Spaces:
Running
Running
| import os | |
| from PIL import ImageOps | |
| from impact.utils import * | |
| from . import core | |
| import random | |
| class PreviewBridge: | |
| 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 | |
| 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: | |
| 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)) | |
| 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, ), | |
| } | |