Updated HF node + Created PDF Caption Layout
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Fillip
+4
-1
@@ -54,6 +54,7 @@ from .nodes.FL_SaveCSV import FL_SaveCSV
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from. nodes.FL_KSamplerXYZPlot import FL_KSamplerXYZPlot
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from .nodes.FL_SamplerStrings import FL_SamplerStrings
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from .nodes.FL_SchedulerStrings import FL_SchedulerStrings
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from .nodes.FL_ImageCaptionLayoutPDF import FL_ImageCaptionLayoutPDF
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@@ -115,6 +116,7 @@ NODE_CLASS_MAPPINGS = {
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"FL_KSamplerXYZPlot": FL_KSamplerXYZPlot,
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"FL_SamplerStrings": FL_SamplerStrings,
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"FL_SchedulerStrings": FL_SchedulerStrings,
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"FL_ImageCaptionLayoutPDF": FL_ImageCaptionLayoutPDF,
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}
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@@ -174,7 +176,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"FL_SaveCSV": "FL Save CSV",
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"FL_KSamplerXYZPlot": "FL KSampler XYZ Plot",
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"FL_SamplerStrings": "FL Sampler String XYZ",
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"FL_SchedulerStrings": "FL Scheduler String XYZ"
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"FL_SchedulerStrings": "FL Scheduler String XYZ",
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"FL_ImageCaptionLayoutPDF": "FL Image Caption Layout PDF",
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}
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@@ -17,7 +17,6 @@ from PIL import Image
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from huggingface_hub import HfApi, create_repo, repo_exists
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from tqdm import tqdm
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class FL_HF_Character:
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@classmethod
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def INPUT_TYPES(cls):
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@@ -36,6 +35,7 @@ class FL_HF_Character:
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"lora_file": ("STRING", {"default": ""}),
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"dataset_zip": ("ZIP",),
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"caption_layout": ("IMAGE",),
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"caption_PDF_layout": ("PDF",),
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"csv_file": ("CSV",),
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}
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}
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@@ -47,7 +47,8 @@ class FL_HF_Character:
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def upload_to_hub(self, api_key: str, owner: str, repo_name: str, studio_name: str, project_name: str,
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character_name: str, create_new_repo: str, repo_type: str,
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lora_file: str = "", dataset_zip: bytes = None,
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caption_layout: torch.Tensor = None, csv_file: bytes = None) -> tuple[str]:
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caption_layout: torch.Tensor = None, caption_PDF_layout: bytes = None,
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csv_file: bytes = None) -> tuple[str]:
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# Initialize Hugging Face API
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api = HfApi(token=api_key)
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@@ -62,8 +63,7 @@ class FL_HF_Character:
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print(f"Repository created or already exists: {repo_url}")
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else:
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if not repo_exists(repo_id=full_repo_id, token=api_key):
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return (
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f"Error: Repository {full_repo_id} does not exist. Please create it first or use the 'Create New Repo' option.",)
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return (f"Error: Repository {full_repo_id} does not exist. Please create it first or use the 'Create New Repo' option.",)
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repo_url = f"https://huggingface.co/{full_repo_id}"
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print(f"Using existing repository: {repo_url}")
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@@ -77,6 +77,8 @@ class FL_HF_Character:
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self.upload_zip(api, dataset_zip, f"{base_path}/dataset", full_repo_id, api_key, "Dataset")
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if caption_layout is not None:
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self.upload_image(api, caption_layout, base_path, full_repo_id, api_key, "caption_layout")
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if caption_PDF_layout is not None:
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self.upload_pdf(api, caption_PDF_layout, base_path, full_repo_id, api_key, "caption_PDF_layout")
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if csv_file is not None:
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self.upload_csv(api, csv_file, base_path, full_repo_id, api_key)
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@@ -164,6 +166,35 @@ class FL_HF_Character:
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)
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print(f"{image_type} uploaded successfully")
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def upload_pdf(self, api, pdf_data, repo_dir, full_repo_id, api_key, pdf_type):
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repo_path = f"{repo_dir}/{pdf_type}.pdf"
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pbar = tqdm(total=100, unit='%', desc=f"Uploading {pdf_type} PDF")
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def update_progress():
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progress = 0
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while progress < 95:
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time.sleep(0.5)
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increment = min(5, 95 - progress)
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progress += increment
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pbar.update(increment)
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progress_thread = threading.Thread(target=update_progress)
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progress_thread.start()
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api.upload_file(
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path_or_fileobj=pdf_data,
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path_in_repo=repo_path,
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repo_id=full_repo_id,
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token=api_key
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)
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progress_thread.join()
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pbar.update(100 - pbar.n)
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pbar.close()
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print(f"{pdf_type} PDF uploaded successfully to {repo_path}")
