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on
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Running
on
Zero
| from .imagefunc import * | |
| NODE_NAME = 'AddGrain' | |
| class AddGrain: | |
| def __init__(self): | |
| pass | |
| def INPUT_TYPES(self): | |
| return { | |
| "required": { | |
| "image": ("IMAGE", ), # | |
| "grain_power": ("FLOAT", {"default": 0.5, "min": 0, "max": 1, "step": 0.01}), | |
| "grain_scale": ("FLOAT", {"default": 1, "min": 0.1, "max": 10, "step": 0.1}), | |
| "grain_sat": ("FLOAT", {"default": 1, "min": 0, "max": 1, "step": 0.01}), | |
| }, | |
| "optional": { | |
| } | |
| } | |
| RETURN_TYPES = ("IMAGE",) | |
| RETURN_NAMES = ("image",) | |
| FUNCTION = 'add_grain' | |
| CATEGORY = '😺dzNodes/LayerFilter' | |
| def add_grain(self, image, grain_power, grain_scale, grain_sat): | |
| ret_images = [] | |
| for i in image: | |
| _canvas = tensor2pil(torch.unsqueeze(i, 0)).convert('RGB') | |
| _canvas = image_add_grain(_canvas, grain_scale, grain_power, grain_sat, toe=0, seed=int(time.time())) | |
| ret_images.append(pil2tensor(_canvas)) | |
| log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish') | |
| return (torch.cat(ret_images, dim=0),) | |
| NODE_CLASS_MAPPINGS = { | |
| "LayerFilter: AddGrain": AddGrain | |
| } | |
| NODE_DISPLAY_NAME_MAPPINGS = { | |
| "LayerFilter: AddGrain": "LayerFilter: Add Grain" | |
| } |