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AI 模型 2119

• sam-audio-base-tv↗

  facebook

• ddpm-celebahq-256↗

  Google

  *We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.* for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead.

• GLM-5.3-Flash-DFlash2↗

  incoai

  This repository contains the DFlash 2 draft model for `zai-org/GLM-5.3-Flash`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution. Benchmark numbers for this checkpoint are being re-measured and will be published here once final.

• Qwen3.8-Flash-Next-NVFP4-Q8-Mix-Strata↗

  Maximilian228

  Qwen's original Qwen3.8-Flash-Next with **our NVFP4 GPTQ experts, and Q8_0 in the layers that move the answers most**, packed for Strata NVFP4. It is the most accurate model that fits a **128 GB PC** with every expert in RAM. Its most likely next token is the Q8_0 model's 95.0% of the time, against 93.6% for the all-NVFP4 GPTQ, and it moves the answers 43% less (KL). It decodes at about half the GPTQ model's speed. **Quick start (Windows):** from the Strata NVFP4 release 0.1.42-nvfp4.2 or newer, run `START-HERE.bat --family qwen-nvfp4-mix`: it checks the PC, downloads this repository (only its experts if `qwen-nvfp4-gptq` is installed: the other files are the same) and starts the model. `pack/mix.json` has the plan. - **Everything but the experts is the same as in the NVFP4 GPTQ repository**, byte for byte: the dense GGUF (Q8_0 projections; router, small tensors and norms BF16 / F32), Qwen's own FP8 PLE table, the BF16 embedding and the MTP draft head. - **The original model, censored, with a switch.** It refuses what Qwen refuses. The fork can remove the refusal direction at run time (layers 8-33, the weights untouched): `--uncensored on` at install, the web app's "Disable censorship" switch, or `"uncensored": true` in a request. Checked on this pack: 5 of 5 held-out harmful requests refused with it off, 0 of 5 with it on. - **Only for Strata NVFP4.** The files are Strata's runtime formats. They do not load in transformers, vLLM or llama.cpp.

• superprompt-v1↗

  roborovski

  A T5 model fine-tuned on the SuperPrompt dataset for upsampling text prompts to more detailed descriptions. Meant to be used as a pre-generation step for text-to-image models that benefit from more detailed prompts. It's important that you use the exact task prefix: `Expand the following prompt to add more detail:` and limit the max tokens to 77 - which is the max prompt length for Stable Diffusion models.

• MOSS-Audio-4B-Instruct-GGUF↗

  cstr

  GGUF / ggml conversions of `OpenMOSS-Team/MOSS-Audio-4B-Instruct` for use with `crispasr --backend moss-audio` from **CrispStrobe/CrispASR**. **JFK sample (samples/jfk.wav, 11s):** > And so, my fellow Americans, ask not what your country can do for you, ask what you can do for your country. 1. **Inspect** the HF safetensors: 3 shards, 901 tensors total -- audio encoder (conv stem + 32 transformer layers + layer_norm), audio adapter (1 GatedMLP), deepstack mergers (3 GatedMLPs), language model (embedding + 36 Qwen3 layers + final norm + lm_head).

• stable-diffusion-xl-1.0-inpainting-0.1↗

  diffusers

  --- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - inpainting inference: false --- SD-XL Inpainting 0.1 is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask. The SD-XL Inpainting 0.1 was initialized with the `stable-diffusion-xl-base-1.0` weights. The model is trained for 40k steps at resolution 1024x1024 and 5% dropping of the text-conditioning to improve classifier-free classifier-free guidance sampling. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and, in 25% mask everything.

• MOSS-TTS-Local-Transformer-v1.5-GGUF↗

  mudler

  GGUF conversion of `OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5` for moss-tts.cpp: a C++17/ggml inference port of the OpenMOSS MOSS-TTS family that runs entirely on stock ggml with no Python, ONNX or torch at inference time. The non-matmul tensors (all `lc.*` heads and embeddings, every norm and bias) are kept in f32 in every quant; only the Qwen3 global and GPT-J local attention/FFN matmuls are quantized, so the CPU gather/head paths stay exact. This port is numerically verified against the reference PyTorch implementation: the language model matches to about 1e-5 with an exact greedy code sequence, and the MOSS-Audio-Tokenizer-v2 decode matches at about 115 dB SNR (bit-exact in f32). On CPU it runs roughly twice as fast per frame as the reference PyTorch at the same f32 precision.

• Llama-3.2-3B-small_Shiro_roleplay-gguf↗

  samunder12

• GLM-5.2-GGUF↗

  unsloth

  You can now run GLM-5.2 in Unsloth Studio with toggles for High and Max thinking. Read our GLM-5.2 guide for analysis and instructions. See below for example of 1-bit UD-IQ1_M GGUF running in Unsloth: We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a **solid 1M-token context**. GLM-5.2's new capabilities include: - **Solid 1M Context:** A solid 1M-token context that stably sustains long-horizon work - **Advanced Coding with Flexible Effort**: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency - **Improved Architecture**: We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP layer for speculative decoding, increasing the acceptance length by up to 20% - **Pure Open**: An MIT open-source license — no regional limits, technical access without borders