259 lines
9.1 KiB
Python
259 lines
9.1 KiB
Python
import os
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from PIL import ImageOps
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from impact.utils import *
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from . import core
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import random
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class PreviewBridge:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"images": ("IMAGE",),
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"image": ("STRING", {"default": ""}),
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},
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"hidden": {"unique_id": "UNIQUE_ID"},
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}
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RETURN_TYPES = ("IMAGE", "MASK", )
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FUNCTION = "doit"
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OUTPUT_NODE = True
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CATEGORY = "ImpactPack/Util"
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def __init__(self):
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super().__init__()
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self.output_dir = folder_paths.get_temp_directory()
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self.type = "temp"
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self.prev_hash = None
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@staticmethod
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def load_image(pb_id):
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is_fail = False
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if pb_id not in core.preview_bridge_image_id_map:
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is_fail = True
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image_path, ui_item = core.preview_bridge_image_id_map[pb_id]
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if not os.path.isfile(image_path):
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is_fail = True
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if not is_fail:
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i = Image.open(image_path)
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i = ImageOps.exif_transpose(i)
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image = i.convert("RGB")
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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if 'A' in i.getbands():
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mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
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mask = 1. - torch.from_numpy(mask)
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else:
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mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
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else:
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image = empty_pil_tensor()
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mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
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ui_item = {
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"filename": 'empty.png',
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"subfolder": '',
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"type": 'temp'
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}
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return image, mask.unsqueeze(0), ui_item
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def doit(self, images, image, unique_id):
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need_refresh = False
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if unique_id not in core.preview_bridge_cache:
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need_refresh = True
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elif core.preview_bridge_cache[unique_id][0] is not images:
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need_refresh = True
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if not need_refresh:
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pixels, mask, path_item = PreviewBridge.load_image(image)
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image = [path_item]
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else:
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res = nodes.PreviewImage().save_images(images, filename_prefix="PreviewBridge/PB-")
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image2 = res['ui']['images']
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pixels = images
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mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
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path = os.path.join(folder_paths.get_temp_directory(), 'PreviewBridge', image2[0]['filename'])
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core.set_previewbridge_image(unique_id, path, image2[0])
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core.preview_bridge_image_id_map[image] = (path, image2[0])
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core.preview_bridge_image_name_map[unique_id, path] = (image, image2[0])
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core.preview_bridge_cache[unique_id] = (images, image2)
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image = image2
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return {
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"ui": {"images": image},
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"result": (pixels, mask, ),
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}
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def decode_latent(latent_tensor, preview_method, vae_opt=None):
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if vae_opt is not None:
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image = nodes.VAEDecode().decode(vae_opt, latent_tensor)[0]
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return image
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from comfy.cli_args import LatentPreviewMethod
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import comfy.latent_formats as latent_formats
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if preview_method.startswith("TAE"):
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if preview_method == "TAESD15":
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decoder_name = "taesd"
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else:
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decoder_name = "taesdxl"
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vae = nodes.VAELoader().load_vae(decoder_name)[0]
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image = nodes.VAEDecode().decode(vae, latent_tensor)[0]
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return image
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else:
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if preview_method == "Latent2RGB-SD15":
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latent_format = latent_formats.SD15()
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method = LatentPreviewMethod.Latent2RGB
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else: # preview_method == "Latent2RGB-SDXL"
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latent_format = latent_formats.SDXL()
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method = LatentPreviewMethod.Latent2RGB
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previewer = core.get_previewer("cpu", latent_format=latent_format, force=True, method=method)
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pil_image = previewer.decode_latent_to_preview(latent_tensor['samples'])
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pixels_size = pil_image.size[0]*8, pil_image.size[1]*8
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resized_image = pil_image.resize(pixels_size, Image.NONE)
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return to_tensor(resized_image).unsqueeze(0)
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class PreviewBridgeLatent:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"latent": ("LATENT",),
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"image": ("STRING", {"default": ""}),
