Add Smart Tile Experimental workflow and nodes
This commit is contained in:
ttplanet
@@ -32,6 +32,80 @@ https://github.com/user-attachments/assets/af06b9d3-9c84-4a83-ba90-eb4ec4bb2e99
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## **Instructions**
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### **Smart Tile Experimental Workflow**
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The **Smart Tile Experimental** nodes provide a manual tile workflow that keeps the existing automatic tile nodes unchanged. They are designed for layouts where you want to split the image by visual structure, such as face, hands, subject, clothes, foreground, and background.
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The first experimental version uses a **large sampling area** and a **smaller paste area**:
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- `TTP Smart Tile Layout (Experimental)`: stores a JSON tile layout.
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- `TTP Smart Tile Crop (Experimental)`: reads the input image directly, crops padded tile images, and outputs tile metadata plus a debug preview.
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- `TTP Smart Tile Visual Crop (Experimental)`: creates a visual/parameter-based crop plan without JSON, using grid controls plus optional focus regions.
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- `TTP Smart Tile Assemble (Experimental)`: assembles sampled tiles back into the final image with feathered weighted blending, priority, and importance weights.
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Visual workflow without JSON:
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```text
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Load Image
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→ TTP Smart Tile Visual Crop (Experimental)
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→ Preview the plan output and tune grid/focus parameters
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→ VAE Encode / Sampler / VAE Decode
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→ TTP Smart Tile Assemble (Experimental)
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→ Final Image
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```
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`TTP Smart Tile Visual Crop (Experimental)` is the recommended starting point if you want to tune the tile plan visually in ComfyUI instead of editing JSON. It provides controls for grid columns/rows, grid padding/blending, optional full-image coverage, and two optional focus boxes. Connect its `preview` output to `PreviewImage` while adjusting parameters.
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JSON workflow:
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```text
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Load Image
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→ TTP Smart Tile Layout (Experimental)
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→ TTP Smart Tile Crop (Experimental)
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→ VAE Encode / Sampler / VAE Decode
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→ TTP Smart Tile Assemble (Experimental)
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→ Final Image
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```
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Example layout:
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```json
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{
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"defaults": {
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"pad": 128,
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"blend": 48,
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"priority": 50,
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"importance": 1.0
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},
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"tiles": [
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{
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"name": "full_image",
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"x": 0,
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"y": 0,
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"w": 1.0,
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"h": 1.0,
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"pad": 0,
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"blend": 96,
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"priority": 10,
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"importance": 0.5
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},
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{
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"name": "face",
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"x": 0.35,
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"y": 0.08,
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"w": 0.30,
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"h": 0.28,
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"pad": 192,
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"blend": 64,
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"priority": 100,
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"importance": 1.0
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}
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]
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}
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```
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Coordinates can be pixel values or normalized values from `0.0` to `1.0`. `pad` expands the crop sent into the sampler, while `blend`, `priority`, and `importance` control how the sampled tile is pasted back.
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### **1. Image Tile Batch Node**
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This node cuts an image into pieces automatically based on your specified width and height. It also records the necessary information for further processing.
