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BackComfyUI Workflow Detail
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Version Type
TypeWorkflow
FeatureImage to Image
Formatgguf
AuthorDavid
Qwen-Image InstantX-ControlNets Depth
Note: All model files and custom nodes below must be downloaded via the client. If you do not have the client, Download >>>
Lotus Depth is used to generate depth maps, and with InstantX ControlNet, the AI can accurately interpret spatial relationships, ensuring that the generated images adhere to the spatial structure defined by the depth maps, thereby achieving precise control over the 3D layout.
Model Files
Custom Nodes
| Name | Size | Target Dir |
|---|---|---|
| Qwen-Image-Lightning-4steps-V2.0.safetensors | 1.70 GB | loras |
| Qwen-Image-Lightning-4steps-V2.0-bf16.safetensors | 850 MB | loras |
| Qwen-Image-Lightning-4steps-V1.0.safetensors | 1.70 GB | loras |
| Qwen-Image-Lightning-8steps-V1.0.safetensors | 1.70 GB | loras |
| Qwen-Image-Lightning-8steps-V1.1.safetensors | 1.70 GB | loras |
| Qwen-Image-Lightning-4steps-V1.0-bf16.safetensors | 849.61 MB | loras |
| Qwen-Image-Lightning-8steps-V1.1-bf16.safetensors | 849.61 MB | loras |
| Name | Size | Target Dir |
|---|---|---|
| Qwen2.5-VL-7B-Instruct-UD-IQ1_S.gguf | 2.07 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-UD-IQ1_M.gguf | 2.20 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-UD-IQ2_XXS.gguf | 2.40 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-UD-IQ2_M.gguf | 2.86 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-Q2_K.gguf | 3.02 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-Q2_K_L.gguf | 3.14 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-UD-IQ3_XXS.gguf | 3.17 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-UD-Q2_K_XL.gguf | 3.24 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-Q3_K_S.gguf | 3.49 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-Q3_K_M.gguf | 3.81 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-UD-Q3_K_XL.gguf | 4.00 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-IQ4_XS.gguf | 4.24 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-IQ4_NL.gguf | 4.44 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-Q4_0.gguf | 4.44 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-Q4_K_S.gguf | 4.46 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-Q4_K_M.gguf | 4.68 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-UD-Q4_K_XL.gguf | 4.79 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-Q4_1.gguf | 4.87 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-Q5_K_S.gguf | 5.32 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-Q5_K_M.gguf | 5.44 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-UD-Q5_K_XL.gguf | 5.46 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-Q6_K.gguf | 6.25 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-UD-Q6_K_XL.gguf | 6.96 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-Q8_0.gguf | 8.10 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-UD-Q8_K_XL.gguf | 10.19 GB | text_encoders |
| Qwen2.5-VL-7B-Instruct-BF16.gguf | 15.24 GB | text_encoders |
| Name | Size | Target Dir |
|---|---|---|
| qwen_image_vae.safetensors | 253.81 MB | vae |
| Name | Size | Target Dir |
|---|---|---|
| qwen-image-Q2_K.gguf | 7.06 GB | unet |
| qwen-image-Q3_K_S.gguf | 8.95 GB | unet |
| qwen-image-Q3_K_M.gguf | 9.68 GB | unet |
| qwen-image-Q4_0.gguf | 11.85 GB | unet |
| qwen-image-Q4_K_S.gguf | 12.14 GB | unet |
| qwen-image-Q4_1.gguf | 12.84 GB | unet |
| qwen-image-Q4_K_M.gguf | 13.07 GB | unet |
| qwen-image-Q5_K_S.gguf | 14.12 GB | unet |
| qwen-image-Q5_0.gguf | 14.40 GB | unet |
| qwen-image-Q5_K_M.gguf | 14.93 GB | unet |
| qwen-image-Q5_1.gguf | 15.39 GB | unet |
| qwen-image-Q6_K.gguf | 16.82 GB | unet |
| qwen-image-Q8_0.gguf | 21.76 GB | unet |
| qwen-image-BF16.gguf | 40.87 GB | unet |
| Name | Size | Target Dir |
|---|---|---|
| Qwen-Image-InstantX-ControlNet-Union.safetensors | 3.54 GB | controlnet |
| Qwen-Image-InstantX-ControlNet-Inpainting.safetensors | 4.23 GB | controlnet |
| Name | Size | Target Dir |
|---|---|---|
| vae-ft-mse-840000-ema-pruned.safetensors | 334.64 MB | vae |
| vae-ft-mse-840000-ema-pruned.ckpt | 334.70 MB | vae |