{"schema":"record/v1","id":"art_01M5NCC1BQFMN294EZRR8XBW8D","type":"article","revision":1,"etag":"\"r1.s0\"","title":"What does ComfyUI-GGUF let me do with quantised diffusion models (Flux, SD3.5), what limits does it state, and does a GGUF conversion change the licence?","summary":"Problem. A diffusion transformer does not fit in VRAM, or a team wants a smaller file to move between machines, and must know what the GGUF route changes. Conditions. city96/ComfyUI-GGUF at commit 6ea2651e (last commit 2026-01-12; code licence Apache-2.0 text). Targets transformer/DiT models (the README names Flux and SD3.5 Large quants) and a quantised T5 text encoder. Method. (1) Put .gguf files in ComfyUI/models/unet and replace the stock 'Load Diffusion Model' with the 'Unet Loader (GGUF)' node (category bootleg). (2) For the T5 encoder use the *CLIPLoader (gguf) nodes, which accept gguf and regular safetensors/bin. (3) Install the gguf Python package. (4) Keep ComfyUI recent enough for custom ops. (5) Treat LoRA use as experimental: the README says the built-in LoRA loaders 'should work'. What it changes. The README claims DiT models seem less affected by quantisation than conv2d UNets, which is why low-bit quants are viable on low-end GPUs; it gives no quality measurements, an...","status":"active","body":{"text":"Problem. A diffusion transformer does not fit in VRAM, or a team wants a smaller file to move between machines, and must know what the GGUF route changes. Conditions. city96/ComfyUI-GGUF at commit 6ea2651e (last commit 2026-01-12; code licence Apache-2.0 text). Targets transformer/DiT models (the README names Flux and SD3.5 Large quants) and a quantised T5 text encoder. Method. (1) Put .gguf files in ComfyUI/models/unet and replace the stock 'Load Diffusion Model' with the 'Unet Loader (GGUF)' node (category bootleg). (2) For the T5 encoder use the *CLIPLoader (gguf) nodes, which accept gguf and regular safetensors/bin. (3) Install the gguf Python package. (4) Keep ComfyUI recent enough for custom ops. (5) Treat LoRA use as experimental: the README says the built-in LoRA loaders 'should work'. What it changes. The README claims DiT models seem less affected by quantisation than conv2d UNets, which is why low-bit quants are viable on low-end GPUs; it gives no quality measurements, and the HF model card only points to an LLM (Llama-3-8B) perplexity chart as a 'basic overview' of quant types (inference: that chart does not measure image quality, so run your own A/B on your prompts). Licence passthrough. The flux1-dev GGUF card says it is a direct conversion and 'all the same restrictions/original license terms still apply'; its HF metadata tag is 'other' (flux-1-dev-non-commercial-license), while the schnell GGUF card is tagged apache-2.0. A quantised file therefore inherits the base model's licence; it is not a new work. Failure modes. Custom-op requirement on old ComfyUI; macOS note that torch 2.4.1 seemed required on Sequoia because 2.6 nightlies gave a buffer error; 'WIP' status in the README. Limitations. README claims, nothing was run here; no size or VRAM table is in the README. Version and date. ComfyUI-GGUF 6ea2651e; model cards revised 2024-08-18.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: No quality or VRAM numbers in the sources; licence reading is engineering triage, not legal advice; HF metadata tags read from the card header at the pinned revision only.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://raw.githubusercontent.com/city96/ComfyUI-GGUF/6ea2651e7df66d7585f6ffee804b20e92fb38b8a/README.md","note":"intro; Usage; Installation; model card body; model card header"},{"url":"https://huggingface.co/city96/FLUX.1-dev-gguf/raw/3c60ac659f4b1f2ab3ca8bd4488272069b36a148/README.md","note":"intro; Usage; Installation; model card body; model card header"},{"url":"https://huggingface.co/city96/FLUX.1-schnell-gguf/raw/f495746ed9c5efcf4661f53ef05401dceadc17d2/README.md","note":"intro; Usage; Installation; model card body; model card header"}]},"categories":["T05","T08","T09"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://raw.githubusercontent.com/city96/ComfyUI-GGUF/6ea2651e7df66d7585f6ffee804b20e92fb38b8a/README.md","claim":"Problem. A diffusion transformer does not fit in VRAM, or a team wants a smaller file to move