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Signed-off-by: Vladimir Mandic <mandic00@live.com>
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Vladimir Mandic
2026-09-05 11:57:53 +02:00
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data/reference-base.json
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data/reference-cloud.json
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{
"Nano Banana": {
"path": "gemini-2.5-flash-image",
"desc": "Our best engine for high-velocity visual creation, offering state-of-the-art speed and efficiency. Gemini 2.5 Flash Image, also known as Nano Banana, is best for high-volume generation, conversational image editing, and low-latency creative workflows that require native multimodal understanding. (Knowledge cutoff June 2025)",
"preview": "gemini-2.5-flash-image.jpg"
"Nano Banana lite": {
"path": "gemini-3.1-flash-lite-image",
"desc": "Nano Banana Lite is designed as the efficiency specialist of the image generation family, offering ultra-low latency and cost-effective image generation and editing. By targeting a sub-2 second latency and significantly reduced TPU compute costs, this model enables high-volume interactive developer use cases and real-time consumer applications. (Knowledge cutoff January 2025)",
"preview": "gemini-3.1-flash-lite-image.jpg"
},
"Nano Banana 2": {
"path": "gemini-3.1-flash-image",
"desc": "Nano Banana 2 provides high-quality image generation and conversational editing at a mainstream price point and low latency. It serves as the high-efficiency counterpart to Gemini 3 Pro Image, optimized for speed and high-volume developer use cases.(Knowledge cutoff January 2025)",
"preview": "gemini-3.1-flash-image.jpg"
},
"Nano Banana lite": {
"path": "gemini-3.1-flash-lite-image",
"desc": "Nano Banana Lite is designed as the efficiency specialist of the image generation family, offering ultra-low latency and cost-effective image generation and editing. By targeting a sub-2 second latency and significantly reduced TPU compute costs, this model enables high-volume interactive developer use cases and real-time consumer applications. (Knowledge cutoff January 2025)",
"preview": "gemini-3.1-flash-lite-image.jpg"
},
"Nano Banana Pro": {
"path": "gemini-3-pro-image",
"desc": "Nano Banana Pro is a sophisticated reasoning-driven engine for professional-grade image editing and generation, offering studio-quality precision and advanced creative control. Nano Banana Pro is best for complex graphic design, high-fidelity product mockups, and factual data visualizations that require accurate text rendering and real-world grounding via Google Search. (Knowledge cutoff January 2025)",
"preview": "gemini-3-pro-image.jpg"
},
"Nano Banana": {
"path": "gemini-2.5-flash-image",
"desc": "Our best engine for high-velocity visual creation, offering state-of-the-art speed and efficiency. Gemini 2.5 Flash Image, also known as Nano Banana, is best for high-volume generation, conversational image editing, and low-latency creative workflows that require native multimodal understanding. (Knowledge cutoff June 2025)",
"preview": "gemini-2.5-flash-image.jpg"
}
}
data/reference-community.json
+99 -99
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@@ -1,17 +1,12 @@
{
"Tempest-by-Vlad XL": {
"path": "tempestByVlad_baseV01.safetensors@https://civitai.com/api/download/models/1301775",
"preview": "tempestByVlad_baseV01.jpg",
"desc": "Flexible SDXL model with custom encoder and finetuned for larger landscape resolutions with high details and high contrast.",
"size": 6.94,
"date": "2025 January"
},
"Tempest-by-Vlad XL Hyper": {
"path": "tempestByVlad_hyperV01.safetensors@https://civitai.com/api/download/models/1343512",
"preview": "tempestByVlad_hyperV01.jpg",
"desc": "Custom distilled variant with goal to get as-normal-as-possible model that works with low steps and guidance-free",
"size": 6.94,
"date": "2025 January"
"Juggernaut SD Reborn": {
"original": true,
"path": "juggernaut_reborn.safetensors@https://civitai.com/api/download/models/274039",
"preview": "juggernaut_reborn.jpg",
"desc": "Showcase finetuned model based on Stable diffusion 1.5",
"date": "2023 December",
"size": 2.28,
"extras": "width: 512, height: 512, sampler: DEIS, steps: 20, cfg_scale: 6.0"
},
"Juggernaut XL XI": {
"path": "juggernautXL_juggXIByRundiffusion.safetensors@https://civitai.com/api/download/models/782002",
@@ -29,28 +24,26 @@
"size": 6.94,
"extras": "sampler: DPM SDE, steps: 6, cfg_scale: 2.0"
},
"Juggernaut SD Reborn": {
"original": true,
"path": "juggernaut_reborn.safetensors@https://civitai.com/api/download/models/274039",
"preview": "juggernaut_reborn.jpg",
"desc": "Showcase finetuned model based on Stable diffusion 1.5",
"date": "2023 December",
"size": 2.28,
"extras": "width: 512, height: 512, sampler: DEIS, steps: 20, cfg_scale: 6.0"
},
"WAI Illustrious XL v15": {
"path": "waiIllustriousSDXL_v150.safetensors@https://civitai.com/api/download/models/2167369",
"preview": "waiIllustriousSDXL_v150.jpg",
"NoobAI XL 1.1 Epsilon": {
"path": "noobaiXLNAIXL_epsilonPred11Version.safetensors@https://huggingface.co/Laxhar/noobai-XL-1.1/resolve/main/NoobAI-XL-v1.1.safetensors",
"preview": "noobaiXLNAIXL_epsilonPred11Version.jpg",
"desc": "",
"size": 6.94,
"date": "2025 August"
"date": "2024 November"
},
"Pony Realism XL v2.3": {
"path": "ponyRealism_V23.safetensors@https://civitai.com/api/download/models/1763661",
"preview": "ponyRealism_V23.jpg",
"desc": "",
