AuraFlow
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View details ↗DISCOVER & COMPARE
Explore AI models 84997
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 ↗AuraSR-v2
fal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 ↗dreamshaper-xl-1-0
fal-collab-models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.2 [dev] Turbo** is a distilled LoRA adapter for [FLUX.2 [dev]](https://huggingface.co/black-forest-labs/FLUX.2-dev) that enables high-quality image generation in just **8 inference steps**. This model inherits the [FLUX [dev] Non-Commercial License](https://huggingface.co/black-forest-labs/FLUX.2-dev/blob/main/LICENSE.txt) from the base model.
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View details ↗Virtual clothing try-on LoRA for Flux Klein 9b Edit. Given a person image and clothing reference images, generates a realistic result of the person wearing the specified outfit. **Try it now:** - Playground (direct) — upload your images and run the model - Workflow with LLM — auto-generates the prompt for you > Example images shown on this page were generated using Nano Banana Pro and are not part of the training data.
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View details ↗Best paired with the render‑to‑real endpoint's `enable_detail_refine` option (extra 2× spatial‑upscale + detailer pass). File: `3DREAL-strong-v2.safetensors`. Made with Strong v2 — a 1930s chase short where every shot starts as a Blender blockout (action, cameras and cuts owned by the 3D scene): This is an **IC‑LoRA** (in‑context LoRA) for **LTX‑2.3** that turns a rough grey 3D viewport animation (a simple low‑poly blockout) into a fully **photorealistic, film‑quality** video, while keeping the **exact composition, camera move and layout** of the input.
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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 ↗A LoRA adapter for MiniMax H3 specialized in realistic people: faces that hold up in close-up, natural skin texture, believable expressions and gestures, film-style lighting and documentary camera movement. 19 pairs, **same prompt, same seed, adapter on vs off** — the only variable is the LoRA. The trigger word is present on both sides, so it is not doing the work. Each pair plays the base model first, then freezes and dims while the adapted version plays beside it. Close-up talking faces, arguments, several people speaking at once, weathered skin, children, ritual and travel scenes. Nothing cherry-picked from a larger render batch: these are the pairs that were kept, in order. (download the comparison, 197s, 1920x1080)
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View details ↗> **Multi-angle camera control LoRA for Qwen-Image-Edit-2511** > > 96 camera positions • Trained on 3000+ Gaussian Splatting renders • Built with fal.ai **This is the first multi-angle camera control LoRA for Qwen-Image-Edit-2511.** While Qwen-Image-Edit-2511 has built-in viewpoint capabilities, this LoRA provides:
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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 ↗Amateur-Photography
falanaja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 ↗GPasT-small-model
Falcon2006VN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 ↗medical_summarization
Falconsai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 ↗nsfw_image_detection
FalconsaiThe **Fine-Tuned Vision Transformer (ViT)** is a variant of the transformer encoder architecture, similar to BERT, that has been adapted for image classification tasks. This specific model, named "google/vit-base-patch16-224-in21k," is pre-trained on a substantial collection of images in a supervised manner, leveraging the ImageNet-21k dataset. The images in the pre-training dataset are resized to a resolution of 224x224 pixels, making it suitable for a wide range of image recognition tasks. During the training phase, meticulous attention was given to hyperparameter settings to ensure optimal model performance. The model was fine-tuned with a judiciously chosen batch size of 16. This choice not only balanced computational efficiency but also allowed for the model to effectively process and learn from a diverse array of images. To facilitate this fine-tuning process, a learning rate of 5e-5 was employed. The learning rate serves as a critical tuning parameter that dictates the magnitude of adjustments made to the model's parameters during training. In this case, a learning rate of 5e-5 was selected to strike a harmonious balance between rapid convergence and steady optimization, resulting in a model that not only learns swiftly but also steadily refines its capabilities throughout the training process.
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View details ↗nsfw_image_detection_26
Falconsai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_summarization
FalconsaiThe **Fine-Tuned T5 Small** is a variant of the T5 transformer model, designed for the task of text summarization. It is adapted and fine-tuned to generate concise and coherent summaries of input text. The model, named "t5-small," is pre-trained on a diverse corpus of text data, enabling it to capture essential information and generate meaningful summaries. Fine-tuning is conducted with careful attention to hyperparameter settings, including batch size and learning rate, to ensure optimal performance for text summarization. During the fine-tuning process, a batch size of 8 is chosen for efficient computation and learning. Additionally, a learning rate of 2e-5 is selected to balance convergence speed and model optimization. This approach guarantees not only rapid learning but also continuous refinement during training.
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View details ↗MN-Violet-Lotus-12B
FallenMerick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 ↗Qwen3.8-27B-Uncensored-GP100-GGUF
fallentree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 ↗R1-Onevision-7B
Fancy-MLLM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 ↗big-asp-v2
fancyfeast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 ↗bigasp-v1
fancyfeast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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