util.getXXXX toooooooooooooo slow

[SagiriMOMO]

any other way?????????

[Chaoses-Ib]

It should be the same speed as ComfyUI, unless you triggered the workflow multiple times.

Anyway, another way is to use real mode, which will convert the image tensor to PIL images directly in memory. For example:

from comfy_script.runtime.real import *
load()
from comfy_script.runtime.real.nodes import *

# Or: with torch.inference_mode()
with Workflow():
    model, clip, vae = CheckpointLoaderSimple('v1-5-pruned-emaonly.ckpt')
    print(model, clip, vae, sep='\n')
    # <comfy.model_patcher.ModelPatcher object at 0x000002198721ECB0>
    # <comfy.sd.CLIP object at 0x000002198721C250>
    # <comfy.sd.VAE object at 0x000002183B128EB0>

    conditioning = CLIPTextEncode('beautiful scenery nature glass bottle landscape, , purple galaxy bottle,', clip)
    conditioning2 = CLIPTextEncode('text, watermark', clip)
    print(conditioning2)
    # [[
    #   tensor([[
    #     [-0.3885, ..., 0.0674],
    #     ...,
    #     [-0.8676, ..., -0.0057]
    #   ]]),
    #   {'pooled_output': tensor([[-1.2670e+00, ..., -1.5058e-01]])}
    # ]]

    latent = EmptyLatentImage(512, 512, 1)
    print(latent)
    # {'samples': tensor([[
    #   [[0., ..., 0.],
    #     ...,
    #    [0., ..., 0.]],
    #   [[0., ..., 0.],
    #     ...,
    #    [0., ..., 0.]],
    #   [[0., ..., 0.],
    #     ...,
    #    [0., ..., 0.]],
    #   [[0., ..., 0.],
    #     ...,
    #    [0., ..., 0.]]
    # ]])}

    latent = KSampler(model, 156680208700286, 20, 8, 'euler', 'normal', conditioning, conditioning2, latent, 1)

    image = VAEDecode(latent, vae)
    print(image)
    # tensor([[
    #   [[0.3389, 0.3652, 0.3428],
    #     ...,
    #     [0.4277, 0.3789, 0.1445]],
    #   ...,
    #   [[0.6348, 0.5898, 0.5270],
    #     ...,
    #     [0.7012, 0.6680, 0.5952]]
    # ]])

    print(SaveImage(image, 'ComfyUI'))
    # {'ui': {'images': [
    #   {'filename': 'ComfyUI_00001_.png',
    #     'subfolder': '',
    #     'type': 'output'}
    # ]}}

    print(util.get_images(image))