ares2324 (ares)

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FlashLabs/Chroma-4B

Any-to-Any • 6B • Updated 8 days ago • 8.79k • 323

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mlx-community/Kimi-K2.5

Text Generation • Updated 8 days ago • 510k • 21

New activity in stepfun-ai/Step3-VL-10B 8 days ago

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#8 opened 17 days ago by

ares2324

reacted to wangbuer999's post with 🔥 9 days ago

Post

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HunyuanImage 3.0-Instruct just dropped

fresh -sourceImage 3.0model! Spent 20 mins testing it on a Messi + retro scrambler fusion case

Ran on diffusers v0.26.3 + CUDA 12.1 | 8B MoE params (1.3B activated) | zero VRAM issues

strength=0.9 Messi #10 kit/tattoo sharp, moto’s rusted metal texture blurred (classic open-source pain)
strength=0.7 Moto/cobblestone background crisp, Messi’s jersey details faded completely

strength=0.75 + prompt "Blend seamlessly, keep all original details": both subject & background sharp
No ControlNet, no manual masking the model’s chain-of-thought reasoning parses image+prompt first
Already outperforms Qwen-Image-Edit 2511 (GSB eval +25.7% on single-image edits) | 100% open-source

👉 Repo: https://hunyuan.tencent.com/chat/HunyuanDefault?from=modelSquare&modelId=Hunyuan-Image-3.0-Instruct

technical report：https://arxiv.org/abs/2509.23951

Anyone else struggled with strength tweaks for fusion? This fixed it for my Messi+moto case did it work as well for yours?

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RuneXX/LTX-2-Workflows

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Danrisi/adorablegirls_qwen

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reacted to sagar007's post with 🔥 17 days ago

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4133

🚀 I built a Multimodal Vision-Language Model from using Gemma-270M + CLIP!

Just finished training my multimodal model on the full LLaVA-Instruct-150K dataset (157K samples) and wanted to share the results!

🔧 What I Built:
A vision-language model that can understand images and answer questions about them, combining:
- Google Gemma-3-270M (language)
- OpenAI CLIP ViT-Large/14 (vision)
- LoRA fine-tuning for efficiency

📊 Training Stats:
- 157,712 training samples (full LLaVA dataset)
- 3 epochs on A100 40GB
- ~9 hours training time
- Final loss: 1.333 training / 1.430 validation
- Only 18.6M trainable params (3.4% of 539M total)

📈 sagar007/multigemma
Benchmark Results:
- VQA Accuracy: 53.8%
- Works great for: animal detection, room identification, scene understanding

🔗 **Try it yourself:**
- 🤗 Model: sagar007/multigemma
- 🎮 Demo: https://huggingface.co/spaces/sagar007/Multimodal-Gemma
- 💻 GitHub: https://github.com/sagar431/multimodal-gemma-270m

Built with PyTorch Lightning + MLflow for experiment tracking. Full MLOps pipeline with CI/CD!

Would love to hear your feedback! 🙏

#multimodal #gemma #clip #llava #vision-language #pytorch