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+541
-15
@@ -1,6 +1,7 @@
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import cv2
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import numpy as np
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from PIL import Image, ImageFilter, ImageChops, ImageEnhance
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import json
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import cv2
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import numpy as np
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from PIL import Image, ImageFilter, ImageChops, ImageEnhance, ImageDraw
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import node_helpers
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import torch
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import comfy.model_management
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@@ -133,7 +134,7 @@ class TTP_Image_Tile_Batch:
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return (tiles, positions, (img_width, img_height), (num_cols, num_rows))
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class TTP_Image_Assy:
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class TTP_Image_Assy:
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def __init__(self, *args, **kwargs):
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pass
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@@ -269,7 +270,524 @@ class TTP_Image_Assy:
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new_final_image.paste(row_images[row], (0, final_image.height))
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final_image = new_final_image
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return pil2tensor(final_image).unsqueeze(0)
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return pil2tensor(final_image).unsqueeze(0)
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def _ttp_clamp(value, minimum, maximum):
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return max(minimum, min(maximum, value))
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def _ttp_round_to(value, multiple):
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if multiple <= 1:
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return int(round(value))
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return int(np.ceil(value / multiple) * multiple)
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def _ttp_parse_box_value(value, size):
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if isinstance(value, float) and 0.0 <= value <= 1.0:
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return int(round(value * size))
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return int(round(value))
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def _ttp_parse_margin_value(value, size):
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if isinstance(value, float) and 0.0 <= value <= 1.0:
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return int(round(value * size))
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return int(round(value))
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def _ttp_normalize_tile_box(tile, image_width, image_height, defaults):
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x = _ttp_parse_box_value(tile.get("x", 0), image_width)
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y = _ttp_parse_box_value(tile.get("y", 0), image_height)
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w = _ttp_parse_box_value(tile.get("w", tile.get("width", image_width)), image_width)
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h = _ttp_parse_box_value(tile.get("h", tile.get("height", image_height)), image_height)
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x = _ttp_clamp(x, 0, image_width)
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y = _ttp_clamp(y, 0, image_height)
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w = _ttp_clamp(w, 1, image_width - x)
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h = _ttp_clamp(h, 1, image_height - y)
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pad_value = tile.get("pad", tile.get("padding", defaults.get("pad", 0)))
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blend_value = tile.get("blend", tile.get("feather", defaults.get("blend", 32)))
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pad = _ttp_parse_margin_value(pad_value, max(image_width, image_height))
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blend = _ttp_parse_margin_value(blend_value, max(w, h))
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return {
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"name": str(tile.get("name", f"tile_{tile.get('id', 0)}")),
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"core_box": [x, y, w, h],
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"pad": max(0, pad),
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"blend": max(0, blend),
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"priority": float(tile.get("priority", defaults.get("priority", 50))),
