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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 21
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查看详情 ↗LFM2.5-24B-A2B-Uncensored-GGUF
pottokaoGGUF quantizations of pottokao/LFM2.5-24B-A2B-Uncensored (LiquidAI LFM2.5-24B-A2B decensored with Heretic ARA, searched and tested with thinking enabled; see the main repo for results). KV cache is small (only 10 of 40 layers are attention, ~20 KB per token in F16), so most of the VRAM goes to weights. The model always thinks before answering; allow a generous output budget. Liquid AI's official DSpark draft targets the original weights (acceptance on this model not yet measured).
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查看详情 ↗MiniMax-H3-TextEncoder-16GB-GGUF
pottokao> ⚠️ **Not a general-purpose LLM.** These files are the **text encoder of MiniMax-H3** and nothing else: cut to the > 50 layers H3 uses, with **no LM head** — they cannot chat or generate text. They exist so H3's 32B encoder fits a > **16 GB GPU in ComfyUI**. Built from the **official** MiniMax-H3 encoder (`MiniMaxAI/MiniMax-H3` → `text_encoder/`, revision `42ed227e`; Qwen3-VL-32B). Not abliterated. For comparison, on the same test: the earlier `MiniMax-H3-TextEncoder-Qwen3VL-32B-abliterated-GGUF` Q3_K_S (11.46 GB text part) scores 14.4% / 14.7%, and a stock llama.cpp Q3_K_S of the official weights 11.8% / 12.0%.
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查看详情 ↗> 🆕 **Newer, better-quality build:** pottokao/MiniMax-H3-TextEncoder-16GB-GGUF — official encoder weights, quantized per tensor by how much each one affects the H3 DiT, K-quants only. At the same 12.65 GB `_vis` size its conditioning error is about half of this Q3_K_S (7.9% vs 14.4%), with 13.7 / 14.8 GB options at 6.4% / 4.9%. > ⚠️ The `_vis` file here stores the visual tower's 5-D `patch_embed` weight as-is; recent `gguf` versions refuse tensors with more than 4 dimensions, so it may fail to load on an up-to-date ComfyUI-GGUF. The new repo stores it the ComfyUI-GGUF way. > 🆕 有新版：pottokao/MiniMax-H3-TextEncoder-16GB-GGUF（官方權重、依 H3 DiT 敏感度逐張量量化、只用 K 系列）。同樣 12.65 GB 誤差約為這顆的一半。這裡的 `_vis` 檔在新版 gguf 套件上可能載不進去。 > 📌 Heads-up: I've been focused on still-image work lately, so I don't have the bandwidth to help with video setups like MiniMax-H3 for now. The files stay up as they are — thanks for understanding! > 最近重心放在平面攝影，影片類（像 MiniMax-H3）暫時比較沒辦法協助排錯。檔案會照舊留著，謝謝體諒！ GGUF (llama.cpp) builds of the **MiniMax-H3 text encoder** — abliterated Qwen3-VL-32B, 50 layers, **Q3_K_S** (≈3.53 BPW).
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查看详情 ↗One file does two jobs: it **rewrites** a short idea into a full MOSS-Image caption, then **encodes** that caption for the DiT. Weights are loaded once. A Q4-class file fits an 8 GB card. The quantization is **DHQ** (DiT-aware quantization): every tensor's format is chosen from measured damage on two rulers at once — the error after MOSS-Image's own text stage (what the DiT sees) and the KL of the rewrite — instead of llama.cpp's fixed per-type rules. At the same file size, every DHQ build beats the standard llama.cpp quant on both rulers. Use the **Dual-Head Dragon** ComfyUI node: **https://github.com/pottokao-dotcom/ComfyUI-DualHead-Dragon** (MOSS-Image support: `products/moss.py`).
