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AI 모델 84755

• M2↗

  Ahad5625

• moderngena-base↗

  AIRI-Institute

• Z-Image-Turbo-Fun-Controlnet-Union-2.1↗

  alibaba-pai

  - **[2026.02.26]** Update to version 2602, with support for Gray Control. - **[2026.01.12]** Update to version 2601, with support for Scribble Control. Added lite models (1.9GB, 5 layers). Retrained Control and Tile models with enriched mask varieties, improved training schedules, and multi-resolution control images (512~1536) to fix mask pattern leakage and large `control_context_scale` artifacts. - **[2025.12.22]** Performed 8-step distillation on v2.1 to restore acceleration lost when applying ControlNet. Uploaded a tile model for super-resolution. - **[2025.12.17]** Fixed v2.0 typo (`control_layers` used instead of `control_noise_refiner`), which caused double forward pass and slow inference. Speed restored in v2.1. - This ControlNet is applied to 15 layer blocks and 2 refiner layer blocks (Lite models: 3 layer blocks and 2 refiner layer blocks). It supports multiple control conditions including Canny, HED, Depth, Pose, and MLSD (supporting Scribble in 2601 models and Gray in 2602 models). - Inpainting mode is also supported. For inpaint mode, use a larger `control_context_scale` for better image continuity. - **Training Process:** - **2.0:** Trained from scratch for 70,000 steps on 1M high-quality images (general and human-centric content) at 1328 resolution with BFloat16 precision, batch size 64, learning rate 2e-5, and text dropout ratio 0.10. - **2.1:** Continued training from 2.0 weights for 11,000 additional steps after fixing a typo, using the same parameters and dataset. - **2.1-8-steps:** Distilled from version 2.1 using an 8-step distillation algorithm for 5,500 steps. - **Note on Steps:** - **2.0 and 2.1:** Higher `control_context_scale` values may require more inference steps for better results, likely because the control model has not been distilled. - **2.1-8-steps:** Use 8 steps for inference. - Adjust `control_context_scale` (optimal range: 0.65–1.00) for stronger control and better detail preservation. A detailed prompt is highly recommended for stability. - In versions 2.0 and 2.1, applying ControlNet to Z-Image-Turbo caused loss of acceleration capability and blurry images. For strength and step count testing details, refer to Scale Test Results (generated with version 2.0). The old 8-steps model had bright spots/artifacts when the control_context_scale was too large, while the new version does not.

• vjepa2.1-vit-large-384↗

  apiantonio

  A HuggingFace-format conversion of Meta AI's **V-JEPA 2.1 ViT-L/16** video encoder and predictor, operating at 384x384 resolution. The weights are Meta's, copied without modification. This repository provides the `transformers`-compatible packaging plus a documented numerical validation against the original implementation. An equivalent community port already exists (`Dev-Jahn/vjepa2.1-vitl-fpc64-384`). This repository adds an independently reproduced conversion together with the validation results below. Forward outputs match the existing port to all reported digits.

• ttskit-coreml↗

  argmaxinc

• Qwen2.5-0.5B-Instruct-Gensyn-Swarm-freckled_peaceful_sealion↗

  Arletha

• qwen2.5-1.5b-uncensored↗

  arzaan789

• AgentFlow_agentflow-planner-7b-GGUF↗

  bartowski

• magnum-72b-v1-GGUF↗

  bartowski

• nvidia_OpenReasoning-Nemotron-7B-GGUF↗

  bartowski