linoyts/yarn-art-z-image-lora
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Martin Viewegger
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linoyts/yarn-art-z-image-lora
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Qwen/Qwen3-ASR-1.7B
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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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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?
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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🔥 SWE-Pruner can save up to 40% of your Claude Code cost without sacrificing performance. Try it out!
📚 SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents (2601.16746)
💻 https://github.com/Ayanami1314/swe-pruner
Drawing inspiration from how human programmers “selectively skim” source code during development and debugging, SWE-Pruner performs task-aware adaptive pruning for long contexts.
📚 SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents (2601.16746)
💻 https://github.com/Ayanami1314/swe-pruner
Drawing inspiration from how human programmers “selectively skim” source code during development and debugging, SWE-Pruner performs task-aware adaptive pruning for long contexts.
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We’ve released two conversational speech datasets from oto on Hugging Face 🤗
Both are based on real, casual, full-duplex conversations, but with slightly different focuses.
Dataset 1: Processed / curated subset
otoearth/otoSpeech-full-duplex-processed-141h
* Full-duplex, spontaneous multi-speaker conversations
* Participants filtered for high audio quality
* PII removal and audio enhancement applied
* Designed for training and benchmarking S2S or dialogue models
Dataset 2: Larger raw(er) release
otoearth/otoSpeech-full-duplex-280h
* Same collection pipeline, with broader coverage
* More diversity in speakers, accents, and conversation styles
* Useful for analysis, filtering, or custom preprocessing experiments
We intentionally split the release to support different research workflows:
clean and ready-to-use vs. more exploratory and research-oriented use.
The datasets are currently private, but we’re happy to approve access requests — feel free to request access if you’re interested.
If you’re working on speech-to-speech (S2S) models or are curious about full-duplex conversational data, we’d love to discuss and exchange ideas together.
Feedback and ideas are very welcome!
Both are based on real, casual, full-duplex conversations, but with slightly different focuses.
Dataset 1: Processed / curated subset
otoearth/otoSpeech-full-duplex-processed-141h
* Full-duplex, spontaneous multi-speaker conversations
* Participants filtered for high audio quality
* PII removal and audio enhancement applied
* Designed for training and benchmarking S2S or dialogue models
Dataset 2: Larger raw(er) release
otoearth/otoSpeech-full-duplex-280h
* Same collection pipeline, with broader coverage
* More diversity in speakers, accents, and conversation styles
* Useful for analysis, filtering, or custom preprocessing experiments
We intentionally split the release to support different research workflows:
clean and ready-to-use vs. more exploratory and research-oriented use.
The datasets are currently private, but we’re happy to approve access requests — feel free to request access if you’re interested.
If you’re working on speech-to-speech (S2S) models or are curious about full-duplex conversational data, we’d love to discuss and exchange ideas together.
Feedback and ideas are very welcome!
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little pig little pig - let me in.
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#1 opened 3 months ago
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johndpope
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