From e91e8845adc04406ebc2a03d6db10db5e2d405fa Mon Sep 17 00:00:00 2001 From: hanwen0529 Date: Mon, 1 Apr 2024 21:56:13 -0400 Subject: [PATCH] code release --- README.md | 10 +- videocrafter/License | 470 ++++++++++++++++++ videocrafter/cog.yaml | 25 + .../configs/inference_i2v_512_v1.0.yaml | 83 ++++ .../configs/inference_t2v_1024_v1.0.yaml | 77 +++ .../configs/inference_t2v_512_v1.0.yaml | 74 +++ .../configs/inference_t2v_512_v2.0.yaml | 77 +++ videocrafter/prompts/i2v_prompts/horse.png | Bin 0 -> 476298 bytes videocrafter/prompts/i2v_prompts/seashore.png | Bin 0 -> 390038 bytes .../prompts/i2v_prompts/test_prompts.txt | 2 + videocrafter/prompts/test_prompts.txt | 3 + .../scripts/evaluation/ddp_wrapper.py | 46 ++ videocrafter/scripts/evaluation/funcs.py | 195 ++++++++ videocrafter/scripts/evaluation/inference.py | 137 +++++ .../scripts/evaluation/videocrafter2_utils.py | 309 ++++++++++++ videocrafter/scripts/gradio/i2v_test.py | 83 ++++ videocrafter/scripts/gradio/t2v_test.py | 77 +++ videocrafter/scripts/run_image2video.sh | 24 + videocrafter/scripts/run_sds.sh | 18 + videocrafter/scripts/run_text2video.sh | 21 + 20 files changed, 1726 insertions(+), 5 deletions(-) create mode 100644 videocrafter/License create mode 100644 videocrafter/cog.yaml create mode 100644 videocrafter/configs/inference_i2v_512_v1.0.yaml create mode 100644 videocrafter/configs/inference_t2v_1024_v1.0.yaml create mode 100644 videocrafter/configs/inference_t2v_512_v1.0.yaml create mode 100644 videocrafter/configs/inference_t2v_512_v2.0.yaml create mode 100644 videocrafter/prompts/i2v_prompts/horse.png create mode 100644 videocrafter/prompts/i2v_prompts/seashore.png create mode 100644 videocrafter/prompts/i2v_prompts/test_prompts.txt create mode 100644 videocrafter/prompts/test_prompts.txt create mode 100644 videocrafter/scripts/evaluation/ddp_wrapper.py create mode 100644 videocrafter/scripts/evaluation/funcs.py create mode 100644 videocrafter/scripts/evaluation/inference.py create mode 100644 videocrafter/scripts/evaluation/videocrafter2_utils.py create mode 100644 videocrafter/scripts/gradio/i2v_test.py create mode 100644 videocrafter/scripts/gradio/t2v_test.py create mode 100644 videocrafter/scripts/run_image2video.sh create mode 100644 videocrafter/scripts/run_sds.sh create mode 100644 videocrafter/scripts/run_text2video.sh diff --git a/README.md b/README.md index d09ac85..67b5d06 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,8 @@ The official implementation of paper "Comp4D: LLM-Guided Compositional 4D Scene Generation". +Dejia Xu*, Hanwen Liang*, Neel P. Bhatt, Hezhen Hu, Hanxue Liang, Konstantinos N. Plataniotis, and Zhangyang Wang + [[Project Page]](https://vita-group.github.io/Comp4D/) | [[Video (narrated)]](https://www.youtube.com/watch?v=9q8SV1Xf_Xw) | [[Video (results)]](https://www.youtube.com/watch?v=gXVoPTGb734) | [[Paper]](https://github.com/VITA-Group/Comp4D/blob/main/assets/Comp4D.pdf) | [[Arxiv]](https://arxiv.org/abs/2403.16993) ## News @@ -73,18 +75,16 @@ We release a set of pre-generated static assets in `data/` directory. During tra ``` # cd /path_to_4dfy/ -# seed=0 -# gpu=0 ## Stage 1 -# python launch.py --config configs/fourdfy_stage_1_low_vram.yaml --train --gpu $gpu exp_root_dir=output/ seed=$seed system.prompt_processor.prompt="a flower" +# python launch.py --config configs/fourdfy_stage_1_low_vram.yaml --train --gpu 0 exp_root_dir=output/ seed=0 system.prompt_processor.prompt="a flower" ## Stage 2 # ckpt=output/fourdfy_stage_1_low_vram/a_flower@timestamp/ckpts/last.ckpt -# python launch.py --config configs/fourdfy_stage_2_low_vram.yaml --train --gpu $gpu exp_root_dir=output/ seed=$seed system.prompt_processor.prompt="a flower" system.weights=$ckpt +# python launch.py --config configs/fourdfy_stage_2_low_vram.yaml --train --gpu 0 exp_root_dir=output/ seed=0 system.prompt_processor.prompt="a flower" system.weights=$ckpt ## Post-Process. Convert to mesh file. -# python launch.py --config output/fourdfy_stage_2_low_vram/a_flower@timestamp/configs/parsed.yaml --export --gpu $gpu \ +# python launch.py --config output/fourdfy_stage_2_low_vram/a_flower@timestamp/configs/parsed.yaml --export --gpu 0 \ # resume=output/fourdfy_stage_2_low_vram/a_flower@timestamp/ckpts/last.ckpt system.exporter_type=mesh-exporter \ # system.exporter.context_type=cuda system.exporter.fmt=obj ## saved to output/fourdfy_stage_2_low_vram/a_flower@timestamp/save/iterations-export/ diff --git a/videocrafter/License b/videocrafter/License new file mode 100644 index 0000000..e2741c4 --- /dev/null +++ b/videocrafter/License @@ -0,0 +1,470 @@ +This license applies to the source codes that are open sourced in connection with the VideoCrafter1. + +Copyright (C) 2023 THL A29 Limited, a Tencent company. + +Apache License +Version 2.0, January 2004 +http://www.apache.org/licenses/ + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + +1. 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We also recommend that a +file or class name and description of purpose be included on the +same "printed page" as the copyright notice for easier +identification within third-party archives. + +Copyright [yyyy] [name of copyright owner] + +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. \ No newline at end of file diff --git a/videocrafter/cog.yaml b/videocrafter/cog.yaml new file mode 100644 index 0000000..b0108f3 --- /dev/null +++ b/videocrafter/cog.yaml @@ -0,0 +1,25 @@ +# Configuration for Cog ⚙️ +# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md + +build: + gpu: true + system_packages: + - "libgl1-mesa-glx" + - "libglib2.0-0" + python_version: "3.11" + python_packages: + - "torch==2.0.1" + - "opencv-python==4.8.1.78" + - "torchvision==0.15.2" + - "pytorch_lightning==2.1.0" + - "einops==0.7.0" + - "imageio==2.31.6" + - "omegaconf==2.3.0" + - "transformers==4.35.0" + - "moviepy==1.0.3" + - "av==10.0.0" + - "decord==0.6.0" + - "kornia==0.7.0" + - "open-clip-torch==2.12.0" + - "xformers==0.0.21" +predict: "predict.py:Predictor" diff --git a/videocrafter/configs/inference_i2v_512_v1.0.yaml b/videocrafter/configs/inference_i2v_512_v1.0.yaml new file mode 100644 index 0000000..7d49007 --- /dev/null +++ b/videocrafter/configs/inference_i2v_512_v1.0.yaml @@ -0,0 +1,83 @@ +model: + target: lvdm.models.ddpm3d.LatentVisualDiffusion + params: + linear_start: 0.00085 + linear_end: 0.012 + num_timesteps_cond: 1 + timesteps: 1000 + first_stage_key: video + cond_stage_key: caption + cond_stage_trainable: false + conditioning_key: crossattn + image_size: + - 40 + - 64 + channels: 4 + scale_by_std: false + scale_factor: 0.18215 + use_ema: false + uncond_type: empty_seq + use_scale: true + scale_b: 0.7 + finegrained: true + unet_config: + target: lvdm.modules.networks.openaimodel3d.UNetModel + params: + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + num_head_channels: 64 + transformer_depth: 1 + context_dim: 1024 + use_linear: true + use_checkpoint: true + temporal_conv: true + temporal_attention: true + temporal_selfatt_only: true + use_relative_position: false + use_causal_attention: false + use_image_attention: true + temporal_length: 16 + addition_attention: true + fps_cond: true + first_stage_config: + target: lvdm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 512 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + cond_stage_config: + target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder + params: + freeze: true + layer: penultimate + cond_img_config: + target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2 + params: + freeze: true \ No newline at end of file diff --git a/videocrafter/configs/inference_t2v_1024_v1.0.yaml b/videocrafter/configs/inference_t2v_1024_v1.0.yaml new file mode 100644 index 0000000..4cb9af1 --- /dev/null +++ b/videocrafter/configs/inference_t2v_1024_v1.0.yaml @@ -0,0 +1,77 @@ +model: + target: lvdm.models.ddpm3d.