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"preview_method": (["Latent2RGB-SDXL", "Latent2RGB-SD15", "TAESDXL", "TAESD15"],),
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},
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"optional": {
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"vae_opt": ("VAE", )
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},
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"hidden": {"unique_id": "UNIQUE_ID"},
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}
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RETURN_TYPES = ("LATENT", "MASK", )
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FUNCTION = "doit"
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OUTPUT_NODE = True
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CATEGORY = "ImpactPack/Util"
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def __init__(self):
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super().__init__()
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self.output_dir = folder_paths.get_temp_directory()
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self.type = "temp"
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self.prev_hash = None
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self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
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@staticmethod
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def load_image(pb_id):
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is_fail = False
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if pb_id not in core.preview_bridge_image_id_map:
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is_fail = True
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image_path, ui_item = core.preview_bridge_image_id_map[pb_id]
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if not os.path.isfile(image_path):
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is_fail = True
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if not is_fail:
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i = Image.open(image_path)
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i = ImageOps.exif_transpose(i)
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image = i.convert("RGB")
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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if 'A' in i.getbands():
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mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
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mask = 1. - torch.from_numpy(mask)
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else:
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mask = None
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else:
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image = empty_pil_tensor()
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mask = None
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ui_item = {
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"filename": 'empty.png',
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"subfolder": '',
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"type": 'temp'
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}
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return image, mask, ui_item
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def doit(self, latent, image, preview_method, vae_opt=None, unique_id=None):
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need_refresh = False
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if unique_id not in core.preview_bridge_cache:
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need_refresh = True
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elif (core.preview_bridge_cache[unique_id][0] is not latent
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or (vae_opt is None and core.preview_bridge_cache[unique_id][2] is not None)
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or (vae_opt is None and core.preview_bridge_cache[unique_id][1] != preview_method)
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or (vae_opt is not None and core.preview_bridge_cache[unique_id][2] is not vae_opt)):
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need_refresh = True
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if not need_refresh:
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pixels, mask, path_item = PreviewBridge.load_image(image)
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if mask is None:
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mask = torch.ones(latent['samples'].shape[2:], dtype=torch.float32, device="cpu").unsqueeze(0)
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if 'noise_mask' in latent:
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res_latent = latent.copy()
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del res_latent['noise_mask']
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else:
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res_latent = latent
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else:
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res_latent = latent.copy()
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res_latent['noise_mask'] = mask
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res_image = [path_item]
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else:
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decoded_image = decode_latent(latent, preview_method, vae_opt)
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if 'noise_mask' in latent:
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mask = latent['noise_mask']
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decoded_pil = to_pil(decoded_image)
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inverted_mask = 1 - mask # invert
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resized_mask = resize_mask(inverted_mask, (decoded_image.shape[1], decoded_image.shape[2]))
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result_pil = apply_mask_alpha_to_pil(decoded_pil, resized_mask)
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full_output_folder, filename, counter, _, _ = folder_paths.get_save_image_path("PreviewBridge/PBL-"+self.prefix_append, folder_paths.get_temp_directory(), result_pil.size[0], result_pil.size[1])
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file = f"{filename}_{counter}.png"
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result_pil.save(os.path.join(full_output_folder, file), compress_level=4)
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res_image = [{
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'filename': file,
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'subfolder': 'PreviewBridge',
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'type': 'temp',
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}]
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else:
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mask = torch.ones(latent['samples'].shape[2:], dtype=torch.float32, device="cpu").unsqueeze(0)
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res = nodes.PreviewImage().save_images(decoded_image, filename_prefix="PreviewBridge/PBL-")
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res_image = res['ui']['images']
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path = os.path.join(folder_paths.get_temp_directory(), 'PreviewBridge', res_image[0]['filename'])
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core.set_previewbridge_image(unique_id, path, res_image[0])
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core.preview_bridge_image_id_map[image] = (path, res_image[0])
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core.preview_bridge_image_name_map[unique_id, path] = (image, res_image[0])
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core.preview_bridge_cache[unique_id] = (latent, preview_method, vae_opt, res_image)
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res_latent = latent
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return {
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"ui": {"images": res_image},
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"result": (res_latent, mask, ),
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}
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