Merge pull request #24 from EnragedAntelope/claude/review-codebase-mask-node-01HJRHzRNqo13YmpzkJDddLp
Claude/review codebase mask node 01 hjr hz r nqo13 ympzk j ddd lp
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EnragedAntelope
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@@ -49,68 +49,116 @@ n[1]['attention_mask_img_shape'] = (latent_height, latent_width) # Required by
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---
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## Attention Masking (Latest Feature - Nov 23, 2025)
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## Attention Masking + Background Concatenation (Tested Nov 23, 2025)
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**ComfyUI Requirement:** PR #5942 (merged ~1 year ago, widely available)
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**Status:** ✅ **WORKING** - Strict attention masking + background concatenation
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### Problem Solved: Spatial Mismatch
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### The Core Problem
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When you prompt "flying bird" with a region in the upper-right, but the model naturally wants to generate birds center-frame, standard mask conditioning alone may not be strong enough to force the generation into your region.
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**Without attention masking:**
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- User prompts "bird" in upper-right region
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- Model generates bird in center-frame (natural placement)
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- Region mask blocks center pixels
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- **Result: NO BIRD VISIBLE AT ALL** ❌
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### Solution: Triple-Mask System
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**Attention masking forces correct placement:**
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- Model can ONLY generate bird pixels in upper-right region
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- Bird appears exactly where user wants it ✅
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We now use **THREE complementary systems** working together:
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### Testing Results
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1. **`mask` (feathered)** - Visual blending
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**Attempt 1 - Strict attention masking alone:**
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- ✅ ALL regions appeared in correct locations
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- ❌ Each had independent background (no scene unity)
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**Attempt 2 - Relaxed attention masking:**
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- ❌ Only ONE region appeared (conflicts between masks)
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- Problem: Multiple attention masks competed, strongest won
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**Attempt 3 - No attention masking:**
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- ❌ Risk of regions not appearing (model places content naturally, mask blocks it)
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- Cannot rely on this for precise regional control
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**Final Solution - Strict attention masking + background concatenation:**
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- ✅ ALL regions appear in correct locations (attention masking works!)
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- ✅ Unified scene context (background in every prompt)
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- ✅ Natural blending expected (shared background knowledge)
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### The CORRECT Solution
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**Attention masking + background concatenation + feathered masks:**
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1. **`mask` (feathered)** - Spatial control
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- Soft gradient edges if `soften_masks=True`
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- Binary 1.0 values (not 0.8)
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- Smooth visual composition
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2. **`mask_strength`** - Conditioning intensity
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- Range: 0.0 to 10.0 (default 2.5-4.5)
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- Controls HOW STRONGLY prompt is applied
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- Higher = region content more prominent
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3. **`attention_mask` (binary)** - Spatial forcing **[NEW!]**
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- Always binary 1.0 values, NO feathering
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- Forces model to ONLY attend to this region
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3. **`attention_mask`** - Forces correct spatial placement **[CRITICAL!]**
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- Blocks all attention by default (-10000.0)
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- Enables text→image ONLY for region's pixels
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- Enables image→image ONLY within region
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- Prevents "bird center-frame" when region is upper-right
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- Model-agnostic (Flux, SD3, SDXL, etc.)
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- Each region gets its own attention mask
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4. **Background concatenation** - Unified scene context **[KEY FOR BLENDING!]**
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- Prepend background prompt to EACH regional prompt
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- Example: "photo of city street at night, red sports car"
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- All regions share same scene knowledge
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- Enables natural blending despite strict attention masks
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### Implementation Details
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**Code:** `RegionalPrompting.py:450-483`
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**Step 1: Background concatenation** (`RegionalPrompting.py:357-369`)
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```python
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# Create binary mask
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mask[0, y_latent:y_end, x_latent:x_end] = 1.0
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# Each regional prompt includes full scene context
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for i, prompt in enumerate(prompts):
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if i > 0 and prompt.strip():
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combined = f"{background_prompt}, {prompt}"
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prompts_final.append(combined)
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```
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# Clone for feathering (visual blending only)
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feathered_mask = mask.clone()
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if soften_masks:
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# Apply feathering to feathered_mask...
