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EnragedAntelope-ComfyUI_Eas…/RegionalPrompting.py
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Claude fd32a7d7e5 Improve UX: reorder widgets, add debug output, fix defaults 
WIDGET ORDER FIX:
- Moved soften_masks from middle of prompts to after height
- Before: clip, width, height, bg, region1, soften_masks, region2, region3, region4
- After: clip, width, height, soften_masks, bg, region1, region2, region3, region4
- More logical: settings grouped together, prompts grouped together

NODE HELP & TOOLTIPS:
- Added concise DESCRIPTION for ? help icon (was too long/verbose before)
- Shortened all tooltips for clarity
- Added must-match reminder to width/height tooltips
- Removed redundant "RECOMMENDED" emoji spam from tooltips

DEFAULT STRENGTH:
- Changed from 1.0 to 0.8 for both RegionalPrompterSimple and RegionalPrompterFlux
- JavaScript: Updated default values arrays to 0.8
- JavaScript: Updated CUSTOM_INT strength slider default to 0.8
- Matches mask_strength default when soften_masks is ON

DEBUG OUTPUT FOR REGIONAL CONDITIONING:
- Added comprehensive debug logging to understand why regions aren't working
- Prints dimensions, region count, soften_masks status, default boxes
- For each region: prompt text, box coords, latent coords, mask/region strength
- Final summary: total conditioning blocks generated, CFG recommendation
- Will help diagnose the "no sports car/vendor" issue user reported

CFG GUIDANCE UPDATE:
- DESCRIPTION now recommends CFG 1.0-3.5 (not 5-7)
- User confirmed Flux likes CFG 1.0, and 5 CFG caused blurry results
- Previous "5-7 for regional control" guidance was incorrect

Location: RegionalPrompting.py, js/RegionalPrompting.js
2025-11-22 04:02:42 +00:00

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# Enhanced All-in-One Regional Prompting Nodes
# Created: 2025-11-22
# Inline prompt boxes - no external CLIP Text Encode nodes needed!
import torch
from nodes import MAX_RESOLUTION
import folder_paths
class RegionalPrompterSimple:
"""
ALL-IN-ONE regional prompting with inline text inputs!
Just connect your CLIP model, type prompts directly, and draw boxes.
No need for external CLIP Text Encode nodes!
Compatible with: SD1.5, SD2.x, SDXL (area-based conditioning)
"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"clip": ("CLIP", {"tooltip": "CLIP model from your checkpoint"}),
"background_prompt": ("STRING", {
"default": "photo of a city street, high quality",
"multiline": True,
"tooltip": "Background/scene description (applies to whole image)"
}),
"region1_prompt": ("STRING", {
"default": "red sports car",
"multiline": True,
"tooltip": "Prompt for first region/box (clear this to disable)"
}),
},
"optional": {
"region2_prompt": ("STRING", {
"default": "street vendor",
"multiline": True,
"tooltip": "Prompt for second region (optional - clear this to disable)"
}),
"region3_prompt": ("STRING", {
"default": "",
"multiline": True,
"tooltip": "Prompt for third region (optional - clear this to disable)"
}),
"region4_prompt": ("STRING", {
"default": "",
"multiline": True,
"tooltip": "Prompt for fourth region (optional - clear this to disable)"
}),
},
"hidden": {"extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"},
}
RETURN_TYPES = ("CONDITIONING", "INT", "INT")
RETURN_NAMES = ("conditioning", "width", "height")
FUNCTION = "encode_regions"
CATEGORY = "Davemane42/Enhanced"
DESCRIPTION = """🎨 ALL-IN-ONE Regional Prompting (EASY MODE!)
Just type your prompts and draw boxes - that's it!