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"importance": float(tile.get("importance", defaults.get("importance", 1.0))),
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"strength": float(tile.get("strength", defaults.get("strength", 1.0))),
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"prompt_tag": str(tile.get("prompt_tag", tile.get("name", ""))),
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"align": bool(tile.get("align", defaults.get("align", False))),
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}
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def _ttp_expand_grid_tiles(grid, defaults):
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x_lines = grid.get("x", [])
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y_lines = grid.get("y", [])
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if len(x_lines) < 2 or len(y_lines) < 2:
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raise ValueError("Smart Tile grid layout requires at least two x lines and two y lines")
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prefix = str(grid.get("prefix", defaults.get("prefix", "grid")))
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grid_defaults = defaults.copy()
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grid_defaults.update({k: v for k, v in grid.items() if k not in {"x", "y", "prefix"}})
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tiles = []
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for row in range(len(y_lines) - 1):
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for col in range(len(x_lines) - 1):
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x0, x1 = x_lines[col], x_lines[col + 1]
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y0, y1 = y_lines[row], y_lines[row + 1]
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tiles.append({
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**grid_defaults,
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"name": f"{prefix}_{row}_{col}",
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"x": x0,
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"y": y0,
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"w": x1 - x0,
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"h": y1 - y0,
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})
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return tiles
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def _ttp_parse_smart_tile_layout(layout_text, image_width, image_height):
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try:
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layout = json.loads(layout_text)
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except json.JSONDecodeError as exc:
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raise ValueError(f"Invalid Smart Tile layout JSON: {exc}") from exc
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defaults = {}
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raw_tiles = []
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if isinstance(layout, list):
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raw_tiles = layout
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elif isinstance(layout, dict):
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defaults = dict(layout.get("defaults", {}))
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if "grid" in layout:
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raw_tiles.extend(_ttp_expand_grid_tiles(layout["grid"], defaults))
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raw_tiles.extend(layout.get("tiles", []))
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raw_tiles.extend(layout.get("custom", []))
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else:
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raise ValueError("Smart Tile layout must be a JSON list or object")
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if not raw_tiles:
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raw_tiles = [{"name": "full_image", "x": 0, "y": 0, "w": image_width, "h": image_height}]
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normalized_tiles = []
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for index, tile in enumerate(raw_tiles):
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if not isinstance(tile, dict):
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raise ValueError(f"Smart Tile entry {index} must be an object")
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normalized = _ttp_normalize_tile_box({**tile, "id": index}, image_width, image_height, defaults)
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normalized["id"] = index
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x, y, w, h = normalized["core_box"]
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pad = normalized["pad"]
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sample_left = _ttp_clamp(x - pad, 0, image_width)
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sample_top = _ttp_clamp(y - pad, 0, image_height)
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sample_right = _ttp_clamp(x + w + pad, 0, image_width)
