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| # Copyright 2023-2025 Marigold Team, ETH Zürich. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # -------------------------------------------------------------------------- | |
| # More information about Marigold: | |
| # https://marigoldmonodepth.github.io | |
| # https://marigoldcomputervision.github.io | |
| # Efficient inference pipelines are now part of diffusers: | |
| # https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage | |
| # https://huggingface.co/docs/diffusers/api/pipelines/marigold | |
| # Examples of trained models and live demos: | |
| # https://huggingface.co/prs-eth | |
| # Related projects: | |
| # https://marigolddepthcompletion.github.io/ | |
| # https://rollingdepth.github.io/ | |
| # Citation (BibTeX): | |
| # https://github.com/prs-eth/Marigold#-citation | |
| # https://github.com/prs-eth/Marigold-DC#-citation | |
| # https://github.com/prs-eth/rollingdepth#-citation | |
| # -------------------------------------------------------------------------- | |
| import os | |
| os.system("pip freeze") | |
| import spaces | |
| import gradio as gr | |
| import torch as torch | |
| from diffusers import MarigoldDepthPipeline, DDIMScheduler | |
| from gradio_dualvision import DualVisionApp | |
| from huggingface_hub import login | |
| from PIL import Image | |
| CHECKPOINT = "prs-eth/marigold-depth-v1-1" | |
| if "HF_TOKEN_LOGIN" in os.environ: | |
| login(token=os.environ["HF_TOKEN_LOGIN"]) | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32 | |
| pipe = MarigoldDepthPipeline.from_pretrained(CHECKPOINT) | |
| pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") | |
| pipe = pipe.to(device=device, dtype=dtype) | |
| try: | |
| import xformers | |
| pipe.enable_xformers_memory_efficient_attention() | |
| except: | |
| pass | |
| class MarigoldDepthApp(DualVisionApp): | |
| DEFAULT_SEED = 2024 | |
| DEFAULT_ENSEMBLE_SIZE = 1 | |
| DEFAULT_DENOISE_STEPS = 4 | |
| DEFAULT_PROCESSING_RES = 768 | |
| def make_header(self): | |
| gr.Markdown( | |
| """ | |
| ## Marigold Depth Estimation | |
| <p align="center"> | |
| <a title="Website" href="https://marigoldmonodepth.github.io/" target="_blank" rel="noopener noreferrer" style="display: inline-block;"> | |
| <img src="https://img.shields.io/badge/%E2%99%A5%20Project%20-Website-blue"> | |
| </a> | |
| <a title="arXiv" href="https://arxiv.org/abs/2312.02145" target="_blank" rel="noopener noreferrer" style="display: inline-block;"> | |
| <img src="https://img.shields.io/badge/%F0%9F%93%84%20Read%20-Paper-AF3436"> | |
| </a> | |
| <a title="Github" href="https://github.com/prs-eth/marigold" target="_blank" rel="noopener noreferrer" style="display: inline-block;"> | |
| <img src="https://img.shields.io/github/stars/prs-eth/marigold?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="badge-github-stars"> | |
| </a> | |
| <a title="Video Depth" href="https://huggingface.co/spaces/prs-eth/rollingdepth" target="_blank" rel="noopener noreferrer" style="display: inline-block;"> | |
| <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Video%20Depth%20-Demo-yellow" alt="videodepth"> | |
| </a> | |
| <a title="Depth-to-3D" href="https://huggingface.co/spaces/prs-eth/depth-to-3d-print" target="_blank" rel="noopener noreferrer" style="display: inline-block;"> | |
| <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Depth--to--3D%20-Demo-yellow" alt="depthto3d"> | |
| </a> | |
| <a title="Social" href="https://twitter.com/antonobukhov1" target="_blank" rel="noopener noreferrer" style="display: inline-block;"> | |
| <img src="https://shields.io/twitter/follow/:?label=Subscribe%20for%20updates!" alt="social"> | |
| </a> | |
| </p> | |
| <p align="center" style="margin-top: 0px;"> | |
| Upload a photo or select an example below to compute depth maps in real time. | |
| Use the slider to reveal areas of interest. | |
| Use the radio-buttons to switch between modalities. | |
| Check our other demo badges above for new or relocated functionality. | |
| </p> | |
| """ | |
| ) | |
| def build_user_components(self): | |
| with gr.Column(): | |
| ensemble_size = gr.Slider( | |
| label="Ensemble size", | |
| minimum=1, | |
| maximum=10, | |
| step=1, | |
| value=self.DEFAULT_ENSEMBLE_SIZE, | |
| ) | |
| denoise_steps = gr.Slider( | |
| label="Number of denoising steps", | |
| minimum=1, | |
| maximum=20, | |
| step=1, | |
| value=self.DEFAULT_DENOISE_STEPS, | |
| ) | |
| processing_res = gr.Radio( | |
| [ | |
| ("Native", 0), | |
| ("Recommended", 768), | |
| ], | |
| label="Processing resolution", | |
| value=self.DEFAULT_PROCESSING_RES, | |
| ) | |
| return { | |
| "ensemble_size": ensemble_size, | |
| "denoise_steps": denoise_steps, | |
| "processing_res": processing_res, | |
| } | |
| def process(self, image_in: Image.Image, **kwargs): | |
| ensemble_size = kwargs.get("ensemble_size", self.DEFAULT_ENSEMBLE_SIZE) | |
| denoise_steps = kwargs.get("denoise_steps", self.DEFAULT_DENOISE_STEPS) | |
| processing_res = kwargs.get("processing_res", self.DEFAULT_PROCESSING_RES) | |
| generator = torch.Generator(device=device).manual_seed(self.DEFAULT_SEED) | |
| pipe_out = pipe( | |
| image_in, | |
| ensemble_size=ensemble_size, | |
| num_inference_steps=denoise_steps, | |
| processing_resolution=processing_res, | |
| batch_size=1 if processing_res == 0 else 2, | |
| output_uncertainty=ensemble_size >= 3, | |
| generator=generator, | |
| ) | |
| depth_vis = pipe.image_processor.visualize_depth(pipe_out.prediction)[0] | |
| depth_16bit = pipe.image_processor.export_depth_to_16bit_png(pipe_out.prediction)[0] | |
| out_modalities = { | |
| "Depth Visualization": depth_vis, | |
| "Depth 16-bit": depth_16bit, | |
| } | |
| if ensemble_size >= 3: | |
| uncertainty = pipe.image_processor.visualize_uncertainty(pipe_out.uncertainty)[0] | |
| out_modalities["Uncertainty"] = uncertainty | |
| out_settings = { | |
| "ensemble_size": ensemble_size, | |
| "denoise_steps": denoise_steps, | |
| "processing_res": processing_res, | |
| } | |
| return out_modalities, out_settings | |
| with MarigoldDepthApp( | |
| title="Marigold Depth", | |
| examples_path="files", | |
| examples_per_page=5, | |
| squeeze_canvas=True, | |
| spaces_zero_gpu_enabled=True, | |
| ) as demo: | |
| demo.queue( | |
| api_open=False, | |
| ).launch( | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| ) | |