Обновил .gitignore и внёс правки в Rayko_Image_Text и Rayko_Save_Image_Pair
This commit is contained in:
Raykosan
@@ -3,6 +3,14 @@ node.zip
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__pycache__/
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*.py[cod]
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*$py.class
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preset_colors/
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preset_models/
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presets/
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prompts/
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rayko_lora_data/
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styles/presets/
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ComfyUI-Custom-Node-Manager/
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python
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# C extensions
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*.so
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+125
-125
@@ -1,126 +1,126 @@
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# SPDX-License-Identifier: Apache-2.0
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# Copyright 2025-2026 Raykosan (RaykoStudio)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import torch
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import numpy as np
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from PIL import Image, PngImagePlugin
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import folder_paths
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def get_image_files():
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input_dir = folder_paths.get_input_directory()
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files = []
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for f in os.listdir(input_dir):
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if os.path.isfile(os.path.join(input_dir, f)) and f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.bmp')):
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files.append(f)
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files.sort()
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return files
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class LoadImageWithText:
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@classmethod
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def INPUT_TYPES(cls):
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files = get_image_files()
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return {
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"required": {
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"mode": (["read", "write"], {"default": "read"}),
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"filename_prefix": ("STRING", {"default": "Civitai/prompt", "tooltip": "Prefix to save to output (write only)"}),
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"text_input": ("STRING", {"default": "", "multiline": True, "tooltip": "Text to write (write only)"}),
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"image": (files, {"image_upload": True}),
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}
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}
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RETURN_TYPES = ("IMAGE", "STRING")
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RETURN_NAMES = ("image", "text")
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FUNCTION = "process"
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CATEGORY = "🦊 RaykoStudio"
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OUTPUT_NODE = True
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DESCRIPTION = "Node embeds any hidden text into the image that can be used later"
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def process(self, mode, image, text_input="", filename_prefix="ComfyUI"):
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try:
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input_path = folder_paths.get_annotated_filepath(image)
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if not os.path.exists(input_path):
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raise FileNotFoundError(f"File not found: {input_path}")
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if mode == "write":
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return self.write_mode(input_path, text_input, filename_prefix)
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else:
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return self.read_mode(input_path)
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except Exception as e:
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print(f"Error in process: {e}")
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import traceback
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traceback.print_exc()
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dummy_img = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
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return (dummy_img, "")
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def read_mode(self, input_path):
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pil_img = Image.open(input_path)
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text = pil_img.info.get("Description", "")
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image_tensor = self.pil_to_tensor(pil_img)
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print(f"[READ] Text: '{text}'")
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return (image_tensor, text)
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def write_mode(self, input_path, text, filename_prefix):
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pil_img = Image.open(input_path)
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image_tensor = self.pil_to_tensor(pil_img)
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pnginfo = PngImagePlugin.PngInfo()
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if text:
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pnginfo.add_text("Description", text)
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print(f"[WRITE] Text is recorded: '{text}'")
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output_dir = folder_paths.get_output_directory()
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counter = 1
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while True:
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output_filename = f"{filename_prefix}_{counter:05}.png"
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output_path = os.path.join(output_dir, output_filename)
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if not os.path.exists(output_path):
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break
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counter += 1
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print(f"[WRITE] Saved in: {output_path}")
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os.makedirs(os.path.dirname(output_path), exist_ok=True)
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if pil_img.mode not in ('RGB', 'RGBA'):
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pil_img = pil_img.convert('RGB')
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pil_img.save(output_path, format="PNG", pnginfo=pnginfo)
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print(f"[WRITE] File saved: {os.path.exists(output_path)}")
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try:
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check_img = Image.open(output_path)
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saved_text = check_img.info.get("Description", "")
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print(f"[CHECK] Text in the saved file: '{saved_text}'")
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except Exception as e:
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print(f"[CHECK] Verification error: {e}")
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return (image_tensor, text)
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def pil_to_tensor(self, pil_img):
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if pil_img.mode == "RGBA":
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img_np = np.array(pil_img).astype(np.float32) / 255.0
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else:
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img_np = np.array(pil_img.convert("RGB")).astype(np.float32) / 255.0
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return torch.from_numpy(img_np)[None, ...]