"size": 6.94,
"date": "2025 May"
"ShuttleAI Shuttle 3.0 Diffusion": {
"path": "shuttleai/shuttle-3-diffusion",
"desc": "Shuttle uses Flux.1 Schnell as its base. It can produce images similar to Flux Dev or Pro in just 4 steps, and it is licensed under Apache 2. The model was partially de-distilled during training. When used beyond 10 steps, it enters refiner mode enhancing image details without altering the composition",
"preview": "shuttleai--shuttle-3-diffusion.jpg",
"date": "2024 November",
"size": 31.41
},
"ShuttleAI Shuttle 3.1 Aesthetic": {
"path": "shuttleai/shuttle-3.1-aesthetic",
"desc": "Shuttle uses Flux.1 Schnell as its base. It can produce images similar to Flux Dev or Pro in just 4 steps, and it is licensed under Apache 2. The model was partially de-distilled during training. When used beyond 10 steps, it enters refiner mode enhancing image details without altering the composition",
"preview": "shuttleai--shuttle-3.1-aesthetic.jpg",
"date": "2024 November",
"size": 31.41
},
"NoobAI XL 1.0 V-Pred": {
"path": "noobaiXLNAIXL_vPred10Version.safetensors@https://huggingface.co/Laxhar/noobai-XL-Vpred-1.0/resolve/main/NoobAI-XL-Vpred-v1.0.safetensors",
@@ -59,12 +52,33 @@
"size": 6.94,
"date": "2024 December"
},
"NoobAI XL 1.1 Epsilon": {
"path": "noobaiXLNAIXL_epsilonPred11Version.safetensors@https://huggingface.co/Laxhar/noobai-XL-1.1/resolve/main/NoobAI-XL-v1.1.safetensors",
"preview": "noobaiXLNAIXL_epsilonPred11Version.jpg",
"Tempest-by-Vlad XL": {
"path": "tempestByVlad_baseV01.safetensors@https://civitai.com/api/download/models/1301775",
"preview": "tempestByVlad_baseV01.jpg",
"desc": "Flexible SDXL model with custom encoder and finetuned for larger landscape resolutions with high details and high contrast.",
"size": 6.94,
"date": "2025 January"
},
"Tempest-by-Vlad XL Hyper": {
"path": "tempestByVlad_hyperV01.safetensors@https://civitai.com/api/download/models/1343512",
"preview": "tempestByVlad_hyperV01.jpg",
"desc": "Custom distilled variant with goal to get as-normal-as-possible model that works with low steps and guidance-free",
"size": 6.94,
"date": "2025 January"
},
"ShuttleAI Shuttle Jaguar": {
"path": "shuttleai/shuttle-jaguar",
"desc": "Shuttle uses Flux.1 Schnell as its base. It can produce images similar to Flux Dev or Pro in just 4 steps, and it is licensed under Apache 2. The model was partially de-distilled during training. When used beyond 10 steps, it enters refiner mode enhancing image details without altering the composition",
"preview": "shuttleai--shuttle-jaguar.jpg",
"date": "2025 January",
"size": 31.41
},
"Pony Realism XL v2.3": {
"path": "ponyRealism_V23.safetensors@https://civitai.com/api/download/models/1763661",
"preview": "ponyRealism_V23.jpg",
"desc": "",
"size": 6.94,
"date": "2024 November"
"date": "2025 May"
},
"WAI-Ani-Pony XL v14": {
"path": "waiANIPONYXL_v140.safetensors@https://civitai.com/api/download/models/1767402",
@@ -73,6 +87,55 @@
"size": 6.94,
"date": "2025 May"
},
"WAI Illustrious XL v15": {
"path": "waiIllustriousSDXL_v150.safetensors@https://civitai.com/api/download/models/2167369",
"preview": "waiIllustriousSDXL_v150.jpg",
"desc": "",
"size": 6.94,
"date": "2025 August"
},
"Tiwaz CenKreChro": {
"path": "Tiwaz/CenKreChro",
"preview": "Tiwaz--CenKreChro.jpg",
"desc": "Based Centerfold Flux 5, trying to merge in Chroma and Krea.",
"date": "2025 September",
"size": 31.42
},
"purplesmartai Pony 7": {
"path": "purplesmartai/pony-v7-base",
"preview": "purplesmartai--pony-v7-base.jpg",
"desc": "Pony V7 is a versatile character generation model based on AuraFlow architecture. It supports a wide range of styles and species types (humanoid, anthro, feral, and more) and handles character interactions through natural language prompts.",
"date": "2025 October",
"size": 33.32
},
"Skywork UniPic3": {
"path": "Skywork/Unipic3",
"preview": "Skywork--Unipic3.jpg",
"desc": "UniPic3 is an image editing and multi-image composition model based. It is a fine-tune of Qwen-Image-Edit.",
"date": "2026 January",
"size": 53.74
},
"Skywork Unipic3-DMD": {
"path": "Skywork/Unipic3-DMD",
"preview": "Skywork--Unipic3-DMD.jpg",
"desc": "UniPic3-DMD-Model is a few-step image editing and multi-image composition model trained using Distribution Matching Distillation (DMD) and is a fine-tune of Qwen-Image-Edit.",
"date": "2026 January",
"size": 53.74
},
"FireRed Image Edit 1.0": {
"path": "FireRedTeam/FireRed-Image-Edit-1.0",
"preview": "FireRedTeam--FireRed-Image-Edit-1.0.jpg",
"desc": "FireRed-Image-Edit is a general-purpose image editing model that delivers high-fidelity and consistent editing across a wide range of scenarios. FireRed is a fine-tune of Qwen-Image-Edit.",
"date": "2026 February",
"size": 53.74
},
"FireRed Image Edit 1.1": {
"path": "FireRedTeam/FireRed-Image-Edit-1.1",
"preview": "FireRedTeam--FireRed-Image-Edit-1.1.jpg",
"desc": "FireRed-Image-Edit is a general-purpose image editing model that delivers high-fidelity and consistent editing across a wide range of scenarios. FireRed is a fine-tune of Qwen-Image-Edit.",
"date": "2026 March",
"size": 53.74
},
"Z-Image-Turbo MoodyRealMix": {
"path": "resonantsky/MoodyRealMix-SDNQ-int8-svd-r32",
"preview": "resonantsky--MoodyRealMix-SDNQ-int8-svd-r32.jpg",
@@ -100,69 +163,6 @@
"tags": "community, Z-image",
"date": "2026 May"
},
"Tiwaz CenKreChro": {
"path": "Tiwaz/CenKreChro",
"preview": "Tiwaz--CenKreChro.jpg",
"desc": "Based Centerfold Flux 5, trying to merge in Chroma and Krea.",
"date": "2025 September",
