Diffusers Sampler
The Diffusers Sampler Is How You A/B Test ELLA (or Skip It)
- diffusers_model
- images
This node is the control group in ELLA's experiment. The whole point of kijai's ComfyUI-ELLA-wrapper pack is putting Tencent's ELLA text encoder on top of an SD 1.5 model, and this sampler is the "without ELLA" side of that comparison. Feed it a DIFFUSERSMODEL from Diffusers Model Loader and it runs a plain diffusers StableDiffusionPipeline with the ordinary CLIP text encoder - no T5, no 6 GB download, none of the ELLA machinery. Same checkpoint, same seed, and you can finally see what ELLA actually buys you.
Mechanically it's a full diffusers pipeline in a node. The prompt and negative prompt get encoded by the stock CLIP text encoder, the scheduler is swapped in at runtime from a ten-choice dropdown, and the whole thing runs under the same autocast/dtype handling ComfyUI sets for your GPU. It returns ordinary IMAGE tensors straight out of PreviewImage or a save node.
The inputs you'll actually touch:
- prompt / n_prompt - multiline strings. Defaults are literally "positive" and "negative"; replace them with your real prompt.
- steps - 1–200, default 25.
- guidance_scale - default 10, which is the SD 1.5 sweet spot.
- seed - standard.
- scheduler - the default
DPMSolverMultistepScheduleris fine; you'd only switch for LCM or TCD distilled LoRAs. - width / height - 64–2048 in steps of 64. The hidden gem here is hidiffusion: flip it on and the node applies the bundled HiDiffusion patch (same lab as ELLA) that lets SD 1.5 render at 1024+ without the classic duplicate-object meltdown.
The one output, images, wires into anything that takes an IMAGE.
The honest use case is A/B testing. Wire up CheckpointLoaderSimple → Diffusers Model Loader → Diffusers Sampler as your baseline, then build the ella_model_loader + ella_t5_embeds + ella_sampler path beside it, keep the seed fixed, and compare. Community testing back in 2024 found ELLA clearly beats CLIP on prompt adherence but doesn't quite reach a properly fine-tuned SDXL model, so a baseline isn't just nice-to-have - it's how you decide whether the extra GBs are worth it. If you decide they're not, this sampler is also just a perfectly serviceable SD 1.5 diffusers sampler on its own.
One warning up front: this pack is effectively retired. The README starts by pointing you to Tencent's official ComfyUI-ELLA nodes instead, and the last commit was May 2024. It still runs - kijai's wrapper was the one people reported "just worked" - but don't expect updates, and check the official pack first if you're starting fresh.
Inputs (10)
| Name | Type | Default | Description |
|---|---|---|---|
| diffusers_model | DIFFUSERSMODEL | — | |
| width | INT | 51264–2048 | — |
| height | INT | 51264–2048 | — |
| steps | INT | 251–200 | — |
| guidance_scale | FLOAT | 10.001.01–20 | — |
| seed | INT | 00–18446744073709550000 | — |
| scheduler | COMBO | DPMSolverMultistepScheduler | 10 options: DPMSolverMultistepScheduler, DPMSolverMultistepScheduler_SDE_karras, DDPMScheduler, LCMScheduler, PNDMScheduler, DEISMultistepScheduler, +4 |
| prompt | STRING | positive | — |
| n_prompt | STRING | negative | — |
| hidiffusion | BOOLEAN | false | — |
Outputs (1)
| Name | Type | Description |
|---|---|---|
| images | IMAGE | — |