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NODE_CLASS_MAPPINGS = {
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"LoadImageWithText": LoadImageWithText,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LoadImageWithText": "🦊 RS Image-Text",
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# SPDX-License-Identifier: Apache-2.0
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# Copyright 2025-2026 Raykosan (RaykoStudio)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import torch
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import numpy as np
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from PIL import Image, PngImagePlugin
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import folder_paths
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def get_image_files():
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input_dir = folder_paths.get_input_directory()
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files = []
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for f in os.listdir(input_dir):
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if os.path.isfile(os.path.join(input_dir, f)) and f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.bmp')):
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files.append(f)
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files.sort()
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return files
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class LoadImageWithText:
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@classmethod
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def INPUT_TYPES(cls):
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files = get_image_files()
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return {
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"required": {
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"mode": (["read", "write"], {"default": "read"}),
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"filename_prefix": ("STRING", {"default": "Civitai/prompt", "tooltip": "Prefix to save to output (write only)"}),
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"text_input": ("STRING", {"default": "", "multiline": True, "tooltip": "Text to write (write only)"}),
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"image": (files, {"image_upload": True}),
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}
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}
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RETURN_TYPES = ("IMAGE", "STRING")
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RETURN_NAMES = ("image", "text")
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FUNCTION = "process"
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CATEGORY = "🦊 RaykoStudio"
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OUTPUT_NODE = True
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DESCRIPTION = "Node embeds any hidden text into the image that can be used later"
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def process(self, mode, image, text_input="", filename_prefix="ComfyUI"):
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try:
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input_path = folder_paths.get_annotated_filepath(image)
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if not os.path.exists(input_path):
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raise FileNotFoundError(f"File not found: {input_path}")
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if mode == "write":
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return self.write_mode(input_path, text_input, filename_prefix)
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else:
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return self.read_mode(input_path)
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except Exception as e:
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print(f"Error in process: {e}")
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import traceback
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traceback.print_exc()
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dummy_img = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
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return (dummy_img, "")
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def read_mode(self, input_path):
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pil_img = Image.open(input_path)
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text = pil_img.info.get("Description", "")
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image_tensor = self.pil_to_tensor(pil_img)
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print(f"[READ] Text: '{text}'")
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return (image_tensor, text)
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def write_mode(self, input_path, text, filename_prefix):
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pil_img = Image.open(input_path)
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image_tensor = self.pil_to_tensor(pil_img)
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pnginfo = PngImagePlugin.PngInfo()
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if text:
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pnginfo.add_text("Description", text)
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print(f"[WRITE] Text is recorded: '{text}'")
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output_dir = folder_paths.get_output_directory()
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counter = 1
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while True:
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output_filename = f"{filename_prefix}_{counter:05}.png"
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output_path = os.path.join(output_dir, output_filename)
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if not os.path.exists(output_path):
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break
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counter += 1
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print(f"[WRITE] Saved in: {output_path}")
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os.makedirs(os.path.dirname(output_path), exist_ok=True)
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if pil_img.mode not in ('RGB', 'RGBA'):
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pil_img = pil_img.convert('RGB')
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pil_img.save(output_path, format="PNG", pnginfo=pnginfo)
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print(f"[WRITE] File saved: {os.path.exists(output_path)}")
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try:
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check_img = Image.open(output_path)
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saved_text = check_img.info.get("Description", "")
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print(f"[CHECK] Text in the saved file: '{saved_text}'")
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except Exception as e:
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print(f"[CHECK] Verification error: {e}")
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return (image_tensor, text)
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def pil_to_tensor(self, pil_img):
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if pil_img.mode == "RGBA":
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img_np = np.array(pil_img).astype(np.float32) / 255.0
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else:
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img_np = np.array(pil_img.convert("RGB")).astype(np.float32) / 255.0
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return torch.from_numpy(img_np)[None, ...]
