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remove resolution from some reference models

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2026-07-28 14:18:55 +02:00
parent e280938e79
commit c6dc750a81
4 changed files with 6 additions and 6 deletions
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data/reference-base.json
+1 -1
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@@ -189,7 +189,7 @@
"path": "CalamitousFelicitousness/Krea-2-Base-Diffusers",
"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, width: 1024, height: 1024",
"extras": "sampler: Default, cfg_scale: 4.5, steps: 52",
"size": 33.5,
"date": "2026 June"
},
data/reference-distilled.json
+2 -2
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@@ -26,7 +26,7 @@
"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, width: 1024, height: 1024",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 8",
"size": 33.5,
"date": "2026 June"
},
@@ -129,7 +129,7 @@
"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": "width: 2048, height: 1024, sampler: DEIS, steps: 40, cfg_scale: 6.0",
"extras": "sampler: DEIS, steps: 40, cfg_scale: 6.0",
"experimental": true
},
"NVLabs Sana 1.5 1.6B 1k Sprint": {
data/reference-quantized.json
+2 -2
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@@ -230,7 +230,7 @@
"path": "vladmandic/Krea-2-Turbo-sdnq-hadamard-uint4",
"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, width: 1024, height: 1024",
"extras": "sampler: Default, cfg_scale: 1.0, steps: 8",
"size": 10.54,
"date": "2026 July"
},
@@ -238,7 +238,7 @@
"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, width: 1024, height: 1024",
"extras": "sampler: Default, cfg_scale: 4.5, steps: 52",
"size": 10.3,
"date": "2026 June"
}
modules/ui_definitions.py
+1 -1
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@@ -150,7 +150,7 @@ def create_settings(cmd_opts):
"caption_offload": OptionInfo(True, "Offload caption models"),
"caption_to_gpu": OptionInfo(True, "Load caption models direct to GPU"),
"offload_balanced_sep": OptionInfo("<h2>Balanced Offload</h2>", "", gr.HTML),
"diffusers_offload_pre": OptionInfo(True, "Offload during pre-forward"),
"diffusers_offload_pre": OptionInfo(True, "Offload during pre-forward", gr.Checkbox, {"visible": False}),
"diffusers_offload_streams": OptionInfo(False, "Offload using streams"),
"diffusers_offload_min_gpu_memory": OptionInfo(startup_offload_min_gpu, "Offload low watermark", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01 }),
"diffusers_offload_max_gpu_memory": OptionInfo(startup_offload_max_gpu, "Offload GPU high watermark", gr.Slider, {"minimum": 0.1, "maximum": 1, "step": 0.01 }),