Preview Bridge (Latent)¶
Documentation¶
- Class name:
PreviewBridgeLatent - Category:
ImpactPack/Util - Output node:
True
The PreviewBridgeLatent node is designed to facilitate the previewing of latent representations by decoding them into visual formats. It leverages various decoding strategies and optional VAE configurations to convert latent tensors into images, supporting different preview methods tailored to specific latent formats.
Input types¶
Required¶
latent- The latent representation to be decoded into an image. This input is crucial as it contains the encoded information of the image in a compressed form, which is then transformed into a visual format based on the specified preview method and optional VAE configuration.
- Comfy dtype:
LATENT - Python dtype:
torch.Tensor
image- unknown
- Comfy dtype:
STRING - Python dtype:
unknown
preview_method- Determines the decoding strategy and the format of the resulting image. This input is essential for selecting the appropriate method to decode the latent representation into its visual counterpart.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
Optional¶
vae_opt- An optional parameter that allows for the customization of the decoding process with specific VAE models and settings. Providing a VAE configuration can significantly influence the decoding outcome by utilizing tailored models.
- Comfy dtype:
VAE - Python dtype:
Optional[Dict]
Output types¶
latent- Comfy dtype:
LATENT - unknown
- Python dtype:
unknown
- Comfy dtype:
mask- Comfy dtype:
MASK - An optional output that represents a mask applied to the decoded image, used for further processing or visualization purposes.
- Python dtype:
Optional[torch.Tensor]
- Comfy dtype:
ui- A UI parameter that encapsulates additional information or metadata about the decoding process, potentially including paths to saved images or other relevant details.
Usage tips¶
- Infra type:
GPU - Common nodes: unknown
Source code¶
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, ),
}