# MIT License # Copyright (c) 2022 Intelligent Systems Lab Org # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # File author: Zhenyu Li from ControlNet.share import * import einops import torch import random import ControlNet.config as config from pytorch_lightning import seed_everything from ControlNet.cldm.model import create_model, load_state_dict from ControlNet.cldm.ddim_hacked import DDIMSampler import gradio as gr import torch import numpy as np from zoedepth.utils.arg_utils import parse_unknown import argparse from zoedepth.models.builder import build_model from zoedepth.utils.config import get_config_user import gradio as gr from ui_prediction import predict_depth import torch.nn.functional as F from huggingface_hub import hf_hub_download DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' def depth_load_state_dict(model, state_dict): """Load state_dict into model, handling DataParallel and DistributedDataParallel. Also checks for "model" key in state_dict. DataParallel prefixes state_dict keys with 'module.' when saving. If the model is not a DataParallel model but the state_dict is, then prefixes are removed. If the model is a DataParallel model but the state_dict is not, then prefixes are added. """ state_dict = state_dict.get('model', state_dict) # if model is a DataParallel model, then state_dict keys are prefixed with 'module.' do_prefix = isinstance( model, (torch.nn.DataParallel, torch.nn.parallel.DistributedDataParallel)) state = {} for k, v in state_dict.items(): if k.startswith('module.') and not do_prefix: k = k[7:] if not k.startswith('module.') and do_prefix: k = 'module.' + k state[k] = v model.load_state_dict(state, strict=True) print("Loaded successfully") return model def load_wts(model, checkpoint_path): ckpt = torch.load(checkpoint_path, map_location='cpu') return depth_load_state_dict(model, ckpt) def load_ckpt(model, checkpoint): model = load_wts(model, checkpoint) print("Loaded weights from {0}".format(checkpoint)) return model pf_ckp = hf_hub_download(repo_id="zhyever/PatchFusion", filename="patchfusion_u4k.pt") parser = argparse.ArgumentParser() parser.add_argument("--ckp_path", type=str, default=pf_ckp) parser.add_argument("-m", "--model", type=str, default="zoedepth_custom") parser.add_argument("--model_cfg_path", type=str, default="./zoedepth/models/zoedepth_custom/configs/config_zoedepth_patchfusion.json") args, unknown_args = parser.parse_known_args() overwrite_kwargs = parse_unknown(unknown_args) overwrite_kwargs['model_cfg_path'] = args.model_cfg_path overwrite_kwargs["model"] = args.model config_depth = get_config_user(args.model, **overwrite_kwargs) config_depth["pretrained_resource"] = '' depth_model = build_model(config_depth) depth_model = load_ckpt(depth_model, args.ckp_path) depth_model.eval() depth_model.to(DEVICE) controlnet_ckp = hf_hub_download(repo_id="zhyever/PatchFusion", filename="control_sd15_depth.pth") model = create_model('./ControlNet/models/cldm_v15.yaml') model.load_state_dict(load_state_dict(controlnet_ckp, location=DEVICE), strict=False) model = model.to(DEVICE) ddim_sampler = DDIMSampler(model) # controlnet title = "# PatchFusion" description = """Official demo for **PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation**. PatchFusion is a deep learning model for high-resolution metric depth estimation from a single image. Please refer to our [paper](???) or [github](???) for more details.""" def rescale(A, lbound=-1, ubound=1): """ Rescale an array to [lbound, ubound]. Parameters: - A: Input data as numpy array - lbound: Lower bound of the scale, default is 0. - ubound: Upper bound of the scale, default is 1. Returns: - Rescaled array """ A_min = np.min(A) A_max = np.max(A) return (ubound - lbound) * (A - A_min) / (A_max - A_min) + lbound def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, mode, patch_number, resolution, patch_size): with torch.no_grad(): w, h = input_image.size detected_map = predict_depth(depth_model, input_image, mode, patch_number, resolution, patch_size, device=DEVICE) detected_map = F.interpolate(torch.from_numpy(detected_map).unsqueeze(dim=0).unsqueeze(dim=0), (image_resolution, image_resolution), mode='bicubic', align_corners=True).squeeze().numpy() H, W = detected_map.shape detected_map_temp = ((1 - detected_map / np.max(detected_map)) * 255) detected_map = detected_map_temp.astype("uint8") detected_map_temp = detected_map_temp[:, :, None] detected_map_temp = np.concatenate([detected_map_temp, detected_map_temp, detected_map_temp], axis=2) detected_map = detected_map[:, :, None] detected_map = np.concatenate([detected_map, detected_map, detected_map], axis=2) control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0 control = torch.stack([control for _ in range(num_samples)], dim=0) control = einops.rearrange(control, 'b h w c -> b c h w').clone() if seed == -1: seed = random.randint(0, 65535) seed_everything(seed) if config.save_memory: model.low_vram_shift(is_diffusing=False) cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]} un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]} shape = (4, H // 8, W // 8) if config.save_memory: model.low_vram_shift(is_diffusing=True) model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13) # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01 samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples, shape, cond, verbose=False, eta=eta, unconditional_guidance_scale=scale, unconditional_conditioning=un_cond) if config.save_memory: model.low_vram_shift(is_diffusing=False) x_samples = model.decode_first_stage(samples) x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255) results = [x_samples[i] for i in range(num_samples)] return_list = [detected_map_temp] + results update_return_list = [] for r in return_list: t_r = torch.from_numpy(r).unsqueeze(dim=0).permute(0, 3, 1, 2) t_r = F.interpolate(t_r, (h, w), mode='bicubic', align_corners=True).squeeze().permute(1, 2, 0).numpy().astype(np.uint8) update_return_list.append(t_r) return update_return_list title = "# PatchFusion" description = """Official demo for **PatchFusion: An End-to-End Tile-Based Framework for High-Resolution Monocular Metric Depth Estimation**. PatchFusion is a deep learning model for high-resolution metric depth estimation from a single image. Please refer to our [paper](https://arxiv.org/abs/2312.02284) or [github](https://github.com/zhyever/PatchFusion) for more details.""" with gr.Blocks() as demo: gr.Markdown(title) gr.Markdown(description) with gr.Row(): gr.Markdown("## Control Stable Diffusion with Depth Maps") with gr.Row(): with gr.Column(): # input_image = gr.Image(source='upload', type="pil") input_image = gr.Image(label="Input Image", type='pil') prompt = gr.Textbox(label="Prompt (input your description)", value='An evening scene with the Eiffel Tower, the bridge under the glow of street lamps and a twilight sky') run_button = gr.Button("Run") with gr.Accordion("Advanced options", open=False): # mode = gr.Radio(["P49", "R"], label="Tiling mode", info="We recommand using P49 for fast evaluation and R with 1024 patches for best visualization results, respectively", elem_id='mode', value='R'), mode = gr.Radio(["P49", "R"], label="Tiling mode", info="We recommand using P49 for fast evaluation and R with 1024 patches for best visualization results, respectively", elem_id='mode', value='R'), patch_number = gr.Slider(1, 1024, label="Please decide the number of random patches (Only useful in mode=R)", step=1, value=256) resolution = gr.Textbox(label="PatchFusion proccessing resolution (Default 4K. Use 'x' to split height and width.)", elem_id='mode', value='2160x3840') patch_size = gr.Textbox(label="Patch size (Default 1/4 of image resolution. Use 'x' to split height and width.)", elem_id='mode', value='540x960') num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1) image_resolution = gr.Slider(label="ControlNet image resolution", minimum=256, maximum=2048, value=1024, step=64) strength = gr.Slider(label="Control strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01) guess_mode = gr.Checkbox(label='Guess Mode', value=False) # detect_resolution = gr.Slider(label="Depth Resolution", minimum=128, maximum=1024, value=384, step=1) ddim_steps = gr.Slider(label="steps", minimum=1, maximum=100, value=20, step=1) scale = gr.Slider(label="guidance scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1) seed = gr.Slider(label="seed", minimum=-1, maximum=2147483647, step=1, randomize=True) eta = gr.Number(label="eta (DDIM)", value=0.0) a_prompt = gr.Textbox(label="Added prompt", value='best quality, extremely detailed') n_prompt = gr.Textbox(label="Negative prompt", value='worst quality, low quality, lose details') with gr.Column(): # result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto') result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery") ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, mode[0], patch_number, resolution, patch_size] run_button.click(fn=process, inputs=ips, outputs=[result_gallery]) examples = gr.Examples(examples=["examples/example_2.jpeg", "examples/example_4.jpeg", "examples/example_5.jpeg"], inputs=[input_image]) if __name__ == '__main__': demo.queue().launch(share=True)