RES4LYF Sampler Comparison Chart

Since RES4LYF was released and gained popularity for having almost a hundred samplers using ClownSharkSampler, I decided to test each one of them to the same prompt and resolution in an attempt to find the most sharp and realistic generative render.

Positive promtp used:

				
					In a futuristic world, there is this beautiful woman wearing heavy make-up in geometric shapes. She is standing in front of a neutral vibrant yellow background, while the dynamic city lights casts high contrasting light on her face and mechanical parts. This is a portrait shot on Leica M6 using 85 mm lens. The skin texture is very sharp to the pore-level and has some imperfections. Film grain. fluxlisimo_cinematic
				
			

Generation time is marked as XX”

Model: flux1-dev-fp8.safetensors
Clip 1: clip_l.safetensors
Clip 2: t5xxl_fp16.safetensors
VAE: ae.safetensors
LORA: 3d_illustration + fluxlisimo_cinematic + grain_scape
Dimension: 640 x 640
Flux Conditioning: 1.5
Fixed Seed: 347746757495977
Steps: 16
eta: 0.8
Scheduler: normal
Rendered using GTX 3080

Veredict

Fastest
Exponential DDIM and Linear Euler in  24,5 seconds

Veredict

Best quality and prompt adeherence
Exponential rk6_7s in 167 seconds

Veredict

Very good result I
Fully implicit radau_iia_7s in 227,5 seconds

Veredict

Very good result II
Exponential Res_4_Minchev in 95 seconds

All samplers compared side to side

Below you will find each Sampler Algorithm Method followed by the sampler’s name and the time (in seconds) it took to generate/render a 640 x 640 px image on a GTX 3080 10GB.

Overall selected samplers for good resulting

At the end of this Article you will find some generations using the Samplers I am electing as the best out-coming results. These generations were rendered at 24 steps and at 512 x 1024 px on a GTX 3080 10GB. Jump directly to this section.

Multistep

Res_2m, Res_3m, Dpmpp_2m, Dpmpp_3m, Abnorsett_2m, Abnorsett_3m, Abnorsett_4m, Deis_2, Deis_3m, DEis_4m

multistep
res_2m
29”

multistep
res_3m
36”

multistep
dpmpp_2m
29”

multistep
dpmpp_3m
36”

multistep
abnorsett_2m
29”

multistep
abnorsett_3m
32”

multistep
abnorsett_4m
42”

multistep
deis_2m
28”

multistep
deis_3m
36”

multistep
deis_4m
47”

Exponential

Res2, Res3, Res4, Res5, res6, Res8, Res10, Res 15, Res16, etdrk2, etdrk3, dpmpp, lawson, ddim

exponential
res_2s
47”

exponential
res_2s_stable
47,5”

exponential
res_2s_rkmk2e
47”

exponential
res_3s
70”

exponential
res_3s_non-monotonic
69,5”

exponential
res_3s_alt
70,5”

exponential
res_3s_cox_matthews
70”

exponential
res_3s_lie
71”

exponential
res_3s_sunstar
71,5”

exponential
res_3s_strehmel_weinner
92,5”

exponential
res_3s_krogstadt
92,5”

exponential
res_4s_cox_matthews
93,5”

exponential
res_4s_cfree4
93”

exponential
res_4s_friedli
106”

exponential
res_4s_minchev
95”

exponential
res_4s_munthe-kaas
91”

exponential
res_5s
116”

exponential
res_5s_hochbruck-ostermann
115”

exponential
res_6s
140”

exponential
res_8s
187”

exponential
res_10s
239”

exponential
res_15s
366”

exponential
res_16s
384”

exponential
etdrk2_2s
46”

exponential
etdrk3_a_3s
69”

exponential
etdrk3_b_3s
69”

exponential
etdrk4_4s
93,5”

exponential
etdrk3_4s_alt
92”

exponential
dpmpp_2s
47,5”

exponential
dpmpp_sde_2s
47”

exponential
dpmpp_3s
70”

exponential
lawson_2a_2s
48”

exponential
lawson_2b_2s
48”

exponential
lawson4_4s
93”

exponential
lawson41-gen_4s
93”

exponential
lawson41-gen-mod_4s
92”

exponential
ddim
24,5”

Hybrid

Pec423, pec 433, abnorsett, lawson

hybrid
pec423_2h3s
47”

hybrid
pec433_2h3s
70”

hybrid
abnorsett2_1h2s
48”

hybrid
abnorsett3_2h2s
47,5”

hybrid
lawson42-gen-mod_1h4s
93”

hybrid
lawson42-gen-mod_2h4s
94”

hybrid
lawson42-gen-mode_3h4s
94”

Linear

ralston, midpoint, heun, houwen-wray, kutta, ssprk3, RK, bogacki_shampine, dormand-prince, tsi, euler

linear
ralston_2s
47,5”

linear
ralston_3s
71”

linear
ralston_4s
93”

linear
midpoint_2s
47”

linear
heun_2s
48,5”

linear
heun_3s
71”

linear
houwen-wray_3s
71,5”

linear
kutta_3s
71”

linear
ssprk3_3s
71”

linear
ssprk4_4s
110”

linear
rk38_4s
95”

linear
rk4_4s
93”

linear
rk6_7s
167”

linear
bogacki_shampine_4s
95”

linear
bogacki_shampine_7s
165”

linear
dormand-prince_6s
141”

linear
dormand-prince_13s
318,5”

linear
tsi_7s
167”

linear
euler
24,5”

