diff --git a/.gitignore b/.gitignore index 4cf2853ee486a98c18868a367b42e25f9d1b9382..a854e73dd40c598b133e01d97d8ff6e4deea82c9 100644 --- a/.gitignore +++ b/.gitignore @@ -33,9 +33,9 @@ .codecc outputs -weights .vscode/ inference.py +# weights # third_party/weights # third_party/dust3r # app_hg.py diff --git a/app_hg.py b/app_hg.py index 59e78ac2c8511e5be3cb0ad9e69c4d72c646b5b8..4dc5add07c3d151ff9982f73bd9c11e5adac3db3 100644 --- a/app_hg.py +++ b/app_hg.py @@ -48,7 +48,6 @@ except Exception as err: check_bake_available() BAKE_AVAILEBLE = False - warnings.simplefilter('ignore', category=UserWarning) warnings.simplefilter('ignore', category=FutureWarning) warnings.simplefilter('ignore', category=DeprecationWarning) @@ -85,7 +84,6 @@ def download_models(): except Exception as e: print(f"Error downloading HunyuanDiT: {e}") -# Download models before starting the app download_models() ################################################################ diff --git a/third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/README.md b/third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ac44d459bfaa06e5c92687313c2f1661dd6de37d --- /dev/null +++ b/third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/README.md @@ -0,0 +1,117 @@ +--- +tags: +- vision +--- + +## DUSt3R + +# Model info + +Project page: https://dust3r.europe.naverlabs.com/ + +# How to use + +Here's how to load the model (after [installing](https://github.com/naver/dust3r?tab=readme-ov-file#installation) the dust3r package): + +```python +from dust3r.model import AsymmetricCroCo3DStereo +import torch + +model = AsymmetricCroCo3DStereo.from_pretrained("nielsr/DUSt3R_ViTLarge_BaseDecoder_512_dpt") + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +model.to(device) +``` + +Next, one can run inference as follows: + +``` +from dust3r.inference import inference +from dust3r.utils.image import load_images +from dust3r.image_pairs import make_pairs +from dust3r.cloud_opt import global_aligner, GlobalAlignerMode + +if __name__ == '__main__': + batch_size = 1 + schedule = 'cosine' + lr = 0.01 + niter = 300 + + # load_images can take a list of images or a directory + images = load_images(['croco/assets/Chateau1.png', 'croco/assets/Chateau2.png'], size=512) + pairs = make_pairs(images, scene_graph='complete', prefilter=None, symmetrize=True) + output = inference(pairs, model, device, batch_size=batch_size) + + # at this stage, you have the raw dust3r predictions + view1, pred1 = output['view1'], output['pred1'] + view2, pred2 = output['view2'], output['pred2'] + # here, view1, pred1, view2, pred2 are dicts of lists of len(2) + # -> because we symmetrize we have (im1, im2) and (im2, im1) pairs + # in each view you have: + # an integer image identifier: view1['idx'] and view2['idx'] + # the img: view1['img'] and view2['img'] + # the image shape: view1['true_shape'] and view2['true_shape'] + # an instance string output by the dataloader: view1['instance'] and view2['instance'] + # pred1 and pred2 contains the confidence values: pred1['conf'] and pred2['conf'] + # pred1 contains 3D points for view1['img'] in view1['img'] space: pred1['pts3d'] + # pred2 contains 3D points for view2['img'] in view1['img'] space: pred2['pts3d_in_other_view'] + + # next we'll use the global_aligner to align the predictions + # depending on your task, you may be fine with the raw output and not need it + # with only two input images, you could use GlobalAlignerMode.PairViewer: it would just convert the output + # if using GlobalAlignerMode.PairViewer, no need to run compute_global_alignment + scene = global_aligner(output, device=device, mode=GlobalAlignerMode.PointCloudOptimizer) + loss = scene.compute_global_alignment(init="mst", niter=niter, schedule=schedule, lr=lr) + + # retrieve useful values from scene: + imgs = scene.imgs + focals = scene.get_focals() + poses = scene.get_im_poses() + pts3d = scene.get_pts3d() + confidence_masks = scene.get_masks() + + # visualize reconstruction + scene.show() + + # find 2D-2D matches between the two images + from dust3r.utils.geometry import find_reciprocal_matches, xy_grid + pts2d_list, pts3d_list = [], [] + for i in range(2): + conf_i = confidence_masks[i].cpu().numpy() + pts2d_list.append(xy_grid(*imgs[i].shape[:2][::-1])[conf_i]) # imgs[i].shape[:2] = (H, W) + pts3d_list.append(pts3d[i].detach().cpu().numpy()[conf_i]) + reciprocal_in_P2, nn2_in_P1, num_matches = find_reciprocal_matches(*pts3d_list) + print(f'found {num_matches} matches') + matches_im1 = pts2d_list[1][reciprocal_in_P2] + matches_im0 = pts2d_list[0][nn2_in_P1][reciprocal_in_P2] + + # visualize a few matches + import numpy as np + from matplotlib import pyplot as pl + n_viz = 10 + match_idx_to_viz = np.round(np.linspace(0, num_matches-1, n_viz)).astype(int) + viz_matches_im0, viz_matches_im1 = matches_im0[match_idx_to_viz], matches_im1[match_idx_to_viz] + + H0, W0, H1, W1 = *imgs[0].shape[:2], *imgs[1].shape[:2] + img0 = np.pad(imgs[0], ((0, max(H1 - H0, 0)), (0, 0), (0, 0)), 'constant', constant_values=0) + img1 = np.pad(imgs[1], ((0, max(H0 - H1, 0)), (0, 0), (0, 0)), 'constant', constant_values=0) + img = np.concatenate((img0, img1), axis=1) + pl.figure() + pl.imshow(img) + cmap = pl.get_cmap('jet') + for i in range(n_viz): + (x0, y0), (x1, y1) = viz_matches_im0[i].T, viz_matches_im1[i].T + pl.plot([x0, x1 + W0], [y0, y1], '-+', color=cmap(i / (n_viz - 1)), scalex=False, scaley=False) + pl.show(block=True) + +``` + +### BibTeX entry and citation info + +```bibtex +@journal{dust3r2023, +title={{DUSt3R: Geometric 3D Vision Made Easy}}, +author={{Wang, Shuzhe and Leroy, Vincent and Cabon, Yohann and Chidlovskii, Boris and Revaud Jerome}}, +journal={arXiv preprint 2312.14132}, +year={2023}} +``` \ No newline at end of file diff --git a/third_party/weights/DUSt3R_ViTLarge_BaseDecoder_512_dpt/config.json 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+ +

