# Juggernaut-XL-v9 by RunDiffusion: Open-Weight Model Source: https://savrn.com/models/juggernaut-xl-v9 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## SAVRN's Notes on Juggernaut-XL-v9 The page states no parameter count, but the files tell the story: a single 7.1 GB safetensors checkpoint plus fp16 shards for the UNet (5.1 GB), both text encoders (246.1 MB and 1.4 GB) and the VAE (167.3 MB), 14.0 GB in all. This is an SDXL-class image model, derived from stabilityai/stable-diffusion-xl-base-1.0, and the 7.1 GB single file is the fp16 artifact you would actually load — there is no Runs On table here to price it, so budget a card that holds that file plus activations. CreativeML Open RAIL-M permits commercial use but carries use-based restrictions that pass through to downstream users, so read the attachment before productizing. The card claims the most-downloaded SDXL fine-tune with 6M+ lifetime downloads; the Index shows 870.1k a month, still a large number. At 26 months in production it is a proven photorealism workhorse — but it is still SDXL, and neighbors like the FLUX family will beat it on prompt fidelity per GB. SAVRN Research ## Model Card By RunDiffusion, published under creativeml-openrail-m, revision cf419233522d. ### Juggernaut XL v9 by RunDiffusion The world's most-downloaded SDXL model — purpose-built for photorealism. 6M+ downloads on Hugging Face · Overwhelmingly Positive on Civitai · 26 months in production Juggernaut XL is the most popular SDXL fine-tune in the world. Version 9 — co-developed by KandooAI and RunDiffusion, integrating RunDiffusion Photo v2 — is the proven photorealism workhorse trusted by hundreds of thousands of creators: 6M+ all-time downloads on Hugging Face, 1.5M+ on Civitai, and an Overwhelmingly Positive rating across 7,780+ reviews. ### Why Juggernaut XL v9 in 2026? The SDXL ecosystem is the single most mature corner of open image generation, and v9 is its most refined photorealism checkpoint. Choose Juggernaut XL v9 when you want: [Read the full model card (817 words)](https://savrn.com/models/juggernaut-xl-v9/card) ## Identity and Version Repository RunDiffusion/Juggernaut-XL-v9 Publisher RunDiffusion Task Text to image Modality Image Library diffusers Parameters Not stated by the source Languages en Revision cf419233522daa0b9ea36c3aff98fa2cab1fb0fb First published 2024-02-18 Last updated 2026-05-08 ## Files and Weights 23 files, 14.0 GB in total. The weights are 5 files totalling 14.0 GB in safetensors. Weights5 files · 14.0 GB Configuration8 files · 5.7 KB Tokenizer6 files · 3.2 MB Documentation1 file · 10.5 KB Other2 files · 2.9 MB Repository1 file · 1.7 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | Juggernaut-XL_v9_RunDiffusionPhoto_v2.safetensors | Weights | 7.1 GB | c9e3e68f89b8 | | text_encoder/model.fp16.safetensors | Weights | 246.1 MB | 698656aa4f45 | | text_encoder_2/model.fp16.safetensors | Weights | 1.4 GB | 9962c194f50a | | unet/diffusion_pytorch_model.fp16.safetensors | Weights | 5.1 GB | cf1ee18eb367 | | vae/diffusion_pytorch_model.fp16.safetensors | Weights | 167.3 MB | 6353737672c9 | | model_index.json | Configuration | 667 B | — | | scheduler/scheduler_config.json | Configuration | 496 B | — | | text_encoder/config.json | Configuration | 565 B | — | | text_encoder_2/config.json | Configuration | 575 B | — | | tokenizer/special_tokens_map.json | Configuration | 588 B | — | | tokenizer_2/special_tokens_map.json | Configuration | 462 B | — | | unet/config.json | Configuration | 1.8 KB | — | | vae/config.json | Configuration | 607 B | — | | README.md | Documentation | 10.5 KB | — | | assets/Juggernaut_v9_banner.png | Other | 2.6 MB | 2b6cf4ee44e4 | | assets/Juggernaut_v9_banner.webp | Other | 379.2 KB | 82652584d311 | | .gitattributes | Repository | 1.7 KB | — | | tokenizer/merges.txt | Tokenizer | 524.6 KB | — | | tokenizer/tokenizer_config.json | Tokenizer | 705 B | — | | tokenizer/vocab.json | Tokenizer | 1.1 MB | — | | tokenizer_2/merges.txt | Tokenizer | 524.6 KB | — | | tokenizer_2/tokenizer_config.json | Tokenizer | 856 B | — | | tokenizer_2/vocab.json | Tokenizer | 1.1 MB | — | ## License and Download License creativeml-openrail-m Access Open weights, no gate Download size 14.0 GB [Download from RunDiffusion](https://huggingface.co/RunDiffusion/Juggernaut-XL-v9) Released by RunDiffusion through its official repository on Hugging Face. ## Built From - Derived from [stabilityai/stable-diffusion-xl-base-1.0](https://savrn.com/models/stable-diffusion-xl-base-1-0) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 14.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About Juggernaut-XL-v9 ### Can I use Juggernaut-XL-v9 commercially? Yes, with conditions. Juggernaut-XL-v9 is released under CreativeML Open RAIL-M. CreativeML Open RAIL-M permits use, including commercial use, subject to the use-based restrictions in its attachment, which must be passed on downstream. ## Similar Models Model · Text to image ### [Qwen-Image-2.1-Uncensored-Quantized](https://savrn.com/models/qwen-image-2-1-uncensored-quantized) [Ahmet Benzer](https://savrn.com/model-publishers/abenzerps) GGUF, Safetensors, and MLX quantized formats of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights. Q4KM is recommended for the best balance of size and quality for GGUF. INT8 ConvRot and NVFP4 offer high performance on supported GPUs, while MLX formats are optimized for Apple Silicon. Companion model files packaged for ComfyUI: Use the model with ComfyUI and ComfyUI-GGUF. All required companion files (GGUF transformer, text encoder, and VAE) are hosted directly in this repository. Download the files and place them in their respective ComfyUI directories: 1. Install ComfyUI-GGUF: Use the maintained fork with native Qwen-Image 2.1 support by cloning… Open weights other gguf [View model](https://savrn.com/models/qwen-image-2-1-uncensored-quantized) Model · Text to image ### [Qwen-Image-2.1-Uncensored-GGUF](https://savrn.com/models/qwen-image-2-1-uncensored-gguf-2) [Ahmet Benzer](https://savrn.com/model-publishers/abenzerps) GGUF quantizations of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights. Q4KM is recommended for the best balance of size and quality. Companion model files packaged for ComfyUI: Use the model with ComfyUI and ComfyUI-GGUF. All required companion files (GGUF transformer, text encoder, and VAE) are hosted directly in this repository. Download the files and place them in their respective ComfyUI directories: 1. Install ComfyUI-GGUF: Use the maintained fork with native Qwen-Image 2.1 support by cloning leejet/ComfyUI-GGUF into your custom nodes: (Note: If you have the older city96/ComfyUI-GGUF installed and encounter an Unknown model architecture! error… Open weights other gguf [View model](https://savrn.com/models/qwen-image-2-1-uncensored-gguf-2) Model · Text to image ### [Qwen-Image-2.1-GGUF](https://savrn.com/models/qwen-image-2-1-gguf-2) [Unsloth AI](https://savrn.com/model-publishers/unsloth) This is a GGUF quantized version of Qwen-Image-2.1. unsloth/Qwen-Image-2.1-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. - Important layers are upcasted to higher precision, per tensor, from a measured sensitivity scan. - Run these with Unsloth Desktop, stable-diffusion.cpp and more. A GGUF is the denoiser only, so it needs the VAE and the Qwen3-VL text encoder alongside it. 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It excels in photorealistic image generation, bilingual text rendering (English & Chinese), and robust instruction adherence. - Z-Image-Base – The non-distilled foundation model. 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