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import os
from PIL import ImageOps
from impact.utils import *
import latent_preview
# NOTE: this should not be `from . import core`.
# I don't know why but... 'from .' and 'from impact' refer to different core modules.
# This separates global variables of the core module and breaks the preview bridge.
from impact import core
# <--
import random
class PreviewBridge:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"images": ("IMAGE",),
"image": ("STRING", {"default": ""}),
},
"optional": {
"block": ("BOOLEAN", {"default": False, "label_on": "if_empty_mask", "label_off": "never", "tooltip": "is_empty_mask: If the mask is empty, the execution is stopped.\nnever: The execution is never stopped."}),
"restore_mask": (["never", "always", "if_same_size"], {"tooltip": "if_same_size: If the changed input image is the same size as the previous image, restore using the last saved mask\nalways: Whenever the input image changes, always restore using the last saved mask\nnever: Do not restore the mask.\n`restore_mask` has higher priority than `block`"}),
},
"hidden": {"unique_id": "UNIQUE_ID", "extra_pnginfo": "EXTRA_PNGINFO"},
}
RETURN_TYPES = ("IMAGE", "MASK", )
FUNCTION = "doit"
OUTPUT_NODE = True
CATEGORY = "ImpactPack/Util"
DESCRIPTION = "This is a feature that allows you to edit and send a Mask over a image.\nIf the block is set to 'is_empty_mask', the execution is stopped when the mask is empty."
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, block=False, restore_mask="never", prompt=None, extra_pnginfo=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 images:
need_refresh = True
if not need_refresh:
pixels, mask, path_item = PreviewBridge.load_image(image)
image = [path_item]
else:
if restore_mask != "never":
mask = core.preview_bridge_last_mask_cache.get(unique_id)
if mask is None or (restore_mask != "always" and mask.shape[1:] != images.shape[1:3]):
mask = None
else:
mask = None
if mask is None:
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
res = nodes.PreviewImage().save_images(images, filename_prefix="PreviewBridge/PB-", prompt=prompt, extra_pnginfo=extra_pnginfo)
else:
masked_images = tensor_convert_rgba(images)
resized_mask = resize_mask(mask, (images.shape[1], images.shape[2])).unsqueeze(3)
resized_mask = 1 - resized_mask
tensor_putalpha(masked_images, resized_mask)
res = nodes.PreviewImage().save_images(masked_images, filename_prefix="PreviewBridge/PB-", prompt=prompt, extra_pnginfo=extra_pnginfo)
image2 = res['ui']['images']
pixels = images
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
is_empty_mask = torch.all(mask == 0)
if block and is_empty_mask and core.is_execution_model_version_supported():
from comfy_execution.graph import ExecutionBlocker
result = ExecutionBlocker(None), ExecutionBlocker(None)
elif block and is_empty_mask:
print(f"[Impact Pack] PreviewBridge: ComfyUI is outdated - blocking feature is disabled.")
result = pixels, mask
else:
result = pixels, mask
if not is_empty_mask:
core.preview_bridge_last_mask_cache[unique_id] = mask
return {
"ui": {"images": image},
"result": result,
}
def decode_latent(latent, preview_method, vae_opt=None):
if vae_opt is not None:
image = nodes.VAEDecode().decode(vae_opt, latent)[0]
return image
from comfy.cli_args import LatentPreviewMethod
import comfy.latent_formats as latent_formats
if preview_method.startswith("TAE"):
decoder_name = None
if preview_method == "TAESD15":
decoder_name = "taesd"
elif preview_method == 'TAESDXL':
decoder_name = "taesdxl"
elif preview_method == 'TAESD3':
decoder_name = "taesd3"
elif preview_method == 'TAEF1':
decoder_name = "taef1"
if decoder_name:
vae = nodes.VAELoader().load_vae(decoder_name)[0]
image = nodes.VAEDecode().decode(vae, latent)[0]
return image
if preview_method == "Latent2RGB-SD15":
latent_format = latent_formats.SD15()
method = LatentPreviewMethod.Latent2RGB
elif preview_method == "Latent2RGB-SDXL":
latent_format = latent_formats.SDXL()
method = LatentPreviewMethod.Latent2RGB
elif preview_method == "Latent2RGB-SD3":
latent_format = latent_formats.SD3()
method = LatentPreviewMethod.Latent2RGB
elif preview_method == "Latent2RGB-SD-X4":
latent_format = latent_formats.SD_X4()
method = LatentPreviewMethod.Latent2RGB
elif preview_method == "Latent2RGB-Playground-2.5":
latent_format = latent_formats.SDXL_Playground_2_5()
method = LatentPreviewMethod.Latent2RGB
elif preview_method == "Latent2RGB-SC-Prior":
latent_format = latent_formats.SC_Prior()
method = LatentPreviewMethod.Latent2RGB
elif preview_method == "Latent2RGB-SC-B":
latent_format = latent_formats.SC_B()
method = LatentPreviewMethod.Latent2RGB
elif preview_method == "Latent2RGB-FLUX.1":
latent_format = latent_formats.Flux()
method = LatentPreviewMethod.Latent2RGB
else:
print(f"[Impact Pack] PreviewBridgeLatent: '{preview_method}' is unsupported preview method.")