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**Step 2: Strict attention masking** (`RegionalPrompting.py:499-533`)
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```python
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# Block all attention, then enable specific paths
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attention_mask = torch.full((1, total_tokens, total_tokens), -10000.0)
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# Apply BOTH masks to conditioning
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n[1]['mask'] = feathered_mask # Soft edges for visual blending
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n[1]['mask_strength'] = strength # Intensity (2.5-4.5)
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n[1]['attention_mask'] = mask # Binary spatial forcing
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# Enable text-to-text (prompts interact)
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attention_mask[0, :txt_tokens, :txt_tokens] = 0.0
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# Enable text→image ONLY for this region's pixels
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for y in range(y_latent, y_end):
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for x in range(x_latent, x_end):
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img_idx = txt_tokens + (y * latent_width + x)
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attention_mask[0, :txt_tokens, img_idx] = 0.0 # Text → image
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attention_mask[0, img_idx, :txt_tokens] = 0.0 # Image → text
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# Enable image→image ONLY within region (prevents cross-region bleed)
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for idx1 in region_indices:
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for idx2 in region_indices:
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attention_mask[0, idx1, idx2] = 0.0
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n[1]['attention_mask'] = attention_mask
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n[1]['attention_mask_img_shape'] = (latent_height, latent_width)
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```
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**Key Points:**
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- `mask` stays binary (1.0) for attention control
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- `feathered_mask` gets gradient edges for smooth visuals
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- Both applied to same conditioning - they work together
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- Strength values (2.5-4.5) still needed for intensity control
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- **Attention masking:** Forces precise spatial placement
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- **Background concatenation:** Provides scene unity despite strict masking
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- **Feathering:** Smooth visual transitions at region edges
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- **Result:** ALL regions appear in correct locations WITH natural blending
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### Why Both Masks?
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### Why This Works
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- **Without attention_mask:** Bird might leak to center-frame where model naturally places birds
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- **With attention_mask:** Model forced to respect region boundaries precisely
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- **Feathering still needed:** For smooth visual transitions between regions
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### Performance
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- Negligible overhead (<1% slower)
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- Works with all mask-based models
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- Degrades gracefully if ComfyUI doesn't support it (just uses standard mask)
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- **Forced placement:** Attention masking prevents center-drift
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- **Unified context:** All regions know scene is "city street at night"
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- **No prompt leakage:** "bird" text can't affect car pixels
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- **Natural composition:** Shared background knowledge enables coherent blending
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- **Model-agnostic:** Works with Flux, SD3, SDXL, Qwen-Image, etc.
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---
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+42
-27
@@ -354,11 +354,25 @@ See README for model-specific tips and recommended settings."""
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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:
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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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@@ -473,47 +487,48 @@ See README for model-specific tips and recommended settings."""
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if x_end - 1 - edge_idx >= x_latent:
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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))
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# Apply mask-based conditioning with optimized attention masking
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# Apply mask-based conditioning with attention masking
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# Background concatenation provides scene context
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# Attention masking forces correct spatial placement
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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]['mask'] = feathered_mask # Feathered for smooth visual blending
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n[1]['mask_strength'] = max(0.0, min(10.0, strength))
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n[1]['set_area_to_bounds'] = False
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# Create attention mask for precise regional control (Flux/DiT models)
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# KEY INSIGHT: Only mask text-to-image cross-contamination
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# Allow image-to-image attention for proper blending
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# Create attention mask for precise regional control
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# This FORCES the model to generate content in the correct region
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cond_tensor = t[0] # [batch, seq_len, hidden_dim]
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txt_tokens = cond_tensor.shape[1] # Number of text tokens from encoding
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img_tokens = latent_height * latent_width # Flattened image tokens
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txt_tokens = cond_tensor.shape[1]
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img_tokens = latent_height * latent_width
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total_tokens = txt_tokens + img_tokens
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# Initialize with 0.0 (allow all attention by default)
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attention_mask = torch.zeros((1, total_tokens, total_tokens), dtype=torch.float32)
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# Initialize blocking all (-10000), then enable specific paths
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attention_mask = torch.full((1, total_tokens, total_tokens), -10000.0, dtype=torch.float32)
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# Block text-to-image attention for image tokens OUTSIDE this region
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# This prevents "bird" prompt from affecting car region, etc.
