Claude
2f213d7d76
This implements the CORRECT solution for regional conditioning: strict attention masking + background concatenation. Key Changes: - ✅ Restored strict attention masking (forces spatial placement) - ✅ Background concatenation to all regional prompts (scene unity) - ✅ Feathered masks for smooth visual blending - ✅ Updated default values to 1344x768 with 3 regions - ✅ Fixed region selector max from 2 to 3 The Problem: Without attention masking, models generate content in natural positions (bird in center), which region masks then block, making content invisible. User was 100% correct about this. The Solution: 1. Attention masking: Forces content to appear in correct region 2. Background concatenation: All regions know scene context 3. Feathered masks: Smooth visual transitions Result: ALL regions appear in correct locations WITH natural blending. Model-agnostic approach works with Flux, Qwen-Image, SD3, SDXL, etc. Files modified: - RegionalPrompting.py: Strict masking + background concat - RegionalPrompting.js: Fix region selector, update defaults - CONTEXT.md: Document correct approach
544 lines
23 KiB
Python
544 lines
23 KiB
Python
# Enhanced All-in-One Regional Prompting Nodes
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# Created: 2025-11-22
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# Inline prompt boxes - no external CLIP Text Encode nodes needed!
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import torch
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from nodes import MAX_RESOLUTION
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import folder_paths
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class EasyRegionSimple:
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"""
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ALL-IN-ONE regional prompting with inline text inputs!
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Just connect your CLIP model, type prompts directly, and draw boxes.
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No need for external CLIP Text Encode nodes!
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Compatible with: SD1.5, SD2.x, SDXL (area-based conditioning)
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"""
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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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"clip": ("CLIP", {"tooltip": "CLIP model from your checkpoint"}),
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"background_prompt": ("STRING", {
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"default": "photo of a city street, high quality",
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"multiline": True,
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"tooltip": "Background/scene description (applies to whole image)"
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}),
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"region1_prompt": ("STRING", {
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"default": "red sports car",
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"multiline": True,
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"tooltip": "Region 1 - First region/box (see canvas below)"
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}),
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},
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"optional": {
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"region2_prompt": ("STRING", {
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"default": "giraffe wearing sunglasses",
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"multiline": True,
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"tooltip": "Region 2 - Second region/box (see canvas below)"
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}),
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"region3_prompt": ("STRING", {
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"default": "",
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"multiline": True,
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"tooltip": "Region 3 - Third region/box (see canvas below)"
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}),
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"region4_prompt": ("STRING", {
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"default": "",
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"multiline": True,
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"tooltip": "Region 4 - Fourth region/box (see canvas below)"
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}),
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},
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"hidden": {
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"extra_pnginfo": "EXTRA_PNGINFO",
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"unique_id": "UNIQUE_ID",
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"region_boxes": "STRING"
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},
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}
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RETURN_TYPES = ("CONDITIONING",)
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RETURN_NAMES = ("conditioning",)
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FUNCTION = "encode_regions"
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CATEGORY = "EasyRegion"
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DESCRIPTION = """🎨 ALL-IN-ONE Regional Prompting (EASY MODE!)
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Just type your prompts and draw boxes - that's it!
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✅ No external CLIP Text Encode nodes needed
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✅ Simple workflow: CLIP → This Node → Sampler
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✅ Up to 4 regions + background
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✅ Visual box drawing interface
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How to use:
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1. Connect CLIP from your checkpoint
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2. Type background prompt (whole image)
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3. Type region prompts (for each box)
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4. Draw boxes on canvas
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5. Connect to sampler!
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Compatible: SD1.5, SD2.x, SDXL"""
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def encode_regions(self, clip, background_prompt, region1_prompt, extra_pnginfo, unique_id, region_boxes="",
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region2_prompt="", region3_prompt="", region4_prompt=""):
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"""Encode all prompts and apply regional conditioning."""
