Simplify CLIP encoding - remove broken Flux detection
CRITICAL FIX: hasattr(clip, 'encode_from_tokens_scheduled') is useless - This method exists in base CLIP class inherited by ALL models - Always returns True (SD/SDXL/Flux/Chroma/SD3/etc.) - Cannot be used for model detection Simplified to universal encoding for ALL models: - Removed guidance parameter from RegionalPrompterFlux - Removed all is_flux detection branching - Use encode_from_tokens(return_pooled=True) universally - ComfyUI's CLIP object handles 1/2/3/4 encoder complexity internally Benefits: - Simpler code (removed 30+ lines of complex branching) - More maintainable (no model-specific special cases) - Actually works correctly (previous logic was broken) Documented in CONTEXT.md with encoder architecture table.
This commit is contained in:
Claude
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@@ -38,6 +38,7 @@
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- **Format:** Must wrap in list: `[[cond, {"pooled_output": pooled}]]`
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- **Works:** Identical to external CLIPTextEncode nodes
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- **Benefit:** Massive UX improvement - no external nodes needed!
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- **Universal:** Same method works for ALL models (SD/SDXL/Flux/Chroma/SD3/etc.) - ComfyUI handles multi-encoder complexity internally
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#### Text Encoder Architectures by Model
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**CRITICAL:** Different models use different numbers and types of text encoders. ComfyUI's CLIP object handles the complexity internally, but understanding the architecture helps with debugging.
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@@ -47,34 +48,42 @@
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| **SD 1.5** | 1 | CLIP-L | Standard `encode_from_tokens` |
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| **SD 2.x** | 1 | OpenCLIP-H | Standard `encode_from_tokens` |
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| **SDXL** | 2 | CLIP-L + OpenCLIP-G | Standard `encode_from_tokens` (handled internally) |
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| **Flux** ✅ | 2 | T5-XXL + CLIP-L | **`encode_from_tokens_scheduled`** with guidance |
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| **Flux** ✅ | 2 | T5-XXL + CLIP-L | Standard `encode_from_tokens` (handled internally) |
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| **Chroma** ✅ | 1 | T5-XXL only | Standard `encode_from_tokens` (Flux-based but single encoder!) |
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| **SD3** | 3 | CLIP-L + CLIP-G + T5-XXL | Standard `encode_from_tokens` (multi-encoder internal) |
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| **SD3.5** | 3 | CLIP-L + CLIP-G + T5-XXL | Standard `encode_from_tokens` (multi-encoder internal) |
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| **HiDream** | 4 | CLIP-L + CLIP-G + 2× T5 variants | ComfyUI uses 4 encoder files |
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| **WAN 2.1** | 1 | UMT5-XXL | Standard encoding (fp16/fp8/bf16 variants) |
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| **WAN 2.2** | 1 | UMT5-XXL + MoE | Standard encoding (nf4 4-bit quantization) |
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| **Qwen-Image** | 1 | Qwen 2.5 VL (qwen_2.5_vl_7b.safetensors) | Standard encoding (vision-language LLM encoder) |
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| **HiDream** | 4 | CLIP-L + CLIP-G + 2× T5 variants | Standard `encode_from_tokens` (multi-encoder internal) |
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| **WAN 2.1** | 1 | UMT5-XXL | Standard `encode_from_tokens` (fp16/fp8/bf16 variants) |
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| **WAN 2.2** | 1 | UMT5-XXL + MoE | Standard `encode_from_tokens` (nf4 4-bit quantization) |
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| **Qwen-Image** | 1 | Qwen 2.5 VL (qwen_2.5_vl_7b.safetensors) | Standard `encode_from_tokens` (vision-language LLM) |
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**Detection Strategy:**
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**Implementation (Simplified - November 2025):**
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```python
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# Current implementation (correct for most models)
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is_flux = hasattr(clip, 'encode_from_tokens_scheduled')
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if is_flux:
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# Flux-specific: dual encoder with guidance scheduling
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cond = clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance})
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else:
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# Standard encoding: SD/SDXL/Chroma/SD3/SD3.5/HiDream/WAN/Qwen
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# ComfyUI's CLIP object handles multi-encoder complexity internally
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
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# Universal encoding for ALL models (1, 2, 3, or 4 encoders)
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# ComfyUI's CLIP object handles multi-encoder complexity internally
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for prompt in prompts:
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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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```
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**Critical Discovery (November 2025):**
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- ❌ **WRONG:** Using `hasattr(clip, 'encode_from_tokens_scheduled')` to detect Flux
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- This method exists in the **base CLIP class** that ALL models inherit from
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- Returns `True` for SD1.5, SDXL, SD3, Flux, Chroma - everything!
