Bokeh Depth ControlNet

--- license: other license_name: stabilityai-ai-community license_link: LICENSE.md language: - en base_model: - tensorart/bokeh_3.5_medium pipeline_tag: text-to-image --- <div align="center"> **Bokeh_Depth_Controlnet** <img src="show.jpg"/> </div> ## Description - Input Image: Depth information map generated by depth estimation model - Output Image: Base model integrates depth information for control, generating more realistic depth-of-field effects and spatial structure control. The depth map enables the model to better understand spatial relationships within images, achieving more precise depth-of-field control and three-dimensional effect generation. ## Example | input | output | Prompt | |:---:|:---:|:---| | <img src="./images/001_depth.png" width="300"/> | <img src="./images/001.png" width="300"/> | a old man | | <img src="./images/002_depth.png" width="300"/> | <img src="./images/002.png" width="300"/> | A lone astronaut in a white suit and helmet, with a backpack, strolls on a rocky, red landscape under a star galaxy | | <img src="./images/003_depth.png" width="300"/> | <img src="./images/003.png" width="300"/> | a eagle , black background | | <img src="./images/004_depth.png" width="300"/> | <img src="./images/004.png" width="300"/> | A crystal heart sits on a gnarly, moss-covered log, with a galaxy background | ## Use We recommend using ComfyUI for local inference  # With Bokeh ```python import torch from diffusers import StableDiffusion3ControlNetPipeline from diffusers import SD3ControlNetModel from diffusers.utils import load_image controlnet = SD3ControlNetModel.from_pretrained("tensorart/Bokeh_Depth_Controlnet") pipe = StableDiffusion3ControlNetPipeline.from_pretrained( "tensorart/bokeh_3.5_medium", controlnet=controlnet ) pipe.to("cuda", torch.float16) control_image = load_image("https://huggingface.co/tensorart/Bokeh_Depth_Controlnet/resolve/main/images/001_depth.png") prompt = "A old man talking" negative_prompt ="anime,render,cartoon,3d,bad hands,extra finger" negative_prompt_3="" image = pipe( prompt, num_inference_steps=28, negative_prompt=negative_prompt, negative_prompt_3=negative_prompt_3, control_image=control_image, height=1440, width=1440, guidance_scale=4, controlnet_conditioning_scale=0.85 ).images[0] image.save('image.jpg') ``` ## Contact * Website: https://tensor.art https://tusiar...