Qwen3-Embedding-8B-AWQ-INT4
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Qwen3-Reranker-0.6B-AWQ-INT4
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View details ↗flux-3-image-klein
drawthingsaiFLUX 3 Image Klein is a 5.6B open weights model for text-to-image generation and image editing. It takes a prompt and up to 10 reference images and returns one image. Text-to-image, single-reference edits and multi-reference compositions run through the same model, with Qwen3-VL-4B-Instruct as the text encoder. This repository holds the distilled checkpoint for generation and image editing. For fine-tuning, LoRA training and research, use FLUX 3 Image Klein Base. Inference code, prompt upsampling and the bounding box format: black-forest-labs/flux-3-image-klein on GitHub.
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View details ↗MOSS-Image-SFT
drawthingsaiMOSS-Image-SFT is a supervised fine-tuned text-to-image model in the **MOSS-Image** family from OpenMOSS, derived from MOSS-Image-Base. It uses the same architecture and inference pipeline, supporting natural-language prompts, tag sequences, and JSON layouts for controlling text placement and object arrangement. Use Python 3.10 or later and an NVIDIA GPU with CUDA support. Install a PyTorch 2.9 or later build compatible with your CUDA environment, then install: The model and custom pipeline code are downloaded and cached automatically. You can also pass a complete local model directory to `from_pretrained`. The transformer and text encoder use bfloat16; the VAE and initial noise use float32. The bundled VAE uses the FLUX.2 small decoder.
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View details ↗Qwen-Image-2.1
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View details ↗Qwen-Image-2.1-Turbo
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View details ↗Qwen3.8-27B-GGUF
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View details ↗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。
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View details ↗We introduce FireRed-Image-Edit-1.1, an upgrade to our general-purpose image editing foundation model. Building upon the capabilities presented in the FireRed-Image-Edit-1.0 Technical Report, version 1.1 significantly enhances identity consistency, multi-image conditioning, and domain-specialized editing performance, bringing the model closer to real-world creative production needs.
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View details ↗HiDream-O1-Image-FP8
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View details ↗Higgs-Audio-v3-Studio
drbaphThis 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.
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View details ↗Hyperflow-Comfyui
drbaphComfyUI-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).
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View details ↗LTX-2.3-FP8
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View details ↗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
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View details ↗OmniVoice-bf16
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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 ↗Qwen-Image-Edit-2511-FP8
drbaphThis 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   
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View details ↗s2-pro-fp8
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View details ↗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).
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View details ↗Viggle-Animate-ComfyUI
drbaphComfyUI-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)
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View details ↗Z-Image-fp8
drbaphThis is the **fp8-e4m3fn-scaled** / **fp8-e4m3fn** and **fp8-e5m2-scaled** / **fp8_e5m2** quantized versions of the Z-Image model, optimized for ComfyUI workflows. These quantized formats significantly reduce VRAM requirements while maintaining high image quality, making the model more accessible for consumer-grade GPUs. **Benefits:** - Reduced memory footprint (~50% VRAM savings) - Faster inference times - Full ComfyUI compatibility - Minimal quality degradation **Z-Image** is the foundation model of the ⚡️- Image family, engineered for good quality, robust generative diversity, broad stylistic coverage, and precise prompt adherence.
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View details ↗Z-Image-Turbo-FP8
drbaphThis is a quantization of Comfy-Org/z_image_turbo to FP8_E5M2 and FP8_E4M3FN ⚡️- Image An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer Z-Image is a powerful and highly efficient image generation model with **6B** parameters. It is currently has three variants:
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View details ↗Dream-Coder-v0-Base-7B
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