186 lines
8.6 KiB
Python
186 lines
8.6 KiB
Python
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from nodes import PreviewImage, LoadImage
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from comfy.model_management import InterruptProcessingException
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import os
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import torch
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from .image_filter_messaging import send_and_wait, Response, TimeoutResponse
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HIDDEN = {
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"prompt": "PROMPT",
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"extra_pnginfo": "EXTRA_PNGINFO",
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"uid":"UNIQUE_ID",
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"node_identifier": "NID",
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}
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class ImageFilter(PreviewImage):
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RETURN_TYPES = ("IMAGE","LATENT","MASK","STRING","STRING","STRING","STRING")
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RETURN_NAMES = ("images","latents","masks","extra1","extra2","extra3","indexes")
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FUNCTION = "func"
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CATEGORY = "image_filter"
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OUTPUT_NODE = False
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DESCRIPTION = "Allows you to preview images and choose which, if any to proceed with"
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"images" : ("IMAGE", ),
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"timeout": ("INT", {"default": 600, "min":1, "max":9999999, "tooltip": "Timeout in seconds."}),
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"ontimeout": (["send none", "send all", "send first", "send last"], {}),
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},
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"optional": {
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"latents" : ("LATENT", {"tooltip": "Optional - if provided, will be output"}),
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"masks" : ("MASK", {"tooltip": "Optional - if provided, will be output"}),
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"tip" : ("STRING", {"default":"", "tooltip": "Optional - if provided, will be displayed in popup window"}),
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"extra1" : ("STRING", {"default":""}),
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"extra2" : ("STRING", {"default":""}),
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"extra3" : ("STRING", {"default":""}),
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"pick_list_start" : ("INT", {"default":0, "tooltip":"The number used in pick_list for the first image"}),
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"pick_list" : ("STRING", {"default":"", "tooltip":"If a comma separated list of integers is provided, the images with these indices will be selected automatically."}),
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"video_frames" : ("INT", {"default":1, "min":1, "tooltip": "treat each block of n images as a video"}),
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},
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"hidden": HIDDEN,
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}
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@classmethod
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def IS_CHANGED(cls, pick_list, **kwargs):
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return pick_list or float("NaN")
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def func(self, images, timeout, ontimeout, uid, node_identifier, tip="", extra1="", extra2="", extra3="", latents=None, masks=None, pick_list_start:int=0, pick_list:str="", video_frames:int=1, **kwargs):
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e1, e2, e3 = extra1, extra2, extra3
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B = images.shape[0]
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if video_frames>1000: video_frames=1
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try: images_to_return = [ int(x.strip())%B for x in pick_list.split(',') ] if pick_list else []
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except Exception as e:
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print(f"{e} parsing pick_list - will manually select")
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images_to_return = []
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if len(images_to_return) == 0:
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all_the_same = ( B and all( (images[i]==images[0]).all() for i in range(1,B) ))
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urls:list[str] = self.save_images(images=images, **kwargs)['ui']['images']
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payload = {"uid": uid, "urls":urls, "allsame":all_the_same, "extras":[extra1, extra2, extra3], "tip":tip, "video_frames":video_frames}
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response:Response = send_and_wait(payload, timeout, uid, node_identifier)
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if isinstance(response, TimeoutResponse):
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if ontimeout=='send none': images_to_return = []
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if ontimeout=='send all': images_to_return = [*range(len(images))]
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if ontimeout=='send first': images_to_return = [0,]
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if ontimeout=='send last': images_to_return = [len(images)//video_frames,]
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else:
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e1, e2, e3 = response.get_extras([extra1, extra2, extra3])
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images_to_return = response.selection
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if images_to_return is None or len(images_to_return) == 0: raise InterruptProcessingException()
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if video_frames>1:
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images_to_return = [ key*video_frames + frm for key in images_to_return for frm in range(video_frames) ]
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images = torch.stack(list(images[int(i)] for i in images_to_return))
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latents = {"samples": torch.stack(list(latents['samples'][int(i)] for i in images_to_return))} if latents is not None else None
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masks = torch.stack(list(masks[int(i)] for i in images_to_return)) if masks is not None else None
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return (images, latents, masks, e1, e2, e3, ",".join(str(x+pick_list_start) for x in images_to_return))
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class TextImageFilterWithExtras(PreviewImage):
