If you post with zit sampler/shedulers test you might know that all of them produced roughly the same result. But for Ernie-Turbo it turned out to not be the case. Some of the combinations have a HUGE impact on image composition.
Generation Info:
8 steps
cfg 1
No prompt enchanter
Full model
Ideally I should have tried a different combination of steps, but that would be too much work to analyze by hand.
Link to all images:
https://drive.google.com/drive/folders/1E7Kklh-5Gh41GT6h0HpzFIxqVfKONws9?usp=sharing
All images that draw my attention are marked as "not bad" in the name. My taste is subjective so you might want to go through them. All combinations that are marked are in the table below
| Sampler | beta | karras | kl_optimal | linear_quadratic | normal | sgm_uniform | sgm_unirform | simple | uniform | (Other) | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|
| ddim | 1 | 1 | |||||||||
| dpm_2 | 2 | 1 | 3 | ||||||||
| dpm_2_ancestral | 2 | 3 | 1 | 6 | |||||||
| dpmpp_2m_sde | 1 | 1 | 1 | 1 | 4 | ||||||
| dpmpp_2m_sde_gpu | 2 | 2 | 1 | 2 | 7 | ||||||
| dpmpp_2m_sde_heun | 1 | 1 | 1 | 3 | |||||||
| dpmpp_2m_sde_heun_gpu | 1 | 2 | 1 | 4 | |||||||
| dpmpp_2s_ancestral | 2 | 2 | 3 | 2 | 9 | ||||||
| dpmpp_sde | 1 | 1 | 1 | 3 | |||||||
| dpmpp_sde_gpu | 2 | 1 | 1 | 1 | 1 | 6 | |||||
| er_sde | 1 | 1 | 2 | ||||||||
| euler | 1 | 1 | |||||||||
| euler_ancestral | 1 | 1 | |||||||||
| euler_ancestral_cfg_pp | 2 | 2 | |||||||||
| euler_cfg_pp | 1 | 1 | 2 | ||||||||
| exp_heun_2_x0 | 1 | 1 | 1 | 3 | |||||||
| exp_heun_2_x0_sde | 2 | 1 | 2 | 1 | 1 | 7 | |||||
| gradient_estimation | 1 | 1 | |||||||||
| heun | 1 | 1 | |||||||||
| heunpp2 | 1 | 1 | |||||||||
| lcm | 1 | 2 | 3 | ||||||||
| res_multistep | 1 | 1 | |||||||||
| sa_solver | 2 | 2 | |||||||||
| sa_solver_pece | 1 | 1 | 2 | ||||||||
| seeds_2 | 2 | 1 | 1 | 1 | 5 | ||||||
| seeds_3 | 3 | 1 | 1 | 1 | 2 | 8 | |||||
| uni_pc | 1 | 1 | 1 | 3 | |||||||
| uni_pc_bh2 | 1 | 1 | 2 | ||||||||
| Total | 27 | 1 | 2 | 19 | 10 | 20 | 1 | 1 | 12 | 1 | 93 |
So, as you can see objectively beta is the best scheduler you can use. Sgm_uniform is also fine. However, subjectively my favorite scheduler is linear_quadratic, it has a big impact on compositions and details, but at some images it can feel too "clean" for the given subject.
For samplers I think the best option is seeds_3, it looks very good on some images. As a downside it can have to much texture where it's not required, as human faces for example. If that's the case you can go with seeds_2. Also seeds_3 one of the slowest.
One of the samplers that I didn't even know existed but produced good results is exp_heun_2_x0_sde. Give it a try.
As for more traditional samplers dpmpp_2s_ancestral, dpmpp_2m_sde_gpu,dpm_2_ancestral are all fine.
List of samplers that produce garbage (at 8 steps): dpm_fast,dpmpp_2s_ancestral_cfg_pp,dpmpp_2m_ancestral_cfg_pp,dpmpp_2m_cfg_pp,dpmpp_3m_sde,dpmpp_3m_sde_gpu,,res_multistep_cfg_pp,res_multistep_ancestral,res_multistep_ancestral_cfg_pp,gradient_estimation_cfg_pp,lms
List of schedulers that produce garbage: ddim_uniform
Since I'm most interested in "stock images" type", my favorite combination is seeds_3/linear_quadratic. But it's probably not the best option for every scenario. I would like to hear what you think, maybe I missed something between the results.
All that analysis should also apply to the base models at 50 steps (side note: comfy workflow suggests only 20 steps, don't believe it all looks like shit. Use 50 steps). The problem is that at 50 steps it is slow, like, it often can produce images that are better than turbo, especially interiors with seeds_3/linear_quadratic have really good composition,texture,details. But it also takes 12 min for one picture. There is probably a better setting (steps/cfg) but I don't have plans to dig that deep.