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This is a direct GGUF conversion of black-forest-labs/FLUX.1-dev As this is a quantized model not a finetune, all the same restrictions/original license terms still apply. The model files can be used with the ComfyUI-GGUF custom node.

Image generation
Input Text→ Output Image
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This is a direct GGUF conversion of black-forest-labs/FLUX.1-schnell The model files can be used with the ComfyUI-GGUF custom node. Place model files in `ComfyUI/models/unet` - see the GitHub readme for further install instructions.

Image generation
Input Text→ Output Image
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This is a direct GGUF conversion of black-forest-labs/FLUX.2-dev. The model files can be used in ComfyUI with the ComfyUI-GGUF custom node. Place the required model(s) in the following folders: > [!NOTE] > As with Qwen-Image, Q5_K_M, Q4_K_M, Q3_K_M, Q3_K_S and Q2_K have some extra logic as to which blocks to keep in high precision. > > The logic is partially based on guesswork, trial & error, and the graph found in the readme for Freepik/flux.1-lite-8B (which in turn quotes this blog by Ostris)

Image editing
Input Image→ Output Image
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As this is a quantized model not a finetune, all the same restrictions/original license terms still apply. Place model files in `ComfyUI/models/unet` - see the GitHub readme for further install instructions.

Video generation
Input Text→ Output Video
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NOTE: Hunyuan recently replaced the original model in the repo above with a **new and updated version** - the files in this repo have been reconverted and replaced. If you want to test against the original models, you can find them on this branch As this is a quantized model not a finetune, all the same restrictions/original license terms still apply. Place model files in `ComfyUI/models/unet` - see the GitHub readme for further install instructions.

Video generation
Input Image→ Output Video
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The model files can be used in ComfyUI with the ComfyUI-GGUF custom node. Place the required model(s) in the following folders: The VAE seems to be largely the same as LTXV 0.9.5 and the difference between the two seems negligible, but I've uploaded the 0.9.6 one to this repo to be safe *As this is a quantized model not a finetune, all the same restrictions/original license terms still apply.*

Video generation
Input Image→ Output Video
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As this is a quantized model not a finetune, all the same restrictions/original license terms still apply. Place model files in `ComfyUI/models/unet` - see the GitHub readme for further install instructions.

Video generation
Input Image→ Output Video
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This is a direct GGUF conversion of Qwen/Qwen-Image. The model files can be used in ComfyUI with the ComfyUI-GGUF custom node. Place the required model(s) in the following folders: > [!NOTE] > The Q5_K_M, Q4_K_M and most importantly the low bitrate quants (Q3_K_M, Q3_K_S, Q2_K) use a new dynamic logic where the first/last layer is kept in high precision. > > For a comparison, see this imgsli page. With this method, even Q2_K remains somewhat usable.

Image generation
Input Text→ Output Image
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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。

Category pending
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The weights can be used with `./llama-embedding` or with the ComfyUI-GGUF custom node together with image generation models. This is a **non imatrix** quant as llama.cpp doesn't support imatrix creation for T5 models at the time of writing. It's therefore recommended to use **Q5_K_M or larger** for the best results, although smaller models may also still provide decent results in resource constrained scenarios.

Category pending
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This is a GGUF conversion of Google's UMT5 xxl model, specifically the encoder part. The weights can be used with `./llama-embedding` or with the ComfyUI-GGUF custom node together with image/video generation models. This is a **non imatrix** quant as llama.cpp doesn't support imatrix creation for T5 models at the time of writing. It's therefore recommended to use **Q5_K_M or larger** for the best results, although smaller models may also still provide decent results in resource constrained scenarios.

Category pending
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Cost analysis

Adjust monthly token usage to compare estimated API costs.

1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

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How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

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Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

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