✅ No external CLIP Text Encode nodes needed
✅ Simple workflow: CLIP → This Node → Sampler
✅ Up to 4 regions + background
✅ Visual box drawing interface
How to use:
1. Connect CLIP from your checkpoint
2. Type background prompt (whole image)
3. Type region prompts (for each box)
4. Draw boxes on canvas
5. Connect to sampler!
Compatible: SD1.5, SD2.x, SDXL"""
def encode_regions(self, clip, background_prompt, region1_prompt, extra_pnginfo, unique_id,
region2_prompt="", region3_prompt="", region4_prompt=""):
"""Encode all prompts and apply regional conditioning."""
# Default template boxes (if workflow not saved yet)
# Region 1 (red sports car): left side, 200x250px starting at (50, 150)
# Region 2 (street vendor): right side, 180x250px starting at (280, 150)
values = [
[50, 150, 200, 250, 1.0], # Region 1
[280, 150, 180, 250, 1.0] # Region 2
]
resolutionX = 512
resolutionY = 512
# Get region data from UI (overrides defaults if workflow saved)
try:
if extra_pnginfo and "workflow" in extra_pnginfo and "nodes" in extra_pnginfo["workflow"]:
for node in extra_pnginfo["workflow"]["nodes"]:
if node["id"] == int(unique_id):
saved_values = node["properties"].get("values", [])
if saved_values: # Only override if we actually have saved values
values = saved_values
resolutionX = node["properties"].get("width", 512)
resolutionY = node["properties"].get("height", 512)
break
except Exception as e:
print(f"ℹ️ Using default template boxes and canvas size 512x512")
# Collect all non-empty prompts
prompts = [background_prompt, region1_prompt, region2_prompt, region3_prompt, region4_prompt]
# Encode each prompt using CLIP (standard SD/SDXL encoding)
encoded_conditionings = []
for prompt in prompts:
if prompt and prompt.strip(): # Only encode non-empty prompts
# Standard CLIP encoding for SD/SDXL
tokens = clip.tokenize(prompt)
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
encoded_conditionings.append([[cond, {"pooled_output": pooled}]])
else:
encoded_conditionings.append(None)
# Apply regional conditioning using area-based method
c = []
# Background (fullscreen)
if encoded_conditionings[0]:
for t in encoded_conditionings[0]:
c.append(t)
# Process each region
for i in range(1, min(len(encoded_conditionings), len(values) + 1)):
if encoded_conditionings[i] is None:
continue
if i - 1 >= len(values):
continue
try:
x, y = int(values[i-1][0]), int(values[i-1][1])
w, h = int(values[i-1][2]), int(values[i-1][3])
strength = float(values[i-1][4]) if len(values[i-1]) > 4 else 1.0
except (IndexError, ValueError, TypeError):
continue
# Skip if fullscreen (already added as background)
if x == 0 and y == 0 and w == resolutionX and h == resolutionY:
continue
# Clip to bounds
if x + w > resolutionX:
w = max(0, resolutionX - x)
if y + h > resolutionY:
h = max(0, resolutionY - y)
if w == 0 or h == 0:
continue
# Apply area to conditioning
for t in encoded_conditionings[i]:
n = [t[0], t[1].copy()]
n[1]['area'] = (h // 8, w // 8, y // 8, x // 8)
n[1]['strength'] = max(0.0, min(10.0, strength))
n[1]['min_sigma'] = 0.0
n[1]['max_sigma'] = 99.0
c.append(n)
return (c, int(resolutionX), int(resolutionY))
class RegionalPrompterFlux:
"""
ALL-IN-ONE regional prompting for FLUX/CHROMA with inline text inputs!
Just connect your CLIP model, type prompts directly, and draw boxes.
Automatically converts to masks optimized for Flux!
