Depth Estimation
Diffusers
Safetensors
English
MarigoldDepthPipeline
depth estimation
image analysis
computer vision
in-the-wild
zero-shot
Instructions to use prs-eth/marigold-depth-v1-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use prs-eth/marigold-depth-v1-1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("prs-eth/marigold-depth-v1-1", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| { | |
| "_class_name": "MarigoldDepthPipeline", | |
| "_diffusers_version": "0.24.0", | |
| "prediction_type": "depth", | |
| "scale_invariant": true, | |
| "shift_invariant": true, | |
| "default_denoising_steps": 4, | |
| "default_processing_resolution": 768, | |
| "unet": [ | |
| "diffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "DDIMScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ] | |
| } |