mirror of
https://github.com/vladmandic/automatic
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CalamitousFelicitousness
6375b42ff7
Reference entries for the bf16 repo and the sdnq uint4 quant load the modular pipeline through the standard dispatch. Image tabs run the model in still mode with audio off; the video tab keeps its own overrides through the shared per-generation hook. Detailer is not supported and is disabled with a warning.
254 lines
13 KiB
JSON
254 lines
13 KiB
JSON
{
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"FLUX.1-Dev sdnq-svd-uint4": {
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"path": "Disty0/FLUX.1-dev-SDNQ-uint4-svd-r32",
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"preview": "Disty0--FLUX.1-dev-SDNQ-uint4-svd-r32.jpg",
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"desc": "Quantization of black-forest-labs/FLUX.1-dev using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
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"size": 12.6,
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"date": "2025 October"
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},
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"FLUX.1-Schnell sdnq-svd-uint4": {
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"path": "Disty0/FLUX.1-schnell-SDNQ-uint4-svd-r32",
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"preview": "Disty0--FLUX.1-schnell-SDNQ-uint4-svd-r32.jpg",
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"desc": "Quantization of black-forest-labs/FLUX.1-schnell using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
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"size": 12.58,
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"date": "2025 October"
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},
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"FLUX.1-Dev Krea sdnq-svd-uint4": {
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"path": "Disty0/FLUX.1-Krea-dev-SDNQ-uint4-svd-r32",
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"preview": "Disty0--FLUX.1-Krea-dev-SDNQ-uint4-svd-r32.jpg",
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"desc": "Quantization of black-forest-labs/FLUX.1-Krea-dev using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
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"size": 12.6,
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"date": "2025 October"
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},
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"FLUX.1-Dev Kontext sdnq-svd-uint4": {
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"path": "Disty0/FLUX.1-Kontext-dev-SDNQ-uint4-svd-r32",
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"preview": "Disty0--FLUX.1-Kontext-dev-SDNQ-uint4-svd-r32.jpg",
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"desc": "Quantization of black-forest-labs/FLUX.1-Kontext-dev using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
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"size": 12.6,
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"date": "2025 October"
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},
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"FLUX.2 Dev sdnq-svd-uint4": {
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"path": "Disty0/FLUX.2-dev-SDNQ-uint4-svd-r32",
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"preview": "Disty0--FLUX.2-dev-SDNQ-uint4-svd-r32.jpg",
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"desc": "Quantization of black-forest-labs/FLUX.2-dev using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
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"size": 31.89,
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"date": "2025 November"
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},
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"Black Forest Labs FLUX.2 Klein 4B sdnq-uint4-dynamic": {
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"path": "Disty0/FLUX.2-klein-4B-SDNQ-4bit-dynamic",
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"preview": "Disty0--FLUX.2-klein-4B-SDNQ-4bit-dynamic.jpg",
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"desc": "Dynamic 4-bit quantization of black-forest-labs/FLUX.2-klein-4B using SDNQ.",
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"extras": "sampler: Default, cfg_scale: 1.0, steps: 4",
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"size": 5.09,
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"date": "2026 January"
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},
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"Black Forest Labs FLUX.2 Klein 9B sdnq-uint4-dynamic-svd": {
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"path": "Disty0/FLUX.2-klein-9B-SDNQ-4bit-dynamic-svd-r32",
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"preview": "Disty0--FLUX.2-klein-9B-SDNQ-4bit-dynamic-svd-r32.jpg",
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"desc": "Dynamic 4-bit quantization of black-forest-labs/FLUX.2-klein-9B using SDNQ with SVD rank 32.",
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"extras": "sampler: Default, cfg_scale: 1.0, steps: 4",
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"size": 11.73,
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"date": "2026 January"
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},
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"Black Forest Labs FLUX.2 Klein 9B KV sdnq-uint4-dynamic-svd": {
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"path": "vladmandic/Flux.2-Klein-9B-KV-sdnq-hadamard-uint4",
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"preview": "black-forest-labs--FLUX.2-klein-9b-kv.jpg",
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"desc": "Dynamic 4-bit quantization of black-forest-labs/FLUX.2-klein-9B-KV using SDNQ with Hadamard.",
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"extras": "sampler: Default, cfg_scale: 1.0, steps: 4",
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"size": 12.26,
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"date": "2026 July"
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},
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"Chroma1-HD sdnq-svd-uint4": {
