从语言到影像，从开源到 API。读懂模型能力、比较真实成本，
为你的下一个想法，找到合适的智能引擎。
See https://comfyanonymous.github.io/ComfyUI_examples/audio/ or https://docs.comfy.org/tutorials/audio/ace-step/ace-step-v1 for how to use it in ComfyUI. ACE Step v1 M2M Editing ACE-Step v1 Text to Instrumentals Music ACE Step v1 Text to Song
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These are repackaged files to work with ComfyUI, original model repo is: https://huggingface.co/ACE-Step/Ace-Step1.5 ACE-Step 1.5XL Turbo: Text to Music ACE-Step 1.5XL SFT： Text to Music ACE-Step 1.5 Music Generation AIO ACE-Step 1.5XL Base: Text to Music ACE-Step 1.5 Music Generation Workflow ACE-Step 1.5 AI Lyric Song Generation ACE-Step 1.5 Music Generation (4B LLM )
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Anima Lllite: Any Control to Image Before After Anima Lllite: Depth Control to Image Before After Anima Lllite: Image Inpainting Before After
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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。
- https://huggingface.co/ZhengPeng7/BiRefNet - https://huggingface.co/egeorcun/lucida
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Boogu image 0.1 Edit Int8 Before After Boogu image 0.1 Edit Before After Boogu Turbo: Text to Image
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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。
Original model repository: - https://huggingface.co/lodestones/Chroma1-HD - https://huggingface.co/silveroxides/Chroma1-HD-fp8-scaled - Chroma1-HD.safetensors by lodestones - Chroma1-HD-fp8mixed.safetensors by silveroxides
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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。
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。
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。
- https://github.com/ByteDance-Seed/Depth-Anything-3 - https://huggingface.co/depth-anything/DA3-BASE - https://huggingface.co/depth-anything/DA3METRIC-LARGE - https://huggingface.co/depth-anything/DA3MONO-LARGE - https://huggingface.co/depth-anything/DA3-SMALL Depth Map: Depth Anything 3 Before After Depth Estimation: Depth Anything 3 Before After
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- https://huggingface.co/baidu/ERNIE-Image - https://huggingface.co/baidu/ERNIE-Image-Turbo
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- https://huggingface.co/briaai/Fibo-2.0-dev - https://huggingface.co/briaai/Fibo-2.0-edit-turbo The model weights are © BRIA AI and licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). You may use them for non-commercial purposes only. Commercial use requires a separate agreement with BRIA AI: https://platform.bria.ai/licensing/pricing?model=fibo-2 The weights are provided as is, without warranties of any kind. See Section 5 of the license (Disclaimer of Warranties and Limitation of Liability).
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This is a smaller checkpoint for flux1-dev that will work better for ComfyUI users with less VRAM (under 24gb). Flux.1 Inpaint Before After Flux.1 Dev USO Reference Image Generation Before After Flux.1 Outpaint Before After Flux.1 Dev fp8: Text to Image Flux.1 Dev: Text to Image Flux.1 Canny Model Before After Flux.1 Depth Lora Before After Flux.1 Redux Model
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Original model repository: https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model
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Original model repository: https://huggingface.co/black-forest-labs/FLUX.1-Krea-dev All diffusion model files in this repo are licensed the same as the ones in the original repo: https://huggingface.co/black-forest-labs/FLUX.1-Krea-dev The files in this repo are best used with ComfyUI using the Flux examples.
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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。
Original model repository: https://huggingface.co/black-forest-labs/FLUX.1-schnell This flux1-schnell model has weights in FP8, which makes running in ComfyUI much faster and use less memory.
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These are quantized weights. The original FLUX.2 repo is here: FLUX.2-dev. - Flux2TurboComfyv2.safetensors by ByteZSzn - Flux_2-Turbo-LoRA_comfyui.safetensors by ByteZSzn Flux.2 Dev Before After Flux.2 Dev Text to Image Product Mockup(Flux.2 Dev FP8)
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- https://huggingface.co/black-forest-labs/FLUX.2-klein-4B - https://huggingface.co/black-forest-labs/FLUX.2-klein-base-4B
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Original model repository: https://huggingface.co/black-forest-labs/FLUX.2-klein-9B
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- https://github.com/hzwer/Practical-RIFE - https://github.com/google-research/frame-interpolation
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- https://huggingface.co/google/gemma-4-E2B-it - https://huggingface.co/google/gemma-4-E4B-it
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可以调整厂商、分类或输入输出条件；新模型正持续收录。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
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。
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。
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。
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。