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# Default template boxes (fallback if no data from UI)
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values = [
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[41, 102, 138, 307, 1.0], # Region 1 - percentage-based for 512x512
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[360, 77, 140, 348, 1.0] # Region 2 - percentage-based for 512x512
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]
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resolutionX = 512
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resolutionY = 512
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# First try to parse from hidden widget (works on fresh nodes)
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if region_boxes:
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try:
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import json
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parsed = json.loads(region_boxes)
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if parsed and len(parsed) > 0:
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values = parsed
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print(f"ℹ️ Using region boxes from widget: {values}")
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except Exception as e:
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print(f"⚠️ Failed to parse region_boxes widget: {e}")
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# Fallback: Get region data from saved workflow (overrides if available)
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try:
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if extra_pnginfo and "workflow" in extra_pnginfo and "nodes" in extra_pnginfo["workflow"]:
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for node in extra_pnginfo["workflow"]["nodes"]:
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if node["id"] == int(unique_id):
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saved_values = node["properties"].get("values", [])
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if saved_values: # Only override if we actually have saved values
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values = saved_values
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resolutionX = node["properties"].get("width", 512)
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resolutionY = node["properties"].get("height", 512)
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break
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except Exception as e:
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print(f"ℹ️ Using default template boxes and canvas size 512x512")
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# Collect all non-empty prompts
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prompts = [background_prompt, region1_prompt, region2_prompt, region3_prompt, region4_prompt]
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# Encode each prompt using CLIP (standard SD/SDXL encoding)
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encoded_conditionings = []
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for prompt in prompts:
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if prompt and prompt.strip(): # Only encode non-empty prompts
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# Standard CLIP encoding for SD/SDXL
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tokens = clip.tokenize(prompt)
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
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encoded_conditionings.append([[cond, {"pooled_output": pooled}]])
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else:
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encoded_conditionings.append(None)
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# Apply regional conditioning using area-based method
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c = []
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# Background (fullscreen)
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if encoded_conditionings[0]:
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for t in encoded_conditionings[0]:
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c.append(t)
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# Process each region
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for i in range(1, min(len(encoded_conditionings), len(values) + 1)):
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if encoded_conditionings[i] is None:
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continue
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if i - 1 >= len(values):
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continue
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try:
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x, y = int(values[i-1][0]), int(values[i-1][1])
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w, h = int(values[i-1][2]), int(values[i-1][3])
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strength = float(values[i-1][4]) if len(values[i-1]) > 4 else 2.0
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except (IndexError, ValueError, TypeError):
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continue
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# Skip if fullscreen (already added as background)
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if x == 0 and y == 0 and w == resolutionX and h == resolutionY:
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continue
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# Clip to bounds
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if x + w > resolutionX:
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w = max(0, resolutionX - x)
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if y + h > resolutionY:
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h = max(0, resolutionY - y)
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if w == 0 or h == 0:
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continue
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# Apply area to conditioning
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for t in encoded_conditionings[i]:
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n = [t[0], t[1].copy()]
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n[1]['area'] = (h // 8, w // 8, y // 8, x // 8)
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n[1]['strength'] = max(0.0, min(10.0, strength))
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n[1]['min_sigma'] = 0.0
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n[1]['max_sigma'] = 99.0
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c.append(n)
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return (c,)
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class EasyRegionMask:
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"""
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ALL-IN-ONE regional prompting with inline text inputs!
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Just connect your CLIP model, type prompts directly, and draw boxes.
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Uses mask-based conditioning for maximum model compatibility.
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Compatible with modern mask-based models (see README for model-specific tips)
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"""
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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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"clip": ("CLIP", {"tooltip": "CLIP model from your checkpoint"}),
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"width": ("INT", {
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"default": 1344,
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"min": 64,
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"max": MAX_RESOLUTION,
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"step": 64,
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"tooltip": "Output width - must match your latent/sampler"
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}),
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"height": ("INT", {
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"default": 768,
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"min": 64,
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"max": MAX_RESOLUTION,
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"step": 64,
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"tooltip": "Output height - must match your latent/sampler"
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}),
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"soften_masks": ("BOOLEAN", {
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"default": True,
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"tooltip": "Enable feathering at region edges - recommended ON"
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}),
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"background_prompt": ("STRING", {
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"default": "photo of empty city street at night, high quality",
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"multiline": True,
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"tooltip": "Scene description (applies to entire image as base)"
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}),
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},
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"optional": {
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"background_strength": ("FLOAT", {
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"default": 0.5,
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"min": 0.0,
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"max": 10.0,
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"step": 0.1,
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"tooltip": "Background conditioning strength (lower = regions show more, try 0.3-0.7)"
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}),
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"region1_prompt": ("STRING", {
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"default": "red sports car",