Diagonal implicit

irk_exp_diag, kraaijevanger_spijkjer, qin_zhang, pareschi_russo, crouzeix

diagonal implicit
irk_exp_diag_2s
74”

diagonal implicit
kraaijevanger_spijkjer_2s
74”

diagonal implicit
qin_zhang_2s
74”

diagonal implicit
pareschi_russo_2s
73”

diagonal implicit
pareschi_russo_alt_2s
76”

diagonal implicit
crouzeix_2s
74”

diagonal implicit
crouzeix_3s
100”

diagonal implicit
crouzeix_3s_alt
102”

Fully implicit

gauss_legendre, RADAU, lobatto

fully implicit
gauss_legendre_2s
74”

fully implicit
gauss_legendre_3s
101”

fully implicit
gauss_legendre_4s
130”

fully implicit
gauss_legendre_5s
160”

fully implicit
gauss_legendre_5s_ascending
159”

fully implicit
radau_ia_2s
73”

fully implicit
radau_ia_3s
76”

fully implicit
radau_iia_2s
74”

fully implicit
radau_iia_3s
99,5”

fully implicit
radau_iia_3s_alt
100”

fully implicit
radau_iia_5s
164”

fully implicit
radau_iia_7s
227,5”

fully implicit
radau_iia_9s
303”

fully implicit
radau_iia_11s
375”

fully implicit
lobatto_iiia_2s
54”

fully implicit
lobatto_iiia_3s
80”

fully implicit
lobatto_iiia_4s
109,5”

fully implicit
lobatto_iib_2s
54”

fully implicit
lobatto_iib_3s
77,5”

fully implicit
lobatto_iib_4s
103,5”

fully implicit
lobatto_iic_2s
50,5”

fully implicit
lobatto_iic_3s
78,5”

fully implicit
lobatto_iic_4s
106,5”

fully implicit
lobatto_iic_star_2s
51”

fully implicit
lobatto_iic_star_3s
78”

fully implicit
lobatto_iid_2s
51”

fully implicit
lobatto_iid_3s
77,5”

… end of the sampler’s list …

Overall good results

RES_4S_MINCHEV, DEIS_3M, RES_2M, RK6_7S, lobatto_iiib_4s, lobatto_iiic_4s

exponential
res_4s_minchev
24 steps
150”

exponential
res_4s_minchev
24 steps
151.5”

exponential
res_4s_minchev
24 steps
151,5”

exponential
res_4s_minchev
24 steps
157”

multistep
deis_3m
24 steps
60”

multistep
deis_3m
24 steps
60”

multistep
deis_3m
24 steps
59”

multistep
deis_3m
24 steps
59”

multistep
res_2m
24 steps
51”

multistep
res_2m
24 steps
51”

multistep
res_2m
24 steps
50”

multistep
res_2m
24 steps
51”

linear
rk6_7s
24 steps
275”

linear
rk6_7s
24 steps
169”

linear
rk6_7s
24 steps
169”

linear
rk6_7s
24 steps
296”

fully implicit
lobatto_iiib_4s
24 steps
169”

fully implicit
lobatto_iiib_4s
24 steps
169”

fully implicit
lobatto_iiib_4s
24 steps
175”

fully implicit
lobatto_iiib_4s
24 steps
173,5”

fully implicit
lobatto_iiic_4s
24 steps
170”

fully implicit
lobatto_iiic_4s
24 steps
167”

fully implicit
lobatto_iiic_4s
24 steps
174”

fully implicit
lobatto_iiic_4s
24 steps
166,5”

End of the first study and batch comparison test.