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sample_bottom = _ttp_clamp(y + h + pad, 0, image_height)
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normalized["sample_box"] = [
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sample_left,
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sample_top,
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max(1, sample_right - sample_left),
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max(1, sample_bottom - sample_top),
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]
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normalized["paste_box"] = [x, y, w, h]
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normalized_tiles.append(normalized)
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return normalized_tiles
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def _ttp_create_feather_mask(width, height, feather):
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if feather <= 0:
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return np.ones((height, width, 1), dtype=np.float32)
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yy, xx = np.mgrid[0:height, 0:width]
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distance_left = xx
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distance_right = width - 1 - xx
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distance_top = yy
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distance_bottom = height - 1 - yy
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distance = np.minimum(np.minimum(distance_left, distance_right), np.minimum(distance_top, distance_bottom))
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mask = np.clip(distance / max(1, feather), 0.0, 1.0).astype(np.float32)
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return mask[:, :, None]
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def _ttp_pad_image_to_size(image, target_width, target_height):
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if image.width == target_width and image.height == target_height:
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return image
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padded = Image.new("RGB", (target_width, target_height))
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padded.paste(image, (0, 0))
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if image.width < target_width:
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right_edge = image.crop((image.width - 1, 0, image.width, image.height))
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right_fill = right_edge.resize((target_width - image.width, image.height), Image.Resampling.NEAREST)
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padded.paste(right_fill, (image.width, 0))
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if image.height < target_height:
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bottom_edge = padded.crop((0, image.height - 1, target_width, image.height))
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bottom_fill = bottom_edge.resize((target_width, target_height - image.height), Image.Resampling.NEAREST)
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padded.paste(bottom_fill, (0, image.height))
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return padded
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def _ttp_crop_smart_tiles_from_meta(pil_image, tiles_meta, round_to):
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image_width, image_height = pil_image.size
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tile_images = []
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positions = []
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for tile in tiles_meta:
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sx, sy, sw, sh = tile["sample_box"]
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sw = max(1, _ttp_round_to(sw, round_to))
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sh = max(1, _ttp_round_to(sh, round_to))
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if sx + sw > image_width:
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sx = max(0, image_width - sw)
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if sy + sh > image_height:
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sy = max(0, image_height - sh)
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sw = min(sw, image_width - sx)
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sh = min(sh, image_height - sy)
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tile["sample_box"] = [sx, sy, sw, sh]
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crop = pil_image.crop((sx, sy, sx + sw, sy + sh))
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tile_images.append(crop)
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tile["tile_canvas_box"] = [0, 0, sw, sh]
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x, y, w, h = tile["paste_box"]
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positions.append((x, y, x + w, y + h))
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max_tile_width = max(tile.width for tile in tile_images)
