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    FLUX.1-dev-ControlNet-Union-Pro-2.0(fp8) - v1.0
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    πŸ˜‰ FLUX.1-dev-ControlNet-Union-Pro-2.0-fp8

    Hugging Face Model Card

    A Good Reference for Parameters

    • Canny: controlnet_conditioning_scale=0.7, control_guidance_end=0.8

    • Depth: use depth-anything, controlnet_conditioning_scale=0.8, control_guidance_end=0.8

    • Pose: use DWPose, controlnet_conditioning_scale=0.9, control_guidance_end=0.65

    • Gray: use Color, controlnet_conditioning_scale=0.9, control_guidance_end=0.8

    Folder Structure

    Organize your models as follows for FLUX dev and ControlNet workflows:

    πŸ“‚ ComfyUI/
    β”œβ”€β”€ πŸ“‚ models/
    β”‚   β”œβ”€β”€ πŸ“‚ diffusion_models/
    β”‚   β”‚   └── πŸ“„ flux-dev.safetensores         # (or gguf)
    β”‚   β”œβ”€β”€ πŸ“‚ text_encoders/
    β”‚   β”‚   β”œβ”€β”€ πŸ“„ clip_l.safetensors
    β”‚   β”‚   └── πŸ“„ t5xxl_fp8_e4m3fn.safetensors # (or t5xxl_fp16 or t5xxl_fp8_e4m3fn_scaled)
    β”‚   β”œβ”€β”€ πŸ“‚ vae/
    β”‚   β”‚   └── πŸ“„ ae.safetensors
    β”‚   β”œβ”€β”€ πŸ“‚ controlnet/
    β”‚   β”‚   └── πŸ“„ FLUX.1-dev-ControlNet-Union-Pro-2.0-fp8.safetensors
    

    Note: Only one T5XXL text encoder is neededβ€”choose based on your hardware and quality/speed needs.



    My FP8 Quantization Solution

    With modest coding experience, I researched quantization and implemented FP8 compression for the model. The quantized version works perfectly for my needs, enabling all ControlNet workflows with much lower memory requirements and no noticeable quality loss.


    Using The Quantized Model

    • Supports all original control types: pose, depth, canny edge, etc.

    • Drop any reference image, select control type, and generate results with lower memory usage.


    Enhanced Prompting with OllamaGemini

    I use my customOllamaGemini node for ComfyUIto generate optimal prompts. This, combined with the quantized model, creates a powerful, memory-efficient pipeline for creative image manipulation.


    Alternatives for High-End Hardware

    If you have a powerful GPU, the original unquantized model from Shakker-Labs offers higher fidelity at the cost of increased memory usage.


    Looking Forward

    I welcome community feedback! If you find these workflows helpful, please show your support with a πŸ‘ on the project. I'm open to opportunities and appreciate encouragement as I develop these resources.


    Feel free to experiment with the model for your creative projectsβ€”whether using the memory-efficient quantized version or the original full-precision implementation!

    πŸ‘¨β€πŸ’» Developer Information

    This guide was created by Abdallah Al-Swaiti:

    1. Hugging Face

    2. GitHub

    3. LinkedIn

    4. ComfyUI-OllamaGemini

    For additional tools and updates, check out my other repositories.

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    Description

    Checkpoint
    Flux.1 D

    Details

    Downloads
    3,429
    Platform
    CivitAI
    Platform Status
    Available
    Created
    4/19/2025
    Updated
    9/27/2025
    Deleted
    -