-diffusion-v1-5 | |
| tags: | |
| - stable-diffusion | |
| - stable-diffusion-diffusers | |
| - text-to-image | |
| - diffusers | |
| - controlnet | |
| inference: true | |
| # controlnet-VecToRoTceV/model_wireframe | |
| These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. | |
| You can find some example images below. | |
| Validation result of 1 round. | |
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| Validation result of 2 round. | |
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| Validation result of 3 round. | |
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| Validation result of 4 round. | |
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| Validation result of 5 round. | |
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| Validation result of 6 round. | |
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| Validation result of 7 round. | |
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| Validation result of 8 round. | |
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| Validation result of 9 round. | |
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| Validation result of 10 round. | |
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| Validation result of 11 round. | |
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| Validation result of 12 round. | |
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| Validation result of 13 round. | |
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| Validation result of 14 round. | |
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| Validation result of 15 round. | |
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| Validation result of 16 round. | |
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| Validation result of 17 round. | |
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| Validation result of 18 round. | |
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| Validation result of 19 round. | |
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| Validation result of 20 round. | |
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