illustrious
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kontext-gguf
calcuis> >GGUF file(s) available. Select which one to use: > >1. flux-kontext-lite-q2_k.gguf >2. flux-kontext-lite-q4_0.gguf >3. flux-kontext-lite-q8_0.gguf > >Enter your choice (1 to 3): _ > note: try experimental lite model with 8-step operation; save up to 70% loading time not all included in the initial test (*tested with a beginner laptop gpu only, if you have highend model, might find q8_0 running surprisingly faster than others), the rest of them, test it yourself; btw, the interesting thing is: the loading time required was not aligning with file size, due to the complexity of each calculation (dequant), and might vary from models
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View details ↗krea-gguf
calcuis> >GGUF file(s) available. Select which one to use: > >1. flux-krea-lite-q2_k.gguf >2. flux-krea-lite-q4_0.gguf >3. flux-krea-lite-q8_0.gguf > >Enter your choice (1 to 3): _ > note: try experimental lite model with 8-step operation; save up to 70% loading time
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View details ↗llava-gguf
calcuis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 ↗ltxv-gguf
calcuis- run it with `gguf-connector`; simply execute the command below in console/terminal > >GGUF file(s) available. Select which one to use: > >1. ltxv-2b-0.9.8-distilled-iq4_nl.gguf >2. ltxv-2b-0.9.8-distilled-q6_k.gguf >4. ltxv-2b-0.9.8-distilled-q8_0.gguf > >Enter your choice (1 to 3): _ > - opt a `gguf` file in your current directory to interact with; nothing else note: for the latest update, should be able to adjust number of steps, i.e., for distilled model, use 15 instead of 30; save a lot of loading time
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View details ↗ltxv0.9.5-gguf
calcuis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 ↗ltxv0.9.6-gguf
calcuis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 ↗ltxv0.9.7-gguf
calcuis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 ↗lumina-gguf
calcuis> >GGUF file(s) available. Select which one to use: > >1. lumina2-q2_k.gguf >2. lumina2-q4_0.gguf >3. lumina2-q8_0.gguf > >Enter your choice (1 to 3): _ > - opt a `gguf` file in your current directory to interact with; nothing else - you will get the image output in few seconds even with the beginner level gpu
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View details ↗mochi
calcuis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 ↗mochi-gguf
calcuis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 ↗omnigen2-gguf
calcuis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 ↗openaudio-gguf
calcuis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 ↗phi4
calcuis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 ↗pig
calcuis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 ↗pig-encoder
calcuis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 ↗pig-vae
calcuis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 ↗pixart
calcuis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 ↗pony
calcuis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-gguf
calcuis> >GGUF file(s) available. Select which one to use: > >1. qwen-image-edit-iq4_nl.gguf >2. qwen-image-edit-q2_k.gguf >3. qwen-image-edit-q4_0.gguf >4. qwen-image-edit-q8_0.gguf > >Enter your choice (1 to 4): _ > - opt a `gguf` file in your current directory to interact with; nothing else *note: option 1 (pig quant) is an all-in-one choice; for option 2 (llama.cpp quant), you need to prepare both text-model and mmproj-clip; option 3 (llama.cpp quant) is an experimental attempt, a merge (text+mmproj), similar to option 1, an all-in-one choice also but pig x llama.cpp crossover
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View details ↗qwen-image-edit-plus-gguf
calcuis> >GGUF file(s) available. Select which one to use: > >1. qwen-image-edit-plus-v2-iq3_s.gguf >2. qwen-image-edit-plus-v2-iq4_nl.gguf >3. qwen-image-edit-plus-v2-mxfp4_moe.gguf > >Enter your choice (1 to 3): _ > - opt a `gguf` file in your current directory to interact with; nothing else > >Safetensors available. Select which one to use: > >1. qwen-image-edit-lite-blackwell-fp4.safetensors >2. qwen-image-edit-lite-int4.safetensors (for non-blackwell card) > >Enter your choice (1 to 2): _ - opt a `safetensors` file in your current directory to interact with; nothing else note: able to generate output with 4/8 steps (see above); surprisingly fast even with low end device; compatible with safetensors in nunchaku repo (depends on your machine; opt the right one)
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View details ↗qwen-image-gguf
calcuis> >GGUF file(s) available. Select which one to use: > >1. qwen-image-iq2_s.gguf >2. qwen-image-iq4_nl.gguf >3. qwen-image-q4_0.gguf >4. qwen-image-q8_0.gguf > >Enter your choice (1 to 4): _ > - drag **qwen-image** to > `./ComfyUI/models/diffusion_models` - drag **qwen2.5-vl-7b** [4.43GB] to > `./ComfyUI/models/text_encoders` - drag **pig** [254MB] to > `./ComfyUI/models/vae` tip: the text encoder used for this model is qwen2.5-vl-**7b**; get more encoder either here (pig quant) or here (llama.cpp quant); the size is different from the one (qwen2.5-vl-**3b**) used in **omnigen2** note: diffusers not yet supported t and i quants; opt gguf-node via comfyui or run it straight with gguf-connector
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View details ↗sd3.5-large-controlnet
calcuis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 ↗sd3.5-large-gguf
calcuis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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