# Emu3.5 ComfyUI Nodes **ComfyUI integration for BAAI's Emu3.5 multimodal models** ✅ **STATUS: ALL NODES FULLY WORKING** - Verified December 19, 2025 ![Complete Workflow](assets/Emu35_complete_workflow.png) ## Overview This repository provides ComfyUI custom nodes for running BAAI's Emu3.5 models for: - **Text-to-Image (T2I)** - Generate images from text descriptions ✅ - **Image Editing (X2I)** - Transform/edit existing images ✅ - **Interleaved Generation (Story Mode)** - Create illustrated stories with text and images ✅ - **Visual Q&A** - Answer questions about images, OCR, image comparison ✅ ### Workflow Screenshots | T2I / Basic Workflow | X2I (Image Edit) | Story Mode | |:---:|:---:|:---:| | ![T2I](assets/Basic_workflow_screenshot.png) | ![X2I](assets/Emu35-X2I-workflow-screenshot.png) | ![Story](assets/Emu35_base_story_screenshot.png) | **Models Supported:** - Emu3.5-Image (34B params - T2I/X2I) - ✅ Working - Emu3.5-Base (65B params - Story/Interleaved/VQA) - ✅ Working - Vision Tokenizer (VQ-VAE for image encoding/decoding) - ✅ Working ## What's New (December 2025) ### V2 Nodes Released! 🎉 - **Emu 3.5 Loader V2** - Improved model loading with memory management - **Emu 3.5 T2I Sampler V2** - Text-to-image with tiled decoding for large images - **Emu 3.5 X2I (Image Edit)** - Transform/edit images with text prompts - **Emu 3.5 Interleaved** - Generate stories/tutorials with multiple images - **Emu 3.5 VQA** - Visual question answering - **Emu 3.5 Memory Manager** - VRAM management utilities ## Credits & Attribution This project is built upon: - **[BAAI Emu3.5](https://github.com/baaivision/Emu3.5)** - Original model and codebase - Paper: [Emu3.5: Native Multimodal Models are World Learners](https://arxiv.org/pdf/2510.26583) - Authors: Emu3.5 Team, Beijing Academy of Artificial Intelligence - License: Apache 2.0 **Development Contributors:** - **Eric Rollei** - ComfyUI node development and integration - **Claude Opus 4.5 (Anthropic)** - AI pair programming assistant for debugging, compatibility fixes, and feature implementation **Technical Contributions:** - Transformers 4.57+ compatibility patches (DynamicCache API changes) - Blackwell GPU (sm_120) eager attention workaround - VQA task presets and multi-image comparison support - Memory management and VRAM optimization All model weights and architecture remain property of BAAI under Apache 2.0 license. ## Installation ### Prerequisites - ComfyUI installed - Python 3.10+ - CUDA-capable GPU: - **Full BF16**: 48GB+ VRAM (RTX A6000, RTX 6000 Ada/Blackwell) - **NF4 Quantized**: 24GB+ VRAM (RTX 4090, RTX A5000) - 100GB+ disk space for model weights ### Method 1: Git Clone (Recommended) ```bash cd ComfyUI/custom_nodes git clone --recursive https://github.com/EricRollei/Emu35-Comfyui-Nodes.git emu35 cd emu35 # Install dependencies pip install -r requirements.txt ``` > **Note:** The `--recursive` flag automatically clones the patched Emu3.5 submodule with transformers 4.57+ compatibility fixes. ### Method 2: Manual Download 1. Download this repository 2. Extract to `ComfyUI/custom_nodes/emu35/` 3. Download [Emu3.5 repo](https://github.com/baaivision/Emu3.5) and place in `emu35/Emu3_5_repo/` 4. Install requirements: `pip install -r requirements.txt` ### Download Model Weights Place models in `ComfyUI/models/emu35/`: **Option A: Full BF16 (48GB+ VRAM - Best Quality)** ```bash huggingface-cli download BAAI/Emu3.5-Image --local-dir models/emu35/Emu3.5-Image huggingface-cli download BAAI/Emu3.5-VisionTokenizer --local-dir models/emu35/vision_tokenizer ``` **Option B: NF4 Quantized (24GB+ VRAM)** ```bash huggingface-cli download wikeeyang/Emu35-Image-NF4 --local-dir models/emu35/Emu3.5-Image-NF4 huggingface-cli download BAAI/Emu3.5-VisionTokenizer --local-dir models/emu35/vision_tokenizer ``` **Directory structure:** ``` ComfyUI/ ├── custom_nodes/ │ └── emu35/ │ ├── nodes.py │ ├── nodes_v2.py # New V2 nodes │ ├── __init__.py │ ├── patched_tokenization_emu3.py │ └── Emu3_5_repo/ # Official repo └── models/ └── emu35/ ├── Emu3.5-Image/ # (or Emu3.5-Image-NF4/) └── vision_tokenizer/ ``` ## Nodes Reference ### V2 Nodes (Recommended) #### 1. Emu 3.5 Loader V2 Loads the Emu3.5 model with improved memory management. | Input | Type | Description | |-------|------|-------------| | `model_name` | dropdown | Model folder (e.g., "Emu3.5-Image") | | `vq_model_name` | dropdown | Vision tokenizer folder | | `precision` | dropdown | bf16, fp16, fp32, or nf4 | | `device` | dropdown | cuda:0, cuda:1, cpu | | `vq_device` | dropdown | Device for VQ model | | `attention_implementation` | dropdown | eager (recommended), sdpa | | Output | Type | Description | |--------|------|-------------| | `EMU35_MODEL` | model | Loaded language model | | `EMU35_TOKENIZER` | tokenizer | Text tokenizer | | `EMU35_VQ` | model | Vision tokenizer | --- #### 2. Emu 3.5 T2I Sampler V2 Text-to-image generation with improved quality and tiled decoding. | Input | Type | Description | |-------|------|-------------| | `model/tokenizer/vq_model` | - | From loader | | `prompt` | string | Text description | | `aspect_ratio` | dropdown | 1:1, 4:3, 3:4, 16:9, 9:16, etc. | | `cfg_scale` | float | Guidance scale (default: 5.0) | | `seed` | int | Random seed | | `image_top_k` | int | Sampling top-k (default: 5120) | | `image_temperature` | float | Sampling temperature (default: 1.0) | | `tiled_decode` | bool | Use tiled VQ decoding (faster for large images) | | `tile_size` | int | Tile size for decoding (default: 32) | | Output | Type | Description | |--------|------|-------------| | `IMAGE` | image | Generated image | | `TEXT_RESPONSE` | string | Any text response | | `REASONING` | string | Chain-of-thought reasoning (if any) | --- #### 3. Emu 3.5 X2I (Image Edit) ⭐ NEW Transform or edit existing images based on text instructions. | Input | Type | Description | |-------|------|-------------| | `model/tokenizer/vq_model` | - | From loader | | `prompt` | string | Edit instruction (e.g., "Make the background a sunset") | | `reference_image_1` | image | Primary reference image | | `image_area` | dropdown | Token resolution: 256x256 to 1024x1024 | | `cfg_scale` | float | Guidance (default: 2.0 for X2I) | | `seed` | int | Random seed | | `reference_image_2` | image | Optional second reference | | `reference_image_3` | image | Optional third reference | | `tiled_decode` | bool | Use tiled VQ decoding | **Example Prompts for X2I:** - "Transform this image into a realistic photo" - "Change the background to a beach sunset" - "Add sunglasses to the person" - "Replace the dog with a cat" - With 2+ images: "Replace the [object] in first image with [object] from second image" | Output | Type | Description | |--------|------|-------------| | `IMAGE` | image | Edited image | | `TEXT_RESPONSE` | string | Any text response | | `REASONING` | string | Chain-of-thought reasoning | --- #### 4. Emu 3.5 Interleaved (Story/HowTo) Generate text with multiple embedded images (stories, tutorials). | Input | Type | Description | |-------|------|-------------| | `model/tokenizer/vq_model` | - | From loader | | `prompt` | string | Topic/story to generate | | `task_type` | dropdown | story, howto, explore | | `max_images` | int | Number of images to generate (1-10) | | `cfg_scale` | float | Guidance scale | | `seed` | int | Random seed | | `reference_image` | image | Optional reference for context | | Output | Type | Description | |--------|------|-------------| | `IMAGES` | image batch | Generated images | | `TEXT_RESPONSE` | string | Full text with [IMAGE_N] markers | | `REASONING` | string | Reasoning if present | --- #### 5. Emu 3.5 VQA (Visual Question Answering) Analyze