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查看详情 ↗Qwen-Image-2.1-DiT-GGUF
pottokao> **Not affiliated with, or endorsed by, Alibaba / Qwen.** Quantized redistribution of the > `Qwen/Qwen-Image-2.1` diffusion transformer under the > **Qwen Research License** (copy included as `LICENSE`). **Non-commercial use only.** > [!IMPORTANT] > **Loads in stock city96/ComfyUI-GGUF — no patch, no extra nodes.** > These files carry `general.architecture = qwen_image`, so `UnetLoaderGGUF` recognises them directly > (GGUFs without that field fail with `Unknown model architecture!`). > > **Text encoder:** our Qwen-Image-2.1 Text Encoder GGUF > works with this DiT, but it needs one extra node first — > ComfyUI-GGUF-Qwen3VL-TE (stock ComfyUI-GGUF > doesn't load Qwen3-VL text encoders yet). With a **safetensors** text encoder (bf16 / int8 / FP8 from the same > repo family) nothing extra is needed. The sample / showcase images in this repo were generated with the technique behind **ComfyUI-QwenImage-PhotoStyles** — a small ComfyUI node (17 photographic styles; one short prompt becomes a full styled prompt via the PE-T2I rewriter). If you're interested in how the images are made, the node and its recipe are there.
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查看详情 ↗> **Not affiliated with, or endorsed by, Alibaba / Qwen.** Community derivative of > `Qwen/Qwen-Image-2.1-PE-I2I`, > redistributed under the **Qwen Research License** (copy included as `LICENSE`, per §3.a). > **Non-commercial use only** (§1.i / §2.a); commercial use needs a separate licence from Qwen. The **image-editing prompt rewriter** for Qwen-Image-2.1 — a fine-tuned Qwen3.5-VL 9B that takes a vague editing instruction plus one or more input images and rewrites it into a precise, actionable editing prompt — with refusal behaviour reduced via Heretic directional ablation. This repo is the **llama.cpp build** and is **fully multimodal** — it ships both the language model GGUF and the vision projector, so it works for **text-only and image+text** editing.
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查看详情 ↗> **Not affiliated with, or endorsed by, Alibaba / Qwen.** Community derivative of > `Qwen/Qwen-Image-2.1-PE-T2I`, > redistributed under the **Qwen Research License** (copy included as `LICENSE`, per §3.a). > **Non-commercial use only**; commercial use needs a separate licence from Qwen. **Q4_K_M** build of `pottokao/Qwen-Image-2.1-PE-T2I-Heretic` — the Qwen-Image-2.1 **prompt rewriter** with refusal behaviour removed (3/100 refusals @ KL 0.036, from 98/100 — best of a 1,600-trial search; dominates the earlier 7/100 @ 0.039, tag `v1-trial384`). > **`system_prompt.txt` is included and the model is useless without it.** That 10 KB > document *is* the output contract — structure, register, length, and the JSON shape. > Load it as the system message.