"size": 31.42
},
"purplesmartai Pony 7": {
"path": "purplesmartai/pony-v7-base",
"preview": "purplesmartai--pony-v7-base.jpg",
"desc": "Pony V7 is a versatile character generation model based on AuraFlow architecture. It supports a wide range of styles and species types (humanoid, anthro, feral, and more) and handles character interactions through natural language prompts.",
"date": "2025 October",
"size": 33.32
},
"ShuttleAI Shuttle 3.0 Diffusion": {
"path": "shuttleai/shuttle-3-diffusion",
"desc": "Shuttle uses Flux.1 Schnell as its base. It can produce images similar to Flux Dev or Pro in just 4 steps, and it is licensed under Apache 2. The model was partially de-distilled during training. When used beyond 10 steps, it enters refiner mode enhancing image details without altering the composition",
"preview": "shuttleai--shuttle-3-diffusion.jpg",
"date": "2024 November",
"size": 31.41
},
"ShuttleAI Shuttle 3.1 Aesthetic": {
"path": "shuttleai/shuttle-3.1-aesthetic",
"desc": "Shuttle uses Flux.1 Schnell as its base. It can produce images similar to Flux Dev or Pro in just 4 steps, and it is licensed under Apache 2. The model was partially de-distilled during training. When used beyond 10 steps, it enters refiner mode enhancing image details without altering the composition",
"preview": "shuttleai--shuttle-3.1-aesthetic.jpg",
"date": "2024 November",
"size": 31.41
},
"ShuttleAI Shuttle Jaguar": {
"path": "shuttleai/shuttle-jaguar",
"desc": "Shuttle uses Flux.1 Schnell as its base. It can produce images similar to Flux Dev or Pro in just 4 steps, and it is licensed under Apache 2. The model was partially de-distilled during training. When used beyond 10 steps, it enters refiner mode enhancing image details without altering the composition",
"preview": "shuttleai--shuttle-jaguar.jpg",
"date": "2025 January",
"size": 31.41
},
"FireRed Image Edit 1.0": {
"path": "FireRedTeam/FireRed-Image-Edit-1.0",
"preview": "FireRedTeam--FireRed-Image-Edit-1.0.jpg",
"desc": "FireRed-Image-Edit is a general-purpose image editing model that delivers high-fidelity and consistent editing across a wide range of scenarios. FireRed is a fine-tune of Qwen-Image-Edit.",
"date": "2026 February",
"size": 53.74
},
"FireRed Image Edit 1.1": {
"path": "FireRedTeam/FireRed-Image-Edit-1.1",
"preview": "FireRedTeam--FireRed-Image-Edit-1.1.jpg",
"desc": "FireRed-Image-Edit is a general-purpose image editing model that delivers high-fidelity and consistent editing across a wide range of scenarios. FireRed is a fine-tune of Qwen-Image-Edit.",
"date": "2026 March",
"size": 53.74
},
"Skywork UniPic3": {
"path": "Skywork/Unipic3",
"preview": "Skywork--Unipic3.jpg",
"desc": "UniPic3 is an image editing and multi-image composition model based. It is a fine-tune of Qwen-Image-Edit.",
"date": "2026 January",
"size": 53.74
},
"Skywork Unipic3-DMD": {
"path": "Skywork/Unipic3-DMD",
"preview": "Skywork--Unipic3-DMD.jpg",
"desc": "UniPic3-DMD-Model is a few-step image editing and multi-image composition model trained using Distribution Matching Distillation (DMD) and is a fine-tune of Qwen-Image-Edit.",
"date": "2026 January",
"size": 53.74
},
"Anima 1.0 Base Merge sdnq-hadamard-uint4": {
"path": "vladmandic/Anima-1.0-Base-Merge-sdnq-hadamard-uint4",
"preview": "vladmandic--Anima-1.0-Base.jpg",
data/reference-distilled.json
+123 -116
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@@ -1,17 +1,20 @@
{
"Boogu Image 0.1 Turbo": {
"path": "Boogu/Boogu-Image-0.1-Turbo",
"preview": "Boogu--Boogu-Image-0.1-Turbo.jpg",
"desc": "Boogu Image 0.1 Turbo is the distilled fast inference variant of Boogu Image with the same Qwen3-VL instruction encoder and Boogu transformer architecture.",
"size": 35.81,
"date": "2026 June"
"Segmind Tiny": {
"path": "segmind/tiny-sd",
"preview": "segmind--tiny-sd.jpg",
"desc": "Segmind's Tiny-SD offers a compact, efficient, and distilled version of Realistic Vision 4.0 and is up to 80% faster than SD1.5",
"extras": "width: 512, height: 512, sampler: Default, cfg_scale: 9.0",
"size": 0.99,
"date": "2023 July"
},
"Boogu Image 0.1 Edit Turbo": {
"path": "Boogu/Boogu-Image-0.1-Edit-Turbo",
"preview": "Boogu--Boogu-Image-0.1-Edit-Turbo.jpg",
"desc": "Boogu Image 0.1 Edit Turbo is the distilled editing variant of Boogu Image with motion-aware instruction encoding and fast flow-match inference.",
"size": 35.81,
"date": "2026 June"
"Segmind SSD-1B": {
"path": "huggingface/segmind/SSD-1B",
"preview": "segmind--SSD-1B.jpg",
"desc": "The Segmind Stable Diffusion Model (SSD-1B) offers a compact, efficient, and distilled version of the SDXL model. At 50% smaller and 60% faster than Stable Diffusion XL (SDXL), it provides quick and seamless performance without sacrificing image quality.",
"variant": "fp16",
"extras": "sampler: Default, cfg_scale: 9.0",
"size": 12.48,
"date": "2023 October"
},
"StabilityAI StableDiffusion XL Turbo": {
"path": "stabilityai/sdxl-turbo",
@@ -22,13 +25,21 @@
"size": 19.38,
"date": "2023 November"
},
"Krea 2 Turbo": {
"path": "CalamitousFelicitousness/Krea-2-Turbo-Diffusers",
"preview": "CalamitousFelicitousness--Krea-2-Turbo-Diffusers.jpg",
"desc": "Krea 2 (K2) Turbo is the 8-step distilled inference model of the Krea 2 family, trained from scratch by Krea. A 12.9B-parameter single-stream flow-matching DiT that uses a Qwen3-VL-4B vision-language model as its text encoder and the Qwen-Image VAE. Runs without classifier-free guidance; LoRAs trained on Krea 2 Base apply directly.",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 8",