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NODE_CLASS_MAPPINGS = {
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"LoadImageWithText": LoadImageWithText,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LoadImageWithText": "🦊 RS Image-Text",
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}
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+239
-239
@@ -1,240 +1,240 @@
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# SPDX-License-Identifier: Apache-2.0
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# Copyright 2025-2026 Raykosan (RaykoStudio)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import numpy as np
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from PIL import Image, PngImagePlugin, ImageDraw, ImageFont, ImageOps
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import json
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import os
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import folder_paths
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import textwrap
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def get_font_list(font_dir):
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if not os.path.isdir(font_dir):
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return []
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return [f for f in os.listdir(font_dir) if f.lower().endswith(('.ttf', '.otf'))]
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def create_text_panel(width, height, text, font_path, font_size, text_color, bg_color):
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panel = Image.new('RGB', (width, height), color=bg_color)
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draw = ImageDraw.Draw(panel)
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try:
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font = ImageFont.truetype(font_path, font_size)
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except Exception:
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font = ImageFont.load_default()
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avg_char_width = font_size * 0.6
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max_chars = int(width / avg_char_width)
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wrapped_lines = []
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for line in text.split('\n'):
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wrapped_lines.extend(textwrap.wrap(line, width=max_chars) if max_chars > 0 else [line])
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if not wrapped_lines:
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return panel
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line_height = font_size + 4
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total_text_height = len(wrapped_lines) * line_height
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y = (height - total_text_height) // 2
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for line in wrapped_lines:
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try:
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bbox = draw.textbbox((0, 0), line, font=font)
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line_width = bbox[2] - bbox[0]
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except AttributeError:
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line_width = draw.textsize(line, font=font)[0]
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x = (width - line_width) // 2
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draw.text((x, y), line, font=font, fill=text_color)
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y += line_height
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return panel
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def combine_images_pil(images, direction='horizontal'):
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if direction == 'horizontal':
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widths, heights = zip(*(img.size for img in images))
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total_width = sum(widths)
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max_height = max(heights)
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combined = Image.new('RGB', (total_width, max_height))
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x_offset = 0
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for img in images:
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combined.paste(img, (x_offset, (max_height - img.height) // 2))
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x_offset += img.width
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else:
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widths, heights = zip(*(img.size for img in images))
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max_width = max(widths)
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total_height = sum(heights)
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combined = Image.new('RGB', (max_width, total_height))
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y_offset = 0
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for img in images:
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combined.paste(img, ((max_width - img.width) // 2, y_offset))
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y_offset += img.height
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return combined
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class SaveImagePair:
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
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self.type = "output"
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self.prefix_append = ""
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self.compress_level = 4
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self.script_dir = os.path.dirname(os.path.abspath(__file__))
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self.font_dir = os.path.join(self.script_dir, "fonts")
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self.font_list = get_font_list(self.font_dir)
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@classmethod
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def INPUT_TYPES(cls):
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script_dir = os.path.dirname(os.path.abspath(__file__))
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font_dir = os.path.join(script_dir, "fonts")
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font_list = get_font_list(font_dir)
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if not font_list:
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font_list = ["none"]
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return {
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"required": {
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"reference": ("IMAGE",),
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"final": ("IMAGE",),
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"filename_prefix": ("STRING", {"default": "ThumbnailPair/img"}),
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"concat_type": (["horizontal", "vertical"], {"default": "horizontal"}),
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},
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"optional": {
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"text1": ("STRING", {"multiline": True, "default": ""}),
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"text2": ("STRING", {"multiline": True, "default": ""}),
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"footer_height": ("INT", {"default": 100, "min": 0, "max": 1024}),
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"font_name": (font_list, {"default": font_list[0] if font_list else "none"}),
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"font_size": ("INT", {"default": 50, "min": 1, "max": 512}),
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"theme": (["dark", "light"], {"default": "light"}),
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"scale_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05}),
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},
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"hidden": {
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"prompt": "PROMPT",
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"extra_pnginfo": "EXTRA_PNGINFO"
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},
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}
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RETURN_TYPES = ()
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FUNCTION = "save_images_pair"
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OUTPUT_NODE = True
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CATEGORY = "🦊 RaykoStudio"
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DESCRIPTION = "Node to save original and final images in a single image, while maintaining the workflow within the image"
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def save_images_pair(self, reference, final, filename_prefix="ThumbnailPair/img", concat_type="horizontal",
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text1="", text2="", footer_height=100, font_name=None, font_size=50,
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theme="light", scale_factor=1.0, prompt=None, extra_pnginfo=None):
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filename_prefix += self.prefix_append
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if theme == "dark":
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text_color_rgb = (255, 255, 255)
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bg_color_rgb = (0, 0, 0)
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else:
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text_color_rgb = (0, 0, 0)
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bg_color_rgb = (255, 255, 255)
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batch_ref = reference.shape[0]
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batch_fin = final.shape[0]
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if batch_ref != batch_fin:
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min_batch = min(batch_ref, batch_fin)
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print(f"Warning: Batch sizes differ ({batch_ref} vs {batch_fin}). Using first {min_batch} images.")
