fluxflow-comfyui 0.1.1

ComfyUI custom nodes for FluxFlow text-to-image generation

FluxFlow ComfyUI

ComfyUI custom nodes for FluxFlow text-to-image generation.

🚧 Checkpoint Availability

Training In Progress: FluxFlow model checkpoints are currently being trained and are not yet available for download.

Status: The ComfyUI nodes are fully implemented and tested, but require trained FluxFlow checkpoints to generate images.

When Available: Trained checkpoints will be published to MODEL_ZOO.md upon completion of training validation. You will then be able to load them using the FluxFlowModelLoader node.

For Developers: You can use this plugin with your own trained FluxFlow checkpoints if you're conducting custom training experiments.

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Installation

Production Install (ComfyUI Users)

Recommended for ComfyUI users: Clone directly into ComfyUI's custom_nodes directory for automatic discovery:

cd ComfyUI/custom_nodes
git clone https://github.com/danny-mio/fluxflow-comfyui.git
cd fluxflow-comfyui
pip install -e .

This method requires no additional symlink setup.

Production Install (via PyPI)

For advanced users who want to manage the package separately:

pip install fluxflow-comfyui

What gets installed:

• fluxflow-comfyui - ComfyUI custom nodes for FluxFlow
• fluxflow core package (automatically installed as dependency)
• Note: Does NOT include training capabilities. Only inference/generation.

Package available on PyPI: fluxflow-comfyui v0.1.1

Additional Setup Required: You must symlink the package into ComfyUI's custom_nodes directory:

# Find where fluxflow-comfyui was installed
PACKAGE_PATH=$(python -c "import comfyui_fluxflow; print(comfyui_fluxflow.__path__[0])")

# Create symlink in ComfyUI's custom_nodes directory
ln -s "$PACKAGE_PATH" ~/ComfyUI/custom_nodes/comfyui_fluxflow

Adjust the ~/ComfyUI path to match your ComfyUI installation location.

Development Install

git clone https://github.com/danny-mio/fluxflow-comfyui.git
cd fluxflow-comfyui
pip install -e ".[dev]"

Features

• Model Loader: Load FluxFlow checkpoints with auto-configuration
• Text Encoding: BERT-based text encoding for prompts
• Sampling: Multiple sampling algorithms (Euler, DPM++, DDIM, etc.)
• VAE Operations: Encode/decode latents
• Latent Generation: Create empty latents at various resolutions

Available Nodes

FluxFlowModelLoader

Load FluxFlow model checkpoints (.safetensors or .pth files).

FluxFlowTextEncode

Encode text prompts using DistilBERT.

FluxFlowSampler

Sample from the diffusion model with 14 schedulers:

• Euler, Euler Ancestral
• DPM++ 2M, DPM++ 2M Karras
• DPM++ SDE, DPM++ SDE Karras
• DDIM, DDPM
• LCM (Latent Consistency Model)
• And more...

FluxFlowVAEEncode / FluxFlowVAEDecode

Encode images to latents and decode latents to images.

FluxFlowEmptyLatent

Generate empty latent tensors at specified dimensions.

Quick Start

1. Load a FluxFlow model using FluxFlowModelLoader
2. Encode your prompt with FluxFlowTextEncode
3. Create empty latents with FluxFlowEmptyLatent
4. Generate with FluxFlowSampler
5. Decode latents with FluxFlowVAEDecode

Example Workflow

[FluxFlowModelLoader] → model
[FluxFlowTextEncode] → conditioning
[FluxFlowEmptyLatent] → latent
[FluxFlowSampler] (model + conditioning + latent) → sampled_latent
[FluxFlowVAEDecode] (model + sampled_latent) → image

Package Contents

• comfyui_fluxflow.nodes - Custom node implementations
• comfyui_fluxflow.schedulers - Sampling scheduler implementations
• comfyui_fluxflow.web - JavaScript extensions for ComfyUI UI

Links

• GitHub Repository
• ComfyUI Documentation
• Core Package
• Training Package

License

MIT License - see LICENSE file for details.