Flux Transparent I2I

You have a photo of a product - say, a man with curly hair on a plain backdrop - and you want a transparent cutout where the edges were never guessed. That's the job this node reaches for. It's image-to-image LayerDiffusion on FLUX: it re-encodes your image through a transparency-aware VAE, diffuses it under a new prompt, and returns an RGBA result with alpha baked in during generation rather than segmented afterwards.

It's the most ambitious node in this pack, and also the most fragile. Let's be honest about both.

How it works

FluxTransparentI2I needs a model from FluxTransparentModelLoader with load_i2i switched on - that builds a special FluxImg2ImgPipeline the node can drive. The pipeline runs the regular SDXL-style trick: your image gets encoded to latents, partially noised, then denoised toward the prompt. The LayerDiffuse twist is that the encoder here is the TransparentVAE, which reads your RGBA input and adds a learned "latent transparency offset" so the diffusion knows where the subject is and what's empty. Decode the result through the same VAE and you get an RGBA image where the background genuinely isn't there.

The inputs that matter:

• image - your starting photo, as an IMAGE tensor. Feed it whatever you generated or loaded; the node handles the conversion.
• prompt - defaults to a handsome man with curly hair, high quality. Rephrase it to describe the subject you actually fed in.
• strength - default 0.8, range 0–1. This is the re-diffusion amount. Lower it to stay closer to the original photo; crank it and you're basically redrawing the subject.
• guidance_scale - default 7 (note: the T2I node defaults to 3.5; this one runs hotter).
• num_inference_steps, width, height, seed - same shapes as the rest of the pack: 50 steps, 1024² by default, multiples of 8.

Output is a single image with four channels. Save as PNG and check in an editor with a checkerboard view - preview tools that flatten to RGB will make the alpha look like it never happened.

Setup and install

Three nodes again, just wired differently: loader (with load_i2i on) → this → save. Remember the loader builds pipelines on demand, so leaving load_t2i on too means FLUX gets loaded twice - flip it off if you're only doing img2img.

Install is the pack's standard story, and Manager was never actually wired up for it:

cd ComfyUI/custom_nodes
git clone https://github.com/leeguandong/ComfyUI_FluxLayerDiffuse.git
pip install diffusers transformers accelerate safetensors opencv-python-headless huggingface_hub

You'll also need a HuggingFace login for the gated black-forest-labs/FLUX.1-dev base model, and a card that can hold a full bf16 FLUX load - budget ~24GB+ VRAM.

The honest verdict

This is the node where the pack's problems concentrate. The img2img path was already flagged "currently broken" in the Forge-side Flux LayerDiffuse ports, and this ComfyUI wrapper inherits the same instability. On top of that, the loader's TransparentVAE(None, ...) bug means you likely won't even get to the sampler - the whole pack currently dies with 'NoneType' object has no attribute 'to', matching an April 2025 r/comfyui report that got no answer, and the repo hasn't moved since. It's also hardcoded to CUDA, so Mac and CPU-only rigs are out.

When it runs, the idea is right - native alpha beats any post-hoc cutout. But treat this one as an experiment with an unmaintained wrapper, not your product pipeline. For shipping cutouts, BiRefNet and friends remain the boring, working answer.