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.012 + num_timesteps_cond: 1 + timesteps: 1000 + first_stage_key: video + cond_stage_key: caption + cond_stage_trainable: false + conditioning_key: crossattn + image_size: + - 72 + - 128 + channels: 4 + scale_by_std: false + scale_factor: 0.18215 + use_ema: false + uncond_type: empty_seq + use_scale: true + fix_scale_bug: true + unet_config: + target: lvdm.modules.networks.openaimodel3d.UNetModel + params: + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + num_head_channels: 64 + transformer_depth: 1 + context_dim: 1024 + use_linear: true + use_checkpoint: true + temporal_conv: false + temporal_attention: true + temporal_selfatt_only: true + use_relative_position: true + use_causal_attention: false + temporal_length: 16 + addition_attention: true + fps_cond: true + first_stage_config: + target: lvdm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 512 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + cond_stage_config: + target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder + params: + freeze: true + layer: penultimate diff --git a/videocrafter/configs/inference_t2v_512_v1.0.yaml b/videocrafter/configs/inference_t2v_512_v1.0.yaml new file mode 100644 index 0000000..849623e --- /dev/null +++ b/videocrafter/configs/inference_t2v_512_v1.0.yaml @@ -0,0 +1,74 @@ +model: + target: lvdm.models.ddpm3d.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.012 + num_timesteps_cond: 1 + timesteps: 1000 + first_stage_key: video + cond_stage_key: caption + cond_stage_trainable: false + conditioning_key: crossattn + image_size: + - 40 + - 64 + channels: 4 + scale_by_std: false + scale_factor: 0.18215 + use_ema: false + uncond_type: empty_seq + unet_config: + target: lvdm.modules.networks.openaimodel3d.UNetModel + params: + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + num_head_channels: 64 + transformer_depth: 1 + context_dim: 1024 + use_linear: true + use_checkpoint: true + temporal_conv: false + temporal_attention: true + temporal_selfatt_only: true + use_relative_position: true + use_causal_attention: false + temporal_length: 16 + addition_attention: true + first_stage_config: + target: lvdm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 512 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + cond_stage_config: + target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder + params: + freeze: true + layer: penultimate diff --git a/videocrafter/configs/inference_t2v_512_v2.0.yaml b/videocrafter/configs/inference_t2v_512_v2.0.yaml new file mode 100644 index 0000000..4a2e6c4 --- /dev/null +++ b/videocrafter/configs/inference_t2v_512_v2.0.yaml @@ -0,0 +1,77 @@ +model: + target: lvdm.models.ddpm3d.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.012 + num_timesteps_cond: 1 + timesteps: 1000 + first_stage_key: video + cond_stage_key: caption + cond_stage_trainable: false + conditioning_key: crossattn + image_size: + - 40 + - 64 + channels: 4 + scale_by_std: false + scale_factor: 0.18215 + use_ema: false + uncond_type: empty_seq + use_scale: true + scale_b: 0.7 + unet_config: + target: lvdm.modules.networks.openaimodel3d.UNetModel + params: + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + num_head_channels: 64 + transformer_depth: 1 + context_dim: 