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"multiline": True,
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"tooltip": "Region 1 - TIP: Include spatial location in prompt (e.g. 'left side', 'top right')"
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}),
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"region1_strength": ("FLOAT", {
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"default": 2.5,
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"min": 0.0,
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"max": 10.0,
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"step": 0.1,
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"tooltip": "Region 1 strength (start with 2-4, adjust if region doesn't show or is too soft)"
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}),
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"region2_prompt": ("STRING", {
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"default": "closeup full body giraffe wearing sunglasses",
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"multiline": True,
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"tooltip": "Region 2 - TIP: Include spatial location in prompt (e.g. 'right third', 'bottom left')"
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}),
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"region2_strength": ("FLOAT", {
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"default": 3.5,
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"min": 0.0,
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"max": 10.0,
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"step": 0.1,
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"tooltip": "Region 2 strength (start with 3-5, adjust if needed)"
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}),
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"region3_prompt": ("STRING", {
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"default": "blue bird flying",
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"multiline": True,
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"tooltip": "Region 3 - Optional third region"
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}),
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"region3_strength": ("FLOAT", {
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"default": 4.0,
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"min": 0.0,
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"max": 10.0,
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"step": 0.1,
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"tooltip": "Region 3 strength (start with 4-6, adjust if needed)"
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}),
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"region4_prompt": ("STRING", {
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"default": "",
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"multiline": True,
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"tooltip": "Region 4 - Optional fourth region"
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}),
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"region4_strength": ("FLOAT", {
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"default": 4.5,
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"min": 0.0,
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"max": 10.0,
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"step": 0.1,
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"tooltip": "Region 4 strength (start with 4-6, adjust if needed)"
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}),
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},
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"hidden": {
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"extra_pnginfo": "EXTRA_PNGINFO",
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"unique_id": "UNIQUE_ID",
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"region_boxes": "STRING"
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},
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}
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RETURN_TYPES = ("CONDITIONING",)
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RETURN_NAMES = ("conditioning",)
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FUNCTION = "encode_regions_mask"
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CATEGORY = "EasyRegion"
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DESCRIPTION = """Mask-based regional prompting for modern diffusion models.
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Quick Start:
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1. Connect CLIP from checkpoint
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2. Set width/height to match your latent exactly
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3. Type prompts (background + regions)
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4. Adjust per-region strength (defaults: 2.5, 3.5, 4.0, 4.5)
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5. Draw/adjust boxes on canvas
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See README for model-specific tips and recommended settings."""
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def encode_regions_mask(self, clip, width, height, background_strength, soften_masks, background_prompt, region1_prompt,
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extra_pnginfo, unique_id, region_boxes="",
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region1_strength=2.5, region2_prompt="", region2_strength=3.5,
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region3_prompt="", region3_strength=4.0, region4_prompt="", region4_strength=4.5):
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"""Encode all prompts and apply mask-based regional conditioning."""
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# Default template boxes (fallback if no data from UI)
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# Matches user's 1344x768 workflow with 3 regions
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values = [
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[0, 320, 368, 462, 2.0], # Region 1 - red sports car (left)
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[448, 64, 384, 704, 2.0], # Region 2 - giraffe (center-right)
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[945, 0, 378, 256, 2.0] # Region 3 - blue bird (top-right)
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]
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# First try to parse from hidden widget (works on fresh nodes)
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if region_boxes:
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try:
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import json
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parsed = json.loads(region_boxes)
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if parsed and len(parsed) > 0:
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values = parsed
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print(f"ℹ️ Using region boxes from widget: {values}")
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except Exception as e:
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print(f"⚠️ Failed to parse region_boxes widget: {e}")
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# Fallback: Get region data from saved workflow (overrides if available)
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try:
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if extra_pnginfo and "workflow" in extra_pnginfo and "nodes" in extra_pnginfo["workflow"]:
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for node in extra_pnginfo["workflow"]["nodes"]:
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if node["id"] == int(unique_id):
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saved_values = node["properties"].get("values", [])
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if saved_values: # Only override if we actually have saved values
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values = saved_values
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# Override with property dimensions if available
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prop_width = node["properties"].get("width", width)
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prop_height = node["properties"].get("height", height)
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break
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except Exception as e:
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print(f"ℹ️ Using default template boxes and dimensions {width}x{height}")
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prop_width = width
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prop_height = height
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# Collect all non-empty prompts
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prompts = [background_prompt, region1_prompt, region2_prompt, region3_prompt, region4_prompt]
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# Count non-empty regions (excluding background)
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num_regions = sum(1 for p in prompts[1:] if p and p.strip())
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print(f"\n🎨 Regional Prompter - Processing:")
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print(f" Dimensions: {width}x{height} (latent: {width//8}x{height//8})")
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print(f" Background strength: {background_strength}")
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print(f" Region strengths: [{region1_strength}, {region2_strength}, {region3_strength}, {region4_strength}]")
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print(f" Regions: {num_regions} active (background + {num_regions} regions)")
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print(f" Soften masks: {soften_masks}")
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print(f" Default boxes: {values}")
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if num_regions > 4:
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print(f"⚠️ Warning: {num_regions} regions detected. Most models work best with 3-4 regions maximum.")