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reference = reference[:min_batch]
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final = final[:min_batch]
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else:
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min_batch = batch_ref
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font_path = None
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if font_name and font_name != "none":
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font_path = os.path.join(self.font_dir, font_name)
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if not os.path.isfile(font_path):
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font_path = None
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combined_images = []
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for i in range(min_batch):
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img_ref = Image.fromarray((reference[i].cpu().numpy() * 255).astype(np.uint8))
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img_fin = Image.fromarray((final[i].cpu().numpy() * 255).astype(np.uint8))
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target_w, target_h = img_ref.size
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if img_fin.size != (target_w, target_h):
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img_fin = img_fin.resize((target_w, target_h), Image.Resampling.LANCZOS)
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||||
|
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panel1 = None
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if footer_height > 0 and text1.strip():
|
||||
panel1 = create_text_panel(target_w, footer_height, text1, font_path, font_size,
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text_color_rgb, bg_color_rgb)
|
||||
panel2 = None
|
||||
if footer_height > 0 and text2.strip():
|
||||
panel2 = create_text_panel(target_w, footer_height, text2, font_path, font_size,
|
||||
text_color_rgb, bg_color_rgb)
|
||||
|
||||
blocks = []
|
||||
if panel1 is not None:
|
||||
ref_block = combine_images_pil([img_ref, panel1], 'vertical')
|
||||
else:
|
||||
ref_block = img_ref
|
||||
if panel2 is not None:
|
||||
fin_block = combine_images_pil([img_fin, panel2], 'vertical')
|
||||
else:
|
||||
fin_block = img_fin
|
||||
|
||||
if concat_type == "horizontal":
|
||||
combined_img = combine_images_pil([ref_block, fin_block], 'horizontal')
|
||||
else:
|
||||
combined_img = combine_images_pil([ref_block, fin_block], 'vertical')
|
||||
|
||||
if scale_factor < 1.0:
|
||||
old_width, old_height = combined_img.size
|
||||
max_side = max(old_width, old_height)
|
||||
new_max_side = int(max_side * scale_factor)
|
||||
if new_max_side == 0:
|
||||
new_max_side = 1
|
||||
ratio = new_max_side / max_side
|
||||
new_width = int(old_width * ratio)
|
||||
new_height = int(old_height * ratio)
|
||||
combined_img = combined_img.resize((new_width, new_height), Image.Resampling.LANCZOS)
|
||||
|
||||
combined_images.append(np.array(combined_img))
|
||||
|
||||
combined_tensor = torch.from_numpy(np.stack(combined_images, axis=0)).float() / 255.0
|
||||
|
||||
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
|
||||
filename_prefix, self.output_dir, combined_tensor[0].shape[1], combined_tensor[0].shape[0])
|
||||
|
||||
results = []
|
||||
for img_tensor in combined_tensor:
|
||||
img_np = (img_tensor.cpu().numpy() * 255.0).clip(0, 255).astype(np.uint8)
|
||||
img_pil = Image.fromarray(img_np)
|
||||
|
||||
metadata = PngImagePlugin.PngInfo()
|
||||
if prompt is not None:
|
||||
metadata.add_text("prompt", json.dumps(prompt))
|
||||
if extra_pnginfo is not None:
|
||||
for key, value in extra_pnginfo.items():
|
||||
metadata.add_text(key, json.dumps(value))
|
||||
|
||||
file = f"{filename}_{counter:05}_.png"
|
||||
img_pil.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
|
||||
results.append({
|
||||
"filename": file,
|
||||
"subfolder": subfolder,
|
||||
"type": self.type
|
||||
})
|
||||
counter += 1
|
||||
|
||||
return {"ui": {"images": results}}
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"SaveImagePair": SaveImagePair,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"SaveImagePair": "🦊 RS Save Image Pair",
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# Copyright 2025-2026 Raykosan (RaykoStudio)
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import torch
|
||||
import numpy as np
|
||||
from PIL import Image, PngImagePlugin, ImageDraw, ImageFont, ImageOps
|
||||
import json
|
||||
import os
|
||||
import folder_paths
|
||||
import textwrap
|
||||
|
||||
def get_font_list(font_dir):
|
||||