images, answer questions, describe content, and read text (OCR). **⚠️ Important:** Use the **emu35-base** model for VQA tasks, not emu35-image! | Input | Type | Description | |-------|------|-------------| | `model/tokenizer/vq_model` | - | From loader (use emu35-base) | | `image` | image | Image to analyze | | `question` | string | Question about the image | | `max_tokens` | int | Max response length (default: 512) | | `task_type` | dropdown | Preset task types (see below) | | `temperature` | float | Response creativity (0.1-0.3 for accuracy, 0.5-0.7 for creativity) | | `image2` | image | Optional second image for comparison tasks | | Output | Type | Description | |--------|------|-------------| | `response` | string | Model's answer | **Task Types:** | Task Type | Description | Best For | |-----------|-------------|----------| | `caption` | One-sentence summary | Quick descriptions | | `describe` | Detailed description of subjects, background, colors, composition | Comprehensive analysis | | `analyze` | Context, mood, artistic style analysis | Art/photo critique | | `ocr` | Read and transcribe text from images | Screenshots, signs, documents | | `question` | Free-form Q&A | Specific questions | | `custom` | Use your own question directly | Any task | **Example Questions for VQA:** *Basic Understanding:* - "Describe this image in detail." - "What is the main subject of this image?" - "What colors are dominant in this image?" *Object Identification:* - "What objects are visible in this image?" - "Is there a person in this image? What are they doing?" - "What type of animal is in the photo?" *Counting & Spatial:* - "How many people are in this image?" - "What is to the left of the car?" - "Where is the sun in this scene?" *OCR (Text Reading):* - "What text appears in this image?" - "Read the sign in the background." - "What does the label say?" *Analysis & Style:* - "What artistic style is this image?" - "What time of day is shown?" - "What emotion does this scene convey?" - "Is this image indoor or outdoor?" *Comparison (with 2 images):* - "What's different between these two images?" - "Which image shows more people?" - "Compare the lighting in both photos." **Tips for Best Results:** 1. **Use emu35-base model** - The Image model is optimized for generation, not understanding 2. **Lower temperature (0.1-0.3)** for factual answers like counting, OCR, or identification 3. **Higher temperature (0.5-0.7)** for creative descriptions or artistic analysis 4. **Be specific** - "What brand is the laptop?" works better than "What's in the image?" 5. **For OCR**, use task_type="ocr" which has an optimized prompt 6. **For comparisons**, connect both images (image + image2) --- #### 6. Emu 3.5 Memory Manager Utilities for VRAM management. | Input | Type | Description | |-------|------|-------------| | `action` | dropdown | clear_cache, report_memory, gc_collect | | `any_input` | any | Pass-through connection | | Output | Type | Description | |--------|------|-------------| | `any_output` | any | Pass-through | | `memory_info` | string | Memory status report | --- ### Legacy Nodes (V1) The original nodes are still available for compatibility: - `Emu35Loader` - Original loader - `Emu35Sampler` - Original T2I sampler - `Emu35VQA` - Original VQA node - `Emu35ClearCache` - Cache clearing ## Performance ### Tested Configurations | Configuration | GPU | VRAM Used | Speed | |---------------|-----|-----------|-------| | Full BF16 + T2I | RTX 6000 Blackwell 96GB | ~65GB | ~5-6 tok/s | | Full BF16 + X2I (1 image) | RTX 6000 Blackwell 96GB | ~82GB | ~5.4 tok/s | | NF4 + X2I (2 images @ 1024) | RTX 6000 Blackwell 96GB | ~50GB | ~4-5 tok/s | | NF4 + T2I | RTX 4090 24GB | ~22GB | ~3-4 tok/s | ### Generation Times | Task | Resolution | Tokens | Time | |------|------------|--------|------| | T2I 