1024 + use_linear: true + use_checkpoint: true + temporal_conv: true + temporal_attention: true + temporal_selfatt_only: true + use_relative_position: false + use_causal_attention: false + temporal_length: 16 + addition_attention: true + fps_cond: true + first_stage_config: + target: lvdm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 512 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + cond_stage_config: + target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder + params: + freeze: true + layer: penultimate diff --git a/videocrafter/prompts/i2v_prompts/horse.png b/videocrafter/prompts/i2v_prompts/horse.png new file mode 100644 index 0000000000000000000000000000000000000000..b0e8f565f38d61f0daf97b71c277fcfa8af27b51 GIT binary patch literal 476298 zcmV)uK$gFWP)Px#1ZP1_K>z@;j|==^1poj532;bRa{vGi!vFvd!vV){sAK>D|D{PpK~#8Ngnb2g zR#(<_id%pPaUt&R?(Xge1b3)Voytsgrk!>=({}1^w54cU3bZ)I-QC?GA+Y|n&zloo 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z5L{cr;Z1_VpGaTcO2S|n9gotaO>!h|^da}|hX5+jeQHVF)&~>uXTA^t*+K+Fx0WHk zV}T%l&&o8IHt!>UDp+0!034=*i}rndbg>WsO#W96cA=p( z1%^}K!qpR(@Z+c7VMcNWp4_~T8|P2q>7DDq^UL^T?P6^EycS#9XQCnB24}X{K$D*08*uFjsPmis^?Hvnn_fNW(124cm|NG-G^CjKRG)|07yYt9`?Qf5x6&GJ$Occ=B^LZhw`GZW404mbv5D#tdBed?vn{ z7YYv|?#6Iatk3qsmRwgXjnE|#U`XW84tX}dEeSxr?RXT}PsH+R0jQ62$NUIabQPtb zF5VRpy6+=GRfg7NQ=Dig=S;%#e47`p)49&A3>6~aRRJJDBHI3Wk_hO-|4aZdBmvO3 zZ{P7>6#&vX`MUaVdlk|(G5K>X!>a<|Wn@5lj~UYR#F79Iq)+6}GebVy&ownfnB8;w zs=+Uk%RZQoM~7z8`%UCYF8SVp8GY%&>A15$OOQWK(f*>^z2v3qzqoA{4u6q?Js&3F z(zdy{wYvhnTeGp9?rR5KFQ-4W1!DWh)3EdNY#jc6HqQQ8A!e++OJd;WzG__FIUh&A z&Z75--Z3I;F^eTRe3009$Znm;{ci;Tg9QMA1pwpmF}jbx695Nt@oe7=B6f29ECP;k z?18zW)`Oc2ZgUOTDW2`H0T+HN#*OXt?jKl;W1r<={aiaNw|WoFro-@s`dj$HV5ATL zHoOE3pT_P9OJ~P#T^#4w(x3q zM*L?Ha4xb%-li> b l c + uc_img = model.get_image_embeds(uc_img) + uc_emb = torch.cat([uc_emb, uc_img], dim=1) + + if isinstance(cond, dict): + uc = {key:cond[key] for key in cond.keys()} + uc.update({'c_crossattn': [uc_emb]}) + else: + uc = uc_emb + else: + uc = None + + x_T = None + batch_variants = [] + #batch_variants1, batch_variants2 = [], [] + for _ in range(n_samples): + if ddim_sampler is not None: + kwargs.update({"clean_cond": True}) + print(f'##### valid infos temporal_cfg_scale {temporal_cfg_scale} kwargs {kwargs.keys()}') + samples, _ = ddim_sampler.sample(S=ddim_steps, + conditioning=cond, + batch_size=noise_shape[0], + shape=noise_shape[1:], + verbose=False, + unconditional_guidance_scale=cfg_scale, + unconditional_conditioning=uc, + eta=ddim_eta, + temporal_length=noise_shape[2], + conditional_guidance_scale_temporal=temporal_cfg_scale, + x_T=x_T, + **kwargs + ) + ## reconstruct from latent to pixel space + batch_images = model.decode_first_stage_2DAE(samples) + batch_variants.append(batch_images) + ## batch, , c, t, h, w + batch_variants = torch.stack(batch_variants, dim=1) + return batch_variants + + +def get_filelist(data_dir, ext='*'): + file_list = glob.glob(os.path.join(data_dir, '*.%s'%ext)) + file_list.sort() + return file_list + +def get_dirlist(path): + list = [] + if (os.path.exists(path)): + files = os.listdir(path) + for file in files: + m = os.path.join(path,file) + if (os.path.isdir(m)): + list.append(m) + list.sort() + return list + + +def load_model_checkpoint(model, ckpt): + def load_checkpoint(model, ckpt, full_strict): + state_dict = torch.load(ckpt, map_location="cpu") + try: + ## deepspeed + new_pl_sd = OrderedDict() + for key in state_dict['module'].keys(): + new_pl_sd[key[16:]]=state_dict['module'][key] + model.load_state_dict(new_pl_sd, strict=full_strict) + except: + if "state_dict" in list(state_dict.keys()): + state_dict = state_dict["state_dict"] + model.load_state_dict(state_dict, strict=full_strict) + return