if not os.path.isdir(font_dir):
|
||||
return []
|
||||
return [f for f in os.listdir(font_dir) if f.lower().endswith(('.ttf', '.otf'))]
|
||||
|
||||
def create_text_panel(width, height, text, font_path, font_size, text_color, bg_color):
|
||||
panel = Image.new('RGB', (width, height), color=bg_color)
|
||||
draw = ImageDraw.Draw(panel)
|
||||
|
||||
try:
|
||||
font = ImageFont.truetype(font_path, font_size)
|
||||
except Exception:
|
||||
font = ImageFont.load_default()
|
||||
|
||||
avg_char_width = font_size * 0.6
|
||||
max_chars = int(width / avg_char_width)
|
||||
wrapped_lines = []
|
||||
for line in text.split('\n'):
|
||||
wrapped_lines.extend(textwrap.wrap(line, width=max_chars) if max_chars > 0 else [line])
|
||||
|
||||
if not wrapped_lines:
|
||||
return panel
|
||||
|
||||
line_height = font_size + 4
|
||||
total_text_height = len(wrapped_lines) * line_height
|
||||
|
||||
y = (height - total_text_height) // 2
|
||||
|
||||
for line in wrapped_lines:
|
||||
try:
|
||||
bbox = draw.textbbox((0, 0), line, font=font)
|
||||
line_width = bbox[2] - bbox[0]
|
||||
except AttributeError:
|
||||
line_width = draw.textsize(line, font=font)[0]
|
||||
x = (width - line_width) // 2
|
||||
draw.text((x, y), line, font=font, fill=text_color)
|
||||
y += line_height
|
||||
|
||||
return panel
|
||||
|
||||
def combine_images_pil(images, direction='horizontal'):
|
||||
if direction == 'horizontal':
|
||||
widths, heights = zip(*(img.size for img in images))
|
||||
total_width = sum(widths)
|
||||
max_height = max(heights)
|
||||
combined = Image.new('RGB', (total_width, max_height))
|
||||
x_offset = 0
|
||||
for img in images:
|
||||
combined.paste(img, (x_offset, (max_height - img.height) // 2))
|
||||
x_offset += img.width
|
||||
else:
|
||||
widths, heights = zip(*(img.size for img in images))
|
||||
max_width = max(widths)
|
||||
total_height = sum(heights)
|
||||
combined = Image.new('RGB', (max_width, total_height))
|
||||
y_offset = 0
|
||||
for img in images:
|
||||
combined.paste(img, ((max_width - img.width) // 2, y_offset))
|
||||
y_offset += img.height
|
||||
return combined
|
||||
|
||||
class SaveImagePair:
|
||||
def __init__(self):
|
||||
self.output_dir = folder_paths.get_output_directory()
|
||||
self.type = "output"
|
||||
self.prefix_append = ""
|
||||
self.compress_level = 4
|
||||
|
||||
self.script_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
self.font_dir = os.path.join(self.script_dir, "fonts")
|
||||
self.font_list = get_font_list(self.font_dir)
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
script_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
font_dir = os.path.join(script_dir, "fonts")
|
||||
font_list = get_font_list(font_dir)
|
||||
if not font_list:
|
||||
font_list = ["none"]
|
||||
|
||||
return {
|
||||
"required": {
|
||||
"reference": ("IMAGE",),
|
||||
"final": ("IMAGE",),
|
||||
"filename_prefix": ("STRING", {"default": "ThumbnailPair/img"}),
|
||||
"concat_type": (["horizontal", "vertical"], {"default": "horizontal"}),
|
||||
},
|
||||
"optional": {
|
||||
"text1": ("STRING", {"multiline": True, "default": ""}),
|
||||
"text2": ("STRING", {"multiline": True, "default": ""}),
|
||||
"footer_height": ("INT", {"default": 100, "min": 0, "max": 1024}),
|
||||
"font_name": (font_list, {"default": font_list[0] if font_list else "none"}),
|
||||
"font_size": ("INT", {"default": 50, "min": 1, "max": 512}),
|
||||
"theme": (["dark", "light"], {"default": "light"}),
|
||||
"scale_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05}),
|
||||
},
|
||||
"hidden": {
|
||||
"prompt": "PROMPT",
|
||||
"extra_pnginfo": "EXTRA_PNGINFO"
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ()
|
||||
FUNCTION = "save_images_pair"
|
||||
OUTPUT_NODE = True
|
||||
CATEGORY = "🦊 RaykoStudio"
|
||||
DESCRIPTION = "Node to save original and final images in a single image, while maintaining the workflow within the image"
|
||||
|
||||
def save_images_pair(self, reference, final, filename_prefix="ThumbnailPair/img", concat_type="horizontal",
|
||||
text1="", text2="", footer_height=100, font_name=None, font_size=50,
|
||||
theme="light", scale_factor=1.0, prompt=None, extra_pnginfo=None):
|
||||
filename_prefix += self.prefix_append
|
||||
|
||||
if theme == "dark":
|
||||
text_color_rgb = (255, 255, 255)
|
||||
bg_color_rgb = (0, 0, 0)
|
||||
else:
|
||||
text_color_rgb = (0, 0, 0)
|
||||
bg_color_rgb = (255, 255, 255)
|
||||
|
||||
batch_ref = reference.shape[0]
|
||||
batch_fin = final.shape[0]
|
||||
if batch_ref != batch_fin:
|
||||
min_batch = min(batch_ref, batch_fin)
|
||||
print(f"Warning: Batch sizes differ ({batch_ref} vs {batch_fin}). Using first {min_batch} images.")