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max_tile_height = max(tile.height for tile in tile_images)
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max_tile_width = max(1, _ttp_round_to(max_tile_width, round_to))
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max_tile_height = max(1, _ttp_round_to(max_tile_height, round_to))
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tile_tensors = []
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for tile, crop in zip(tiles_meta, tile_images):
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tile["tile_canvas_size"] = [max_tile_width, max_tile_height]
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padded_crop = _ttp_pad_image_to_size(crop, max_tile_width, max_tile_height)
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tile_tensors.append(pil2tensor(padded_crop))
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tiles = torch.cat(tile_tensors, dim=0)
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preview = pil_image.copy()
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draw = ImageDraw.Draw(preview)
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colors = ["red", "lime", "cyan", "yellow", "magenta", "orange", "blue", "white"]
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for tile in tiles_meta:
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color = colors[tile["id"] % len(colors)]
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sx, sy, sw, sh = tile["sample_box"]
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x, y, w, h = tile["paste_box"]
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draw.rectangle((sx, sy, sx + sw, sy + sh), outline=color, width=2)
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draw.rectangle((x, y, x + w, y + h), outline="white", width=2)
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draw.text((x + 4, y + 4), f'{tile["id"]}:{tile["name"]}', fill=color)
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tile_meta = {
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"version": 2,
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"type": "ttp_smart_tile",
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"original_size": [image_width, image_height],
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"tiles": tiles_meta,
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}
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return tiles, tile_meta, positions, pil2tensor(preview)
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class TTP_Smart_Tile_Layout_Experimental:
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@classmethod
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def INPUT_TYPES(cls):
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default_layout = json.dumps({
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"defaults": {"pad": 128, "blend": 48, "priority": 50, "importance": 1.0},
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"tiles": [
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{"name": "full_image", "x": 0, "y": 0, "w": 1.0, "h": 1.0, "pad": 0, "blend": 96, "priority": 10, "importance": 0.5}
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]
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}, indent=2)
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return {
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"required": {
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"layout_json": ("STRING", {"default": default_layout, "multiline": True}),
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}
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("layout_json",)
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FUNCTION = "pass_layout"
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CATEGORY = "TTP/Smart Tile Experimental"
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def pass_layout(self, layout_json):
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return (layout_json,)
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class TTP_Smart_Tile_Crop_Experimental:
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@classmethod
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def INPUT_TYPES(cls):
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default_layout = json.dumps({
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"defaults": {"pad": 128, "blend": 48, "priority": 50, "importance": 1.0},
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"tiles": [
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{"name": "full_image", "x": 0, "y": 0, "w": 1.0, "h": 1.0, "pad": 0, "blend": 96, "priority": 10, "importance": 0.5},
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{"name": "center_detail", "x": 0.25, "y": 0.25, "w": 0.5, "h": 0.5, "pad": 128, "blend": 48, "priority": 100, "importance": 1.0}
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]
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}, indent=2)
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return {
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"required": {
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"image": ("IMAGE",),
