[inference providers] tutorial for image editing with inference providers #1880
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
NielsRogge
reviewed
Aug 22, 2025
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| # Building an AI Image Editor with Gradio and Inference Providers | |||
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| In this guide, we'll build an AI-powered image editor that lets users upload images and edit them using natural language prompts. This project demonstrates how to combine Inference Providers with image-to-image models like [Qwen's Image Edit](https://huggingface.co/Qwen/Qwen-Image-Edit). | |||
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I would include both Qwen-Image-Edit and Flux Kontext since both are SOTA
pcuenca
approved these changes
Aug 22, 2025
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| 1. **Accept image uploads** through a web interface | ||
| 2. **Process natural language prompts** editing instructions like "Turn the cat into a tiger" | ||
| 3. **Transform images** using Qwen Image Edit |
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| 3. **Transform images** using Qwen Image Edit | |
| 3. **Transform images** using Qwen Image Edit or FLUX.1 Kontext |
| export HF_TOKEN="your_token_here" | ||
| ``` | ||
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| When you set this environment variable, it handles authentication automatically for all your inference calls. You can generate a token from [your settings page](https://huggingface.co/settings/tokens/new?ownUserPermissions=inference.serverless.write&tokenType=fineGrained). |
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nice to link with the correct permission
| uv add huggingface-hub>=0.34.4 gradio>=5.0.0 pillow>=11.3.0 | ||
| ``` | ||
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| The dependencies are now installed and ready to use! Also, `uv` will create a handy `pyproject.toml` file for you to manage your dependencies as a project. |
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| The dependencies are now installed and ready to use! Also, `uv` will create a handy `pyproject.toml` file for you to manage your dependencies as a project. | |
| The dependencies are now installed and ready to use! Also, `uv` will maintain the `pyproject.toml` file for you as you add dependencies. |
(just because we already mentioned that pyproject.toml was created by uv init)
| - **Object removal**: Remove unwanted objects from images | ||
| - **Provider comparison**: Benchmark different providers for your use case | ||
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| Happy building! And remember to share your app with the community on the hub. |
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| Happy building! And remember to share your app with the community on the hub. | |
| Happy building! And remember to share your app with the community on the Hub. |
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| 1. **Create a new Space**: Go to [huggingface.co/new-space](https://huggingface.co/new-space) | ||
| 2. **Choose Gradio SDK** and make it public | ||
| 3. **Upload your files**: Upload `main.py` and any example images |
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Also requirements.txt if necessary, no? Users would have to create it manually from the toml file (unless there's a way to automate it)
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This PR adds a guide on using Qwen Image Edit with inference providers.