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"path": "Disty0/Chroma1-HD-SDNQ-uint4-svd-r32",
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"preview": "Disty0--Chroma1-HD-SDNQ-uint4-svd-r32.jpg",
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"desc": "Quantization of lodestones/Chroma1-HD using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
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"size": 11.08,
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"date": "2025 October"
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},
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"Wan-AI Wan2.2 A14B T2I sdnq-svd-uint4": {
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"path": "Disty0/Wan2.2-T2V-A14B-SDNQ-uint4-svd-r32",
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"preview": "Wan-AI--Wan2.2-T2V-A14B-Diffusers.jpg",
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"desc": "Quantization of black-forest-labs/FLUX.1-dev using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
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"date": "2025 October",
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"size": 23.53
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},
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"Wan-AI Wan2.2 A14B I2I sdnq-svd-uint4": {
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"path": "Disty0/Wan2.2-I2V-A14B-SDNQ-uint4-svd-r32",
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"preview": "Wan-AI--Wan2.2-T2V-A14B-Diffusers.jpg",
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"desc": "Quantization of Laxhar/noobai-XL-1.1 using SDNQ: sdnq-svd 4-bit uint with svd rank 128",
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"date": "2025 October",
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"size": 23.53
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},
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"MiniMaxAI MiniMax-H3 sdnq-uint4": {
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"path": "OzzyGT/MiniMax_H3_sdnq_dynamic_4bit",
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"preview": "MiniMaxAI--MiniMax-H3.jpg",
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"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.",
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"extras": "sampler: None",
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"size": 51,
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"date": "2026 August"
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},
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"Z-Image-Turbo sdnq-svd-uint4": {
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"path": "Disty0/Z-Image-Turbo-SDNQ-uint4-svd-r32",
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"preview": "Disty0--Z-Image-Turbo-SDNQ-uint4-svd-r32.jpg",
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"desc": "Quantization of Tongyi-MAI/Z-Image-Turbo using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
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"extras": "sampler: Default, cfg_scale: 1.0, steps: 9",
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"size": 6.05,
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"date": "2025 November"
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},
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"Qwen-Image sdnq-svd-uint4": {
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"path": "Disty0/Qwen-Image-SDNQ-uint4-svd-r32",
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"preview": "Qwen--Qwen-Image.jpg",
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"desc": "Quantization of Qwen/Qwen-Image using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
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"date": "2025 October",
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"size": 16.09
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},
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"Qwen-Image-2512 sdnq-svd-uint4": {
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"path": "Disty0/Qwen-Image-2512-SDNQ-uint4-svd-r32",
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"preview": "Disty0--Qwen-Image-2512-SDNQ-uint4-svd-r32.jpg",
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"desc": "Quantization of Qwen/Qwen-Image-2512 using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
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"size": 16.09,
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"date": "2026 January"
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},
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"Qwen-Image-Edit sdnq-svd-uint4": {
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"path": "Disty0/Qwen-Image-Edit-SDNQ-uint4-svd-r32",
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"preview": "Qwen--Qwen-Image-Edit.jpg",
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"desc": "Quantization of Qwen/Qwen-Image-Edit using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
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"date": "2025 October",
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"size": 16.09
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},
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"Qwen-Image-Edit-2509 sdnq-svd-uint4": {
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"path": "Disty0/Qwen-Image-Edit-2509-SDNQ-uint4-svd-r32",
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"preview": "Qwen--Qwen-Image-Edit-2509.jpg",
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"desc": "Quantization of Qwen/Qwen-Image-Edit-2509 using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
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"date": "2025 October",
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"size": 16.09
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},
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"Qwen-Image-Edit-2511 sdnq-svd-uint4": {
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"path": "Disty0/Qwen-Image-Edit-2511-SDNQ-uint4-svd-r32",
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"preview": "Disty0--Qwen-Image-Edit-2511-SDNQ-uint4-svd-r32.jpg",