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查看详情 ↗> 🍱 **All-GGUF Qwen-Image-2.1:** text encoder GGUF + DiT GGUF (Q8_0 / Q6_K / Q4_K_M, loads in stock ComfyUI-GGUF) + PE-T2I GGUF. > [!IMPORTANT] > **The 4 `model-0000x-of-00004` shards are the HF `transformers` checkpoint (bf16) — they do not load in ComfyUI.** > ComfyUI text encoders use a different key layout (the `model.language_model.` prefix is dropped > when repacking), so pointing ComfyUI at these shards will not work. > > For ComfyUI use one of these instead — all keep the vision tower, which 2.1 needs for editing. > **Mac / non-CUDA:** use the single file `qwen3vl_8b_bf16_heretic.safetensors` in *this* repo (full bf16, 17.5 GB — also in the GGUF repo) — > put it in `models/text_encoders/` and load it with the stock `CLIPLoader`, type `qwen_image`, then `TextEncodeQwenImage21`. > It is the only format here that needs no CUDA-specific kernels. > > | Repo | File | Loader | > |---|---|---| > | **this repo** | `qwen3vl_8b_bf16_heretic.safetensors` (bf16 single file, any device) | `CLIPLoader`, type `qwen_image` | > | `…-NVFP4` | `qwen3vl_8b_nvfp4_heretic.safetensors` | `CLIPLoader`, type `qwen_image` | > | `…-W4A8` | `qwen3vl_8b_w4a8_heretic.safetensors` | `CLIPLoader`, type `qwen_image` | > | `…-GGUF` | `qwen3vl_8b_heretic-Q4_K_M.gguf` + `mmproj-…-f16.gguf` | `CLIPLoaderGGUF` + the ComfyUI-GGUF-Qwen3VL-TE add-on node (without it: `[1, 512, 12288]` shape error) | > > You also need a ComfyUI new enough to know `QwenImage21`: **0.34.2 does not, 0.36.0 does.** > If `TextEncodeQwenImage21` is missing from your node list, that is why. > > Use *this* repo for `transformers` / diffusers / vLLM, or as the base for your own quantization. > **Not affiliated with, or endorsed by, Alibaba / Qwen.** Community derivative > (refusal-ablated) of `Qwen/Qwen3-VL-8B-Instruct` > — the model Qwen-Image-2.1 uses, unmodified, as its text encoder. Qwen releases that > model under **Apache-2.0**, so this derivative is redistributed under Apache-2.0 > (see `LICENSE` and `NOTICE`).
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查看详情 ↗**DHQ = Dual-Head Quant.** Same heretic weights as Qwen-Image-2.1-Text-Encoder-Heretic-GGUF, quantized a different way: instead of llama.cpp's fixed rules, every tensor gets the precision that matters to **what the Qwen-Image-2.1 DiT actually reads** (measured per tensor with gradients through the DiT's text input), plus an importance matrix weighted the same way. Each file is **exactly the size of the standard llama.cpp file of the same name**, and better at every level. Tuned for **Qwen-Image-2.1 only**. Other models that use Qwen3-VL-8B (Ideogram 4, Boogu, JoyImageEdit) can load these files, but they were not tuned for them. Need Q5/Q6/Q8? Use the standard files in the GGUF repo — at those sizes the standard recipe is already as good. DHQ files are quantized **for use as a diffusion model's text encoder**: each tensor's precision is chosen by how much its quantization error changes what the target DiT reads (measured through the DiT's own text-input stage), not by next-token loss. They stay normal LLM GGUFs, so the same file can still write or rewrite prompts: where a model is also used as a prompt enhancer (Z-Image-Engineer-V6, the heretic Qwen3-VL-8B), every file is also checked for that (the PE column) and the tables show where it gets worse.
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查看详情 ↗> [!IMPORTANT] > **Got `got input of size [1, 512, 12288]` with the GGUF? It's fixed.** > The GGUF files were fine all along — ComfyUI-GGUF just didn't load the Qwen3-VL vision tower. > Install the add-on node **ComfyUI-GGUF-Qwen3VL-TE**, > make sure the `mmproj` file sits next to the GGUF, restart ComfyUI. No need to re-download anything. > The same patch also fixes `Unknown model architecture!` for Qwen-Image-2.1 **DiT** GGUFs — so the whole > pipeline can run on GGUF. The sample / showcase images in this repo were generated with the technique behind **ComfyUI-QwenImage-PhotoStyles** — a small ComfyUI node (17 photographic styles; one short prompt becomes a full styled prompt via the PE-T2I rewriter). If you're interested in how the images are made, the node and its recipe are there. All three are in **this repo**. Every loader uses **type `qwen_image`** and feeds `TextEncodeQwenImage21`.
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查看详情 ↗Qwen3-4B-Text-Encoder-DHQ-GGUF
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查看详情 ↗Qwen3.8-27B-NVFP4-MTP-2x16GB
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查看详情 ↗Z-Image-Engineer-V6-DFlash-b16
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查看详情 ↗Z-Image-Engineer-V6-DHQ-GGUF
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。