"size": 33.5,
"date": "2026 June"
"Tencent HunyuanDiT 1.1 Distilled": {
"path": "Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled",
"desc": "Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding.",
"preview": "Tencent-Hunyuan--HunyuanDiT-v1.1-Diffusers-Distilled.jpg",
"extras": "sampler: Default, cfg_scale: 2.0",
"size": 13.49,
"date": "2024 June"
},
"Tencent HunyuanDiT 1.2 Distilled": {
"path": "Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers-Distilled",
"desc": "Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding.",
"preview": "Tencent-Hunyuan--HunyuanDiT-v1.2-Diffusers-Distilled.jpg",
"extras": "sampler: Default, cfg_scale: 2.0",
"size": 13.43,
"date": "2024 July"
},
"StabilityAI Stable Diffusion 3.5 Turbo": {
"path": "stabilityai/stable-diffusion-3.5-large-turbo",
@@ -39,28 +50,12 @@
"size": 36.12,
"date": "2024 October"
},
"Microsoft Lens Turbo": {
"path": "Jinstudio/Lens-Turbo",
"preview": "microsoft--Lens-Turbo.jpg",
"desc": "Microsoft Lens-Turbo is the distilled Lens variant optimized for faster text-to-image generation with fewer steps.",
"size": 28.43,
"date": "2026 May"
},
"Tencent FLUX.1 Dev SRPO": {
"path": "vladmandic/flux.1-dev-SRPO",
"preview": "vladmandic--flux.1-dev-SRPO.jpg",
"desc": "FLUX.1 Dev SRPO is Tencent trained with specific technique: Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference",
"extras": "sampler: Default, cfg_scale: 4.5",
"size": 31.42,
"date": "2025 September"
},
"HiDream-O1 Image Dev": {
"path": "HiDream-ai/HiDream-O1-Image-Dev",
"preview": "HiDream-ai--HiDream-O1-Image-Dev.jpg",
"desc": "HiDream-O1-Image-Dev is the distilled 8B HiDream-O1 variant tuned for 28-step fast generation using flash flow scheduling.",
"extras": "sampler: Flash, steps: 28, cfg_scale: 0.0",
"size": 35.2,
"date": "2026 May"
"NVLabs Sana 1.5 1.6B 1k Sprint": {
"path": "Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers",
"desc": "SANA-Sprint is an ultra-efficient diffusion model for text-to-image (T2I) generation, reducing inference steps from 20 to 1-4 while achieving state-of-the-art performance.",
"preview": "Efficient-Large-Model--Sana15_Sprint_1600M_1024px_diffusers.jpg",
"size": 9.03,
"date": "2025 March"
},
"Qwen-Image-Lightning": {
"path": "vladmandic/Qwen-Lightning",
@@ -78,13 +73,20 @@
"size": 56.1,
"date": "2025 August"
},
"Baidu ERNIE-Image-Turbo": {
"path": "baidu/ERNIE-Image-Turbo",
"preview": "baidu--ERNIE-Image-Turbo.jpg",
"desc": "ERNIE-Image-Turbo is a distilled ERNIE-Image variant optimized for fast generation with fewer denoising steps.",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 8",
"size": 22.29,
"date": "2026 April"
"lodestones Chroma1 Flash": {
"path": "lodestones/Chroma1-Flash",
"preview": "lodestones--Chroma1-Flash.jpg",
"desc": "Chroma is a 8.9B parameter model based on FLUX.1-schnell. It’s fully Apache 2.0 licensed, ensuring that anyone can use, modify, and build on top of it—no corporate gatekeeping. A fine-tuned version of the Chroma1-Base made to find the best way to make these flow matching models faster.",
"size": 25.6,
"date": "2025 August"
},
"Tencent FLUX.1 Dev SRPO": {
"path": "vladmandic/flux.1-dev-SRPO",
"preview": "vladmandic--flux.1-dev-SRPO.jpg",
"desc": "FLUX.1 Dev SRPO is Tencent trained with specific technique: Directly Aligning the Full Diffusion Trajectory with Fine-Grained Human Preference",
"extras": "sampler: Default, cfg_scale: 4.5",
"size": 31.42,
"date": "2025 September"
},
"Qwen-Image-Lightning-Edit": {
"path": "vladmandic/Qwen-Lightning-Edit",
@@ -110,6 +112,13 @@
"date": "2025 September",
"size": 41.08
},
"Tencent HunyuanImage 2.1 Distilled": {
"path": "hunyuanvideo-community/HunyuanImage-2.1-Distilled-Diffusers",
"desc": "HunyuanImage-2.1, a highly efficient text-to-image model that is capable of generating 2K (2048 × 2048) resolution images.",
"preview": "hunyuanvideo-community--HunyuanImage-2.1-Distilled-Diffusers.jpg",
"size": 49.53,
"date": "2025 September"
},
"Qwen-Image-Edit-2509 Pruning-13B": {
"path": "OPPOer/Qwen-Image-Edit-2509-Pruning",
"subfolder": "Qwen-Image-Edit-2509-13B-4steps",
@@ -118,51 +127,6 @@
"date": "2025 October",
"size": 42.34
},
"lodestones Chroma1 Flash": {
"path": "lodestones/Chroma1-Flash",
"preview": "lodestones--Chroma1-Flash.jpg",
"desc": "Chroma is a 8.9B parameter model based on FLUX.1-schnell. It’s fully Apache 2.0 licensed, ensuring that anyone can use, modify, and build on top of it—no corporate gatekeeping. A fine-tuned version of the Chroma1-Base made to find the best way to make these flow matching models faster.",
"size": 25.6,
"date": "2025 August"
},
"SDXL Flash Mini": {
"path": "SDXL-Flash_Mini.safetensors@https://huggingface.co/sd-community/sdxl-flash-mini/resolve/main/SDXL-Flash_Mini.safetensors?download=true",
"preview": "SDXL-Flash_Mini.jpg",
"desc": "Introducing the new fast model SDXL Flash (Mini), we learned that all fast XL models work fast, but the quality decreases, and we also made a fast model, but it is not as fast as LCM, Turbo, Lightning and Hyper, but the quality is higher.",
"extras": "sampler: DEIS, steps: 40, cfg_scale: 6.0",
"experimental": true
},
"NVLabs Sana 1.5 1.6B 1k Sprint": {
"path": "Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers",
"desc": "SANA-Sprint is an ultra-efficient diffusion model for text-to-image (T2I) generation, reducing inference steps from 20 to 1-4 while achieving state-of-the-art performance.",