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"desc": "Quantization of Qwen/Qwen-Image-Edit-2511 using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
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"date": "2025 December",
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"size": 16.09
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},
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"Qwen-Image-Layered sdnq-svd-uint4": {
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"path": "Disty0/Qwen-Image-Layered-SDNQ-uint4-svd-r32",
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"preview": "Disty0--Qwen-Image-Layered-SDNQ-uint4-svd-r32.jpg",
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"desc": "Quantization of Qwen/Qwen-Image-Layered using SDNQ: sdnq-svd 4-bit uint with svd rank 32",
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"date": "2025 December",
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"size": 16.09
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},
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"nVidia ChronoEdit sdnq-svd-uint4": {
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"path": "Disty0/ChronoEdit-14B-SDNQ-uint4-svd-r32",
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"preview": "Disty0--ChronoEdit-14B-SDNQ-uint4-svd-r32.jpg",
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"desc": "Quantization of nvidia/ChronoEdit-14B-Diffusers using SDNQ: sdnq-svd 4-bit uint with svd rank 32.",
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"date": "2025 October",
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"size": 16.9
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},
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"Tencent HunyuanImage 3.0 sdnq-svd-uint4": {
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"path": "Disty0/HunyuanImage3-SDNQ-uint4-svd-r32",
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"desc": "Quantization of tencent/HunyuanImage-3.0 using SDNQ: sdnq-svd 4-bit uint with svd rank 32.",
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"preview": "Disty0--HunyuanImage3-SDNQ-uint4-svd-r32.jpg",
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"size": 57.06,
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"date": "2025 October"
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},
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"Tempest-by-Vlad XL sdnq-svd-uint4": {
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"path": "vladmandic/tempestByVlad_baseV01-SDNQ-uint4-svd",
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"preview": "vladmandic--tempestByVlad_baseV01-SDNQ-uint4-svd.jpg",
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"desc": "Quantization of vladmandic/tempestByVlad_baseV01 using SDNQ: sdnq-svd 4-bit uint with svd rank 128",
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"size": 2.64,
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"date": "2025 October"
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},
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"NoobAI-XL v1.1 epsilon sdnq-svd-uint4": {
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"path": "Disty0/NoobAI-XL-v1.1-SDNQ-uint4-svd-r128",
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"preview": "Disty0--NoobAI-XL-v1.1-SDNQ-uint4-svd-r128.jpg",
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"desc": "Quantization of Laxhar/noobai-XL-1.1 using SDNQ: sdnq-svd 4-bit uint with svd rank 128",
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"size": 3.37,
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"date": "2025 October"
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},
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"NoobAI-XL v1.0 v-pred sdnq-svd-uint4": {
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"path": "Disty0/NoobAI-XL-Vpred-v1.0-SDNQ-uint4-svd-r128",
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"preview": "Disty0--NoobAI-XL-Vpred-v1.0-SDNQ-uint4-svd-r128.jpg",
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"desc": "Quantization of Laxhar/noobai-XL-Vpred-1.0 using SDNQ: sdnq-svd 4-bit uint with svd rank 128",
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"size": 3.37,
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"date": "2025 October"
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},
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"ZAI GLM-Image sdnq-dynamic-uint4": {
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"path": "Disty0/GLM-Image-SDNQ-4bit-dynamic",
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"preview": "zai-org--GLM-Image.jpg",
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"desc": "Quantization of ZAI GLM-Image using SDNQ: sdnq-dynamic 4-bit uint",
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"extras": "sampler: Default, cfg_scale: 1.5, steps: 50",
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"size": 5.19,
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"date": "2026 January"
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},
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"Baidu ERNIE-Image sdnq-dynamic-int4": {
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"path": "OzzyGT/ERNIE_Image_sdnq_dynamic_int4",
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"preview": "OzzyGT--ERNIE_Image_sdnq_dynamic_int4.jpg",
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"desc": "ERNIE-Image is a text-to-image diffusion transformer model that combines a Mistral3 text encoder with a FlowMatch transformer and Flux2-style VAE for 1024px image generation.",
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"extras": "sampler: Default, cfg_scale: 4.0, steps: 50",
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"size": 7.01,
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"date": "2026 April"
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},
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"Baidu ERNIE-Image-Turbo sdnq-dynamic-int4": {
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"path": "OzzyGT/ERNIE_Image_Turbo_sdnq_dynamic_int4",
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"preview": "OzzyGT--ERNIE_Image_Turbo_sdnq_dynamic_int4.jpg",
