Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use NadaGh/working with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use NadaGh/working with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("NadaGh/working", dtype=torch.bfloat16, device_map="cuda") prompt = "tst chair" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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| Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with | |
| the License. You may obtain a copy of the License at | |
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| an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | |
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| --> | |
| # Kandinsky 2.1 | |
| Kandinsky 2.1 is created by [Arseniy Shakhmatov](https://github.com/cene555), [Anton Razzhigaev](https://github.com/razzant), [Aleksandr Nikolich](https://github.com/AlexWortega), [Vladimir Arkhipkin](https://github.com/oriBetelgeuse), [Igor Pavlov](https://github.com/boomb0om), [Andrey Kuznetsov](https://github.com/kuznetsoffandrey), and [Denis Dimitrov](https://github.com/denndimitrov). | |
| The description from it's GitHub page is: | |
| *Kandinsky 2.1 inherits best practicies from Dall-E 2 and Latent diffusion, while introducing some new ideas. As text and image encoder it uses CLIP model and diffusion image prior (mapping) between latent spaces of CLIP modalities. This approach increases the visual performance of the model and unveils new horizons in blending images and text-guided image manipulation.* | |
| The original codebase can be found at [ai-forever/Kandinsky-2](https://github.com/ai-forever/Kandinsky-2). | |
| <Tip> | |
| Check out the [Kandinsky Community](https://huggingface.co/kandinsky-community) organization on the Hub for the official model checkpoints for tasks like text-to-image, image-to-image, and inpainting. | |
| </Tip> | |
| <Tip> | |
| Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines. | |
| </Tip> | |
| ## KandinskyPriorPipeline | |
| [[autodoc]] KandinskyPriorPipeline | |
| - all | |
| - __call__ | |
| - interpolate | |
| ## KandinskyPipeline | |
| [[autodoc]] KandinskyPipeline | |
| - all | |
| - __call__ | |
| ## KandinskyCombinedPipeline | |
| [[autodoc]] KandinskyCombinedPipeline | |
| - all | |
| - __call__ | |
| ## KandinskyImg2ImgPipeline | |
| [[autodoc]] KandinskyImg2ImgPipeline | |
| - all | |
| - __call__ | |
| ## KandinskyImg2ImgCombinedPipeline | |
| [[autodoc]] KandinskyImg2ImgCombinedPipeline | |
| - all | |
| - __call__ | |
| ## KandinskyInpaintPipeline | |
| [[autodoc]] KandinskyInpaintPipeline | |
| - all | |
| - __call__ | |
| ## KandinskyInpaintCombinedPipeline | |
| [[autodoc]] KandinskyInpaintCombinedPipeline | |
| - all | |
| - __call__ | |