Breeze-TTS-2-comfyui
drbaph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。
Basic profile indexed · More details to follow
From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.
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。
Basic profile indexed · More details to follow
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。
Basic profile indexed · More details to follow
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。
Basic profile indexed · More details to follow
This repository hosts the downloadable runtime files for **Higgs Audio v3 Studio**, a Windows desktop app for local Higgs Audio v3 TTS, voice cloning, speech continuation, and multi-speaker generation. This repository is not the original upstream model release. It provides GGUF model builds, the Windows CUDA engine DLL package, checksums, and a manifest used by the desktop app downloader. Higgs Audio v3 Studio is a Rust/Tauri desktop application that runs a ported native C++/CUDA implementation of Higgs Audio v3 locally.
ComfyUI-converted builds of HyperFlow (Video Rebirth's 8-step LoRA for MiniMax-H3, video + audio), for the **ComfyUI-Hyperflow** custom node. > **These are not the standalone LoRAs.** The files here require the custom node (they carry the time-embedder/endpoint LoRA + fixed 8-step grid the node consumes). If you want to use HyperFlow **without** the custom node, get the extracted standalone builds from drbaph/MiniMax-H3-Turbo-Lora-ComfyUI — those load with the stock `Load LoRA` node (backbone only, no two-time conditioning, output deviates from the released model). **Usage:** `Load Diffusion Model (MiniMax-H3) → ApplyHyperFlow → SamplerCustomAdvanced + euler` (feed the node's SIGMAS output). Optional sparse attention: core `Model Sparse Attention` (sol-attn: start_percent 0.25, dense_blocks "0,1", tau 1.0).
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。
Basic profile indexed · More details to follow
This repository contains MiniMax-H3 Turbo and few-step LoRAs converted and optimized for ComfyUI. These LoRAs accelerate MiniMax-H3 video and synchronized-audio generation by reducing the required number of sampling steps. - Video-Rebirth/hyperflow — GitHub - videorebirth/hyperflow — Hugging Face
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。
Basic profile indexed · More details to follow
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。
Basic profile indexed · More details to follow
This is the fp8_e4m3fn quantized version of Qwen-Image-Edit-2511 for lower memory usage and faster inference (compatible with ComfyUI) approx. 20GB VRAM 💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    🖥️ Demo    |   💬 WeChat (微信)    |   🫨 Discord   |    Github   
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。
Basic profile indexed · More details to follow
Pre-quantized **Comfy Kitchen INT8 ConvRot** version of the 8-step checkpoint (`stage-dmd-step-250`) from OpenVDN/vdn-minimax-h3. For use with ComfyUI-VDN-H3. Faster: the branch's dispatched matmuls run **2.7x** faster than bf16, and the A/B render finished **~1.2x** faster end-to-end (~111 s -> ~95 s, single run each). 8 steps, er_sde / beta, 1280x736 / 61 frames, merge, `cache_gpu`, same seed. Identical output; wall clock from a single run each (±5 s sampling).
ComfyUI-native conversions of **Viggle/Viggle-Animate** — a 33.1 B full finetune of MiniMax-H3's `ref2va` transformer for **character replacement in video**: a driving clip provides motion, camera, background and lighting; one reference still provides the character. No text encoder — conditioning is a frozen 362-token embedding. The sampler is DMD2-distilled: **4 steps (3 forward passes)**. **ComfyUI node pack (required for the conditioning layout):** github.com/Saganaki22/ComfyUI-Viggle-Animate-H3 - minimax_h3_ref2va_viggle_pruned_int8_convrot.safetensors (21 GB) - minimax_h3_ref2va_viggle_int8_convrot.safetensors (47 GB) - minimax_h3_ref2va_viggle_bf16.safetensors (66.3 GB, max quality)
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。
Z-Image is a powerful and highly efficient image generation model with **6B** parameters. It is currently has three variants:
Adjust provider, category or input/output filters. New models are added continuously.
Adjust monthly token usage to compare estimated API costs.
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.