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"desc": "ERNIE-Image-Turbo is a distilled ERNIE-Image variant optimized for fast generation with fewer denoising steps.",
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"extras": "sampler: Default, cfg_scale: 1.0, steps: 8",
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"size": 7.01,
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"date": "2026 April"
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},
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"Anima 1.0 Base sdnq-svd-dynamic-uint4": {
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"path": "vladmandic/Anima-1.0-Base-sdnq-svd-dynamic-uint4",
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"preview": "vladmandic--Anima-1.0-Base.jpg",
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"desc": "Anima 1.0 Base with extended 1024-resolution training and expanded dataset coverage for less common artists. A 2B parameter anime-focused text-to-image model based on modified Cosmos-Predict-2B with Qwen3-0.6B text encoder, created by CircleStone Labs and Comfy Org.",
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"date": "2026 May",
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"size": 2.03
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},
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"Anima 1.0 Turbo sdnq-svd-dynamic-uint4": {
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"path": "vladmandic/Anima-1.0-Turbo-sdnq-svd-dynamic-uint4",
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"preview": "vladmandic--Anima-1.0-Turbo-sdnq-svd-dynamic-uint4.jpg",
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"desc": "Anima 1.0 Turbo with extended 1024-resolution training and expanded dataset coverage for less common artists. A 2B parameter anime-focused text-to-image model based on modified Cosmos-Predict-2B with Qwen3-0.6B text encoder, created by CircleStone Labs and Comfy Org.",
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"date": "2026 May",
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"size": 2.03
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},
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"HiDream-O1 Image sdnq-dynamic-int8": {
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"path": "vladmandic/HiDream-O1-Image-SDNQ-8bit-dynamic",
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"desc": "HiDream-O1-Image is an 8B pixel-level unified transformer model for text-to-image generation, instruction editing, and multi-reference personalization up to 2048x2048.",
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"preview": "HiDream-ai--HiDream-O1-Image.jpg",
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"extras": "sampler: Default",
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"size": 9.63,
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"date": "2026 May"
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},
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"HiDream-O1 Image Dev sdnq-dynamic-int8": {
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"path": "vladmandic/HiDream-O1-Image-Dev-SDNQ-8bit-dynamic",
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"desc": "HiDream-O1-Image is an 8B pixel-level unified transformer model for text-to-image generation, instruction editing, and multi-reference personalization up to 2048x2048.",
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"preview": "HiDream-ai--HiDream-O1-Image.jpg",
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"extras": "sampler: Default",
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"size": 9.63,
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"date": "2026 May"
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},
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"Ideogram 4 sdnq-hadamard-uint4": {
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"path": "Disty0/Ideogram-4-SDNQ-4bit-dynamic-hadamard",
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"desc": "Ideogram 4 is Ideogram's first open-weight text-to-image model: a two 9.3B flow-matching DiTs that uses a Qwen3-VL vision-language model as its text encoder, with strong in-image text rendering. Requires structured JSON-caption prompts; prompt-enhance (on by default) rewrites a plain prompt into one.",
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"preview": "Disty0--Ideogram-4-SDNQ-4bit-dynamic-hadamard.jpg",
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"extras": "sampler: Default, cfg_scale: 7.0, steps: 20, width: 1024, height: 1024",
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"size": 17.3,
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"date": "2026 June"
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},
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"Krea 2 Turbo sdnq-hadamard-uint4": {
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"path": "vladmandic/Krea-2-Turbo-sdnq-hadamard-uint4",
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"preview": "CalamitousFelicitousness--Krea-2-Turbo-Diffusers.jpg",
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"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.",
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"extras": "sampler: Default, cfg_scale: 1.0, steps: 8",
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"size": 10.54,
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"date": "2026 July"
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},
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"Krea 2 Base sdnq-hadamard-uint4": {
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"path": "vladmandic/Krea-2-Base-sdnq-hadamard-uint4",
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"preview": "CalamitousFelicitousness--Krea-2-Base-Diffusers.jpg",
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"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.",
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"extras": "sampler: Default, cfg_scale: 4.5, steps: 52",
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"size": 10.3,
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"date": "2026 June"
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}
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}
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