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Stable Diffusion 1.5 LoRA Inference in ComfyUI | RunComfy Workflow (Training-Matched Results)

Updated: Mar 31, 2026

toolai-toolkit-lora-inference

Type

Workflows

Stats

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Published

Mar 31, 2026

Base Model

SD 1.5

Hash

AutoV2
98AB9C3B7E
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RunComfy

Run AI Toolkit-trained Stable Diffusion 1.5 LoRAs in ComfyUI with training-matched behavior using a single RCSD15 custom node.

Who it's for: creators who want this pipeline in ComfyUI without assembling nodes from scratch. Not for: one-click results with zero tuning — you still choose inputs, prompts, and settings.

Open preloaded workflow on RunComfy

Open preloaded workflow on RunComfy (browser)

Why RunComfy first
- Fewer missing-node surprises — run the graph in a managed environment before you mirror it locally.
- Quick GPU tryout — useful if your local VRAM or install time is the bottleneck.
- Matches the published JSON — the zip follows the same runnable workflow you can open on RunComfy.

When downloading for local ComfyUI makes sense — you want full control over models on disk, batch scripting, or offline runs.

How to use (local ComfyUI)
1. Load inputs (images/video/audio) in the marked loader nodes.
2. Set prompts, resolution, and seeds; start with a short test run.
3. Export from the Save / Write nodes shown in the graph.

Expectations — First run may pull large weights; cloud runs may require a free RunComfy account.


Overview

Stable Diffusion 1.5 LoRA Inference is a ready-to-run ComfyUI workflow for applying AI Toolkit-trained LoRAs to SD1.5 on RunComfy. Rather than recreating the job as a generic sampler graph, it runs generation through RC SD 1.5 (RCSD15), which wraps a Stable Diffusion 1.5 pipeline aligned with AI Toolkit preview sampling. Start from a preview-matching baseline, then tune prompts and LoRA strength once the pipeline-aligned output is stable.

Important nodes:

  • SaveImage

  • RC SD 1.5 (RCSD15)

Notes

Stable Diffusion 1.5 LoRA Inference in ComfyUI | RunComfy Workflow (Training-Matched Results) — see RunComfy page for the latest node requirements.