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"layout_json": ("STRING", {"default": default_layout, "multiline": True}),
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"round_to": ("INT", {"default": 8, "min": 1, "max": 128, "step": 1}),
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}
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}
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RETURN_TYPES = ("IMAGE", "TTP_SMART_TILE_META", "LIST", "IMAGE")
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RETURN_NAMES = ("tiles", "tile_meta", "positions", "preview")
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FUNCTION = "crop_tiles"
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CATEGORY = "TTP/Smart Tile Experimental"
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def crop_tiles(self, image, layout_json, round_to=8):
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pil_image = tensor2pil(image[0].unsqueeze(0)).convert("RGB")
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image_width, image_height = pil_image.size
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tiles_meta = _ttp_parse_smart_tile_layout(layout_json, image_width, image_height)
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return _ttp_crop_smart_tiles_from_meta(pil_image, tiles_meta, round_to)
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class TTP_Smart_Tile_Visual_Crop_Experimental:
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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": ("IMAGE",),
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"grid_columns": ("INT", {"default": 2, "min": 1, "max": 8, "step": 1}),
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"grid_rows": ("INT", {"default": 2, "min": 1, "max": 8, "step": 1}),
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"include_full_image": ("BOOLEAN", {"default": True}),
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"grid_pad": ("INT", {"default": 128, "min": 0, "max": 1024, "step": 8}),
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"grid_blend": ("INT", {"default": 64, "min": 0, "max": 512, "step": 8}),
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"grid_priority": ("FLOAT", {"default": 40.0, "min": 0.0, "max": 200.0, "step": 1.0}),
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"grid_importance": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 4.0, "step": 0.05}),
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"round_to": ("INT", {"default": 8, "min": 1, "max": 128, "step": 1}),
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"focus_1_enabled": ("BOOLEAN", {"default": False}),
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"focus_1_x": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.01}),
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"focus_1_y": ("FLOAT", {"default": 0.08, "min": 0.0, "max": 1.0, "step": 0.01}),
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"focus_1_w": ("FLOAT", {"default": 0.30, "min": 0.01, "max": 1.0, "step": 0.01}),
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"focus_1_h": ("FLOAT", {"default": 0.28, "min": 0.01, "max": 1.0, "step": 0.01}),
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"focus_2_enabled": ("BOOLEAN", {"default": False}),
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"focus_2_x": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}),
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"focus_2_y": ("FLOAT", {"default": 0.55, "min": 0.0, "max": 1.0, "step": 0.01}),
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"focus_2_w": ("FLOAT", {"default": 0.50, "min": 0.01, "max": 1.0, "step": 0.01}),
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"focus_2_h": ("FLOAT", {"default": 0.20, "min": 0.01, "max": 1.0, "step": 0.01}),
|
||||
"focus_pad": ("INT", {"default": 192, "min": 0, "max": 1024, "step": 8}),
|
||||
"focus_blend": ("INT", {"default": 64, "min": 0, "max": 512, "step": 8}),
|
||||
"focus_priority": ("FLOAT", {"default": 110.0, "min": 0.0, "max": 300.0, "step": 1.0}),
|
||||
"focus_importance": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 4.0, "step": 0.05}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE", "TTP_SMART_TILE_META", "LIST", "IMAGE")
|
||||
RETURN_NAMES = ("tiles", "tile_meta", "positions", "preview")
|
||||
FUNCTION = "visual_crop_tiles"
|
||||
CATEGORY = "TTP/Smart Tile Experimental"
|
||||
|
||||
def _add_tile_meta(self, raw_tiles, image_width, image_height):
|
||||
tiles_meta = []
|
||||
for index, tile in enumerate(raw_tiles):
|
||||
normalized = _ttp_normalize_tile_box({**tile, "id": index}, image_width, image_height, {})
|
||||
normalized["id"] = index
|
||||
x, y, w, h = normalized["core_box"]
|
||||
pad = normalized["pad"]
|
||||
sample_left = _ttp_clamp(x - pad, 0, image_width)
|
||||
sample_top = _ttp_clamp(y - pad, 0, image_height)
|
||||
sample_right = _ttp_clamp(x + w + pad, 0, image_width)
|
||||
sample_bottom = _ttp_clamp(y + h + pad, 0, image_height)
|
||||
normalized["sample_box"] = [
|
||||
sample_left,
|
||||
sample_top,
|
||||
max(1, sample_right - sample_left),