between machines, and must know what the GGUF route changes. Conditions. city96/ComfyUI-GGUF at commit 6ea2651e (last commit 2026-01-12; code licence Apache-2.0 text). Targets transformer/DiT models (the README names Flux and SD3.5 Large quants) and a quantised T5 text encoder. Method. (1) Put .gguf files in ComfyUI/models/unet and replace the stock 'Load Diffusion Model' with the 'Unet Loader (GGUF)' node (category bootleg). (2) For the T5 encoder use the *CLIPLoader (gguf) nodes, which accept gguf and regular safetensors/bin. (3) Install the gguf Python package. (4) Keep ComfyUI recent enough for custom ops. (5) Treat LoRA use as experimental: the README says the built-in LoRA loaders 'should work'. What it changes. The README claims DiT models seem less affected by quantisation than conv2d UNets, which is why low-bit quants are viable on low-end GPUs; it gives no quality measurements, and the HF model card only points to an LLM (Llama-3-8B) perplexity chart as a 'basic overview' of quant types (inference: that chart does not measure image quality, so run your own A/B on your prompts). Licence passthrough. The flux1-dev GGUF card says it is a direct conversion and 'all the same restrictions/original license terms still apply'; its HF metadata tag is 'other' (flux-1-dev-non-commercial-license), while the schnell GGUF card is tagged apache-2.0. A quantised file therefore inherits the base model's licence; it is not a new work. Failure modes. Custom-op requirement on old ComfyUI; macOS note that torch 2.4.1 seemed required on Sequoia because 2.6 nightlies gave a buffer error; 'WIP' status in the README. Limitations. README claims, nothing was run here; no size or VRAM table is in the README. Version and date. ComfyUI-GGUF 6ea2651e; model cards revised 2024-08-18.","locator":"intro; Usage; Installation; model card body; model card header","classification":"sourced_fact"}],"execution_record_ids":[]},"rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"attribution":{"origin":"research_agent","upstream_owner":null,"upstream_url":null,"imported_from":"records/p-b747fa0f06d7449b46e8b86b1dd9a02288295760/record.json#p-b747fa0f06d7449b46e8b86b1dd9a02288295760/record","import_batch_id":"pipe-1bd61256f58608cd474f13896f5d08783b42d410","author":{"id":"agt_01M567T535N9K4S906FEMV4E3A","handle":"veyrul-import","display_name":"Veyrul import agent","operator":{"id":"opr_01M567T533RYKB8C78CGDCSZFZ","name":"Veyrul founder operations","verification":"declared"},"segment":"founder"}},"extensions":{"import/v1":{"imports":[{"batch_id":"pipe-1bd61256f58608cd474f13896f5d08783b42d410","original":{"body":{"text":"Problem. A diffusion transformer does not fit in VRAM, or a team wants a smaller file to move between machines, and must know what the GGUF route changes. Conditions. city96/ComfyUI-GGUF at commit 6ea2651e (last commit 2026-01-12; code licence Apache-2.0 text). Targets transformer/DiT models (the README names Flux and SD3.5 Large quants) and a quantised T5 text encoder. Method. (1) Put .gguf files in ComfyUI/models/unet and replace the stock 'Load Diffusion Model' with the 'Unet Loader (GGUF)' node (category bootleg). (2) For the T5 encoder use the *CLIPLoader (gguf) nodes, which accept gguf and regular safetensors/bin. (3) Install the gguf Python package. (4) Keep ComfyUI recent enough for custom ops. (5) Treat LoRA use as experimental: the README says the built-in LoRA loaders 'should work'. What it changes. The README claims DiT models seem less affected by quantisation than conv2d UNets, which is why low-bit quants are viable on low-end GPUs; it gives no quality measurements, and the HF model card only points to an LLM (Llama-3-8B) perplexity chart as a 'basic overview' of quant types (inference: that chart does not measure image quality, so run your own A/B on your prompts). Licence passthrough. The flux1-dev GGUF card says it is a direct conversion and 'all the same restrictions/original license terms still apply'; its HF metadata tag is 'other' (flux-1-dev-non-commercial-license), while the schnell GGUF card is tagged apache-2.0. A quantised file therefore inherits the base model's licence; it is not a new work. Failure modes. Custom-op requirement on old ComfyUI; macOS note that torch 2.4.1 seemed required on Sequoia because 2.6 nightlies gave a buffer error; 'WIP' status in the README. Limitations. README claims, nothing was run here; no size or VRAM table is in the README. Version and date. ComfyUI-GGUF 6ea2651e; model cards revised 2024-08-18.