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def upload_csv(self, api, csv_data, repo_dir, full_repo_id, api_key):
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repo_path = f"{repo_dir}/metadata.csv"
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@@ -195,5 +226,5 @@ class FL_HF_Character:
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@classmethod
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def IS_CHANGED(cls, api_key, owner, repo_name, studio_name, project_name, character_name,
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create_new_repo, repo_type, lora_file, dataset_zip, caption_layout, csv_file):
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create_new_repo, repo_type, lora_file, dataset_zip, caption_layout, caption_PDF_layout, csv_file):
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return float("NaN")
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@@ -0,0 +1,184 @@
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import os
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import math
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import torch
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import numpy as np
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from PIL import Image, ImageDraw, ImageFont
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import textwrap
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from reportlab.lib.pagesizes import letter, portrait, landscape
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from reportlab.pdfgen import canvas
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from reportlab.lib.units import inch
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from reportlab.pdfbase import pdfmetrics
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from reportlab.pdfbase.ttfonts import TTFont
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from io import BytesIO
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class FL_ImageCaptionLayoutPDF:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image_directory": ("STRING", {"default": ""}),
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"images_per_row": ("INT", {"default": 6, "min": 1, "max": 10}),
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"display_size": ("INT", {"default": 100, "min": 64, "max": 512}),
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"caption_height": ("INT", {"default": 80, "min": 32, "max": 256}),
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"font_size": ("INT", {"default": 4, "min": 4, "max": 32}),
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"padding": ("INT", {"default": 10, "min": 0, "max": 100}),
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"output_directory": ("STRING", {"default": ""}),
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"output_filename": ("STRING", {"default": "output"}),
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"orientation": (["horizontal", "vertical"], {"default": "horizontal"}),
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},
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}
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RETURN_TYPES = ("STRING", "IMAGE", "PDF")
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FUNCTION = "create_layout"
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CATEGORY = "🏵️Fill Nodes/Captioning"
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OUTPUT_NODE = True
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def create_layout(self, image_directory, images_per_row, display_size, caption_height, font_size, padding,
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output_directory, output_filename, orientation):
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output_path, pdf_bytes = self.create_pdf_layout(image_directory, images_per_row, display_size, caption_height,
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font_size, padding, output_directory, output_filename,
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orientation)
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preview_image = self.create_image_preview(image_directory, images_per_row, display_size, caption_height,
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font_size, padding, orientation)
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return (output_path, preview_image, pdf_bytes)
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def create_pdf_layout(self, image_directory, images_per_row, display_size, caption_height, font_size, padding,
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output_directory, output_filename, orientation):
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# Get the path to the fonts directory
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current_dir = os.path.dirname(os.path.abspath(__file__))
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fonts_dir = os.path.join(os.path.dirname(current_dir), "fonts")
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font_path = os.path.join(fonts_dir, "arial.ttf")
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# Check if the font file exists
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if not os.path.exists(font_path):
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raise FileNotFoundError(f"Font file not found: {font_path}")
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# Register the font
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pdfmetrics.registerFont(TTFont('Arial', font_path))
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# Get all image files and their corresponding caption files
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image_files = [f for f in os.listdir(image_directory) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
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image_files.sort() # Sort files to ensure consistent order
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# PDF setup
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page_size = landscape(letter) if orientation == "horizontal" else portrait(letter)
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width, height = page_size
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output_path = os.path.join(output_directory, f"{output_filename}.pdf")
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pdf_buffer = BytesIO()
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c = canvas.Canvas(pdf_buffer, pagesize=page_size)
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# Calculate layout dimensions
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display_size_pt = display_size
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caption_height_pt = caption_height
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padding_pt = padding
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item_width = display_size_pt + padding_pt
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item_height = display_size_pt + caption_height_pt + padding_pt
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# Calculate how many items can fit on a page
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items_per_row = min(images_per_row, math.floor((width - padding_pt) / item_width))
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rows_per_page = math.floor((height - padding_pt) / item_height)
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items_per_page = items_per_row * rows_per_page
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for i in range(0, len(image_files), items_per_page):
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page_images = image_files[i:i + items_per_page]
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for j, image_file in enumerate(page_images):
