ace-gguf
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bagel-gguf
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View details ↗cat-encoder
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View details ↗chatterbox-gguf
calcuis*note: for c3, you might need to downgrade `transformers` to 4.46.3 (c2 below compatible with the latest version); upgrade `chichat` to 0.0.4, and opt `t3_23lang` clip instead of `t3_cfg` > >GGUF file(s) available. Select which one for **ve**: > >1. s3gen-bf16.gguf >2. s3gen-f16.gguf >3. s3gen-f32.gguf >4. t3_cfg-q2_k.gguf >5. t3_cfg-q4_k_m.gguf >6. t3_cfg-q6_k.gguf >7. ve_fp32-f16.gguf (recommended) >8. ve_fp32-f32.gguf > >Enter your choice (1 to 8): 7 > >ve file: ve_fp32-f16.gguf is selected! > >GGUF file(s) available. Select which one for **t3**: > >1. s3gen-bf16.gguf >2. s3gen-f16.gguf >3. s3gen-f32.gguf >4. t3_cfg-q2_k.gguf >5. t3_cfg-q4_k_m.gguf (recommended) >6. t3_cfg-q6_k.gguf >7. ve_fp32-f16.gguf >8. ve_fp32-f32.gguf > >Enter your choice (1 to 8): 5 > >t3 file: t3_cfg-q4_k_m.gguf is selected! > >GGUF file(s) available. Select which one for **s3gen**: > >1. s3gen-bf16.gguf (recommended) >2. s3gen-f16.gguf (for non-cuda user) >3. s3gen-f32.gguf >4. t3_cfg-q2_k.gguf >5. t3_cfg-q4_k_m.gguf >6. t3_cfg-q6_k.gguf >7. ve_fp32-f16.gguf >8. ve_fp32-f32.gguf > >Enter your choice (1 to 8): _ >
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View details ↗cosmos-predict2-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 ↗cow-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 ↗deepseek-r1
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 ↗dia-gguf
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View details ↗flux1-gguf
calcuisget the comfy pack*: - decompress the 7z bundle file: Extract All... (it includes everything you need to run a model) - download flux1-dev-Q4_0.gguf (6.32GB); pull it to ./ComfyUI/models/unet - download clip_l.safetensors (246MB); pull it to ./ComfyUI/models/clip - download t5xxl_fp8_e4m3fn.safetensors (4.89GB); pull it to ./ComfyUI/models/clip - download ae.safetensors (319MB); pull it to ./ComfyUI/models/vae - run the .bat file in the main directory (it will activate the py backend as well as the js frontend) - drag gguf-workflow.json to the activated browser
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View details ↗gguf
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View details ↗gguf-node
calcuis- drag gguf file(s) to diffusion_models folder (`./ComfyUI/models/diffusion_models`) - drag clip or encoder(s) to text_encoders folder (`./ComfyUI/models/text_encoders`) - drag controlnet adapter(s), if any, to controlnet folder (`./ComfyUI/models/controlnet`) - drag lora adapter(s), if any, to loras folder (`./ComfyUI/models/loras`) - drag vae decoder(s) to vae folder (`./ComfyUI/models/vae`) - get the comfy pack with the new gguf-node (pack) - run the .bat file in the main directory - you could git clone the node to your `./ComfyUI/custom_nodes` (more details here) - either navigate to `./ComfyUI/custom_nodes` first or drag and drop the node clone (gguf repo) there
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View details ↗hidream-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 ↗higgs-gguf
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View details ↗humo-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 ↗hunyuan-gguf
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View details ↗hunyuanimage-gguf
calcuisfor standard model, all files should work - running them with gguf node via comfyui; and v2 is more lightweighted; both of them should be able to generate quality output with 12-15 steps note: for refiner model, please use **v2**; initial test for refining blur image (runnable test only); could load any picture, i.e., output from q2, blur, distorted, poor in quality, etc., to refine/sharpen it note: for distilled model, please use **v2**; able to generate output with merely 8 steps (see picture)
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View details ↗hy3d-gguf
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View details ↗hyvid
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 ↗hyvid-i2v-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 ↗illustrious
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 ↗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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