Instructions to use kandinsky-community/kandinsky-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kandinsky-community/kandinsky-3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-3", 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
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@@ -33,9 +33,6 @@ We release our two models:
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+ Base: Base text-to-image diffusion model. This model was trained over 2M steps on 400 A100
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+ Inpainting: Inpainting version of the model. The model was initialized from final checkpoint of base model and trained 250k steps on 300 A100.
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Weights of the model are loaded internally but if want to change them one can use the following example:
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## Installing
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To install repo first one need to create conda environment:
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+ Base: Base text-to-image diffusion model. This model was trained over 2M steps on 400 A100
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+ Inpainting: Inpainting version of the model. The model was initialized from final checkpoint of base model and trained 250k steps on 300 A100.
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## Installing
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To install repo first one need to create conda environment:
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