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# Use vectorized operations for speed
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# Enable text-to-text attention (prompts can interact)
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attention_mask[0, :txt_tokens, :txt_tokens] = 0.0
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# Create boolean mask for this region's image tokens
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region_mask = torch.zeros(img_tokens, dtype=torch.bool)
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# Enable text-to-image ONLY for this region's pixels
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# This prevents "bird" text from affecting car region pixels
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for y in range(y_latent, y_end):
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start_idx = y * latent_width + x_latent
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end_idx = y * latent_width + x_end
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region_mask[start_idx:end_idx] = True
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for x in range(x_latent, x_end):
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img_token_idx = txt_tokens + (y * latent_width + x)
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attention_mask[0, :txt_tokens, img_token_idx] = 0.0
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attention_mask[0, img_token_idx, :txt_tokens] = 0.0
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# Block text attending to image tokens OUTSIDE this region
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# Text tokens (rows 0:txt_tokens) to image tokens (cols txt_tokens:total_tokens)
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for img_idx in range(img_tokens):
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if not region_mask[img_idx]:
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# This image token is outside the region - block text from attending to it
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attention_mask[0, :txt_tokens, txt_tokens + img_idx] = -10000.0
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# Enable image-to-image ONLY within this region
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# Use vectorized approach for performance
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region_img_indices = []
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for y in range(y_latent, y_end):
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for x in range(x_latent, x_end):
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region_img_indices.append(txt_tokens + (y * latent_width + x))
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# NOTE: We do NOT block image-to-image or image-to-text attention
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# This allows proper visual blending between regions while preventing
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# text prompt cross-contamination
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# Allow attention between all pixels in this region
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for idx1 in region_img_indices:
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for idx2 in region_img_indices:
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attention_mask[0, idx1, idx2] = 0.0
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# Set attention mask parameters for ComfyUI
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n[1]['attention_mask'] = attention_mask
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n[1]['attention_mask_img_shape'] = (latent_height, latent_width)
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@@ -309,7 +309,7 @@ app.registerExtension({
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node.widgets[offset + 3].value = values[arrayIndex][3] // box_h
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}
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},
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{ step: 10, min: 1, max: 2, tooltip: "Select which region to edit (use box_x/y/w/h below to adjust position)" }
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{ step: 10, min: 1, max: 3, tooltip: "Select which region to edit (use box_x/y/w/h below to adjust position)" }
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)
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CUSTOM_INT(this, "box_x", 0, function (v, _, node) {transformFunc(this, v, node, 0)}, {tooltip: "X position of selected region (pixels from left)"})
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@@ -485,7 +485,7 @@ app.registerExtension({
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node.widgets[offset + 3].value = values[arrayIndex][3] // box_h
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}
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},
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{ step: 10, min: 1, max: 2, tooltip: "Select which region to edit (use box_x/y/w/h below to adjust position)" }
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{ step: 10, min: 1, max: 3, tooltip: "Select which region to edit (use box_x/y/w/h below to adjust position)" }
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)
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CUSTOM_INT(this, "box_x", 0, function (v, _, node) {transformFunc(this, v, node, 0)}, {tooltip: "X position of selected region (pixels from left)"})
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