+ +# Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding +# 混元-DiT: 具有细粒度中文理解的多分辨率Diffusion Transformer + +[[Arxiv]](https://arxiv.org/abs/2405.08748) [[project page]](https://dit.hunyuan.tencent.com/) [[github]](https://github.com/Tencent/HunyuanDiT) + +This repo contains the distilled Hunyuan-DiT in 🤗 [Diffusers](https://github.com/huggingface/diffusers) format. + +It supports 25-step text-to-image generation. + +## Dependency +Please install PyTorch first, following the instruction in [https://pytorch.org](https://pytorch.org) + +Install the latest version of transformers with `pip`: +``` +pip install --upgrade transformers +``` + +Then install the latest github version of 🤗 Diffusers with `pip`: +``` +pip install git+https://github.com/huggingface/diffusers.git +``` + +## Example Usage with 🤗 Diffusers +```py +import torch +from diffusers import HunyuanDiTPipeline + +pipe = HunyuanDiTPipeline.from_pretrained("Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled", torch_dtype=torch.float16) +pipe.to("cuda") + +# You may also use English prompt as HunyuanDiT supports both English and Chinese +# prompt = "An astronaut riding a horse" +prompt = "一个宇航员在骑马" +image = pipe(prompt).images[0] +``` + +![image/png](https://cdn-uploads.huggingface.co/production/uploads/646b0bbdec9a61e871799339/xoO_-5N7eZ-aCt4KpBYY6.png) + +## 📈 Comparisons +In order to comprehensively compare the generation capabilities of HunyuanDiT and other models, we constructed a 4-dimensional test set, including Text-Image Consistency, Excluding AI Artifacts, Subject Clarity, Aesthetic. More than 50 professional evaluators performs the evaluation. + +