1:1 | 1024x1024 | ~4096 | ~12 min | | T2I 4:3 | 1168x880 | ~4000 | ~11 min | | X2I | Same as input | ~4000 | ~13 min | ## Technical Details ### Architecture - **Model Size**: 34B parameters - **Training**: 10T+ multimodal tokens - **Image Tokenization**: VQ-VAE (IBQ) with 262,144 codebook - **Visual Tokens**: Token IDs 151855-413998 - **Max Resolution**: 2048x2048 (128x128 latents) ### Special Token IDs ```python BOS = 151849 # <|extra_203|> Begin generation EOS = 151850 # <|extra_204|> End generation IMG = 151851 # <|image token|> BOI = 151852 # <|image start|> EOI = 151853 # <|image end|> EOL = 151846 # <|extra_200|> End of line VISUAL_START = 151854 # First visual token ``` ### Task Templates **T2I (Text-to-Image):** ``` <|extra_203|>You are a helpful assistant for t2i task. USER: {prompt} ASSISTANT: <|extra_100|> ``` **X2I (Image Edit):** ``` <|extra_203|>You are a helpful assistant for x2i task. USER: <|IMAGE|>{prompt} ASSISTANT: <|extra_100|> ``` ## Known Issues & Solutions ### 1. SDPA Attention on Blackwell GPUs **Issue**: SDPA attention produces noise/garbage on Blackwell (sm_120) with CUDA 12.8. **Solution**: Use `attention_implementation="eager"` (default in V2 loader). ### 2. Tokenizer Crashes **Issue**: Missing visual tokens in tokenizer. **Solution**: Patched tokenizer auto-synthesizes missing tokens. ### 3. Out of Memory **Solutions**: - Use NF4 quantization (24GB VRAM) - Reduce `image_area` in X2I node - Use smaller aspect ratios - Enable tiled decoding ### 4. X2I Generates Only 1 Token **Issue**: Stopping criteria triggered by reference image tokens. **Solution**: Fixed in V2 - stopping criteria now ignores input tokens. ### GPU Compatibility | GPU Architecture | SDPA | Eager | Recommended | |-----------------|------|-------|-------------| | Ampere (sm_80) | ✅ | ✅ | SDPA | | Ada Lovelace (sm_89) | ✅ | ✅ | SDPA | | Blackwell (sm_120) | ❌ | ✅ | **Eager** | ## Example Workflows ### Basic T2I ``` [Emu 3.5 Loader V2] → [Emu 3.5 T2I Sampler V2] → [Preview Image] ↑ prompt: "a red apple on a wooden table, studio lighting, photorealistic" ``` ### Image Editing (X2I) ``` [Load Image] → [Emu 3.5 X2I] → [Preview Image] ↑ [Emu 3.5 Loader V2] ↑ prompt: "Transform into an oil painting" ``` ### Multi-Image Edit ``` [Load Image 1] → [Emu 3.5 X2I] → [Preview Image] [Load Image 2] → ↑ prompt: "Replace the background from image 1 with the scene from image 2" ``` ## Development ### File Structure ``` emu35/ ├── nodes.py # V1 nodes (legacy) ├── nodes_v2.py # V2 nodes (recommended) ├── __init__.py # ComfyUI registration ├── patched_tokenization_emu3.py # Fixed tokenizer ├── download_nf4.py # NF4 download helper ├── Emu3_5_repo/ # Official Emu3.5 code └── dev/ # Development/test scripts ``` ### Testing ```bash cd ComfyUI/custom_nodes/emu35/dev # Test tokenizer python test_tokenizer.py # Test minimal generation python test_minimal.py ``` ## Contributing Contributions welcome! Priority areas: - vLLM integration for faster generation - Additional sampling strategies - Workflow examples - Documentation improvements ## License - **This integration code**: MIT License - **Emu3.5 models and code**: Apache 2.0 (BAAI) ## Citation ```bibtex @article{emu3.5, title={Emu3.5: Native Multimodal Models are World Learners}, author={Emu3.5 Team}, journal={arXiv preprint arXiv:2510.26583}, year={2024} } ``` ## Links - **Emu3.5 Project**: https://emu.world/ - **Paper**: https://arxiv.org/pdf/2510.26583 - **Official Code**: https://github.com/baaivision/Emu3.5 - **Model Weights**: https://huggingface.co/BAAI/Emu3.5-Image - **NF4 Quantized**: https://huggingface.co/wikeeyang/Emu35-Image-NF4 - **ComfyUI**: https://github.com/comfyanonymous/ComfyUI

Name	Type	Description
any_input	*	—
modelopt	EMU_MODEL	—
vq_modelopt	EMU_VQ	—

Emu 3.5 Clear Cache

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