model + load_checkpoint(model, ckpt, full_strict=True) + print('>>> model checkpoint loaded.') + return model + + +def load_prompts(prompt_file): + f = open(prompt_file, 'r') + prompt_list = [] + for idx, line in enumerate(f.readlines()): + l = line.strip() + if len(l) != 0: + prompt_list.append(l) + f.close() + return prompt_list + + +def load_video_batch(filepath_list, frame_stride, video_size=(256,256), video_frames=16): + ''' + Notice about some special cases: + 1. video_frames=-1 means to take all the frames (with fs=1) + 2. when the total video frames is less than required, padding strategy will be used (repreated last frame) + ''' + fps_list = [] + batch_tensor = [] + assert frame_stride > 0, "valid frame stride should be a positive interge!" + for filepath in filepath_list: + padding_num = 0 + vidreader = VideoReader(filepath, ctx=cpu(0), width=video_size[1], height=video_size[0]) + fps = vidreader.get_avg_fps() + total_frames = len(vidreader) + max_valid_frames = (total_frames-1) // frame_stride + 1 + if video_frames < 0: + ## all frames are collected: fs=1 is a must + required_frames = total_frames + frame_stride = 1 + else: + required_frames = video_frames + query_frames = min(required_frames, max_valid_frames) + frame_indices = [frame_stride*i for i in range(query_frames)] + + ## [t,h,w,c] -> [c,t,h,w] + frames = vidreader.get_batch(frame_indices) + frame_tensor = torch.tensor(frames.asnumpy()).permute(3, 0, 1, 2).float() + frame_tensor = (frame_tensor / 255. - 0.5) * 2 + if max_valid_frames < required_frames: + padding_num = required_frames - max_valid_frames + frame_tensor = torch.cat([frame_tensor, *([frame_tensor[:,-1:,:,:]]*padding_num)], dim=1) + print(f'{os.path.split(filepath)[1]} is not long enough: {padding_num} frames padded.') + batch_tensor.append(frame_tensor) + sample_fps = int(fps/frame_stride) + fps_list.append(sample_fps) + + return torch.stack(batch_tensor, dim=0) + +from PIL import Image +def load_image_batch(filepath_list, image_size=(256,256)): + batch_tensor = [] + for filepath in filepath_list: + _, filename = os.path.split(filepath) + _, ext = os.path.splitext(filename) + if ext == '.mp4': + vidreader = VideoReader(filepath, ctx=cpu(0), width=image_size[1], height=image_size[0]) + frame = vidreader.get_batch([0]) + img_tensor = torch.tensor(frame.asnumpy()).squeeze(0).permute(2, 0, 1).float() + elif ext == '.png' or ext == '.jpg': + img = Image.open(filepath).convert("RGB") + rgb_img = np.array(img, np.float32) + #bgr_img = cv2.imread(filepath, cv2.IMREAD_COLOR) + #bgr_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB) + rgb_img = cv2.resize(rgb_img, (image_size[1],image_size[0]), interpolation=cv2.INTER_LINEAR) + img_tensor = torch.from_numpy(rgb_img).permute(2, 0, 1).float() + else: + print(f'ERROR: <{ext}> image loading only support format: [mp4], [png], [jpg]') + raise NotImplementedError + img_tensor = (img_tensor / 255. - 0.5) * 2 + batch_tensor.append(img_tensor) + return torch.stack(batch_tensor, dim=0) + + +def save_videos(batch_tensors, savedir, filenames, fps=10): + # b,samples,c,t,h,w + n_samples = batch_tensors.shape[1] + for idx, vid_tensor in enumerate(batch_tensors): + video = vid_tensor.detach().cpu() + video = torch.clamp(video.float(), -1., 1.) + video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w + frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(n_samples)) for framesheet in video] #[3, 1*h, n*w] + grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, n*h, w] + grid = (grid + 1.0) / 2.0 + grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1) + savepath = os.path.join(savedir, f"{filenames[idx]}.mp4") + torchvision.io.write_video(savepath, grid, fps=fps, video_codec='h264', options={'crf': '10'}) + diff --git a/videocrafter/scripts/evaluation/inference.py b/videocrafter/scripts/evaluation/inference.py new file mode 100644 index 0000000..5ddcc68 --- /dev/null +++ b/videocrafter/scripts/evaluation/inference.py @@ -0,0 +1,137 @@ +import argparse, os, sys, glob, yaml, math, random +import datetime, time +import numpy as np +from omegaconf import OmegaConf +from collections import OrderedDict +from tqdm import trange, tqdm +from einops import repeat +from einops import rearrange, repeat +from functools import partial +import torch +from pytorch_lightning import seed_everything + +from funcs import load_model_checkpoint, load_prompts, load_image_batch, get_filelist, save_videos +from funcs import batch_ddim_sampling +from utils.utils import instantiate_from_config + + +def get_parser(): + parser = argparse.ArgumentParser() + parser.add_argument("--seed", type=int, default=20230211, help="seed for seed_everything") + parser.add_argument("--mode", default="base", type=str, help="which kind of inference mode: {'base', 'i2v'}") + parser.add_argument("--ckpt_path", type=str, default=None, help="checkpoint path") + parser.add_argument("--config", type=str, help="config (yaml) path") + parser.add_argument("--prompt_file", type=str, default=None, help="a text file containing many prompts") + parser.add_argument("--savedir", type=str, default=None, help="results saving path") + parser.add_argument("--savefps", type=str, default=10, help="video fps to generate") + parser.add_argument("--n_samples", type=int, default=1, help="num of samples per prompt",) + parser.add_argument("--ddim_steps", type=int, default=50, help="steps of ddim if positive, otherwise use DDPM",) + parser.add_argument("--ddim_eta", type=float, default=1.0, help="eta for ddim sampling (0.0 yields deterministic sampling)",) + parser.add_argument("--bs", type=int, default=1, help="batch size for inference") + parser.add_argument("--height", type=int, default=512, help="image height, in pixel space") + parser.add_argument("--width", type=int, default=512, help="image width, in pixel space") + parser.add_argument("--frames", type=int, default=-1, help="frames num to inference") + parser.add_argument("--fps", type=int, default=24) + parser.add_argument("--unconditional_guidance_scale", type=float, default=1.0, help="prompt classifier-free guidance") + parser.add_argument("--unconditional_guidance_scale_temporal", type=float, default=None, help="temporal consistency guidance") + ## for conditional i2v only + parser.add_argument("--cond_input", type=str, default=None, help="data dir of conditional input") + return parser + + +def run_inference(args, gpu_num, gpu_no, **kwargs): + ## step 1: model config + ## ----------------------------------------------------------------- + config = OmegaConf.load(args.config) + #data_config = config.pop("data", OmegaConf.create()) + model_config = config.pop("model", OmegaConf.create()) + model = instantiate_from_config(model_config) + model = model.cuda(gpu_no) + assert os.path.exists(args.ckpt_path), f"Error: checkpoint [{args.ckpt_path}] Not Found!" + model = load_model_checkpoint(model, args.ckpt_path) + model.eval() + + ## sample shape + assert (args.height % 16 == 0) and (args.width % 16 == 0), "Error: image size [h,w] should be multiples of 16!" + ## latent noise shape + h, w = args.height // 8, args.width // 8 + frames = model.temporal_length if args.frames < 0 else args.frames + channels = model.channels + + ## saving folders + os.makedirs(args.savedir, exist_ok=True) + + ## step 2: load data + ## ----------------------------------------------------------------- + assert os.path.exists(args.prompt_file), "Error: prompt file