Create and verify a transparent PNG with LayerDiffuse in Forge

No. The official Forge announcement links a separate sd-forge-layerdiffuse repository. The current original Forge core and the extension must be versioned independently.

Use the repository linked by announcement #854 through Forge’s extension installation flow, then apply the change and restart. Do not use a fork bundle or relabelled binary as an official download.

The extension code registers an always-visible LayerDiffuse accordion for Txt2img and Img2img. If it is absent after restart, inspect extension loading in the console before downloading models.

The inspected extension code exposes explicitly labelled SDXL and SD1.5 methods. Match the checkpoint family to that label; this does not prove support for every fine-tune.

The inspected method list contains SDXL and SD1.5 only. Support for FLUX, SD3.5 or another family is UNKNOWN on this evidence and should not be inferred from other forks.

Start with the Attention Injection method labelled for your checkpoint family. It requires no foreground, background or blending input and exercises the shortest alpha-output path.

Both inspected SDXL methods generate transparency. The README describes Conv Injection as an alternative that may preserve prompt understanding differently and may introduce stronger style influence; its own author reports better results from Attention Injection in their tests.

The extension exposes Weight from 0 to 2 in 0.001 steps and displays 1.0. It controls the loaded SDXL layer patch. Keep 1.0 for the baseline; no universal best value is claimed.

The control runs from 0 to 1 and displays 1.0. Current code converts it into a sigma boundary, after which sampling can return to the original UNet. Treat changes as a controlled experiment.

Current extension code creates models/layer_model under Forge’s active models path and downloads the file required by the selected method there.

No. The README says downloads and selection are automatic, and current code requests files by method. The official model repository totals about 12.54 GB, so downloading everything is unnecessary for a one-method baseline.

The decoder returns a real PNG candidate and a visualization. Current code assigns the PNG to the processed image and appends the visualization as an extra result. Saving behavior still needs local verification.

No. A checkerboard may be baked into an ordinary RGB image. Confirm a real alpha channel and composite the downloaded PNG over several solid backgrounds.

Open the downloaded file in an editor or inspector that shows RGBA/channels, then place it over white, black and a saturated color. A screenshot of the Forge gallery is not evidence.

Open issues report this in several dated environments, while the repository owner could not reproduce one report. Separate gallery output from the automatic output folder and preserve exact commits before testing any workaround.

The README says Img2img is finished, current code has an Img2img initialization hook, and the official Forge announcement demonstrates transparent-image editing. Compatibility still depends on the recorded build.

Announcement #854 demonstrates transparent inpainting and sketch-plus-Img2img. Load the real RGBA input, preserve a baseline, and verify the alpha channel after the edit rather than trusting the preview.

The official README presents those as the reason to use latent transparency rather than simple background removal. Output quality is probabilistic and must be checked over multiple backgrounds.

For SDXL, use the transparent foreground as input to From Foreground to Blending, then optionally use foreground plus blending to generate a background. Composite the real layers outside Forge for the final alignment proof.

SDXL uses Background to Blending followed by Background and Blending to Foreground. SD1.5 exposes a one-step From Background to Foreground method that requires batch size divisible by 2.

The conditional methods group outputs in pairs and the joint method groups foreground, background and blend in threes. The README warns that incomplete groups can produce noise.

The SD1.5 joint workflow exposes additional foreground, background and blending prompts. The README uses them to separate subject content from scene content; keep the global prompt and seed fixed when testing.

It is not guaranteed. An open issue reports lost transparency, and the extension repository contains dated discussion around rebuilding alpha after post-processing. Prove Hires. fix separately on your exact build.

Compatibility is UNKNOWN until tested. Callback-order reports show that a post-processor may affect the visualization but not the alpha output. Add one extension at a time after the base PNG passes.

There is no universal winner. The README records DPM artifacts in some conditional SDXL tests and uses Euler A or UniPC as a dated probe. Reproduce the sanity check before generalizing.

The alpha may contain matte contamination or weak edge opacity even when the file is RGBA. Compare white, black and saturated composites and inspect semi-transparent edges at 100%.

First prove one image and confirm which output is actually saved. Batch grouping matters for SD1.5 conditional and joint methods, and open reports show automatic alpha saving is not consistent across environments.

Include both repository commits, environment, checkpoint family and hash, method, Weight, Stop At, complete generation receipt, input PNGs, downloaded outputs and the console traceback. State whether the gallery, download or output folder failed.

No. A remover estimates a mask after an ordinary image exists. LayerDiffuse changes diffusion and decodes latent transparency, which can represent glass, glow and internal semi-transparency; neither guarantees perfect edges.

No. The page verifies current source code, repository state and reports, but no GPU generation was run in this workspace. Output quality and exact runtime compatibility remain local tests.