I get much better results with Automatic1111 compared to ComfyUI. Can I fix this (and how)?

I consistently get much better results with Automatic1111's webUI compared to ComfyUI even for seemingly identical workflows. The more complex the workflows get (e.g. multiple LoRas, negative prompting, upscaling), the more Comfy results break down.

I've attached some example pictures and also a link to a Miro Board which shows the exact workflows.

Miro: https://miro.com/app/board/uXjVNRna5Hc=/?share_link_id=661735166309

ComfyUI images look much more cartoonish and unrealistic. With LoRas and latent upscaling I get totally corrupted images for the same workflow. Auto1111 holds up just fine.

I have some nice workflows in Auto and would love to replicate them - if possible _exactly_ - in Comfy. From what I understand, exact same parameters should give the exact same image as a result. However, ComfyUI and Automatic1111 seem to be processing Stable Diffusion quite differently.

Does anyone know how I can get my exact Auto1111 results in Comfy?

I noticed that reducing the cfg scale in ComfyUI gets me more pleasing results, but it's still not at all the same as in Automatic1111. And I do wonder why they are so extremely different. (Seeds in Comfy are much longer by default. Does that mean anything?) Any ideas?

Examples: Comfy (left) vs. Automatic1111 (right)
(Model: photon, 20 steps, cfg: 6, scheduler: DPM++ SDE Karras/dpmpp_sde karras, seed: 2776553318, upscaling: latent (bicubic) with 10 steps)