Image-to-Image
Diffusers
English
stable-diffusion
stable-diffusion-diffusers
controlnet
jax-diffusers-event
Instructions to use mfidabel/controlnet-segment-anything with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mfidabel/controlnet-segment-anything with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("mfidabel/controlnet-segment-anything") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
Added Environmental Impact
Browse files
README.md
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- **Resolution**: 512
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**Environmental Impact**
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Based on the [Machine Learning Emissions Calculator](https://mlco2.github.io/impact#compute) with the following characteristics:
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- **Hardware Type**: TPUv3 Chip (TPUv4 wasn't available yet at the time of calculating)
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- **Training Hours**: 8 hours
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- **Cloud Provider**: Google Cloud Platform
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- **Compute Region**: us-central1
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- **Carbon Emitted (Power consumption x Time x Carbon Produced Based on the Local Power Grid)**:
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283W x 8h = 2.26 kWh x 0.57 kg eq. CO2/kWh = 1.29 kg eq. CO2
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