Workflow: {“id”: “72f6123a-4c3f-4675-9e45-86722ffa8aeb”, “revision”: 0, “last_node_id”: 22, “last_link_id”: 33, “nodes”: [{“id”: 13, “type”: “VAEDecode”, “pos”: [859.5225830078125, 217.15052795410156], “size”: [140, 46], “flags”: {}, “order”: 8, “mode”: 0, “inputs”: [{“name”: “samples”, “type”: “LATENT”, “link”: 12}, {“name”: “vae”, “type”: “VAE”, “link”: 13}], “outputs”: [{“name”: “IMAGE”, “type”: “IMAGE”, “links”: [14, 16]}], “properties”: {“cnr_id”: “comfy-core”, “ver”: “0.3.44”, “Node name for S&R”: “VAEDecode”}, “widgets_values”: [], “color”: “#322”, “bgcolor”: “#533”, “shape”: 1}, {“id”: 10, “type”: “CLIPTextEncode”, “pos”: [115.9236831665039, 642.4409790039062], “size”: [400, 200], “flags”: {“collapsed”: true}, “order”: 3, “mode”: 0, “inputs”: [{“name”: “clip”, “type”: “CLIP”, “link”: 7}], “outputs”: [{“name”: “CONDITIONING”, “type”: “CONDITIONING”, “links”: [9]}], “title”: “Negative Prompt”, “properties”: {“cnr_id”: “comfy-core”, “ver”: “0.3.44”, “Node name for S&R”: “CLIPTextEncode”}, “widgets_values”: [“”], “color”: “#322”, “bgcolor”: “#533”, “shape”: 1}, {“id”: 14, “type”: “SaveImageExtended”, “pos”: [1060, 210], “size”: [231.4296875, 718], “flags”: {}, “order”: 9, “mode”: 0, “inputs”: [{“name”: “images”, “type”: “IMAGE”, “link”: 14}, {“name”: “positive_text_opt”, “shape”: 7, “type”: “STRING”, “link”: null}, {“name”: “negative_text_opt”, “shape”: 7, “type”: “STRING”, “link”: null}], “outputs”: [], “properties”: {“cnr_id”: “save-image-extended-comfyui”, “ver”: “2.64.0”, “Node name for S&R”: “SaveImageExtended”}, “widgets_values”: [“RES4LYF_”, “sampler_name, cfg, steps, %F %H-%M-%S”, “comparisionbatch”, “”, “-“, “disabled”, false, “”, true, 4, “last”, true, true, “.jpg”, 90], “shape”: 1}, {“id”: 1, “type”: “ClownModelLoader”, “pos”: [-203.28634643554688, 222.3040313720703], “size”: [270, 266], “flags”: {}, “order”: 0, “mode”: 0, “inputs”: [], “outputs”: [{“name”: “model”, “type”: “MODEL”, “links”: [33]}, {“name”: “clip”, “type”: “CLIP”, “links”: [7, 28]}, {“name”: “vae”, “type”: “VAE”, “links”: [10, 13]}], “properties”: {“cnr_id”: “RES4LYF”, “ver”: “81fe5f4d02c1a86d4cbb1ef92c652055f574810a”, “Node name for S&R”: “ClownModelLoader”}, “widgets_values”: [“flux1-dev-fp8.safetensors”, “default”, “clip_l.safetensors”, “t5xxl_fp16.safetensors”, “.none”, “.none”, “flux”, “ae.safetensors”], “shape”: 1}, {“id”: 11, “type”: “FluxGuidance”, “pos”: [-211.47117614746094, 563.1766357421875], “size”: [270, 58], “flags”: {}, “order”: 1, “mode”: 0, “inputs”: [{“name”: “conditioning”, “type”: “CONDITIONING”, “link”: null}], “outputs”: [{“name”: “CONDITIONING”, “type”: “CONDITIONING”, “links”: null}], “properties”: {“cnr_id”: “comfy-core”, “ver”: “0.3.44”, “Node name for S&R”: “FluxGuidance”}, “widgets_values”: [1.5]}, {“id”: 22, “type”: “Lora Loader Stack (rgthree)”, “pos”: [120, 90], “size”: [271.8785095214844, 246], “flags”: {}, “order”: 4, “mode”: 0, “inputs”: [{“name”: “model”, “type”: “MODEL”, “link”: 33}, {“name”: “clip”, “type”: “CLIP”, “link”: 28}], “outputs”: [{“name”: “MODEL”, “type”: “MODEL”, “links”: [32]}, {“name”: “CLIP”, “type”: “CLIP”, “links”: [29]}], “properties”: {“cnr_id”: “rgthree-comfy”, “ver”: “944d5353a1b0a668f40844018c3dc956b95a67d7”, “Node name for S&R”: “Lora Loader Stack (rgthree)”}, “widgets_values”: [“3d-illustration.safetensors”, 0.5000000000000001, “fluxlisimo_cinematic_v1_FLUX.safetensors”, 0.8500000000000002, “GrainScape.safetensors”, 0.5000000000000001, “None”, 0], “color”: “#233”, “bgcolor”: “#355”, “shape”: 1}, {“id”: 9, “type”: “CLIPTextEncode”, “pos”: [111.22306060791016, 391.9552917480469], “size”: [418.7828674316406, 200], “flags”: {}, “order”: 6, “mode”: 0, “inputs”: [{“name”: “clip”, “type”: “CLIP”, “link”: 29}], “outputs”: [{“name”: “CONDITIONING”, “type”: “CONDITIONING”, “links”: [8]}], “title”: “Positive Prompt”, “properties”: {“cnr_id”: “comfy-core”, “ver”: “0.3.44”, “Node name for S&R”: “CLIPTextEncode”}, “widgets_values”: [“In a futuristic world, there is this beautiful woman wearing heavy make-up in geometric shapes. She is standing in front of a neutral vibrant yellow background, while the dynamic city lights casts high contrasting light on her face and mechanical parts. This is a portrait shot on Leica M6 using 85 mm lens. The skin texture is very sharp to the pore-level and has some imperfections. 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RES4LYF ClownsharKSampler

Comparison chart generated on 19/20 July 2025