"preview": "Efficient-Large-Model--Sana15_Sprint_1600M_1024px_diffusers.jpg",
"size": 9.03,
"date": "2025 March"
},
"Segmind SSD-1B": {
"path": "huggingface/segmind/SSD-1B",
"preview": "segmind--SSD-1B.jpg",
"desc": "The Segmind Stable Diffusion Model (SSD-1B) offers a compact, efficient, and distilled version of the SDXL model. At 50% smaller and 60% faster than Stable Diffusion XL (SDXL), it provides quick and seamless performance without sacrificing image quality.",
"variant": "fp16",
"extras": "sampler: Default, cfg_scale: 9.0",
"size": 12.48,
"date": "2023 October"
},
"Segmind Tiny": {
"path": "segmind/tiny-sd",
"preview": "segmind--tiny-sd.jpg",
"desc": "Segmind's Tiny-SD offers a compact, efficient, and distilled version of Realistic Vision 4.0 and is up to 80% faster than SD1.5",
"extras": "width: 512, height: 512, sampler: Default, cfg_scale: 9.0",
"size": 0.99,
"date": "2023 July"
},
"Tencent HunyuanImage 2.1 Distilled": {
"path": "hunyuanvideo-community/HunyuanImage-2.1-Distilled-Diffusers",
"desc": "HunyuanImage-2.1, a highly efficient text-to-image model that is capable of generating 2K (2048 × 2048) resolution images.",
"preview": "hunyuanvideo-community--HunyuanImage-2.1-Distilled-Diffusers.jpg",
"size": 49.53,
"date": "2025 September"
},
"Bria Fibo-Lite": {
"path": "briaai/Fibo-lite",
"preview": "briaai--Fibo-lite.jpg",
@@ -171,22 +135,6 @@
"size": 22.47,
"date": "2025 November"
},
"Tencent HunyuanDiT 1.2 Distilled": {
"path": "Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers-Distilled",
"desc": "Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding.",
"preview": "Tencent-Hunyuan--HunyuanDiT-v1.2-Diffusers-Distilled.jpg",
"extras": "sampler: Default, cfg_scale: 2.0",
"size": 13.43,
"date": "2024 July"
},
"Tencent HunyuanDiT 1.1 Distilled": {
"path": "Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled",
"desc": "Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding.",
"preview": "Tencent-Hunyuan--HunyuanDiT-v1.1-Diffusers-Distilled.jpg",
"extras": "sampler: Default, cfg_scale: 2.0",
"size": 13.49,
"date": "2024 June"
},
"Black Forest Labs FLUX.2 Klein 4B": {
"path": "black-forest-labs/FLUX.2-klein-4B",
"preview": "black-forest-labs--FLUX.2-klein-4B.jpg",
@@ -203,6 +151,13 @@
"size": 32.32,
"date": "2026 January"
},
"Meituan LongCat Image-Edit Turbo": {
"path": "meituan-longcat/LongCat-Image-Edit-Turbo",
"preview": "meituan-longcat--LongCat-Image-Edit.jpg",
"desc": "LongCat-Image-Edit-Turbo, the distilled version of LongCat-Image-Edit. It achieves high-quality image editing with only 8 NFEs (Number of Function Evaluations) , offering extremely low inference latency.",
"size": 27.28,
"date": "2026 February"
},
"Black Forest Labs FLUX.2 Klein 9B KV": {
"path": "black-forest-labs/FLUX.2-klein-9b-kv",
"preview": "black-forest-labs--FLUX.2-klein-9b-kv.jpg",
@@ -211,6 +166,51 @@
"size": 32.32,
"date": "2026 March"
},
"Baidu ERNIE-Image-Turbo": {
"path": "baidu/ERNIE-Image-Turbo",
"preview": "baidu--ERNIE-Image-Turbo.jpg",
"desc": "ERNIE-Image-Turbo is a distilled ERNIE-Image variant optimized for fast generation with fewer denoising steps.",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 8",
"size": 22.29,
"date": "2026 April"
},
"Microsoft Lens Turbo": {
"path": "Jinstudio/Lens-Turbo",
"preview": "microsoft--Lens-Turbo.jpg",
"desc": "Microsoft Lens-Turbo is the distilled Lens variant optimized for faster text-to-image generation with fewer steps.",
"size": 28.43,
"date": "2026 May"
},
"HiDream-O1 Image Dev": {
"path": "HiDream-ai/HiDream-O1-Image-Dev",
"preview": "HiDream-ai--HiDream-O1-Image-Dev.jpg",
"desc": "HiDream-O1-Image-Dev is the distilled 8B HiDream-O1 variant tuned for 28-step fast generation using flash flow scheduling.",
"extras": "sampler: Flash, steps: 28, cfg_scale: 0.0",
"size": 35.2,
"date": "2026 May"
},
"Krea 2 Turbo": {
"path": "CalamitousFelicitousness/Krea-2-Turbo-Diffusers",
"preview": "CalamitousFelicitousness--Krea-2-Turbo-Diffusers.jpg",
"desc": "Krea 2 (K2) Turbo is the 8-step distilled inference model of the Krea 2 family, trained from scratch by Krea. A 12.9B-parameter single-stream flow-matching DiT that uses a Qwen3-VL-4B vision-language model as its text encoder and the Qwen-Image VAE. Runs without classifier-free guidance; LoRAs trained on Krea 2 Base apply directly.",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 8",
"size": 33.5,
"date": "2026 June"
},
"Boogu Image 0.1 Turbo": {
"path": "Boogu/Boogu-Image-0.1-Turbo",
"preview": "Boogu--Boogu-Image-0.1-Turbo.jpg",
"desc": "Boogu Image 0.1 Turbo is the distilled fast inference variant of Boogu Image with the same Qwen3-VL instruction encoder and Boogu transformer architecture.",
"size": 35.81,
"date": "2026 June"
},
"Boogu Image 0.1 Edit Turbo": {
"path": "Boogu/Boogu-Image-0.1-Edit-Turbo",
"preview": "Boogu--Boogu-Image-0.1-Edit-Turbo.jpg",
"desc": "Boogu Image 0.1 Edit Turbo is the distilled editing variant of Boogu Image with motion-aware instruction encoding and fast flow-match inference.",
"size": 35.81,
"date": "2026 June"
},
"Anima 1.0 Turbo": {
"path": "CalamitousFelicitousness/Anima-1.0-Turbo-Diffusers",
"preview": "CalamitousFelicitousness--Anima-1.0-Turbo-Diffusers.jpg",
@@ -219,13 +219,6 @@
"date": "2026 July",
"size": 4.99
},
"Meituan LongCat Image-Edit Turbo": {
"path": "meituan-longcat/LongCat-Image-Edit-Turbo",
"preview": "meituan-longcat--LongCat-Image-Edit.jpg",
