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Amarsingh Thakur
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🎨 Ghibli-Style Image Generation with Multilingual Text Integration: FLUX.1 Hugging Face Edition 🌏✨
Hello creators! Today I'm introducing a special image generator that combines the beautiful aesthetics of Studio Ghibli with multilingual text integration! 😍
seawolf2357/Ghibli-Multilingual-Text-rendering
✨ Key Features
Ghibli-Style Image Generation - High-quality animation-style images based on FLUX.1
Multilingual Text Rendering - Support for Korean, Japanese, English, and all languages! 🇰🇷🇯🇵🇬🇧
Automatic Image Editing with Simple Prompts - Just input your desired text and you're done!
Two Stylistic Variations Provided - Get two different results from a single prompt
Full Hugging Face Spaces Support - Deploy and share instantly!
🚀 How Does It Work?
Enter a prompt describing your desired image (e.g., "a cat sitting by the window")
Input the text you want to add (any language works!)
Select the text position, size, and color
Two different versions are automatically generated!
💯 Advantages of This Model
No Tedious Post-Editing Needed - Text is perfectly integrated during generation
Natural Text Integration - Text automatically adjusts to match the image style
Perfect Multilingual Support - Any language renders beautifully!
User-Friendly Interface - Easily adjust text size, position, and color
One-Click Hugging Face Deployment - Use immediately without complex setup
🎭 Use Cases
Creating multilingual greeting cards
Animation-style social media content
Ghibli-inspired posters or banners
Character images with dialogue in various languages
Sharing with the community through Hugging Face Spaces
This project leverages Hugging Face's FLUX.1 model to open new possibilities for seamlessly integrating high-quality Ghibli-style images with multilingual text using just prompts! 🌈
Try it now and create your own artistic masterpieces! 🎨✨
#GhibliStyle #MultilingualSupport #AIImageGeneration #TextRendering #FLUX #HuggingFace
Hello creators! Today I'm introducing a special image generator that combines the beautiful aesthetics of Studio Ghibli with multilingual text integration! 😍
seawolf2357/Ghibli-Multilingual-Text-rendering
✨ Key Features
Ghibli-Style Image Generation - High-quality animation-style images based on FLUX.1
Multilingual Text Rendering - Support for Korean, Japanese, English, and all languages! 🇰🇷🇯🇵🇬🇧
Automatic Image Editing with Simple Prompts - Just input your desired text and you're done!
Two Stylistic Variations Provided - Get two different results from a single prompt
Full Hugging Face Spaces Support - Deploy and share instantly!
🚀 How Does It Work?
Enter a prompt describing your desired image (e.g., "a cat sitting by the window")
Input the text you want to add (any language works!)
Select the text position, size, and color
Two different versions are automatically generated!
💯 Advantages of This Model
No Tedious Post-Editing Needed - Text is perfectly integrated during generation
Natural Text Integration - Text automatically adjusts to match the image style
Perfect Multilingual Support - Any language renders beautifully!
User-Friendly Interface - Easily adjust text size, position, and color
One-Click Hugging Face Deployment - Use immediately without complex setup
🎭 Use Cases
Creating multilingual greeting cards
Animation-style social media content
Ghibli-inspired posters or banners
Character images with dialogue in various languages
Sharing with the community through Hugging Face Spaces
This project leverages Hugging Face's FLUX.1 model to open new possibilities for seamlessly integrating high-quality Ghibli-style images with multilingual text using just prompts! 🌈
Try it now and create your own artistic masterpieces! 🎨✨
#GhibliStyle #MultilingualSupport #AIImageGeneration #TextRendering #FLUX #HuggingFace
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I wrote an article about G2P: https://hf.co/blog/hexgrad/g2p
G2P is an underrated piece of small TTS models, like offensive linemen who do a bunch of work and get no credit.
Instead of relying on explicit G2P, larger speech models implicitly learn this task by eating many thousands of hours of audio data. They often use a 500M+ parameter LLM at the front to predict latent audio tokens over a learned codebook, then decode these tokens into audio.
Kokoro instead relies on G2P preprocessing, is 82M parameters, and thus needs less audio to learn. Because of this, we can cherrypick high fidelity audio for training data, and deliver solid speech for those voices. In turn, this excellent audio quality & lack of background noise helps explain why Kokoro is very competitive in single-voice TTS Arenas.
G2P is an underrated piece of small TTS models, like offensive linemen who do a bunch of work and get no credit.
Instead of relying on explicit G2P, larger speech models implicitly learn this task by eating many thousands of hours of audio data. They often use a 500M+ parameter LLM at the front to predict latent audio tokens over a learned codebook, then decode these tokens into audio.
Kokoro instead relies on G2P preprocessing, is 82M parameters, and thus needs less audio to learn. Because of this, we can cherrypick high fidelity audio for training data, and deliver solid speech for those voices. In turn, this excellent audio quality & lack of background noise helps explain why Kokoro is very competitive in single-voice TTS Arenas.
Nice ~90x real-time generation on 3090TI. Quickstart provided.
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