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RETURN_TYPES = ("IMAGE","STRING","STRING","STRING","STRING")
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RETURN_NAMES = ("image","text","extra1","extra2","extra3")
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FUNCTION = "func"
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CATEGORY = "image_filter"
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OUTPUT_NODE = False
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image" : ("IMAGE", ),
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"text" : ("STRING", {"default":""}),
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"timeout": ("INT", {"default": 600, "min":1, "max":9999999, "tooltip": "Timeout in seconds."}),
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},
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"optional": {
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"mask" : ("MASK", {"tooltip": "Optional - if provided, will be overlaid on image"}),
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"tip" : ("STRING", {"default":"", "tooltip": "Optional - if provided, will be displayed in popup window"}),
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"extra1" : ("STRING", {"default":""}),
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"extra2" : ("STRING", {"default":""}),
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"extra3" : ("STRING", {"default":""}),
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"textareaheight" : ("INT", {"default": 150, "min": 50, "max": 500, "tooltip": "Height of text area in pixels"}),
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},
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"hidden": HIDDEN,
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}
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@classmethod
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def IS_CHANGED(cls, **kwargs):
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return float("NaN")
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def func(self, image, text, timeout, uid, node_identifier, extra1="", extra2="", extra3="", mask=None, tip="", textareaheight=None, **kwargs):
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urls:list[str] = self.save_images(images=image, **kwargs)['ui']['images']
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payload = {"uid": uid, "urls":urls, "text":text, "extras":[extra1, extra2, extra3], "tip":tip}
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if textareaheight is not None: payload['textareaheight'] = textareaheight
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if mask is not None: payload['mask_urls'] = self.save_images(images=mask_to_image(mask), **kwargs)['ui']['images']
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response = send_and_wait(payload, timeout, uid, node_identifier)
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if isinstance(response, TimeoutResponse):
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return (image, text, extra1, extra2, extra3)
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return (image, response.text, *response.get_extras([extra1, extra2, extra3]))
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def mask_to_image(mask:torch.Tensor):
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return torch.stack([mask, mask, mask, 1.0-mask], -1)
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class MaskImageFilter(PreviewImage, LoadImage):
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RETURN_TYPES = ("IMAGE","MASK","STRING","STRING","STRING")
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RETURN_NAMES = ("image","mask","extra1","extra2","extra3")
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FUNCTION = "func"
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CATEGORY = "image_filter"
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OUTPUT_NODE = False
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image" : ("IMAGE", ),
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"timeout": ("INT", {"default": 600, "min":1, "max":9999999, "tooltip": "Timeout in seconds."}),
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"if_no_mask": (["cancel", "send blank"], {}),
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},
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"optional": {
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"mask" : ("MASK", {"tooltip":"optional initial mask"}),
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"tip" : ("STRING", {"default":"", "tooltip": "Optional - if provided, will be displayed in popup window"}),
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"extra1" : ("STRING", {"default":""}),
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"extra2" : ("STRING", {"default":""}),
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"extra3" : ("STRING", {"default":""}),
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},
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"hidden": HIDDEN,
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}
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@classmethod
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def IS_CHANGED(cls, **kwargs):
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return float("NaN")
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@classmethod
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def VALIDATE_INPUTS(cls, **kwargs): return True
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def func(self, image, timeout, uid, if_no_mask, node_identifier, mask=None, extra1="", extra2="", extra3="", tip="", **kwargs):
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if mask is not None and mask.shape[:3] == image.shape[:3] and not torch.all(mask==0):
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saveable = torch.cat((image, mask.unsqueeze(-1)), dim=-1)
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else:
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saveable = image
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urls:list[str] = self.save_images(images=saveable, **kwargs)['ui']['images']
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payload = {"uid": uid, "urls":urls, "maskedit":True, "extras":[extra1, extra2, extra3], "tip":tip}
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response = send_and_wait(payload, timeout, uid, node_identifier)
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if (response.masked_image):
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try:
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return ( *(self.load_image(os.path.join('clipspace', response.masked_image)+" [input]")), *response.get_extras([extra1, extra2, extra3]) )
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except FileNotFoundError:
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pass
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if if_no_mask == 'cancel':
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raise InterruptProcessingException()
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return ( *(self.load_image(urls[0]['filename']+" [temp]")), *response.get_extras([extra1, extra2, extra3]) )
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