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# Concatenate background to regional prompts for visual coherence
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# This ensures all regions share the same scene context
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prompts_final = []
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for i, prompt in enumerate(prompts):
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if i == 0: # Background
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prompts_final.append(prompt if prompt and prompt.strip() else "")
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else: # Regional prompts - prepend background for unified composition
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if prompt and prompt.strip():
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combined = f"{background_prompt}, {prompt}" if background_prompt and background_prompt.strip() else prompt
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prompts_final.append(combined)
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print(f" 📝 Region {i} combined prompt: '{combined[:60]}...'")
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else:
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prompts_final.append("")
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# Encode each prompt using CLIP
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# ComfyUI's CLIP object handles multi-encoder complexity internally
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encoded_conditionings = []
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for prompt in prompts_final:
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if prompt and prompt.strip():
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tokens = clip.tokenize(prompt)
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
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encoded_conditionings.append([[cond, {"pooled_output": pooled}]])
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else:
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encoded_conditionings.append(None)
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# Apply mask-based conditioning
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combined_conditioning = []
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# Mask tensor values: use 1.0 (full binary mask), let mask_strength param control intensity
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# The per-region strength parameter in conditioning dict controls actual strength
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mask_tensor_value = 1.0 # Always use binary mask (0.0 or 1.0)
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# Background (fullscreen - no mask)
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# Apply background_strength to allow users to reduce background influence
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if encoded_conditionings[0]:
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for t in encoded_conditionings[0]:
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n = [t[0], t[1].copy()]
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if background_strength != 1.0:
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# Scale the conditioning tensor by background_strength
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n[0] = t[0] * background_strength
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combined_conditioning.append(n)
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# Per-region strength values from inputs (not canvas)
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region_strengths = [region1_strength, region2_strength, region3_strength, region4_strength]
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# Process each region with masks
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latent_width = width // 8
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latent_height = height // 8
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for i in range(1, min(len(encoded_conditionings), len(values) + 1)):
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if encoded_conditionings[i] is None:
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continue
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if i - 1 >= len(values):
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continue
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try:
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x, y = int(values[i-1][0]), int(values[i-1][1])
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w, h = int(values[i-1][2]), int(values[i-1][3])
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# Use per-region strength from inputs instead of canvas values
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strength = region_strengths[i-1] if i-1 < len(region_strengths) else 5.0
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except (IndexError, ValueError, TypeError):
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continue
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# Get canvas dimensions
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canvas_width = prop_width if 'prop_width' in locals() else width
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canvas_height = prop_height if 'prop_height' in locals() else height
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# Skip fullscreen (already added as background)
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if x == 0 and y == 0 and w == canvas_width and h == canvas_height:
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continue
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# Scale to output dimensions if needed
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if canvas_width != width or canvas_height != height:
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x = int(x * width / canvas_width)
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y = int(y * height / canvas_height)
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w = int(w * width / canvas_width)
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h = int(h * height / canvas_height)
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# Clip to bounds
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x = max(0, min(x, width))
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y = max(0, min(y, height))
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w = max(0, min(w, width - x))
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h = max(0, min(h, height - y))
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if w == 0 or h == 0:
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continue
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# Convert to latent space
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x_latent = x // 8
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y_latent = y // 8
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w_latent = max(1, w // 8)
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h_latent = max(1, h // 8)
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# Create binary mask for region
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mask = torch.zeros((1, latent_height, latent_width), dtype=torch.float32)
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x_end = min(x_latent + w_latent, latent_width)
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y_end = min(y_latent + h_latent, latent_height)
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# Fill mask region with 1.0 (binary mask)
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mask[0, y_latent:y_end, x_latent:x_end] = mask_tensor_value
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print(f" Region {i}: '{prompts[i][:40]}...'")
