While everyone is waiting for Alibaba to drop the weights for Z-Image Edit, Meituan just released LongCat. It is a complete ecosystem that competes in the same space and is available for use right now.
Why LongCat is interesting
LongCat-Image and Z-Image are models of comparable scale that utilize the same VAE component (Flux VAE). The key distinction lies in their text encoders: Z-Image uses Qwen 3 (4B), while LongCat uses Qwen 2.5-VL (7B).
This allows the model to actually see the image structure during editing, unlike standard diffusion models that rely mostly on text. LongCat Turbo is also one of the few official 8-step distilled models made specifically for image editing.
Model List
LongCat-Image-Edit: SOTA instruction following for editing. LongCat-Image-Edit-Turbo: Fast 8-step inference model. LongCat-Image-Dev: The specific checkpoint needed for training LoRAs, as the base version is too rigid for fine-tuning. LongCat-Image: The base generation model. It can produce uncanny results if not prompted carefully.
Current Reality
The model shows outstanding text rendering and follows instructions precisely. The training code is fully open-source, including scripts for SFT, LoRA, and DPO.
However, VRAM usage is high since there are no quantized versions (GGUF/NF4) yet. There is no native ComfyUI support, though custom nodes are available. It currently only supports editing one image at a time.
Training and Future Updates
SimpleTuner now supports LongCat, including both Image and Edit training modes.
The developers confirmed that multi-image editing is the top priority for the next release. They also plan to upgrade the Text Encoder to Qwen 3 VL in the future.
Links
Edit Turbo: https://huggingface.co/meituan-longcat/LongCat-Image-Edit-Turbo
Dev Model: https://huggingface.co/meituan-longcat/LongCat-Image-Dev
GitHub: https://github.com/meituan-longcat/LongCat-Image
Demo: https://huggingface.co/spaces/lenML/LongCat-Image-Edit
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