Compatible with: Flux, Chroma, SD3, SD3.5 (mask-based conditioning)
"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"clip": ("CLIP", {"tooltip": "CLIP model from your checkpoint"}),
"width": ("INT", {
"default": 1024,
"min": 64,
"max": MAX_RESOLUTION,
"step": 64,
"tooltip": "Output width - must match your latent/sampler"
}),
"height": ("INT", {
"default": 1024,
"min": 64,
"max": MAX_RESOLUTION,
"step": 64,
"tooltip": "Output height - must match your latent/sampler"
}),
"soften_masks": ("BOOLEAN", {
"default": True,
"tooltip": "Enable feathering at region edges - recommended ON"
}),
"background_prompt": ("STRING", {
"default": "photo of a city street, high quality",
"multiline": True,
"tooltip": "Scene description (applies to entire image as base)"
}),
"region1_prompt": ("STRING", {
"default": "red sports car",
"multiline": True,
"tooltip": "First region - draw box on canvas to position"
}),
},
"optional": {
"region2_prompt": ("STRING", {
"default": "street vendor",
"multiline": True,
"tooltip": "Second region (optional - clear to disable)"
}),
"region3_prompt": ("STRING", {
"default": "",
"multiline": True,
"tooltip": "Third region (optional - Flux works best with 3-4 max)"
}),
"region4_prompt": ("STRING", {
"default": "",
"multiline": True,
"tooltip": "Fourth region (optional - Flux works best with 3-4 max)"
}),
},
"hidden": {"extra_pnginfo": "EXTRA_PNGINFO", "unique_id": "UNIQUE_ID"},
}
RETURN_TYPES = ("CONDITIONING",)
RETURN_NAMES = ("conditioning",)
FUNCTION = "encode_regions_flux"
CATEGORY = "Davemane42/Enhanced"
DESCRIPTION = """Mask-based regional prompting with inline text encoding.
Usage:
1. Connect CLIP from checkpoint
2. Set width/height to match your latent
3. Type prompts for background and regions
4. Draw boxes on canvas to position regions
5. Connect conditioning to sampler
Compatible: Flux, Chroma, SD3, SD3.5, Qwen-Image
Recommended: CFG 1.0-3.5, soften_masks ON, 3-4 regions max for Flux"""
def encode_regions_flux(self, clip, width, height, soften_masks, background_prompt, region1_prompt,
extra_pnginfo, unique_id,
region2_prompt="", region3_prompt="", region4_prompt=""):
"""Encode all prompts and apply mask-based regional conditioning for Flux/Chroma/SD3."""
# Default template boxes (if workflow not saved yet)
# Region 1 (red sports car): left side, 400x500px starting at (100, 300)
# Region 2 (street vendor): right side, 350x500px starting at (560, 300)
values = [
[100, 300, 400, 500, 1.0], # Region 1
[560, 300, 350, 500, 1.0] # Region 2
]
# Get region data from UI (overrides defaults if workflow saved)
try:
if extra_pnginfo and "workflow" in extra_pnginfo and "nodes" in extra_pnginfo["workflow"]:
for node in extra_pnginfo["workflow"]["nodes"]:
if node["id"] == int(unique_id):
saved_values = node["properties"].get("values", [])
if saved_values: # Only override if we actually have saved values
values = saved_values
# Override with property dimensions if available
prop_width = node["properties"].get("width", width)
prop_height = node["properties"].get("height", height)
break
except Exception as e:
print(f"ℹ️ Using default template boxes and dimensions {width}x{height}")
prop_width = width
prop_height = height
# Collect all non-empty prompts
prompts = [background_prompt, region1_prompt, region2_prompt, region3_prompt, region4_prompt]
# Count non-empty regions (excluding background)
num_regions = sum(1 for p in prompts[1:] if p and p.strip())
print(f"\n🎨 Regional Prompter - Processing:")
print(f" Dimensions: {width}x{height} (latent: {width//8}x{height//8})")
print(f" Regions: {num_regions} active (background + {num_regions} regions)")
print(f" Soften masks: {soften_masks}")
print(f" Default boxes: {values}")
if num_regions > 4:
print(f"⚠️ Warning: {num_regions} regions detected. Flux works best with 3-4 regions maximum.")