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model Open Source Text-Image Consistency (%) Excluding AI Artifacts (%) Subject Clarity (%) Aesthetics (%) Overall (%)
SDXL ✔ 64.3 60.6 91.1 76.3 42.7
PixArt-α ✔ 68.3 60.9 93.2 77.5 45.5
Playground 2.5 ✔ 71.9 70.8 94.9 83.3 54.3
SD 3 ✘ 77.1 69.3 94.6 82.5 56.7
MidJourney v6✘ 73.5 80.2 93.5 87.2 63.3
DALL-E 3✘ 83.9 80.3 96.5 89.4 71.0
Hunyuan-DiT✔ 74.2 74.3 95.4 86.6 59.0
+

+ +## 🎥 Visualization + +* **Chinese Elements** +

+ +

+ +* **Long Text Input** + + +

+ +

+ + +## 🔥🔥🔥 Tencent Hunyuan Bot + +Welcome to [Tencent Hunyuan Bot](https://hunyuan.tencent.com/bot/chat), where you can explore our innovative products in multi-round conversation! \ No newline at end of file diff --git a/weights/hunyuanDiT/model_index.json b/weights/hunyuanDiT/model_index.json new file mode 100644 index 0000000000000000000000000000000000000000..2e7a0f14ca4c81041e30aa0df476be0d5cfb1b8c --- /dev/null +++ b/weights/hunyuanDiT/model_index.json @@ -0,0 +1,41 @@ +{ + "_class_name": "HunyuanDiTPipeline", + "_diffusers_version": "0.29.0", + "feature_extractor": [ + null, + null + ], + "requires_safety_checker": true, + "safety_checker": [ + null, + null + ], + "scheduler": [ + "diffusers", + "DDPMScheduler" + ], + "text_encoder": [ + "transformers", + "BertModel" + ], + "text_encoder_2": [ + "transformers", + "T5EncoderModel" + ], + "tokenizer": [ + "transformers", + "BertTokenizer" + ], + "tokenizer_2": [ + "transformers", + "T5Tokenizer" + ], + "transformer": [ + "diffusers", + "HunyuanDiT2DModel" + ], + "vae": [ + "diffusers", + "AutoencoderKL" + ] +} diff --git a/weights/hunyuanDiT/scheduler/scheduler_config.json b/weights/hunyuanDiT/scheduler/scheduler_config.json new file mode 100644 index 0000000000000000000000000000000000000000..1161ab5e7df72b96bcd278fe1a57228b8243199d --- /dev/null +++ b/weights/hunyuanDiT/scheduler/scheduler_config.json @@ -0,0 +1,21 @@ +{ + "_class_name": "DDPMScheduler", + "_diffusers_version": "0.29.0", + "beta_end": 0.03, + "beta_schedule": "scaled_linear", + "beta_start": 0.00085, + "clip_sample": false, + "clip_sample_range": 1.0, + "dynamic_thresholding_ratio": 0.995, + "num_train_timesteps": 1000, + "prediction_type": "v_prediction", + "rescale_betas_zero_snr": false, + "sample_max_value": 1.0, + "set_alpha_to_one": false, + "skip_prk_steps": true, + "steps_offset": 1, + "thresholding": false, + "timestep_spacing": "leading", + "trained_betas": null, + "variance_type": "fixed_small" +} diff --git 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