Cyber-Prime-1-2.6B
Akahsizrr基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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fuse-1-Lite-GGUF
Akahsizrr基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Text-to-Image
Akalabeth12基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Flourish-3M
akanemind基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ddpm-cifar10-32-dot.in.name
akasharidas*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.
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View details ↗Hausa_xlsr
Akashpb13基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗MiniMax-H3-Character-Swap-LoRA
akatz-ai**V1.5 is the recommended checkpoint for new character swaps.** It replaces a selected person in a source performance with a character from an image reference. - **Download V1.5** — recommended, strength 1.0. - **Download V1** — retained for existing workflows and reproducible comparisons. Install H3 Relay 2.0 and follow the workflow guide for models, runtime and input paths.
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View details ↗MiniMax-H3-Person-Remover-LoRA
akatz-ai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.5-2B-Uncensored-Zero-Refusal-SOMA
AkaumiKaedeLarge-scale extreme, hardcore prompt tests were conducted, with no soft refusals or hard refusals appearing in any case. (Special note: There are numerous misleading "uncensored" or "low-refusal" models on HF. Although these models claim to be fully decensored or to have extremely low refusal rates, testing them with slightly more hardcore prompts will cause the model to produce refusals or soft censorship — specifically manifested as moral/legal statements/warnings, or answers that dodge the topic/intentionally steer the conversation toward compliance/NSFW content with erotic details censored.) This decensoring experiment did **not** use publicly available garbage prompt sets. Instead, it used a **completely original prompt set** covering fields such as extensive R18 (adult/NSFW), anti-human, real crimes, unethical/illegal content, etc. Through **extensive experiments**, it can be said that SRA, RDO, and other decensoring methods are all trash in terms of decensoring power. The strongest method is still **SOM**.
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View details ↗qwen2.5-3b-instruct-motion
akcit-motion基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗qwen2.5-7b-instruct-motion
akcit-motion基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗bert-base-turkish-cased-ner
akdeniz27This model is the fine-tuned model of "dbmdz/bert-base-turkish-cased" using a reviewed version of well known Turkish NER dataset (https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt). Pls refer "https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html" for entity grouping with aggregation_strategy parameter. * accuracy: 0.9933935699477056 * f1: 0.9592969472710453 * precision: 0.9543530277931161 * recall: 0.9642923563325274
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View details ↗bert-base-turkish-cased-ner-lora
akdeniz27基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mbert-base-albanian-cased-ner
akdeniz27基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗roberta-base-cuad
akdeniz27基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Llama3.2.1B.0.1-H
akhadangi基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗drama-3-int4
akhaliq基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗flux-3-image-klein-base-gguf
akhaliq基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗flux-3-image-klein-sd-gguf
akhaliq基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗MOSS-Image-SFT-4-steps
akhaliq> ⚡ **Demo:** MOSS-Image 4-Step Turbo Space > 🆕 **v2.0 (2026-10-18) — major update, usage changed.** The model is now > **Euler-native**: a single `lora.safetensors` that runs on the **stock > flow-matching Euler sampler** (any LoRA loader, 4 steps, CFG 1) — the custom > data-space sampler and the `time_embedding.safetensors` / > `dataspace.safetensors` files are **no longer needed and were removed from > the repo root**. If you used v1.x adapters, grab the old data-space variant > from `checkpoint-update-400/` (it keeps its own sampler), or switch to the > new one-line loader below. New users: just follow the Quickstart. The fastest high-fidelity MOSS-Image-SFT: **4 steps, no CFG, one LoRA file, stock sampler.** No custom loop, no auxiliary safetensors, no pipeline patches — it drops into any standard LoRA loader.
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Feed a frame, direct the next shot. This LoRA teaches **Qwen-Image-2.1** cinematic scene continuation: it takes an input image and a director-style instruction and generates the *next scene* — camera moves, reveals, lighting shifts — while keeping the world, characters and style consistent. Chain outputs into storyboards. This is the **Qwen-Image-2.1 port of the next-scene format** pioneered by lovis93/next-scene-qwen-image-lora-2509 (313K downloads on the 2509 edit model) — retrained natively on the newest Qwen image base, where this capability did not exist yet. Companion piece to akhaliq/Qwen-Image-2.1-Multiple-Angles-LoRA (camera control) — together they cover *where the camera is* and *where the story goes next*. **ComfyUI:** load in any Qwen-Image-2.1 workflow with a LoRA node (strength **0.7–0.9**), pass your image as the edit/reference input.
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