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Update nodes.py

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kijai 2024-05-02 20:59:29 +03:00
parent 7b2b1c35c1
commit b47aa365f5
1 changed files with 6 additions and 5 deletions
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nodes/nodes.py
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@ -18,6 +18,7 @@ from nodes import MAX_RESOLUTION
import folder_paths import folder_paths
from ..utility.utility import tensor2pil, pil2tensor from ..utility.utility import tensor2pil, pil2tensor
from comfy.utils import ProgressBar
script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
folder_paths.add_model_folder_path("kjnodes_fonts", os.path.join(script_directory, "fonts")) folder_paths.add_model_folder_path("kjnodes_fonts", os.path.join(script_directory, "fonts"))
@ -751,7 +752,7 @@ controls the number of images processed at once.
black, white = white, black black, white = white, black
steps = images.shape[0] steps = images.shape[0]
pbar = comfy.utils.ProgressBar(steps) pbar = ProgressBar(steps)
tensors_out = [] tensors_out = []
for start_idx in range(0, images.shape[0], per_batch): for start_idx in range(0, images.shape[0], per_batch):
@ -1345,15 +1346,15 @@ Segments an image or batch of images using CLIPSeg.
device = torch.device("cuda") device = torch.device("cuda")
else: else:
device = torch.device("cpu") device = torch.device("cpu")
dtype = comfy.model_management.unet_dtype() dtype = model_management.unet_dtype()
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined") model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
model.to(dtype) model.to(dtype)
model.to(device) model.to(device)
images = images.to(device) images = images.to(device)
processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
pbar = comfy.utils.ProgressBar(images.shape[0]) pbar = ProgressBar(images.shape[0])
autocast_condition = (dtype != torch.float32) and not comfy.model_management.is_device_mps(device) autocast_condition = (dtype != torch.float32) and not model_management.is_device_mps(device)
with torch.autocast(comfy.model_management.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext(): with torch.autocast(model_management.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
for image in images: for image in images:
image = (image* 255).type(torch.uint8) image = (image* 255).type(torch.uint8)
prompt = text prompt = text