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AI 模型 2119
- controlnet-union-sdxl-1.0↗
xinsir
- Use bucket training like novelai, can generate high resolutions images of any aspect ratio - Use large amount of high quality data(over 10000000 images), the dataset covers a diversity of situation - Use re-captioned prompt like DALLE.3, use CogVLM to generate detailed description, good prompt following ability - Use many useful tricks during training. Including but not limited to date augmentation, mutiple loss, multi resolution - Use almost the same parameter compared with original ControlNet. No obvious increase in network parameter or computation. - Support 10+ control conditions, no obvious performance drop on any single condition compared with training independently - Support multi condition generation, condition fusion is learned during training. No need to set hyperparameter or design prompts. - Compatible with other opensource SDXL models, such as BluePencilXL, CounterfeitXL. Compatible with other Lora models. ***We design a new architecture that can support 10+ control types in condition text-to-image generation and can generate high resolution images visually comparable with midjourney***. The network is based on the original ControlNet architecture, we propose two new modules to: 1 Extend the original ControlNet to support different image conditions using the same network parameter. 2 Support multiple conditions input without increasing computation offload, which is especially important for designers who want to edit image in detail, different conditions use the same condition encoder, without adding extra computations or parameters. We do thoroughly experiments on SDXL and achieve superior performance both in control ability and aesthetic score. We release the method and the model to the open source community to make everyone can enjoy it. Inference scripts and more details can found: https://github.com/xinsir6/ControlNetPlus/tree/main
- Yi-1.5-6B↗
01-ai
Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. Model | Context Length | Pre-trained Tokens | :------------: | :------------: | :------------: | | Yi-1.5 | 4K, 16K, 32K | 3.6T
- control_v11f1e_sd15_tile↗
lllyasviel
This checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. This checkpoint corresponds to the ControlNet conditioned on **tiled image**. Conceptually, it is similar to a super-resolution model, but its usage is not limited to that. It is also possible to generate details at the same size as the input (conditione) image. @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} }
- GLM-5.3-GGUF↗
unsloth
You can now run GLM-5.3 in Unsloth Desktop with toggles for Low, High and Max thinking. Read our GLM-5.3 guide for analysis and instructions. GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks: + Stronger Coding: GLM-5.3 is the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench. It also achieve open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam. + Emergent Cyber Capability: As we scaled post-training, cyber capability developed faster than we expected. GLM-5.3 is state of the art on CyberGym for vulnerability discovery, and its gains are largest further up the exploitation chain, where it more than doubles GLM-5.2 on exploitation benchmarks.
- LTX-2.5-GGUF↗
unsloth
This is a GGUF quantized version of LTX-2.5 by Lightricks, the 22B audio-video DiT, both the **distilled** (8 + 3 steps, CFG 1) and the **dev** (full, 30 steps, CFG 3) checkpoint. unsloth/LTX-2.5-GGUF uses Unsloth Dynamic 2.0 methodology: the `UD-` files are mixed precision, with every layer's type chosen from a measured sensitivity scan, and they beat the uniform files at the same size. - Loads in Unsloth Studio and in ComfyUI through ComfyUI-GGUF (`Unet Loader (GGUF)`), both tested on every file here. - Same layout as unsloth/LTX-2.3-GGUF: dev files at the root, distilled files in `distilled/`. The text encoder, VAEs and latent upscaler are unchanged; take them from Lightricks/LTX-2.5. **Recommended:** `UD-Q4_K_XL` (14.0 GB) for most GPUs: clearly better than `Q4_K_M` and on par with `Q5_K_M`, which is 1.6 GB larger. For output closest to bf16 use `Q8_0`; on small cards use `UD-Q2_K_XL` (9.5 GB), which is far cleaner than `Q2_K` and close to `Q3_K_M` at 1.8 GB less. `distilled/ltx-2.5-22b-distilled-UD-Q4_K_XL.gguf`, 1280x704, 121 frames, 24 fps, seed 42, with audio: text to video, image to video.
- Qwen3.8-27B-AWQ-INT4-FP8KV↗
abhishekchohan
The scales were measured on the quantized model itself, not on the BF16 base, because activation-aware smoothing and int4 rounding change the K/V activations the served model actually writes to cache. Calibration replayed the exact same sequences that were used to calibrate the AWQ model: **112 packed sequences × 262,144 tokens (~29.4M tokens)** from the same Nemotron Post-Training v3 SFT blend (instruction-following chat, math, science, agentic code, multilingual), same shuffle seed, rendered with thinking enabled, so the measured KV distribution matches the deployment regime the weights were tuned for, at the exact context length the model serves at. While the model processed those sequences, we tracked the running maximum absolute value of the K states (after QK-norm) and V states each of the 16 global attention layers writes to the cache. Each scale is `amax / 448` (the fp8-e4m3 representable maximum), symmetric, per tensor, so no cached value can clip. The result is 32 per-layer scalars (`k_scale` / `v_scale`). Only the 16 global attention layers accumulate KV; the 48 DeltaNet linear-attention layers carry recurrent state and have no KV scales.
- Nanbeige4.1-3B↗
Nanbeige
Nanbeige4.1-3B is built upon Nanbeige4-3B-Base and represents an enhanced iteration of our previous reasoning model, Nanbeige4-3B-Thinking-2511, achieved through further post-training optimization with supervised fine-tuning (SFT) and reinforcement learning (RL). As a highly competitive open-source model at a small parameter scale, Nanbeige4.1-3B illustrates that compact models can simultaneously achieve robust **reasoning**, **preference alignment**, and **effective agentic behaviors**. We evaluate Nanbeige4.1-3B across a broad and diverse set of benchmarks covering **general reasoning**, and **deep-search capabilities**. On general reasoning tasks including **code**, **math**, **science**, **alignment**, and **tool-use** benchmarks, Nanbeige4.1-3B not only significantly outperforms same-scale models such as **Qwen3-4B**, but also demonstrates overall superior performance compared to larger models including **Qwen3-30B-A3B-2507** and **Qwen3-32B**.
- MiniMax-M3-EAGLE3↗
Inferact
**Inferact/MiniMax-M3-EAGLE3** is an EAGLE3 draft model for accelerating inference of MiniMax-M3. It is served end-to-end with **vLLM** and was trained using **TorchSpec** — a torch-native online speculative-decoding training framework that runs FSDP training and vLLM-based target inference concurrently, learning from **MiniMax-M3-regenerated responses and live vLLM-generated hidden states** to match the base model's exact token distribution. The draft is a **1-layer** dense Llama (`LlamaForCausalLMEagle3`, ~3.3 B params) operating on MiniMax-M3's `hidden_size=6144` / `vocab_size=200064`; at serve time it shares the target's embedding and LM head (EAGLE3). See `config.json` for the full architecture. All numbers are measured end-to-end against `MiniMaxAI/MiniMax-M3-MXFP8` served with vLLM at `tensor-parallel-size=4`, `num_speculative_tokens=3`, and `--enforce-eager`. Greedy draft sampling (`topk=1`).
- Qwen3-0.6B-Gensyn-Swarm-burrowing_subtle_parrot↗
SamsBuk
- evf-sam2-multitask↗
YxZhang
This is the checkpoint holder of EVF-SAM. Please refer to `"inference.py"` in the source code for detailed usage. We haven't supported `"AutoModel.from_pretrained(...)"` yet, please import the model script from source code.