<div data-youtube-video="https://youtu.be/4YEU8Ui6u1w"><iframe src="https://www.youtube.com/embed/4YEU8Ui6u1w" width="853" height="480" allowfullscreen></iframe></div> 
 
Qwen Image 2.1 + ControlNet Union 9-in-1 workflow: 8 CN control modes plus Mask-based local repaint in a single graph. One input image, nine parallel branches, all outputs in one pass. 
 
Main features: 
- 9 parallel branches on Qwen Image 2.1 (bf16) with the Fun ControlNet Union patch and the Turbo LoRA (rank 178) 
- 7 AIO preprocessor modes at 512: Canny, MiDaS depth, HED, LineArt, M-LSD (structure), DW depth, Scribble 
- Structural-guidance branch (raw image condition) and a Mask-based inpainting branch (ImageToMask -&gt; ImageCompositeMasked) for local repaint 
- Gemini 3.1 Flash Lite (RHLLMChatNode) auto prompt analysis: describe the source image automatically for consistent prompts across branches (auto prompt reconstruction / image wash) 
- Per-branch ManualSigmas, image resize and size-math (pysssss) for matched output dimensions 
 
Suggested workflow: 
1. Load the source image; optionally let Gemini reconstruct the prompt automatically 
2. Run the graph; all 9 branches (7 control modes + structure + mask inpaint) render together 
3. Pick the control mode per task: pose / depth / line / structure for control, mask branch for local edits 
 
Dependencies: 
- qwen_image_2.1_bf16.safetensors (base UNET) 
- qwen3vl_8b_fp8_scaled.safetensors (CLIP) 
- qwen_image_2.1_vae_bf16.safetensors (VAE) 
- Qwen-Image-2.1-Fun-Controlnet-Union-ComfyUI.safetensors (Union patch) 
- qwen_image_2.1_turbo_lora_avg_rank_178_bf16.safetensors (Turbo LoRA) 
- pysssss MathExpression, easy showAnything, JWString nodes, AIO Preprocessor, RHLLMChatNode 
 
RunningHub workflow page: https://www.runninghub.ai/zh-cn/post/2109549130926755842?inviteCode=rh-v1111 
Bilibili tutorial video: https://www.bilibili.com/video/BV14zp96REo9/ 
RunningHub registration with invite code rh-v1111 (1000 RH coins): https://www.runninghub.ai/?inviteCode=rh-v1111

Qwen Image 2.1 CN Union 9-in-1 + Inpaint

<div data-youtube-video="https://youtu.be/4YEU8Ui6u1w"></div> 
 
Qwen Image 2.1 + ControlNet Union 9-in-1 workflow: 8 CN control modes plus Mask-based local repaint in a single graph. One input image, nine parallel branches, all outputs in one pass. 
 
Main features: 
- 9 parallel branches on Qwen Image 2.1 (bf16) with the Fun ControlNet Union patch and the Turbo LoRA (rank 178) 
- 7 AIO preprocessor modes at 512: Canny, MiDaS depth, HED, LineArt, M-LSD (structure), DW depth, Scribble 
- Structural-guidance branch (raw image condition) and a Mask-based inpainting branch (ImageToMask -&gt; ImageCompositeMasked) for local repaint 
- Gemini 3.1 Flash Lite (RHLLMChatNode) auto prompt analysis: describe the source image automatically for consistent prompts across branches (auto prompt reconstruction / image wash) 
- Per-branch ManualSigmas, image resize and size-math (pysssss) for matched output dimensions 
 
Suggested workflow: 
1. Load the source image; optionally let Gemini reconstruct the prompt automatically 
2. Run the graph; all 9 branches (7 control modes + structure + mask inpaint) render together 
3. Pick the control mode per task: pose / depth / line / structure for control, mask branch for local edits 
 
Dependencies: 
- qwen_image_2.1_bf16.safetensors (base UNET) 
- qwen3vl_8b_fp8_scaled.safetensors (CLIP) 
- qwen_image_2.1_vae_bf16.safetensors (VAE) 
- Qwen-Image-2.1-Fun-Controlnet-Union-ComfyUI.safetensors (Union patch) 
- qwen_image_2.1_turbo_lora_avg_rank_178_bf16.safetensors (Turbo LoRA) 
- pysssss MathExpression, easy showAnything, JWString nodes, AIO Preprocessor, RHLLMChatNode 
 
RunningHub workflow page: https://www.runninghub.ai/zh-cn/post/2109549130926755842?inviteCode=rh-v1111 
Bilibili tutorial video: https://www.bilibili.com/video/BV14zp96REo9/ 
RunningHub registration with invite code rh-v1111 (1000 RH coins): https://www.runninghub.ai/?inviteCode=rh-v1111

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