|
||||
max(1, sample_bottom - sample_top),
|
||||
]
|
||||
normalized["paste_box"] = [x, y, w, h]
|
||||
tiles_meta.append(normalized)
|
||||
return tiles_meta
|
||||
|
||||
def visual_crop_tiles(
|
||||
self,
|
||||
image,
|
||||
grid_columns,
|
||||
grid_rows,
|
||||
include_full_image,
|
||||
grid_pad,
|
||||
grid_blend,
|
||||
grid_priority,
|
||||
grid_importance,
|
||||
round_to,
|
||||
focus_1_enabled,
|
||||
focus_1_x,
|
||||
focus_1_y,
|
||||
focus_1_w,
|
||||
focus_1_h,
|
||||
focus_2_enabled,
|
||||
focus_2_x,
|
||||
focus_2_y,
|
||||
focus_2_w,
|
||||
focus_2_h,
|
||||
focus_pad,
|
||||
focus_blend,
|
||||
focus_priority,
|
||||
focus_importance,
|
||||
):
|
||||
pil_image = tensor2pil(image[0].unsqueeze(0)).convert("RGB")
|
||||
image_width, image_height = pil_image.size
|
||||
raw_tiles = []
|
||||
|
||||
if include_full_image:
|
||||
raw_tiles.append({
|
||||
"name": "full_image",
|
||||
"x": 0.0,
|
||||
"y": 0.0,
|
||||
"w": 1.0,
|
||||
"h": 1.0,
|
||||
"pad": 0,
|
||||
"blend": max(grid_blend, focus_blend),
|
||||
"priority": max(0.0, grid_priority * 0.25),
|
||||
"importance": max(0.0, grid_importance * 0.5),
|
||||
})
|
||||
|
||||
for row in range(grid_rows):
|
||||
for col in range(grid_columns):
|
||||
raw_tiles.append({
|
||||
"name": f"grid_{row}_{col}",
|
||||
"x": col / grid_columns,
|
||||
"y": row / grid_rows,
|
||||
"w": 1.0 / grid_columns,
|
||||
"h": 1.0 / grid_rows,
|
||||
"pad": grid_pad,
|
||||
"blend": grid_blend,
|
||||
"priority": grid_priority,
|
||||
"importance": grid_importance,
|
||||
})
|
||||
|
||||
focus_tiles = [
|
||||
(focus_1_enabled, "focus_1", focus_1_x, focus_1_y, focus_1_w, focus_1_h),
|
||||
(focus_2_enabled, "focus_2", focus_2_x, focus_2_y, focus_2_w, focus_2_h),
|
||||
]
|
||||
for enabled, name, x, y, w, h in focus_tiles:
|
||||
if enabled:
|
||||
raw_tiles.append({
|
||||
"name": name,
|
||||
"x": x,
|
||||
"y": y,
|
||||
"w": w,
|
||||
"h": h,
|
||||
"pad": focus_pad,
|
||||
"blend": focus_blend,
|
||||
"priority": focus_priority,
|
||||
"importance": focus_importance,
|
||||
})
|
||||
|
||||
tiles_meta = self._add_tile_meta(raw_tiles, image_width, image_height)
|
||||
return _ttp_crop_smart_tiles_from_meta(pil_image, tiles_meta, round_to)
|
||||
|
||||
|
||||
class TTP_Smart_Tile_Assemble_Experimental:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"sampled_tiles": ("IMAGE",),
|
||||
"tile_meta": ("TTP_SMART_TILE_META", {"forceInput": True}),
|
||||
"blend_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 4.0, "step": 0.05}),
|
||||
"output_scale": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 16.0, "step": 0.05}),
|
||||
"use_priority": ("BOOLEAN", {"default": True}),
|
||||
},
|
||||
"optional": {
|
||||
"base_image": ("IMAGE",),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE", "IMAGE")
|
||||
RETURN_NAMES = ("image", "weight_preview")
|
||||
FUNCTION = "assemble_tiles"
|
||||
CATEGORY = "TTP/Smart Tile Experimental"
|
||||
|
||||
def assemble_tiles(self, sampled_tiles, tile_meta, blend_multiplier=1.0, output_scale=0.0, use_priority=True, base_image=None):
|
||||
if not isinstance(tile_meta, dict) or tile_meta.get("type") != "ttp_smart_tile":
|
||||
raise ValueError("tile_meta must come from TTP Smart Tile Crop (Experimental)")
|
||||
|
||||
tiles_info = tile_meta.get("tiles", [])
|
||||
if len(tiles_info) != sampled_tiles.shape[0]:
|
||||
raise ValueError(f"sampled_tiles batch ({sampled_tiles.shape[0]}) does not match tile_meta tiles ({len(tiles_info)})")
|
||||
|
||||
original_width, original_height = tile_meta["original_size"]
|
||||
if output_scale <= 0:
|
||||
inferred_scales = []
|
||||
for index, tile in enumerate(tiles_info):
|
||||
_, _, sw, sh = tile["sample_box"]
|
||||
canvas_width, canvas_height = tile.get("tile_canvas_size", [sw, sh])
|
||||
canvas_box = tile.get("tile_canvas_box", [0, 0, sw, sh])
|
||||
content_width = float(sampled_tiles[index].shape[1]) * (canvas_box[2] / max(1, canvas_width))
|
||||
content_height = float(sampled_tiles[index].shape[0]) * (canvas_box[3] / max(1, canvas_height))
|
||||
inferred_scales.append(content_width / max(1, sw))
|
||||
inferred_scales.append(content_height / max(1, sh))
|
||||
output_scale = float(np.median(inferred_scales)) if inferred_scales else 1.0
|
||||
output_scale = max(0.01, output_scale)
|
||||
|
||||
output_width = max(1, int(round(original_width * output_scale)))
|
||||
output_height = max(1, int(round(original_height * output_scale)))
|
||||
|
||||
if base_image is not None:
|
||||
base_pil = tensor2pil(base_image[0].unsqueeze(0)).convert("RGB")
|
||||
if base_pil.size != (output_width, output_height):
|
||||
base_pil = base_pil.resize((output_width, output_height), Image.Resampling.LANCZOS)
|
||||
canvas = np.array(base_pil).astype(np.float32) / 255.0
|
||||
weights = np.ones((output_height, output_width, 1), dtype=np.float32)
|
||||
else:
|
||||
canvas = np.zeros((output_height, output_width, 3), dtype=np.float32)
|
||||
weights = np.zeros((output_height, output_width, 1), dtype=np.float32)
|
||||
|
||||
for index, tile in enumerate(tiles_info):
|
||||
tile_pil = tensor2pil(sampled_tiles[index].unsqueeze(0)).convert("RGB")
|
||||