"desc": "LongCat-Image-Edit-Turbo, the distilled version of LongCat-Image-Edit. It achieves high-quality image editing with only 8 NFEs (Number of Function Evaluations) , offering extremely low inference latency.",
"size": 27.28,
"date": "2026 February"
},
"Microsoft Mage-Flow Turbo": {
"path": "vladmandic/Mage-Flow-4B-Turbo",
"preview": "vladmandic--Mage-Flow-Turbo-4B.jpg",
@@ -257,5 +250,19 @@
"extras": "sampler: Default",
"size": 17.69,
"date": "2026 July"
},
"inclusionAI LLaDA-Image Turbo": {
"path": "inclusionAI/LLaDA-Image-Turbo",
"desc": "LLaDA-Image-Turbo is the distilled fast-generation and editing variant of LLaDA-Image.",
"extras": "steps: 4, cfg_scale: 1.0",
"size": 37.15,
"date": "2026 September"
},
"SDXL Flash Mini": {
"path": "SDXL-Flash_Mini.safetensors@https://huggingface.co/sd-community/sdxl-flash-mini/resolve/main/SDXL-Flash_Mini.safetensors?download=true",
"preview": "SDXL-Flash_Mini.jpg",
"desc": "Introducing the new fast model SDXL Flash (Mini), we learned that all fast XL models work fast, but the quality decreases, and we also made a fast model, but it is not as fast as LCM, Turbo, Lightning and Hyper, but the quality is higher.",
"extras": "sampler: DEIS, steps: 40, cfg_scale: 6.0",
"experimental": true
}
}
data/reference-nunchaku.json
+58 -58
View File
@@ -1,4 +1,27 @@
{
"SDXL Base Nunchaku SVDQuant": {
"path": "stabilityai/stable-diffusion-xl-base-1.0",
"subfolder": "nunchaku",
"preview": "stabilityai--stable-diffusion-xl-base-1.0.jpg",
"desc": "Nunchaku SVDQuant quantization of SDXL Base 1.0 UNet with INT4 and SVD rank 32",
"nunchaku": [
"Model"
],
"size": 32.0,
"date": "2023 July"
},
"SDXL Turbo Nunchaku SVDQuant": {
"path": "stabilityai/sdxl-turbo",
"subfolder": "nunchaku",
"preview": "stabilityai--sdxl-turbo.jpg",
"desc": "Nunchaku SVDQuant quantization of SDXL Turbo UNet with INT4 and SVD rank 32",
"nunchaku": [
"Model"
],
"extras": "sampler: Default, cfg_scale: 1.0, steps: 4",
"size": 19.38,
"date": "2023 November"
},
"FLUX.1-Dev Nunchaku SVDQuant": {
"path": "black-forest-labs/FLUX.1-dev",
"subfolder": "nunchaku",
@@ -24,30 +47,6 @@
"size": 31.41,
"date": "2024 July"
},
"FLUX.1-Kontext Nunchaku SVDQuant": {
"path": "black-forest-labs/FLUX.1-Kontext-dev",
"subfolder": "nunchaku",
"preview": "black-forest-labs--FLUX.1-Kontext-dev.jpg",
"desc": "Nunchaku SVDQuant quantization of FLUX.1-Kontext-dev transformer with INT4 and SVD rank 32",
"nunchaku": [
"Model",
"TE"
],
"size": 31.42,
"date": "2025 May"
},
"FLUX.1-Krea Nunchaku SVDQuant": {
"path": "black-forest-labs/FLUX.1-Krea-dev",
"subfolder": "nunchaku",
"preview": "black-forest-labs--FLUX.1-Krea-dev.jpg",
"desc": "Nunchaku SVDQuant quantization of FLUX.1-Krea-dev transformer with INT4 and SVD rank 32",
"nunchaku": [
"Model",
"TE"
],
"size": 31.42,
"date": "2025 July"
},
"FLUX.1-Fill Nunchaku SVDQuant": {
"path": "black-forest-labs/FLUX.1-Fill-dev",
"subfolder": "nunchaku",
@@ -74,6 +73,17 @@
"size": 40.68,
"date": "2024 November"
},
"Sana 1.6B 1k Nunchaku SVDQuant": {
"path": "Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers",
"subfolder": "nunchaku",
"preview": "Efficient-Large-Model--Sana_1600M_1024px_diffusers.jpg",
"desc": "Nunchaku SVDQuant quantization of Sana 1.6B 1024px transformer with INT4 and SVD rank 32",
"nunchaku": [
"Model"
],
"size": 22.22,
"date": "2024 December"
},
"Shuttle Jaguar Nunchaku SVDQuant": {
"path": "shuttleai/shuttle-jaguar",
"subfolder": "nunchaku",
@@ -86,6 +96,30 @@
"size": 31.41,
"date": "2025 January"
},
"FLUX.1-Kontext Nunchaku SVDQuant": {
"path": "black-forest-labs/FLUX.1-Kontext-dev",
"subfolder": "nunchaku",
"preview": "black-forest-labs--FLUX.1-Kontext-dev.jpg",
"desc": "Nunchaku SVDQuant quantization of FLUX.1-Kontext-dev transformer with INT4 and SVD rank 32",
"nunchaku": [
"Model",
"TE"
],
"size": 31.42,
"date": "2025 May"
},
"FLUX.1-Krea Nunchaku SVDQuant": {
"path": "black-forest-labs/FLUX.1-Krea-dev",
"subfolder": "nunchaku",
"preview": "black-forest-labs--FLUX.1-Krea-dev.jpg",
"desc": "Nunchaku SVDQuant quantization of FLUX.1-Krea-dev transformer with INT4 and SVD rank 32",
"nunchaku": [
"Model",
"TE"
],
"size": 31.42,
"date": "2025 July"
},
"Qwen-Image Nunchaku SVDQuant": {
"path": "Qwen/Qwen-Image",
"subfolder": "nunchaku",
@@ -167,17 +201,6 @@
"size": 53.74,
"date": "2025 September"
},
"Sana 1.6B 1k Nunchaku SVDQuant": {
"path": "Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers",
"subfolder": "nunchaku",
"preview": "Efficient-Large-Model--Sana_1600M_1024px_diffusers.jpg",
"desc": "Nunchaku SVDQuant quantization of Sana 1.6B 1024px transformer with INT4 and SVD rank 32",
"nunchaku": [
"Model"
],
"size": 22.22,
"date": "2024 December"
},
"Z-Image-Turbo Nunchaku SVDQuant": {
"path": "Tongyi-MAI/Z-Image-Turbo",
"subfolder": "nunchaku",
@@ -190,29 +213,6 @@
"size": 30.58,
"date": "2025 November"
},
"SDXL Base Nunchaku SVDQuant": {
"path": "stabilityai/stable-diffusion-xl-base-1.0",
"subfolder": "nunchaku",
"preview": "stabilityai--stable-diffusion-xl-base-1.0.jpg",
"desc": "Nunchaku SVDQuant quantization of SDXL Base 1.0 UNet with INT4 and SVD rank 32",
"nunchaku": [
"Model"
],
"size": 32.0,
"date": "2023 July"
},
"SDXL Turbo Nunchaku SVDQuant": {
"path": "stabilityai/sdxl-turbo",
"subfolder": "nunchaku",
"preview": "stabilityai--sdxl-turbo.jpg",
"desc": "Nunchaku SVDQuant quantization of SDXL Turbo UNet with INT4 and SVD rank 32",
"nunchaku": [
"Model"
],
"extras": "sampler: Default, cfg_scale: 1.0, steps: 4",
"size": 19.38,
"date": "2023 November"
},
"Z-Image-Turbo Nunchaku-Lite": {
