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Sunday

  14. Story9 reports · 2 facts

    Qwen-Image-2.1 supports ComfyUI

    On September 20, 2026, the Qwen team open-sourced the Qwen-Image-2.1 image model, unifying text-to-image and image editing into a single model that supports a variety of image editing tasks. According to the official introduction, the model's visual generation component is a 7B-parameter, 32-layer Single-Stream DiT that natively supports transparent image generation and editing, supports up to 10 reference images as well as local editing methods such as circling and masking, and reduces inference overhead and VRAM usage during multi-image editing through mixed-granularity attention and KV cache reuse; the model weights have been released on GitHub, Hugging Face, and ModelScope. On the same day, Qwen announced that Qwen-Image-2.1 is natively supported in ComfyUI; users can update ComfyUI or use Comfy Cloud, download the weights from Hugging Face, and load the template to use it.

    up to 10 reference images as well as local editing methods such as circling and masking, and reduces inference overhead and VRAM usage during multi-image editing through mixed-granularity attention and KV cache reuse; the model weights have been released on GitHub, Hugging Face, and ModelScope. On the same day, Qwen announced that Qwen-Image-2.1 is natively supported in ComfyUI; users can update ComfyUI or use Comfy Cloud, download the weights from Hugging Face, and load the template to use it.

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Tuesday

Sunday

  1. Story1 reports · 1 facts

    Fireworks publishes GLM 5.2 numerical alignment experiment

    On September 30, 2026, Fireworks published a GLM 5.2 numerical alignment experiment. The experiment shows that when running the same reinforcement learning task on GLM 5.2, an inconsistency between the trainer and the rollout engine in how token probabilities are computed leads to a KL of about 0.013, roughly 45% of samples discarded per batch, and the reward dropping from about 0.9 to below 0.2 around step 20; after numerical alignment, the KL is 0 and the reward remains stable throughout. The same material also documents a Qwen3.5-MoE experiment: precision differences in the weighted sum of expert outputs brought the k3 of image tokens to 0.296, about 300 times the equivalence threshold, and k3 dropped to zero after merely replacing the MoE module with the Hugging Face reference implementation. Currently, this experiment is published by Fireworks as a first-party source, and no subsequent independent progress has emerged.

    ent, the KL is 0 and the reward remains stable throughout. The same material also documents a Qwen3.5-MoE experiment: precision differences in the weighted sum of expert outputs brought the k3 of image tokens to 0.296, about 300 times the equivalence threshold, and k3 dropped to zero after merely replacing the MoE module with the Hugging Face reference implementation. Currently, this experiment is published by Fireworks as a first-party source, and no subsequent independent progress has emerged.

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Wednesday