sx, sy, sw, sh = tile["sample_box"]
|
||||
px, py, pw, ph = tile["paste_box"]
|
||||
|
||||
canvas_width, canvas_height = tile.get("tile_canvas_size", [sw, sh])
|
||||
canvas_box = tile.get("tile_canvas_box", [0, 0, sw, sh])
|
||||
content_left = int(round(tile_pil.width * canvas_box[0] / max(1, canvas_width)))
|
||||
content_top = int(round(tile_pil.height * canvas_box[1] / max(1, canvas_height)))
|
||||
content_width = max(1, int(round(tile_pil.width * canvas_box[2] / max(1, canvas_width))))
|
||||
content_height = max(1, int(round(tile_pil.height * canvas_box[3] / max(1, canvas_height))))
|
||||
|
||||
tile_scale_x = content_width / max(1, sw)
|
||||
tile_scale_y = content_height / max(1, sh)
|
||||
local_left = int(round((px - sx) * tile_scale_x))
|
||||
local_top = int(round((py - sy) * tile_scale_y))
|
||||
local_right = int(round(local_left + pw * tile_scale_x))
|
||||
local_bottom = int(round(local_top + ph * tile_scale_y))
|
||||
local_left = _ttp_clamp(content_left + local_left, 0, tile_pil.width - 1)
|
||||
local_top = _ttp_clamp(content_top + local_top, 0, tile_pil.height - 1)
|
||||
local_right = _ttp_clamp(content_left + local_right, local_left + 1, tile_pil.width)
|
||||
local_bottom = _ttp_clamp(content_top + local_bottom, local_top + 1, tile_pil.height)
|
||||
|
||||
paste_region = tile_pil.crop((local_left, local_top, local_right, local_bottom))
|
||||
out_x = int(round(px * output_scale))
|
||||
out_y = int(round(py * output_scale))
|
||||
out_w = max(1, int(round(pw * output_scale)))
|
||||
out_h = max(1, int(round(ph * output_scale)))
|
||||
if paste_region.size != (out_w, out_h):
|
||||
paste_region = paste_region.resize((out_w, out_h), Image.Resampling.LANCZOS)
|
||||
|
||||
blend = int(round(tile.get("blend", 0) * output_scale * blend_multiplier))
|
||||
mask = _ttp_create_feather_mask(out_w, out_h, blend)
|
||||
importance = max(0.0, float(tile.get("importance", 1.0)))
|
||||
priority = max(0.0, float(tile.get("priority", 50.0)))
|
||||
priority_scale = (1.0 + priority / 100.0) if use_priority else 1.0
|
||||
mask = mask * importance * priority_scale
|
||||
|
||||
region = np.array(paste_region).astype(np.float32) / 255.0
|
||||
x_end = min(output_width, out_x + out_w)
|
||||
y_end = min(output_height, out_y + out_h)
|
||||
region_w = x_end - out_x
|
||||
region_h = y_end - out_y
|
||||
if region_w <= 0 or region_h <= 0:
|
||||
continue
|
||||
canvas[out_y:y_end, out_x:x_end] += region[:region_h, :region_w] * mask[:region_h, :region_w]
|
||||
weights[out_y:y_end, out_x:x_end] += mask[:region_h, :region_w]
|
||||
|
||||
safe_weights = np.maximum(weights, 1e-6)
|
||||
output = canvas / safe_weights
|
||||
output = np.clip(output, 0.0, 1.0)
|
||||
weight_preview = np.clip(weights / max(1e-6, weights.max()), 0.0, 1.0)
|
||||
weight_preview = np.repeat(weight_preview, 3, axis=2)
|
||||
|
||||
return (
|
||||
torch.from_numpy(output.astype(np.float32)).unsqueeze(0),
|
||||
torch.from_numpy(weight_preview.astype(np.float32)).unsqueeze(0),
|
||||
)
|
||||
|
||||
|
||||
class TTP_CoordinateSplitter:
|
||||
@@ -1108,12 +1626,16 @@ NODE_CLASS_MAPPINGS = {
|
||||
"TTP_condsetarea_merge": TTP_condsetarea_merge,
|
||||
"TTP_Tile_image_size": Tile_imageSize,
|
||||
"TTP_condsetarea_merge_test": TTP_condsetarea_merge_test,
|
||||
"TTP_Expand_And_Mask": TTP_Expand_And_Mask,
|
||||
"TTP_text_mix": TTP_text_mix,
|
||||
"TeaCacheHunyuanVideoSampler": TeaCacheHunyuanVideoSampler
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"TTP_Expand_And_Mask": TTP_Expand_And_Mask,
|
||||
"TTP_text_mix": TTP_text_mix,
|
||||
"TeaCacheHunyuanVideoSampler": TeaCacheHunyuanVideoSampler,
|
||||
"TTP_Smart_Tile_Layout_Experimental": TTP_Smart_Tile_Layout_Experimental,
|
||||
"TTP_Smart_Tile_Crop_Experimental": TTP_Smart_Tile_Crop_Experimental,
|
||||
"TTP_Smart_Tile_Visual_Crop_Experimental": TTP_Smart_Tile_Visual_Crop_Experimental,
|
||||
"TTP_Smart_Tile_Assemble_Experimental": TTP_Smart_Tile_Assemble_Experimental,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"TTPlanet_Tile_Preprocessor_Simple": "TTP Tile Preprocessor Simple",
|
||||
"TTP_Image_Tile_Batch": "TTP_Image_Tile_Batch",
|
||||
"TTP_Image_Assy": "TTP_Image_Assy",
|
||||
@@ -1122,7 +1644,11 @@ NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"TTP_condsetarea_merge": "TTP_condsetarea_merge",
|
||||
"TTP_Tile_image_size": "TTP_Tile_image_size",
|
||||
"TTP_condsetarea_merge_test": "TTP_condsetarea_merge_test",
|
||||
"TTP_Expand_And_Mask": "TTP_Expand_And_Mask",
|
||||
"TTP_text_mix": "TTP_text_mix",
|
||||
"TeaCacheHunyuanVideoSampler": "TTP_TeaCache HunyuanVideo Sampler"
|
||||
}
|
||||
"TTP_Expand_And_Mask": "TTP_Expand_And_Mask",
|
||||
"TTP_text_mix": "TTP_text_mix",
|
||||
"TeaCacheHunyuanVideoSampler": "TTP_TeaCache HunyuanVideo Sampler",
|
||||
"TTP_Smart_Tile_Layout_Experimental": "TTP Smart Tile Layout (Experimental)",
|
||||
"TTP_Smart_Tile_Crop_Experimental": "TTP Smart Tile Crop (Experimental)",
|
||||
"TTP_Smart_Tile_Visual_Crop_Experimental": "TTP Smart Tile Visual Crop (Experimental)",
|
||||
"TTP_Smart_Tile_Assemble_Experimental": "TTP Smart Tile Assemble (Experimental)",
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user