"path": "lite-infer/z-image-turbo-nunchaku-lite-int4_r32-bnb4-text-encoder",
"preview": "Tongyi-MAI--Z-Image-Turbo.jpg",
data/reference-quantized.json
+101 -101
View File
@@ -27,37 +27,6 @@
"size": 12.6,
"date": "2025 October"
},
"FLUX.2 Dev sdnq-svd-uint4": {
"path": "Disty0/FLUX.2-dev-SDNQ-uint4-svd-r32",
"preview": "Disty0--FLUX.2-dev-SDNQ-uint4-svd-r32.jpg",
"desc": "Quantization of black-forest-labs/FLUX.2-dev using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
"size": 31.89,
"date": "2025 November"
},
"Black Forest Labs FLUX.2 Klein 4B sdnq-uint4-dynamic": {
"path": "Disty0/FLUX.2-klein-4B-SDNQ-4bit-dynamic",
"preview": "Disty0--FLUX.2-klein-4B-SDNQ-4bit-dynamic.jpg",
"desc": "Dynamic 4-bit quantization of black-forest-labs/FLUX.2-klein-4B using SDNQ.",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 4",
"size": 5.09,
"date": "2026 January"
},
"Black Forest Labs FLUX.2 Klein 9B sdnq-uint4-dynamic-svd": {
"path": "Disty0/FLUX.2-klein-9B-SDNQ-4bit-dynamic-svd-r32",
"preview": "Disty0--FLUX.2-klein-9B-SDNQ-4bit-dynamic-svd-r32.jpg",
"desc": "Dynamic 4-bit quantization of black-forest-labs/FLUX.2-klein-9B using SDNQ with SVD rank 32.",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 4",
"size": 11.73,
"date": "2026 January"
},
"Black Forest Labs FLUX.2 Klein 9B KV sdnq-uint4-dynamic-svd": {
"path": "vladmandic/Flux.2-Klein-9B-KV-sdnq-hadamard-uint4",
"preview": "black-forest-labs--FLUX.2-klein-9b-kv.jpg",
"desc": "Dynamic 4-bit quantization of black-forest-labs/FLUX.2-klein-9B-KV using SDNQ with Hadamard.",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 4",
"size": 12.26,
"date": "2026 July"
},
"Chroma1-HD sdnq-svd-uint4": {
"path": "Disty0/Chroma1-HD-SDNQ-uint4-svd-r32",
"preview": "Disty0--Chroma1-HD-SDNQ-uint4-svd-r32.jpg",
@@ -79,48 +48,6 @@
"date": "2025 October",
"size": 23.53
},
"MiniMaxAI MiniMax-H3 sdnq-uint4": {
"path": "OzzyGT/MiniMax_H3_sdnq_dynamic_4bit",
"preview": "MiniMaxAI--MiniMax-H3.jpg",
"desc": "Quantization of MiniMaxAI/MiniMax-H3 using SDNQ: dynamic 4-bit uint. Video with synchronized audio; in image tabs the model runs in experimental still mode.",
"extras": "sampler: Default",
"size": 64.80,
"date": "2026 August"
},
"MiniMaxAI MiniMax-H3 sdnq-uint4 Ref2VA": {
"path": "OzzyGT/MiniMax_H3_sdnq_dynamic_4bit",
"preview": "MiniMaxAI--MiniMax-H3.jpg",
"subfolder": "ref2va",
"desc": "Quantization of MiniMaxAI/MiniMax-H3 using SDNQ: dynamic 4-bit uint. Video with synchronized audio; in image tabs the model runs in experimental still mode.",
"extras": "sampler: Default",
"size": 64.80,
"date": "2026 August"
},
"MiniMaxAI MiniMax-H3 Pruned sdnq-uint4": {
"path": "OzzyGT/MiniMax_H3_sdnq_4bit_pruned",
"preview": "OzzyGT--MiniMax_H3_sdnq_4bit_pruned.jpg",
"desc": "Quantization of MiniMaxAI/MiniMax-H3 using SDNQ: dynamic 4-bit uint. Video with synchronized audio; in image tabs the model runs in experimental still mode.",
"extras": "sampler: Default",
"size": 23.70,
"date": "2026 August"
},
"MiniMaxAI MiniMax-H3 Pruned sdnq-uint4 Ref2VA": {
"path": "OzzyGT/MiniMax_H3_sdnq_4bit_pruned",
"preview": "OzzyGT--MiniMax_H3_sdnq_4bit_pruned.jpg",
"subfolder": "ref2va",
"desc": "Quantization of MiniMaxAI/MiniMax-H3 using SDNQ: dynamic 4-bit uint. Video with synchronized audio; in image tabs the model runs in experimental still mode.",
"extras": "sampler: Default",
"size": 23.70,
"date": "2026 August"
},
"Z-Image-Turbo sdnq-svd-uint4": {
"path": "Disty0/Z-Image-Turbo-SDNQ-uint4-svd-r32",
"preview": "Disty0--Z-Image-Turbo-SDNQ-uint4-svd-r32.jpg",
"desc": "Quantization of Tongyi-MAI/Z-Image-Turbo using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 9",
"size": 6.05,
"date": "2025 November"
},
"Qwen-Image sdnq-svd-uint4": {
"path": "Disty0/Qwen-Image-SDNQ-uint4-svd-r32",
"preview": "Qwen--Qwen-Image.jpg",
@@ -128,13 +55,6 @@
"date": "2025 October",
"size": 16.09
},
"Qwen-Image-2512 sdnq-svd-uint4": {
"path": "Disty0/Qwen-Image-2512-SDNQ-uint4-svd-r32",
"preview": "Disty0--Qwen-Image-2512-SDNQ-uint4-svd-r32.jpg",
"desc": "Quantization of Qwen/Qwen-Image-2512 using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
"size": 16.09,
"date": "2026 January"
},
"Qwen-Image-Edit sdnq-svd-uint4": {
"path": "Disty0/Qwen-Image-Edit-SDNQ-uint4-svd-r32",
"preview": "Qwen--Qwen-Image-Edit.jpg",
@@ -149,20 +69,6 @@
"date": "2025 October",
"size": 16.09
},
"Qwen-Image-Edit-2511 sdnq-svd-uint4": {
"path": "Disty0/Qwen-Image-Edit-2511-SDNQ-uint4-svd-r32",
"preview": "Disty0--Qwen-Image-Edit-2511-SDNQ-uint4-svd-r32.jpg",
"desc": "Quantization of Qwen/Qwen-Image-Edit-2511 using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
"date": "2025 December",
"size": 16.09
},
"Qwen-Image-Layered sdnq-svd-uint4": {
"path": "Disty0/Qwen-Image-Layered-SDNQ-uint4-svd-r32",
"preview": "Disty0--Qwen-Image-Layered-SDNQ-uint4-svd-r32.jpg",
"desc": "Quantization of Qwen/Qwen-Image-Layered using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
"date": "2025 December",
"size": 16.09
},
"nVidia ChronoEdit sdnq-svd-uint4": {
"path": "Disty0/ChronoEdit-14B-SDNQ-uint4-svd-r32",
"preview": "Disty0--ChronoEdit-14B-SDNQ-uint4-svd-r32.jpg",
@@ -198,6 +104,58 @@
"size": 3.37,
"date": "2025 October"
},
"FLUX.2 Dev sdnq-svd-uint4": {
"path": "Disty0/FLUX.2-dev-SDNQ-uint4-svd-r32",
"preview": "Disty0--FLUX.2-dev-SDNQ-uint4-svd-r32.jpg",
"desc": "Quantization of black-forest-labs/FLUX.2-dev using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
"size": 31.89,
"date": "2025 November"
},
"Z-Image-Turbo sdnq-svd-uint4": {
