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    FLUX 3 Klein Base vs Distilled - v1.0
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    FLUX 3 Image Klein A/B workflow: base and distilled checkpoints run side by side from the same seed, so you can judge exactly what the speed-up costs.

    Main features:

    - Two checkpoints in one graph: flux3_image_klein_base_int8_convrot (25 steps, CFG 3.5) and flux3_image_klein_sd_int8_convrot (16 steps, no classifier-free guidance), sharing one qwen3vl_4b text encoder and one flux2 VAE
    - Identical latent, identical seed, identical caption - only the model and sampler settings differ, so the comparison is fair
    - ImageCompare renders both outputs next to the source for direct judging
    - Demonstrates the practical difference: the base path is slower and slightly more literal, the distilled path is roughly 3x faster with close-but-not-identical detail
    - No LoRA and no upscale, so you are comparing raw checkpoints

    Suggested workflow:

    1. Write one detailed caption and set a fixed seed.
    2. Run the graph. Both checkpoints render; ImageCompare lines them up.
    3. Judge by task, not by score: portraits, product text and pose edits usually favour one path consistently, so run a handful of your own subjects before deciding.

    Dependencies:

    - flux3_image_klein_base_int8_convrot.safetensors (base UNET)
    - flux3_image_klein_sd_int8_convrot.safetensors (distilled UNET)
    - qwen3vl_4b_int8_convrot.safetensors (text encoder / CLIP)
    - flux2-vae.safetensors (VAE)
    - ImageCompare (rgthree Node Suite)

    RunningHub workflow page: https://www.runninghub.ai/zh-cn/user-center/1846601088051884033/webapp?inviteCode=rh-v1111

    Bilibili tutorial video: https://www.bilibili.com/video/BV1btJb6LEGS/

    RunningHub registration with invite code rh-v1111: https://www.runninghub.ai/?inviteCode=rh-v1111

    Description



    FLUX 3 Image Klein A/B workflow: base and distilled checkpoints run side by side from the same seed, so you can judge exactly what the speed-up costs.

    Main features:

    - Two checkpoints in one graph: flux3_image_klein_base_int8_convrot (25 steps, CFG 3.5) and flux3_image_klein_sd_int8_convrot (16 steps, no classifier-free guidance), sharing one qwen3vl_4b text encoder and one flux2 VAE
    - Identical latent, identical seed, identical caption - only the model and sampler settings differ, so the comparison is fair
    - ImageCompare renders both outputs next to the source for direct judging
    - Demonstrates the practical difference: the base path is slower and slightly more literal, the distilled path is roughly 3x faster with close-but-not-identical detail
    - No LoRA and no upscale, so you are comparing raw checkpoints

    Suggested workflow:

    1. Write one detailed caption and set a fixed seed.
    2. Run the graph. Both checkpoints render; ImageCompare lines them up.
    3. Judge by task, not by score: portraits, product text and pose edits usually favour one path consistently, so run a handful of your own subjects before deciding.

    Dependencies:

    - flux3_image_klein_base_int8_convrot.safetensors (base UNET)
    - flux3_image_klein_sd_int8_convrot.safetensors (distilled UNET)
    - qwen3vl_4b_int8_convrot.safetensors (text encoder / CLIP)
    - flux2-vae.safetensors (VAE)
    - ImageCompare (rgthree Node Suite)

    RunningHub workflow page: https://www.runninghub.ai/zh-cn/user-center/1846601088051884033/webapp?inviteCode=rh-v1111

    Bilibili tutorial video: https://www.bilibili.com/video/BV1btJb6LEGS/

    RunningHub registration with invite code rh-v1111: https://www.runninghub.ai/?inviteCode=rh-v1111
    Workflows
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    Details

    Downloads
    47
    Platform
    CivitAI
    Platform Status
    Available
    Created
    10/18/2026
    Updated
    10/24/2026
    Deleted
    -

    Files

    flux3KleinBaseVs_v10.json

    Mirrors

    CivitAI (1 mirrors)