|
||||
reference = reference[:min_batch]
|
||||
final = final[:min_batch]
|
||||
else:
|
||||
min_batch = batch_ref
|
||||
|
||||
font_path = None
|
||||
if font_name and font_name != "none":
|
||||
font_path = os.path.join(self.font_dir, font_name)
|
||||
if not os.path.isfile(font_path):
|
||||
font_path = None
|
||||
|
||||
combined_images = []
|
||||
for i in range(min_batch):
|
||||
img_ref = Image.fromarray((reference[i].cpu().numpy() * 255).astype(np.uint8))
|
||||
img_fin = Image.fromarray((final[i].cpu().numpy() * 255).astype(np.uint8))
|
||||
|
||||
target_w, target_h = img_ref.size
|
||||
if img_fin.size != (target_w, target_h):
|
||||
img_fin = img_fin.resize((target_w, target_h), Image.Resampling.LANCZOS)
|
||||
|
||||
panel1 = None
|
||||
if footer_height > 0 and text1.strip():
|
||||
panel1 = create_text_panel(target_w, footer_height, text1, font_path, font_size,
|
||||
text_color_rgb, bg_color_rgb)
|
||||
panel2 = None
|
||||
if footer_height > 0 and text2.strip():
|
||||
panel2 = create_text_panel(target_w, footer_height, text2, font_path, font_size,
|
||||
text_color_rgb, bg_color_rgb)
|
||||
|
||||
blocks = []
|
||||
if panel1 is not None:
|
||||
ref_block = combine_images_pil([img_ref, panel1], 'vertical')
|
||||
else:
|
||||
ref_block = img_ref
|
||||
if panel2 is not None:
|
||||
fin_block = combine_images_pil([img_fin, panel2], 'vertical')
|
||||
else:
|
||||
fin_block = img_fin
|
||||
|
||||
if concat_type == "horizontal":
|
||||
combined_img = combine_images_pil([ref_block, fin_block], 'horizontal')
|
||||
else:
|
||||
combined_img = combine_images_pil([ref_block, fin_block], 'vertical')
|
||||
|
||||
if scale_factor < 1.0:
|
||||
old_width, old_height = combined_img.size
|
||||
max_side = max(old_width, old_height)
|
||||
new_max_side = int(max_side * scale_factor)
|
||||
if new_max_side == 0:
|
||||
new_max_side = 1
|
||||
ratio = new_max_side / max_side
|
||||
new_width = int(old_width * ratio)
|
||||
new_height = int(old_height * ratio)
|
||||
combined_img = combined_img.resize((new_width, new_height), Image.Resampling.LANCZOS)
|
||||
|
||||
combined_images.append(np.array(combined_img))
|
||||
|
||||
combined_tensor = torch.from_numpy(np.stack(combined_images, axis=0)).float() / 255.0
|
||||
|
||||
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
|
||||
filename_prefix, self.output_dir, combined_tensor[0].shape[1], combined_tensor[0].shape[0])
|
||||
|
||||
results = []
|
||||
for img_tensor in combined_tensor:
|
||||
img_np = (img_tensor.cpu().numpy() * 255.0).clip(0, 255).astype(np.uint8)
|
||||
img_pil = Image.fromarray(img_np)
|
||||
|
||||
metadata = PngImagePlugin.PngInfo()
|
||||
if prompt is not None:
|
||||
metadata.add_text("prompt", json.dumps(prompt))
|
||||
if extra_pnginfo is not None:
|
||||
for key, value in extra_pnginfo.items():
|
||||
metadata.add_text(key, json.dumps(value))
|
||||
|
||||
file = f"{filename}_{counter:05}_.png"
|
||||
img_pil.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
|
||||
results.append({
|
||||
"filename": file,
|
||||
"subfolder": subfolder,
|
||||
"type": self.type
|
||||
})
|
||||
counter += 1
|
||||
|
||||
return {"ui": {"images": results}}
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"SaveImagePair": SaveImagePair,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"SaveImagePair": "🦊 RS Save Image Pair",
|
||||
}
|
||||
Reference in New Issue
Block a user