comfyui-fluxflow 0.8.0

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 comfyui-fluxflow

What gets installed:

• comfyui-fluxflow - 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: comfyui-fluxflow v0.8.0

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

# Find where comfyui-fluxflow 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
• Classifier-Free Guidance (CFG): Improved prompt adherence with guidance scaling
• Negative Prompts: Support for negative text conditioning
• 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).

v0.8.0 model detection: The loader automatically detects v0.8.0 pillar-attention checkpoints (identified by pillar_cross_attn or film_p0 keys in the state dict). When a v0.8.0 checkpoint is detected, the loader returns a clear error directing you to use versioned loading via load_versioned_checkpoint() instead of the legacy loader. This prevents silent architecture mismatches.

FluxFlowTextEncode

Encode text prompts using DistilBERT.

FluxFlowTextEncodeNegative

Encode negative text prompts for Classifier-Free Guidance (CFG).

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 Workflows

Basic Workflow (No CFG)

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

CFG Workflow (Recommended)

[FluxFlowModelLoader] → model
[FluxFlowTextEncode] (positive prompt) → conditioning
[FluxFlowEmptyLatent] → latent
[FluxFlowSampler] (model + conditioning + latent + use_cfg=True + guidance_scale=5.0) → sampled_latent
[FluxFlowVAEDecode] (model + sampled_latent) → image

Advanced CFG with Negative Prompt

[FluxFlowModelLoader] → model
[FluxFlowTextEncode] (positive prompt) → conditioning
[FluxFlowTextEncodeNegative] (negative prompt) → negative_conditioning
[FluxFlowEmptyLatent] → latent
[FluxFlowSampler] (model + conditioning + negative_conditioning + latent + use_cfg=True + guidance_scale=5.0) → sampled_latent
[FluxFlowVAEDecode] (model + sampled_latent) → image

Classifier-Free Guidance (CFG)

FluxFlow supports CFG for improved prompt adherence and higher quality outputs.

How CFG Works

CFG performs two forward passes during sampling:

1. Conditional pass: Using your positive prompt
2. Unconditional pass: Using null/negative embeddings

The final prediction is guided by: v_guided = v_uncond + guidance_scale * (v_cond - v_uncond)

Using CFG

Basic CFG (recommended for most use cases):

1. Load a FluxFlow checkpoint trained with CFG support
2. Encode your positive prompt with FluxFlowTextEncode
3. In FluxFlowSampler:
   • Set use_cfg to True
   • Set guidance_scale between 1.0-15.0 (recommended: 3.0-7.0)
   • Leave negative_conditioning empty (uses null embeddings)

Advanced CFG with Negative Prompts:

1. Encode positive prompt with FluxFlowTextEncode
2. Encode negative prompt with FluxFlowTextEncodeNegative
3. Connect both to FluxFlowSampler
4. Set use_cfg=True and adjust guidance_scale

Guidance Scale Guidelines

Recommended range: 3.0–7.0. At 1.0, guidance has no amplification effect (standard conditional generation). Above 7.0 may oversaturate or reduce diversity. Higher values increase computation (2x forward passes per step). See fluxflow-core CFG documentation for full guidance.

CFG Performance

• Memory: CFG requires ~2x VRAM due to dual forward passes
• Speed: CFG sampling takes ~2x longer than standard sampling
• Quality: Improves prompt adherence and output coherence
• Compatibility: Requires checkpoints trained with CFG dropout

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
• INSTALL / QUICKSTART
• Core Package
• Training Package

License

MIT License - see LICENSE file for details.