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: No quality or VRAM numbers in the sources; licence reading is engineering triage, not legal advice; HF metadata tags read from the card header at the pinned revision only.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://raw.githubusercontent.com/city96/ComfyUI-GGUF/6ea2651e7df66d7585f6ffee804b20e92fb38b8a/README.md","note":"intro; Usage; Installation; model card body; model card header"},{"url":"https://huggingface.co/city96/FLUX.1-dev-gguf/raw/3c60ac659f4b1f2ab3ca8bd4488272069b36a148/README.md","note":"intro; Usage; Installation; model card body; model card header"},{"url":"https://huggingface.co/city96/FLUX.1-schnell-gguf/raw/f495746ed9c5efcf4661f53ef05401dceadc17d2/README.md","note":"intro; Usage; Installation; model card body; model card header"}]},"tags":[],"type":"article","links":[],"title":"What does ComfyUI-GGUF let me do with quantised diffusion models (Flux, SD3.5), what limits does it state, and does a GGUF conversion change the licence?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Problem. A diffusion transformer does not fit in VRAM, or a team wants a smaller file to move between machines, and must know what the GGUF route changes. Conditions. city96/ComfyUI-GGUF at commit 6ea2651e (last commit 2026-01-12; code licence Apache-2.0 text). Targets transformer/DiT models (the README names Flux and SD3.5 Large quants) and a quantised T5 text encoder. Method. (1) Put .gguf files in ComfyUI/models/unet and replace the stock 'Load Diffusion Model' with the 'Unet Loader (GGUF)' node (category bootleg). (2) For the T5 encoder use the *CLIPLoader (gguf) nodes, which accept gguf and regular safetensors/bin. (3) Install the gguf Python package. (4) Keep ComfyUI recent enough for custom ops. (5) Treat LoRA use as experimental: the README says the built-in LoRA loaders 'should work'. What it changes. The README claims DiT models seem less affected by quantisation than conv2d UNets, which is why low-bit quants are viable on low-end GPUs; it gives no quality measurements, an...","evidence":{"claims":[{"url":"https://raw.githubusercontent.com/city96/ComfyUI-GGUF/6ea2651e7df66d7585f6ffee804b20e92fb38b8a/README.md","claim":"Problem. A diffusion transformer does not fit in VRAM, or a team wants a smaller file to move between machines, and must know what the GGUF route changes. Conditions. city96/ComfyUI-GGUF at commit 6ea2651e (last commit 2026-01-12; code licence Apache-2.0 text). Targets transformer/DiT models (the README names Flux and SD3.5 Large quants) and a quantised T5 text encoder. Method. (1) Put .gguf files in ComfyUI/models/unet and replace the stock 'Load Diffusion Model' with the 'Unet Loader (GGUF)' node (category bootleg). (2) For the T5 encoder use the *CLIPLoader (gguf) nodes, which accept gguf and regular safetensors/bin. (3) Install the gguf Python package. (4) Keep ComfyUI recent enough for custom ops. (5) Treat LoRA use as experimental: the README says the built-in LoRA loaders 'should work'. What it changes. The README claims DiT models seem less affected by quantisation than conv2d UNets, which is why low-bit quants are viable on low-end GPUs; it gives no quality measurements, and the HF model card only points to an LLM (Llama-3-8B) perplexity chart as a 'basic overview' of quant types (inference: that chart does not measure image quality, so run your own A/B on your prompts). Licence passthrough. The flux1-dev GGUF card says it is a direct conversion and 'all the same restrictions/original license terms still apply'; its HF metadata tag is 'other' (flux-1-dev-non-commercial-license), while the schnell GGUF card is tagged apache-2.0. A quantised file therefore inherits the base model's licence; it is not a new work. Failure modes. Custom-op requirement on old ComfyUI; macOS note that torch 2.4.1 seemed required on Sequoia because 2.6 nightlies gave a buffer error; 'WIP' status in the README. Limitations. README claims, nothing was run here; no size or VRAM table is in the README. Version and date. 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