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print(f" Box: x={x}, y={y}, w={w}, h={h}")
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print(f" Latent: x={x_latent}, y={y_latent}, w={w_latent}, h={h_latent}")
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print(f" Mask: binary 1.0, Conditioning strength: {strength}")
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# Create separate feathered mask for visual blending
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# Keep original mask binary for attention control
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feathered_mask = mask.clone()
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# Apply feathering if enabled (gentle edge blending)
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if soften_masks:
|
||
# Apply gaussian-like feather at edges (40-60px = 5-8 latent pixels)
|
||
feather_size = min(6, w_latent // 4, h_latent // 4)
|
||
if feather_size > 0:
|
||
for edge_idx in range(feather_size):
|
||
fade = (edge_idx + 1) / feather_size # 0.0 to 1.0 gradient
|
||
# Top edge
|
||
if y_latent + edge_idx < y_end:
|
||
feathered_mask[0, y_latent + edge_idx, x_latent:x_end] = fade
|
||
# Bottom edge
|
||
if y_end - 1 - edge_idx >= y_latent:
|
||
feathered_mask[0, y_end - 1 - edge_idx, x_latent:x_end] = fade
|
||
# Left edge
|
||
if x_latent + edge_idx < x_end:
|
||
feathered_mask[0, y_latent:y_end, x_latent + edge_idx] = torch.minimum(feathered_mask[0, y_latent:y_end, x_latent + edge_idx], torch.tensor(fade))
|
||
# Right edge
|
||
if x_end - 1 - edge_idx >= x_latent:
|
||
feathered_mask[0, y_latent:y_end, x_end - 1 - edge_idx] = torch.minimum(feathered_mask[0, y_latent:y_end, x_end - 1 - edge_idx], torch.tensor(fade))
|
||
|
||
# Apply mask-based conditioning with attention masking
|
||
# Background concatenation provides scene context
|
||
# Attention masking forces correct spatial placement
|
||
for t in encoded_conditionings[i]:
|
||
n = [t[0], t[1].copy()]
|
||
n[1]['mask'] = feathered_mask # Feathered for smooth visual blending
|
||
n[1]['mask_strength'] = max(0.0, min(10.0, strength))
|
||
n[1]['set_area_to_bounds'] = False
|
||
|
||
# Create attention mask for precise regional control
|
||
# This FORCES the model to generate content in the correct region
|
||
cond_tensor = t[0] # [batch, seq_len, hidden_dim]
|
||
txt_tokens = cond_tensor.shape[1]
|
||
img_tokens = latent_height * latent_width
|
||
total_tokens = txt_tokens + img_tokens
|
||
|
||
# Initialize blocking all (-10000), then enable specific paths
|
||
attention_mask = torch.full((1, total_tokens, total_tokens), -10000.0, dtype=torch.float32)
|
||
|
||
# Enable text-to-text attention (prompts can interact)
|
||
attention_mask[0, :txt_tokens, :txt_tokens] = 0.0
|
||
|
||
# Enable text-to-image ONLY for this region's pixels
|
||
# This prevents "bird" text from affecting car region pixels
|
||
for y in range(y_latent, y_end):
|
||
for x in range(x_latent, x_end):
|
||
img_token_idx = txt_tokens + (y * latent_width + x)
|
||
attention_mask[0, :txt_tokens, img_token_idx] = 0.0
|
||
attention_mask[0, img_token_idx, :txt_tokens] = 0.0
|
||
|
||
# Enable image-to-image ONLY within this region
|
||
# Use vectorized approach for performance
|
||
region_img_indices = []
|
||
for y in range(y_latent, y_end):
|
||
for x in range(x_latent, x_end):
|
||
region_img_indices.append(txt_tokens + (y * latent_width + x))
|
||
|
||
# Allow attention between all pixels in this region
|
||
for idx1 in region_img_indices:
|
||
for idx2 in region_img_indices:
|
||
attention_mask[0, idx1, idx2] = 0.0
|
||
|
||
n[1]['attention_mask'] = attention_mask
|
||
n[1]['attention_mask_img_shape'] = (latent_height, latent_width)
|
||
|
||
combined_conditioning.append(n)
|
||
|
||
print(f" ✅ Generated {len(combined_conditioning)} conditioning blocks\n")
|
||
|
||
return (combined_conditioning,)
|
||
|
||
|
||
# Note: These enhanced nodes need the same JavaScript UI as the original nodes
|
||
# They will use the canvas interface from MultiAreaConditioning/MultiAreaConditioningMask
|