latent_format = latent_formats.SD15()
method = LatentPreviewMethod.Latent2RGB
previewer = core.get_previewer("cpu", latent_format=latent_format, force=True, method=method)
samples = latent_format.process_in(latent['samples'])
pil_image = previewer.decode_latent_to_preview(samples)
pixels_size = pil_image.size[0]*8, pil_image.size[1]*8
resized_image = pil_image.resize(pixels_size, resample=LANCZOS)
return to_tensor(resized_image).unsqueeze(0)
class PreviewBridgeLatent:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"latent": ("LATENT",),
"image": ("STRING", {"default": ""}),
"preview_method": (["Latent2RGB-FLUX.1",
"Latent2RGB-SDXL", "Latent2RGB-SD15", "Latent2RGB-SD3",
"Latent2RGB-SD-X4", "Latent2RGB-Playground-2.5",
"Latent2RGB-SC-Prior", "Latent2RGB-SC-B",
"TAEF1", "TAESDXL", "TAESD15", "TAESD3"],),
},
"optional": {
"vae_opt": ("VAE", ),
"block": ("BOOLEAN", {"default": False, "label_on": "if_empty_mask", "label_off": "never", "tooltip": "is_empty_mask: If the mask is empty, the execution is stopped.\nnever: The execution is never stopped. Instead, it returns a white mask."}),
"restore_mask": (["never", "always", "if_same_size"], {"tooltip": "if_same_size: If the changed input latent is the same size as the previous latent, restore using the last saved mask\nalways: Whenever the input latent changes, always restore using the last saved mask\nnever: Do not restore the mask.\n`restore_mask` has higher priority than `block`\nIf the input latent already has a mask, do not restore mask."}),
},
"hidden": {"unique_id": "UNIQUE_ID", "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}
RETURN_TYPES = ("LATENT", "MASK", )
FUNCTION = "doit"
OUTPUT_NODE = True
CATEGORY = "ImpactPack/Util"
DESCRIPTION = "This is a feature that allows you to edit and send a Mask over a latent image.\nIf the block is set to 'is_empty_mask', the execution is stopped when the mask is empty."
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, block=False, unique_id=None, restore_mask='never', prompt=None, extra_pnginfo=None):
latent_channels = latent['samples'].shape[1]
preview_method_channels = 16 if 'SD3' in preview_method or 'SC-Prior' in preview_method or 'FLUX.1' in preview_method or 'TAEF1' == preview_method else 4
if vae_opt is None and latent_channels != preview_method_channels:
print(f"[PreviewBridgeLatent] The version of latent is not compatible with preview_method.\nSD3, SD1/SD2, SDXL, SC-Prior, SC-B and FLUX.1 are not compatible with each other.")
raise Exception("The version of latent is not compatible with preview_method.<BR>SD3, SD1/SD2, SDXL, SC-Prior, SC-B and FLUX.1 are not compatible with each other.")
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
is_empty_mask = True
else:
res_latent = latent.copy()
res_latent['noise_mask'] = mask
is_empty_mask = torch.all(mask == 1)
res_image = [path_item]
else:
decoded_image = decode_latent(latent, preview_method, vae_opt)
if 'noise_mask' in latent:
mask = latent['noise_mask'].squeeze(0) # 4D mask -> 3D 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',
}]
is_empty_mask = False
else:
if restore_mask != "never":
mask = core.preview_bridge_last_mask_cache.get(unique_id)
if mask is None or (restore_mask != "always" and mask.shape[1:] != decoded_image.shape[1:3]):
mask = None
else:
mask = None
if mask is None:
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-", prompt=prompt, extra_pnginfo=extra_pnginfo)
else:
masked_images = tensor_convert_rgba(decoded_image)
resized_mask = resize_mask(mask, (decoded_image.shape[1], decoded_image.shape[2])).unsqueeze(3)
resized_mask = 1 - resized_mask
tensor_putalpha(masked_images, resized_mask)
res = nodes.PreviewImage().save_images(masked_images, filename_prefix="PreviewBridge/PBL-", prompt=prompt, extra_pnginfo=extra_pnginfo)
res_image = res['ui']['images']
is_empty_mask = torch.all(mask == 1)
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
if block and is_empty_mask and core.is_execution_model_version_supported():
from comfy_execution.graph import ExecutionBlocker
result = ExecutionBlocker(None), ExecutionBlocker(None)
elif block and is_empty_mask:
print(f"[Impact Pack] PreviewBridgeLatent: ComfyUI is outdated - blocking feature is disabled.")
result = res_latent, mask
else:
result = res_latent, mask
if not is_empty_mask:
core.preview_bridge_last_mask_cache[unique_id] = mask
return {
"ui": {"images": res_image},
"result": result,
}
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