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# Calculate position
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row = j // items_per_row
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col = j % items_per_row
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x = padding_pt + col * item_width
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y = height - padding_pt - (row + 1) * item_height
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# Load and draw image
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img_path = os.path.join(image_directory, image_file)
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img = Image.open(img_path)
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aspect_ratio = img.width / img.height
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display_height = display_size_pt / aspect_ratio
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c.drawImage(img_path, x, y + caption_height_pt, width=display_size_pt, height=display_height,
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preserveAspectRatio=True, anchor='sw')
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# Load caption
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caption_file = os.path.splitext(image_file)[0] + '.txt'
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caption_path = os.path.join(image_directory, caption_file)
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try:
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with open(caption_path, 'r') as f:
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caption = f.read().strip()
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except FileNotFoundError:
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caption = "No caption found"
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# Draw caption
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c.setFont("Arial", font_size)
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text_object = c.beginText(x, y + caption_height_pt - font_size)
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wrapped_text = textwrap.fill(caption, width=int(display_size_pt / (font_size * 0.6)))
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for line in wrapped_text.split('\n'):
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text_object.textLine(line)
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c.drawText(text_object)
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c.showPage() # Start a new page after each set of items
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c.save()
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pdf_bytes = pdf_buffer.getvalue()
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# Save the PDF to file
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with open(output_path, 'wb') as f:
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f.write(pdf_bytes)
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print(f"PDF saved as {output_path}")
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return output_path, pdf_bytes
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def create_image_preview(self, image_directory, images_per_row, display_size, caption_height, font_size, padding,
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orientation):
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# Get all image files and their corresponding caption files
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image_files = [f for f in os.listdir(image_directory) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
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image_files.sort() # Sort files to ensure consistent order
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# Calculate layout dimensions
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total_width = images_per_row * (display_size + padding) + padding
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rows = (len(image_files) + images_per_row - 1) // images_per_row
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total_height = rows * (display_size + caption_height + padding) + padding
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# Create the layout
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layout = Image.new('RGB', (total_width, total_height), color=(255, 255, 255))
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# Load font
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font_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "fonts", "arial.ttf")
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try:
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font = ImageFont.truetype(font_path, font_size)
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except IOError:
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font = ImageFont.load_default()
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for i, image_file in enumerate(image_files):
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# Load and resize image
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img_path = os.path.join(image_directory, image_file)
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img = Image.open(img_path).convert('RGB')
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img.thumbnail((display_size, display_size), Image.LANCZOS)
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# Calculate position
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row = i // images_per_row
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col = i % images_per_row
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x = padding + col * (display_size + padding)
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y = padding + row * (display_size + caption_height + padding)
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# Paste image
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layout.paste(img, (x, y))
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# Load caption
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caption_file = os.path.splitext(image_file)[0] + '.txt'
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caption_path = os.path.join(image_directory, caption_file)
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try:
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with open(caption_path, 'r') as f:
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caption = f.read().strip()
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except FileNotFoundError:
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caption = "No caption found"
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# Draw caption
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draw = ImageDraw.Draw(layout)
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wrapped_text = textwrap.fill(caption, width=int(display_size / (font_size * 0.6)))
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draw.text((x, y + display_size + 5), wrapped_text, font=font, fill=(0, 0, 0))
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# Convert to tensor for preview
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preview_tensor = torch.from_numpy(np.array(layout).astype(np.float32) / 255.0).unsqueeze(0)
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return preview_tensor
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+2
-1
@@ -7,4 +7,5 @@ scipy >=1.13.1
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requests
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aiohttp
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moviepy
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matplotlib
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matplotlib
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reportlab
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