Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0
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Awario
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Article
The AI tools for Art Newsletter - Issue 1
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reacted to
alibabasglab's
post with π
4 months ago
Post
2617
We are thrilled to present the improved "ClearerVoice-Studio", an open-source platform designed to make speech processing easy use for everyone! Whether youβre working on speech enhancement, speech separation, speech super-resolution, or target speaker extraction, this unified platform has you covered.
** Why Choose ClearerVoice-Studio?**
- Pre-Trained Models: Includes cutting-edge pre-trained models, fine-tuned on extensive, high-quality datasets. No need to start from scratch!
- Ease of Use: Designed for seamless integration with your projects, offering a simple yet flexible interface for inference and training.
**Where to Find Us?**
- GitHub Repository: ClearerVoice-Studio (https://github.com/modelscope/ClearerVoice-Studio)
- Try Our Demo: Hugging Face Space ( alibabasglab/ClearVoice)
**What Can You Do with ClearerVoice-Studio?**
- Enhance noisy speech recordings to achieve crystal-clear quality.
- Separate speech from complex audio mixtures with ease.
- Transform low-resolution audio into high-resolution audio. A full upscaled LJSpeech-1.1-48kHz dataset can be downloaded from alibabasglab/LJSpeech-1.1-48kHz .
- Extract target speaker voices with precision using audio-visual models.
**Join Us in Growing ClearerVoice-Studio!**
We believe in the power of open-source collaboration. By starring our GitHub repository and sharing ClearerVoice-Studio with your network, you can help us grow this community-driven platform.
**Support us by:**
- Starring it on GitHub.
- Exploring and contributing to our codebase .
- Sharing your feedback and use cases to make the platform even better.
- Joining our community discussions to exchange ideas and innovations.
- Together, letβs push the boundaries of speech processing! Thank you for your support! :sparkling_heart:
** Why Choose ClearerVoice-Studio?**
- Pre-Trained Models: Includes cutting-edge pre-trained models, fine-tuned on extensive, high-quality datasets. No need to start from scratch!
- Ease of Use: Designed for seamless integration with your projects, offering a simple yet flexible interface for inference and training.
**Where to Find Us?**
- GitHub Repository: ClearerVoice-Studio (https://github.com/modelscope/ClearerVoice-Studio)
- Try Our Demo: Hugging Face Space ( alibabasglab/ClearVoice)
**What Can You Do with ClearerVoice-Studio?**
- Enhance noisy speech recordings to achieve crystal-clear quality.
- Separate speech from complex audio mixtures with ease.
- Transform low-resolution audio into high-resolution audio. A full upscaled LJSpeech-1.1-48kHz dataset can be downloaded from alibabasglab/LJSpeech-1.1-48kHz .
- Extract target speaker voices with precision using audio-visual models.
**Join Us in Growing ClearerVoice-Studio!**
We believe in the power of open-source collaboration. By starring our GitHub repository and sharing ClearerVoice-Studio with your network, you can help us grow this community-driven platform.
**Support us by:**
- Starring it on GitHub.
- Exploring and contributing to our codebase .
- Sharing your feedback and use cases to make the platform even better.
- Joining our community discussions to exchange ideas and innovations.
- Together, letβs push the boundaries of speech processing! Thank you for your support! :sparkling_heart:
reacted to
sanaka87's
post with π₯
4 months ago
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1813
π Excited to Share Our Latest Work: 3DIS & 3DIS-FLUX for Multi-Instance Layout-to-Image Generation! β€οΈβ€οΈβ€οΈ
π¨ Daily Paper: 3DIS-FLUX: simple and efficient multi-instance generation with DiT rendering (2501.05131)
π Code is now open source!
π Project Website: https://limuloo.github.io/3DIS/
π GitHub Repository: https://github.com/limuloo/3DIS
π 3DIS Paper: https://arxiv.org/abs/2410.12669
π 3DIS-FLUX Tech Report: https://arxiv.org/abs/2501.05131
π₯ Why 3DIS & 3DIS-FLUX?
Current SOTA multi-instance generation methods are typically adapter-based, requiring additional control modules trained on pre-trained models for layout and instance attribute control. However, with the emergence of more powerful models like FLUX and SD3.5, these methods demand constant retraining and extensive resources.
