| ## Examples | |
| Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) in a simple and efficient manner. | |
| ```python | |
| from PIL import Image | |
| import os | |
| import torch | |
| from diffusers import StableDiffusionUpscaleLDM3DPipeline, StableDiffusionLDM3DPipeline | |
| #Generate a rgb/depth output from LDM3D | |
| pipe_ldm3d = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d-4c") | |
| pipe_ldm3d.to("cuda") | |
| prompt =f"A picture of some lemons on a table" | |
| output = pipe_ldm3d(prompt) | |
| rgb_image, depth_image = output.rgb, output.depth | |
| rgb_image[0].save(f"lemons_ldm3d_rgb.jpg") | |
| depth_image[0].save(f"lemons_ldm3d_depth.png") | |
| #Upscale the previous output to a resolution of (1024, 1024) | |
| pipe_ldm3d_upscale = StableDiffusionUpscaleLDM3DPipeline.from_pretrained("Intel/ldm3d-sr") | |
| pipe_ldm3d_upscale.to("cuda") | |
| low_res_img = Image.open(f"lemons_ldm3d_rgb.jpg").convert("RGB") | |
| low_res_depth = Image.open(f"lemons_ldm3d_depth.png").convert("L") | |
| outputs = pipe_ldm3d_upscale(prompt="high quality high resolution uhd 4k image", rgb=low_res_img, depth=low_res_depth, num_inference_steps=50, target_res=[1024, 1024]) | |
| upscaled_rgb, upscaled_depth =outputs.rgb[0], outputs.depth[0] | |
| upscaled_rgb.save(f"upscaled_lemons_rgb.png") | |
| upscaled_depth.save(f"upscaled_lemons_depth.png") | |
| ``` | |