"path": "Disty0/Z-Image-Turbo-SDNQ-uint4-svd-r32",
"preview": "Disty0--Z-Image-Turbo-SDNQ-uint4-svd-r32.jpg",
"desc": "Quantization of Tongyi-MAI/Z-Image-Turbo using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 9",
"size": 6.05,
"date": "2025 November"
},
"Qwen-Image-Edit-2511 sdnq-svd-uint4": {
"path": "Disty0/Qwen-Image-Edit-2511-SDNQ-uint4-svd-r32",
"preview": "Disty0--Qwen-Image-Edit-2511-SDNQ-uint4-svd-r32.jpg",
"desc": "Quantization of Qwen/Qwen-Image-Edit-2511 using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
"date": "2025 December",
"size": 16.09
},
"Qwen-Image-Layered sdnq-svd-uint4": {
"path": "Disty0/Qwen-Image-Layered-SDNQ-uint4-svd-r32",
"preview": "Disty0--Qwen-Image-Layered-SDNQ-uint4-svd-r32.jpg",
"desc": "Quantization of Qwen/Qwen-Image-Layered using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
"date": "2025 December",
"size": 16.09
},
"Black Forest Labs FLUX.2 Klein 4B sdnq-uint4-dynamic": {
"path": "Disty0/FLUX.2-klein-4B-SDNQ-4bit-dynamic",
"preview": "Disty0--FLUX.2-klein-4B-SDNQ-4bit-dynamic.jpg",
"desc": "Dynamic 4-bit quantization of black-forest-labs/FLUX.2-klein-4B using SDNQ.",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 4",
"size": 5.09,
"date": "2026 January"
},
"Black Forest Labs FLUX.2 Klein 9B sdnq-uint4-dynamic-svd": {
"path": "Disty0/FLUX.2-klein-9B-SDNQ-4bit-dynamic-svd-r32",
"preview": "Disty0--FLUX.2-klein-9B-SDNQ-4bit-dynamic-svd-r32.jpg",
"desc": "Dynamic 4-bit quantization of black-forest-labs/FLUX.2-klein-9B using SDNQ with SVD rank 32.",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 4",
"size": 11.73,
"date": "2026 January"
},
"Qwen-Image-2512 sdnq-svd-uint4": {
"path": "Disty0/Qwen-Image-2512-SDNQ-uint4-svd-r32",
"preview": "Disty0--Qwen-Image-2512-SDNQ-uint4-svd-r32.jpg",
"desc": "Quantization of Qwen/Qwen-Image-2512 using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
"size": 16.09,
"date": "2026 January"
},
"ZAI GLM-Image sdnq-dynamic-uint4": {
"path": "Disty0/GLM-Image-SDNQ-4bit-dynamic",
"preview": "zai-org--GLM-Image.jpg",
@@ -260,6 +218,22 @@
"size": 17.3,
"date": "2026 June"
},
"Krea 2 Base sdnq-hadamard-uint4": {
"path": "vladmandic/Krea-2-Base-sdnq-hadamard-uint4",
"preview": "CalamitousFelicitousness--Krea-2-Base-Diffusers.jpg",
"desc": "Krea 2 (K2) Base is the undistilled foundation model of the Krea 2 family, trained from scratch by Krea. A 12.9B-parameter single-stream flow-matching DiT that uses a Qwen3-VL-4B vision-language model as its text encoder and the Qwen-Image VAE. The base checkpoint is intended for fine-tuning and LoRA training; LoRAs trained on it apply to Krea 2 Turbo.",
"extras": "sampler: Default, cfg_scale: 4.5, steps: 52",
"size": 10.3,
"date": "2026 June"
},
"Black Forest Labs FLUX.2 Klein 9B KV sdnq-uint4-dynamic-svd": {
"path": "vladmandic/Flux.2-Klein-9B-KV-sdnq-hadamard-uint4",
"preview": "black-forest-labs--FLUX.2-klein-9b-kv.jpg",
"desc": "Dynamic 4-bit quantization of black-forest-labs/FLUX.2-klein-9B-KV using SDNQ with Hadamard.",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 4",
"size": 12.26,
"date": "2026 July"
},
"Krea 2 Turbo sdnq-hadamard-uint4": {
"path": "vladmandic/Krea-2-Turbo-sdnq-hadamard-uint4",
"preview": "CalamitousFelicitousness--Krea-2-Turbo-Diffusers.jpg",
@@ -268,12 +242,38 @@
"size": 10.54,
"date": "2026 July"
},
"Krea 2 Base sdnq-hadamard-uint4": {
"path": "vladmandic/Krea-2-Base-sdnq-hadamard-uint4",
"preview": "CalamitousFelicitousness--Krea-2-Base-Diffusers.jpg",
"desc": "Krea 2 (K2) Base is the undistilled foundation model of the Krea 2 family, trained from scratch by Krea. A 12.9B-parameter single-stream flow-matching DiT that uses a Qwen3-VL-4B vision-language model as its text encoder and the Qwen-Image VAE. The base checkpoint is intended for fine-tuning and LoRA training; LoRAs trained on it apply to Krea 2 Turbo.",
"extras": "sampler: Default, cfg_scale: 4.5, steps: 52",
"size": 10.3,
"date": "2026 June"
"MiniMaxAI MiniMax-H3 sdnq-uint4": {
"path": "OzzyGT/MiniMax_H3_sdnq_dynamic_4bit",
"preview": "MiniMaxAI--MiniMax-H3.jpg",
"desc": "Quantization of MiniMaxAI/MiniMax-H3 using SDNQ: dynamic 4-bit uint. Video with synchronized audio; in image tabs the model runs in experimental still mode.",
"extras": "sampler: Default",
"size": 64.80,
"date": "2026 August"
},
"MiniMaxAI MiniMax-H3 sdnq-uint4 Ref2VA": {
"path": "OzzyGT/MiniMax_H3_sdnq_dynamic_4bit",
"preview": "MiniMaxAI--MiniMax-H3.jpg",
"subfolder": "ref2va",
"desc": "Quantization of MiniMaxAI/MiniMax-H3 using SDNQ: dynamic 4-bit uint. Video with synchronized audio; in image tabs the model runs in experimental still mode.",
"extras": "sampler: Default",
"size": 64.80,
"date": "2026 August"
},
"MiniMaxAI MiniMax-H3 Pruned sdnq-uint4": {
"path": "OzzyGT/MiniMax_H3_sdnq_4bit_pruned",
"preview": "OzzyGT--MiniMax_H3_sdnq_4bit_pruned.jpg",
"desc": "Quantization of MiniMaxAI/MiniMax-H3 using SDNQ: dynamic 4-bit uint. Video with synchronized audio; in image tabs the model runs in experimental still mode.",
"extras": "sampler: Default",
"size": 23.70,
"date": "2026 August"
},
"MiniMaxAI MiniMax-H3 Pruned sdnq-uint4 Ref2VA": {
"path": "OzzyGT/MiniMax_H3_sdnq_4bit_pruned",
"preview": "OzzyGT--MiniMax_H3_sdnq_4bit_pruned.jpg",
"subfolder": "ref2va",
"desc": "Quantization of MiniMaxAI/MiniMax-H3 using SDNQ: dynamic 4-bit uint. Video with synchronized audio; in image tabs the model runs in experimental still mode.",
"extras": "sampler: Default",
"size": 23.70,
"date": "2026 August"
}
}