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- Completely useless for model detection
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- ✅ **CORRECT:** Use standard `encode_from_tokens(return_pooled=True)` for ALL models
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- Works universally: SD (1 encoder), SDXL (2), Flux (2), SD3 (3), HiDream (4)
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- ComfyUI's CLIP object abstracts encoder complexity
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- No model-specific branching needed
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- 📝 **Note:** `encode_from_tokens_scheduled` is what CLIPTextEncode uses internally, but for regional prompting without temporal effects, `encode_from_tokens` works perfectly
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**Key Insights:**
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- ✅ **Chroma is Flux-based architecture but uses ONLY 1 encoder** (T5-XXL, not CLIP-L+T5-XXL)
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- ✅ **SD3/SD3.5 use 3 encoders** but ComfyUI's CLIP handles it transparently via standard method
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- ✅ **SD3/SD3.5 use 3 encoders** but ComfyUI's CLIP handles it transparently
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- ✅ **HiDream uses 4 encoders** in ComfyUI (CLIP-L, CLIP-G, T5 variants)
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- ✅ **Flux is the ONLY model** currently requiring `encode_from_tokens_scheduled` method
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- ✅ **ALL models** use the same encoding method - no special cases needed
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- ⚠️ **Don't assume encoder count from architecture** - Chroma proves Flux-based ≠ dual encoder!
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#### JavaScript UI
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+7
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@@ -207,13 +207,6 @@ class RegionalPrompterFlux:
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"default": True,
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"tooltip": "✅ RECOMMENDED: Softer masks (0.8 strength + gentle edge blend) work better than harsh full-strength (1.0) masks. Confirmed better for Flux, works well with other mask-based models. Disable if you prefer sharper region edges."
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}),
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"guidance": ("FLOAT", {
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"default": 3.5,
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"min": 0.0,
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"max": 100.0,
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"step": 0.1,
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"tooltip": "Guidance strength for Flux (default 3.5). Higher values = stronger prompt adherence. Try 5-7 for better regional control."
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}),
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},
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"optional": {
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"region2_prompt": ("STRING", {
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@@ -267,15 +260,15 @@ How to use:
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• Works well with other mask-based models - try it first!
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Model-Specific Tips:
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• FLUX: Use 3-4 regions max (quality degrades with more), increase CFG to 5-7
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• FLUX: Use 3-4 regions max (quality degrades with more)
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• FLUX-BASED (Chroma, etc.): Likely 3-4 region limit too (untested), try and see!
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• SD3/SD3.5: No known region limit, standard CFG values
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• SD3/SD3.5: No known region limit
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• QWEN-IMAGE: Experimental - try mask-based conditioning and report results!
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Compatible: Flux (all variants), Chroma, SD3, SD3.5, and other mask-based models"""
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def encode_regions_flux(self, clip, width, height, background_prompt, region1_prompt,
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soften_masks, guidance, extra_pnginfo, unique_id,
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soften_masks, extra_pnginfo, unique_id,
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region2_prompt="", region3_prompt="", region4_prompt=""):
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"""Encode all prompts and apply mask-based regional conditioning for Flux/Chroma/SD3."""
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@@ -314,43 +307,15 @@ Compatible: Flux (all variants), Chroma, SD3, SD3.5, and other mask-based models
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print(f"⚠️ Warning: {num_regions} regions detected. Flux works best with 3-4 regions maximum.")
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# Encode each prompt using CLIP
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# Flux uses dual-encoder (CLIP-L + T5-XXL), Chroma/Qwen use single encoder
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# ComfyUI's CLIP object handles multi-encoder complexity internally
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# Works for all models: Flux (2 encoders), SD3 (3 encoders), Chroma/Qwen (1 encoder), etc.
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encoded_conditionings = []
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# Detect if this is Flux by checking for encode_from_tokens_scheduled method
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is_flux = hasattr(clip, 'encode_from_tokens_scheduled')
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for prompt in prompts:
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if prompt and prompt.strip():
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tokens = clip.tokenize(prompt)
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if is_flux:
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# Flux-specific: Dual encoder (CLIP-L + T5-XXL) with guidance
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# Check if t5xxl tokens are available
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if "t5xxl" in tokens:
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# Already has both encoders in single tokenize call
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pass
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else:
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# Need to merge t5xxl tokens manually
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try:
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t5_result = clip.tokenize(prompt)
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if "t5xxl" in t5_result:
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tokens["t5xxl"] = t5_result["t5xxl"]
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except:
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pass
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# Use Flux's scheduled encoding with guidance
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try:
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cond = clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance})
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encoded_conditionings.append(cond)
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except Exception as e:
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print(f"⚠️ Flux encoding failed: {e}, using fallback")
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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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# Standard encoding for Chroma, Qwen, SD3, etc. (single encoder)
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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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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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