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