# Encode each prompt using CLIP
# ComfyUI's CLIP object handles multi-encoder complexity internally
# Works for all models: Flux (2 encoders), SD3 (3 encoders), Chroma/Qwen (1 encoder), etc.
encoded_conditionings = []
for prompt in prompts:
if prompt and prompt.strip():
tokens = clip.tokenize(prompt)
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
encoded_conditionings.append([[cond, {"pooled_output": pooled}]])
else:
encoded_conditionings.append(None)
# Apply mask-based conditioning
combined_conditioning = []
# Determine mask strength based on soften setting
# Research shows 0.8 (softer masks) work better than full 1.0 strength for Flux
# Users can try it for Chroma/SD3 (default ON), disable if they prefer sharper edges
mask_strength = 0.8 if soften_masks else 1.0
# Background (fullscreen - no mask)
if encoded_conditionings[0]:
for t in encoded_conditionings[0]:
combined_conditioning.append(t)
# Process each region with masks
latent_width = width // 8
latent_height = height // 8
for i in range(1, min(len(encoded_conditionings), len(values) + 1)):
if encoded_conditionings[i] is None:
continue
if i - 1 >= len(values):
continue
try:
x, y = int(values[i-1][0]), int(values[i-1][1])
w, h = int(values[i-1][2]), int(values[i-1][3])
strength = float(values[i-1][4]) if len(values[i-1]) > 4 else 1.0
except (IndexError, ValueError, TypeError):
continue
# Get canvas dimensions
canvas_width = prop_width if 'prop_width' in locals() else width
canvas_height = prop_height if 'prop_height' in locals() else height
# Skip fullscreen (already added as background)
if x == 0 and y == 0 and w == canvas_width and h == canvas_height:
continue
# Scale to output dimensions if needed
if canvas_width != width or canvas_height != height:
x = int(x * width / canvas_width)
y = int(y * height / canvas_height)
w = int(w * width / canvas_width)
h = int(h * height / canvas_height)
# Clip to bounds
x = max(0, min(x, width))
y = max(0, min(y, height))
w = max(0, min(w, width - x))
h = max(0, min(h, height - y))
if w == 0 or h == 0:
continue
# Convert to latent space
x_latent = x // 8
y_latent = y // 8
w_latent = max(1, w // 8)
h_latent = max(1, h // 8)
# Create mask with Flux-optimized strength
mask = torch.zeros((1, latent_height, latent_width), dtype=torch.float32)
x_end = min(x_latent + w_latent, latent_width)
y_end = min(y_latent + h_latent, latent_height)
# Fill mask with specified strength
mask[0, y_latent:y_end, x_latent:x_end] = mask_strength
print(f" Region {i}: '{prompts[i][:40]}...'")
print(f" Box: x={x}, y={y}, w={w}, h={h}")
print(f" Latent: x={x_latent}, y={y_latent}, w={w_latent}, h={h_latent}")
print(f" Mask strength: {mask_strength}, Region strength: {strength}")
# Apply feathering if softening enabled (gentle edge blending to avoid harsh boundaries)
if soften_masks:
# Apply gentle 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 * mask_strength
# Top edge
if y_latent + edge_idx < y_end:
mask[0, y_latent + edge_idx, x_latent:x_end] *= (fade / mask_strength)
# Bottom edge
if y_end - 1 - edge_idx >= y_latent:
mask[0, y_end - 1 - edge_idx, x_latent:x_end] *= (fade / mask_strength)
# Left edge
if x_latent + edge_idx < x_end:
mask[0, y_latent:y_end, x_latent + edge_idx] *= (fade / mask_strength)
# Right edge
if x_end - 1 - edge_idx >= x_latent:
mask[0, y_latent:y_end, x_end - 1 - edge_idx] *= (fade / mask_strength)
# Apply mask to conditioning
for t in encoded_conditionings[i]:
n = [t[0], t[1].copy()]
n[1]['mask'] = mask
n[1]['strength'] = max(0.0, min(10.0, strength))
n[1]['set_area_to_bounds'] = False
combined_conditioning.append(n)
print(f" ✅ Generated {len(combined_conditioning)} conditioning blocks")
print(f" 💡 Tip: Flux works best at CFG 1.0-3.5 with regional prompting\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
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