QIE-Object-Remover-Bbox Demo removes objects and artifacts from selected regions using bounding box grounding. Built on Qwen-Image-Edit-2509 with Rapid Diffusers acceleration, it delivers fast 4-step inference via the QIE-2509 adapter. ๐ค๐ฅ
-> Paired the EditPlusPipeline with the Diffusers-compatible transformer weights of Rapid AIO from Qwen-Image-Edit. (experimental) -> This fusion delivers more accurate instruction following, higher image quality, and consistent visual coherence @ 4-step fast inference. -> Better maintains text styles with high fidelity, along with high-quality old photo restoration, enhancement, and best-in-class virtual try-on.
Dropping the Qwen3 VL Series of Unredacted MAX-VL models. These models have undergone multi-stage training to minimize refusal rates through continuous abliterated optimization. You can find the models in BF16, FP8-Dynamic, and GGUF formats at the links below.๐ฅ๐
Introducing FLUX.2-Klein-LoRA-Studio, a demo for image editing using specialized LoRA adapters built for the FLUX.2-Klein-Distilled model. It features an edit-style gallery for multi-style image editing, including de-light, face swap, mannequin, and more. Try the demo below.
GLM OCR, a multimodal OCR model for complex document understanding, built on the GLM-V encoderโdecoder architecture. It delivers high accuracy and strong generalization with a blazing-fast inference pipeline. The demo is live . Try it now. ๐ค๐
Introducing the Qwen-Image-Edit-3D-Lighting-Control app, featuring 8ร horizontal and 3ร elevational lighting positions for precise 3D lighting control. It enables studio-level lighting using fast Qwen Image Edit fast inference, paired with Multi-Angle-Lighting adapters. ๐ฆ
Daggr UI version of the Qwen3-TTS demo.๐ฅ (custom voice, voice design, qwen3-asr and voice cloning) nodes. No remote spaces used for API inference; all functions run in-app fn. Powered by t4-m and built with daggr@0.5.2 and gradio@6.
Qwen-Image-Edit-Object-Manipulator Space is now featured in Hugging Face Space of the Week. It enables object manipulation such as extracting objects, adding designs, and removing objects or designs from the red highlighted area using specialized adapters.
๐๏ธ Hugging Face Community Post Title: ๐งฌ Experimenting with "Dynamic Chaos" in Tamil SLMs
Hi everyone! I just published a new experimental study on Small Language Model (SLM) resilience.
I took the Qwen2.5-0.5B model and put it through a "Chaos Phase" to see how much weight data a tiny model can lose before its understanding of classical Tamil grammar breaks.
Key highlights of the study:
Target Data: Fine-tuned on the Thirukkural (1,330 couplets + modern explanations). The Chaos Step: Applied 20% random weight pruning but implemented "Layer Protection" for the Token Embeddings and LM Head to keep the characters readable. Compression: 4-bit (Q4_K_M) quantization for extreme efficiency. Result: A surrealist classical Tamil model that is ultra-light (~300MB) and ultra-fast!
Introducing QIE-2511-Zoom-Master for highlight-guided area zoom-in, enabling lossless zooming within a drawn square area, and QIE-2511-Object-Remover-v2 for precise object or highlight-guided area cleanup. These experimental adapters are trained based on QIE-2511. Find the adapters below.
Iโm excited to release hawky-ai-Qwen3-0.6B-Marketing-MoT, a specialized SLM designed for deep strategic reasoning in performance marketing.
While small at 0.6B parameters, this model punches way above its weight class by utilizing a Mixture of Thoughts (MoT) framework. It doesn't just give you an answer; it thinks through the logic of Meta Ads scaling, GA4 attribution, and unit economics before providing a strategic recommendation.
Key Features:
Thinking-First: Trained on 1,500+ critical thinking scenarios. MoT Framework: 5 distinct reasoning styles (Linear, Exploratory, Critical, Deconstructive, Analogical). SLM Speed: Perfect for low-latency, high-precision marketing audits. Check it out on Hugging Face: ๐ Sri-Vigneshwar-DJ/hawky-ai-Qwen3-0.6B-Marketing-MoT
LTX-2 Camera-Control LoRA demo with dolly-in/out and dolly-left/right is now available on Hugging Face, paired with ltx-2-19b-distilled-lora for fast inference. It also includes dynamic GPU duration adjustments for long video generations. Click the related Space links below.
Introducing Hawky-AI H1 4B PM: The First Open-Source LLM for Performance Marketing ๐ฏ
Hey HF Community! ๐
Just released the first LLM fine-tuned specifically for Performance Marketing. What is it? Gemma 3 4B distilled from Claude Opus 4.5 with expert-level marketing knowledge. Covers: ๐ฑ Meta Ads (campaign structure, bidding, scaling, creative fatigue) ๐ Google Ads (Quality Score, Performance Max, lead gen) ๐ Measurement (ROAS vs MER, incrementality, LTV:CAC) ๐จ Creative Strategy (hook rates, A/B testing, funnel creative) Why we built it: Generic LLMs say "optimize your targeting" โ not helpful. This model gives specific frameworks like "frequency at 4.5 + CTR drop = creative fatigue, here's the fix..." Technical:
Base: Gemma 3 4B Method: QLoRA (r=64) Teacher: Claude Opus 4.5
๐ฆ Introducing Hawky AI H1 Mini 4B: A Domain-Specific Model for Performance Marketing
Hey HuggingFace community! ๐
We're excited to share our first open-source release: **Hawky AI H1 Mini 4B Experimental** - a Gemma 3 4B model fine-tuned specifically for Meta advertising and performance marketing strategy.
๐ฏ Why We Built This
At [Hawky.ai](https://hawky.ai), we build AI-powered creative intelligence tools for performance marketers. We work with major agencies (WPP, Madison, GroupM) and brands (TVS Motors, Tanishq, Bajaj Finserv) on campaign optimization.
We wanted to explore: Can a small, domain-specific model provide expert-level guidance on performance marketing?
Specifically, we focused on Meta's Andromeda algorithm - the AI system that now powers ad delivery across Facebook and Instagram. Understanding Andromeda is crucial for modern media buying, but the knowledge is scattered and constantly evolving.
๐ง What Makes This Different
Chain-of-Thought Reasoning The model doesn't just answer - it **thinks through problems** step-by-step:
Qwen-Image-Edit-2511-Object-Remover is an adapter (LoRA) developed for Qwenโs Qwen-Image-Edit-2511 image-to-image model. It is specifically designed for precise object removal from images.
Qwen-Image-Edit-2511-Object-Adder is an adapter (LoRA) developed for Qwenโs Qwen-Image-Edit-2511 image-to-image model. It is specifically designed for precise object addition to images.
Domain-specific reasoning is crucial when working with big-budget campaigns on Meta. That's why we've launched an experimental Chain-of-Thought (CoT) reasoning model for critical thinking, tailored to Meta's Andromeda algorithm-based campaign structuring and optimization.
The recent update to Meta's ad algorithm is very difficult to crack, and even the latest models struggle to keep up with it. To address this, we've created a small experimental dataset for fine-tuning models to better tackle Meta's Andromeda algorithm: Sri-Vigneshwar-DJ/hawky-ai-andromeda-dataset