β¨ Our Solution: 3DIS
We introduce a decoupled approach that only requires training a low-resolution Layout-to-Depth model to convert layouts into coarse-grained scene depth maps. Leveraging community and company pre-trained models like ControlNet + SAM2, we enable training-free controllable image generation on high-resolution models such as SDXL and FLUX.
π Benefits of Our Decoupled Multi-Instance Generation:
1. Enhanced Control: By constructing scenes using depth maps in the first stage, the model focuses on coarse-grained scene layout, improving control over instance placement.
2. Flexibility & Preservation: The second stage employs training-free rendering methods, allowing seamless integration with various models (e.g., fine-tuned weights, LoRA) while maintaining the generative capabilities of pre-trained models.
Join us in advancing Layout-to-Image Generation! Follow and star our repository to stay updated! β
π¨ Daily Paper: 3DIS-FLUX: simple and efficient multi-instance generation with DiT rendering (2501.05131)
π Code is now open source!
π Project Website: https://limuloo.github.io/3DIS/
π GitHub Repository: https://github.com/limuloo/3DIS
π 3DIS Paper: https://arxiv.org/abs/2410.12669
π 3DIS-FLUX Tech Report: https://arxiv.org/abs/2501.05131
π₯ Why 3DIS & 3DIS-FLUX?
Current SOTA multi-instance generation methods are typically adapter-based, requiring additional control modules trained on pre-trained models for layout and instance attribute control. However, with the emergence of more powerful models like FLUX and SD3.5, these methods demand constant retraining and extensive resources.
β¨ Our Solution: 3DIS
We introduce a decoupled approach that only requires training a low-resolution Layout-to-Depth model to convert layouts into coarse-grained scene depth maps. Leveraging community and company pre-trained models like ControlNet + SAM2, we enable training-free controllable image generation on high-resolution models such as SDXL and FLUX.
π Benefits of Our Decoupled Multi-Instance Generation:
1. Enhanced Control: By constructing scenes using depth maps in the first stage, the model focuses on coarse-grained scene layout, improving control over instance placement.
2. Flexibility & Preservation: The second stage employs training-free rendering methods, allowing seamless integration with various models (e.g., fine-tuned weights, LoRA) while maintaining the generative capabilities of pre-trained models.
Join us in advancing Layout-to-Image Generation! Follow and star our repository to stay updated! β
upvoted
a
paper
4 months ago
liked
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4 months ago
reacted to
MonsterMMORPG's
post with π
4 months ago
Post
1814
Famous IC-Light - Relight Images - Advanced Gradio APP with Windows, RunPod, Massed Compute and Free Kaggle Account Installers Published
Installers are shared here : https://www.patreon.com/posts/famous-ic-light-119566071
1-Click to install and use on Windows, RunPod, Massed Compute and a free Kaggle account notebook
All working perfect with more advanced Gradio app than what was officially published on official repo : https://github.com/lllyasviel/IC-Light
Moreover,
Started another experimental product training for a client. Doing FLUX Dreambooth / Finetuning via Kohya SS GUI. GPU is L40S and batch size is 7. Config name : Batch_Size_7_48GB_GPU_46250MB_29.1_second_it_Tier_1.json
Full workflow, step by step tutorial and configs : https://youtu.be/FvpWy1x5etM
Check out the attached images in full resolution fore more info
Installers are shared here : https://www.patreon.com/posts/famous-ic-light-119566071
1-Click to install and use on Windows, RunPod, Massed Compute and a free Kaggle account notebook
All working perfect with more advanced Gradio app than what was officially published on official repo : https://github.com/lllyasviel/IC-Light
Moreover,
Started another experimental product training for a client. Doing FLUX Dreambooth / Finetuning via Kohya SS GUI. GPU is L40S and batch size is 7. Config name : Batch_Size_7_48GB_GPU_46250MB_29.1_second_it_Tier_1.json
Full workflow, step by step tutorial and configs : https://youtu.be/FvpWy1x5etM
Check out the attached images in full resolution fore more info
reacted to
dylanebert's
post with π€
4 months ago
Post
2034
π¦ New Image-to-3D model from Stability AI
stabilityai/stable-point-aware-3d
here's how it looks, with TRELLIS for comparison
stabilityai/stable-point-aware-3d
here's how it looks, with TRELLIS for comparison
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