File size: 3,928 Bytes
edb09f2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | import random
import math
import comfy.sample
import latent_preview
class SimpleSampler:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"sampler": (
[
"Normal - euler",
"Normal - uni_pc",
"LCM Lora - lcm",
"SDXL Turbo - dpmpp_sde karras",
],
),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"latents": ("LATENT",),
"mode": (["txt2img", "img2img"],),
},
"optional": {
"seed": (
"INT",
{
"forceInput": True,
},
),
},
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "sample"
CATEGORY = "Chibi-Nodes"
@classmethod
def IS_CHANGED(s, **kwargs):
random.seed()
return float("NaN")
def sample(
self,
model,
sampler,
positive,
negative,
latents,
mode,
seed=None,
scheduler="normal",
sampler_name="euler",
):
# ['euler', 'euler_ancestral', 'heun', 'heunpp2', 'dpm_2', 'dpm_2_ancestral', 'lms', 'dpm_fast', 'dpm_adaptive','dpmpp_2s_ancestral', 'dpmpp_sde', 'dpmpp_sde_gpu', 'dpmpp_2m', 'dpmpp_2m_sde', 'dpmpp_2m_sde_gpu', 'dpmpp_3m_sde', 'dpmpp_3m_sde_gpu', 'ddpm', 'lcm', 'ddim', 'uni_pc', 'uni_pc_bh2']
# ['normal', 'karras', 'exponential', 'sgm_uniform', 'simple', 'ddim_uniform']
match sampler:
case "Normal - euler":
sampler_name = "uni_pc"
steps = 20
cfg = 7
case "Normal - uni_pc":
sampler_name = "uni_pc"
steps = 20
cfg = 7
case "LCM Lora - lcm":
sampler_name = "lcm"
steps = 8
cfg = 1.8
case "SDXL Turbo - dpmpp_sde karras":
sampler_name = "ddmpp_sde"
steps = 8
cfg = 1.8
scheduler = "karras"
case _:
steps = 20
cfg = 7
match mode:
case "txt2img":
denoise = 1.0
case "img2img":
denoise = 0.6
case _:
denoise = 1.0
if seed is not None:
random.seed(seed)
else:
random.seed()
seed = math.floor(random.random() * 10000000000000000)
latent_image = latents["samples"]
batch_inds = latents["batch_index"] if "batch_index" in latents else None
noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)
noise_mask = None
if "noise_mask" in latents:
noise_mask = latents["noise_mask"]
callback = latent_preview.prepare_callback(model, steps)
samples = comfy.sample.sample(
model=model,
noise=noise,
steps=steps,
cfg=cfg,
sampler_name=sampler_name,
scheduler=scheduler,
positive=positive,
negative=negative,
latent_image=latent_image,
denoise=denoise,
disable_noise=False,
start_step=0,
last_step=steps,
force_full_denoise=True,
noise_mask=noise_mask,
callback=callback,
disable_pbar=False,
seed=seed,
)
out = latents.copy()
out["samples"] = samples
return (out,)
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