NOT Found!" + prompt_list = load_prompts(args.prompt_file) + num_samples = len(prompt_list) + filename_list = [f"{id+1:04d}" for id in range(num_samples)] + + samples_split = num_samples // gpu_num + residual_tail = num_samples % gpu_num + print(f'[rank:{gpu_no}] {samples_split}/{num_samples} samples loaded.') + indices = list(range(samples_split*gpu_no, samples_split*(gpu_no+1))) + if gpu_no == 0 and residual_tail != 0: + indices = indices + list(range(num_samples-residual_tail, num_samples)) + prompt_list_rank = [prompt_list[i] for i in indices] + + ## conditional input + if args.mode == "i2v": + ## each video or frames dir per prompt + cond_inputs = get_filelist(args.cond_input, ext='[mpj][pn][4gj]') # '[mpj][pn][4gj]' + assert len(cond_inputs) == num_samples, f"Error: conditional input ({len(cond_inputs)}) NOT match prompt ({num_samples})!" + filename_list = [f"{os.path.split(cond_inputs[id])[-1][:-4]}" for id in range(num_samples)] + cond_inputs_rank = [cond_inputs[i] for i in indices] + + filename_list_rank = [filename_list[i] for i in indices] + + ## step 3: run over samples + ## ----------------------------------------------------------------- + start = time.time() + n_rounds = len(prompt_list_rank) // args.bs + n_rounds = n_rounds+1 if len(prompt_list_rank) % args.bs != 0 else n_rounds + for idx in range(0, n_rounds): + print(f'[rank:{gpu_no}] batch-{idx+1} ({args.bs})x{args.n_samples} ...') + idx_s = idx*args.bs + idx_e = min(idx_s+args.bs, len(prompt_list_rank)) + batch_size = idx_e - idx_s + filenames = filename_list_rank[idx_s:idx_e] + noise_shape = [batch_size, channels, frames, h, w] + fps = torch.tensor([args.fps]*batch_size).to(model.device).long() + + prompts = prompt_list_rank[idx_s:idx_e] + if isinstance(prompts, str): + prompts = [prompts] + #prompts = batch_size * [""] + text_emb = model.get_learned_conditioning(prompts) + + if args.mode == 'base': + cond = {"c_crossattn": [text_emb], "fps": fps} + elif args.mode == 'i2v': + #cond_images = torch.zeros(noise_shape[0],3,224,224).to(model.device) + cond_images = load_image_batch(cond_inputs_rank[idx_s:idx_e], (args.height, args.width)) + cond_images = cond_images.to(model.device) + img_emb = model.get_image_embeds(cond_images) + imtext_cond = torch.cat([text_emb, img_emb], dim=1) + cond = {"c_crossattn": [imtext_cond], "fps": fps} + else: + raise NotImplementedError + + ## inference + batch_samples = batch_ddim_sampling(model, cond, noise_shape, args.n_samples, \ + args.ddim_steps, args.ddim_eta, args.unconditional_guidance_scale, **kwargs) + ## b,samples,c,t,h,w + save_videos(batch_samples, args.savedir, filenames, fps=args.savefps) + + print(f"Saved in {args.savedir}. Time used: {(time.time() - start):.2f} seconds") + + +if __name__ == '__main__': + now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + print("@CoLVDM Inference: %s"%now) + parser = get_parser() + args = parser.parse_args() + seed_everything(args.seed) + rank, gpu_num = 0, 1 + run_inference(args, gpu_num, rank) \ No newline at end of file diff --git a/videocrafter/scripts/evaluation/videocrafter2_utils.py b/videocrafter/scripts/evaluation/videocrafter2_utils.py new file mode 100644 index 0000000..9fb2aa7 --- /dev/null +++ b/videocrafter/scripts/evaluation/videocrafter2_utils.py @@ -0,0 +1,309 @@ +from typing import List + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import imageio + + +def seed_everything(seed): + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + # torch.backends.cudnn.deterministic = True + # torch.backends.cudnn.benchmark = True +import sys +import argparse, os +sys.path.insert(1, os.path.join(sys.path[0], '..', '..')) +from omegaconf import OmegaConf +import matplotlib.pyplot as plt +import torchvision, tqdm +# from utils.utils import instantiate_from_config +# from funcs import load_model_checkpoint, load_prompts, load_image_batch, get_filelist, save_videos +# from funcs import batch_ddim_sampling + +# from lvdm.models.samplers.ddim import DDIMSampler +import importlib +from collections import OrderedDict + + + + +def load_prompts(prompt_file): + f = open(prompt_file, 'r') + prompt_list = [] + for idx, line in enumerate(f.readlines()): + l = line.strip() + if len(l) != 0: + prompt_list.append(l) + f.close() + return prompt_list + +def load_model_checkpoint(model, ckpt): + def load_checkpoint(model, ckpt, full_strict): + state_dict = torch.load(ckpt, map_location="cpu") + try: + ## deepspeed + new_pl_sd = OrderedDict() + for key in state_dict['module'].keys(): + new_pl_sd[key[16:]]=state_dict['module'][key] + model.load_state_dict(new_pl_sd, strict=full_strict) + except: + if "state_dict" in list(state_dict.keys()): + state_dict = state_dict["state_dict"] + model.load_state_dict(state_dict, strict=full_strict) + return model + load_checkpoint(model, ckpt, full_strict=True) + print('>>> model checkpoint loaded.') + return model + +def count_params(model, verbose=False): + total_params = sum(p.numel() for p in model.parameters()) + if verbose: + print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") + return total_params + + +def check_istarget(name, para_list): + """ + name: full name of source para + para_list: partial name of target para + """ + istarget=False + for para in para_list: + if para in name: + return True + return istarget + + +def instantiate_from_config(config): + if not "target" in config: + if config == '__is_first_stage__': + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + +def get_parser(): + parser = argparse.ArgumentParser() + + parser.add_argument("--seed", type=int, default=20230211, help="seed for seed_everything") + parser.add_argument("--mode", default="base", type=str, help="which kind of inference mode: {'base', 'i2v'}") + parser.add_argument("--ckpt_path", type=str, default=None, help="checkpoint path") + parser.add_argument("--config", type=str, help="config (yaml) path") + parser.add_argument("--prompt_file", type=str, default=None, help="a text file containing many prompts") + parser.add_argument("--savedir", type=str, default=None, help="results saving path") + parser.add_argument("--savefps", type=str, default=16, help="video fps to generate") + parser.add_argument("--n_samples", type=int, default=1, help="num of samples per prompt",) + # parser.add_argument("--ddim_steps", type=int, default=50, help="steps of ddim if positive, otherwise use DDPM",) + # parser.add_argument("--ddim_eta", type=float, default=1.0, help="eta for ddim sampling (0.0 yields deterministic sampling)",) + parser.add_argument("--bs", type=int, default=1, help="batch size for inference") + parser.add_argument("--height", type=int, default=512, help="image height, in pixel space") + parser.add_argument("--width", type=int, default=512, help="image width, in pixel space") + parser.add_argument("--frames", type=int, default=-1, help="frames num to inference") + parser.add_argument("--fps", type=int, default=24) + parser.add_argument("--lr", type=float, default=0.05) + parser.add_argument("--cfg", type=float, default=1.0, help="prompt classifier-free guidance") + parser.add_argument("--cfg_temporal", type=float, default=0.0, help="prompt classifier-free guidance") + # parser.add_argument("--unconditional_guidance_scale_temporal", type=float, default=None, help="temporal consistency guidance") + ## for conditional i2v only + parser.add_argument("--cond_input", type=str, default=None, help="data dir of conditional input") + + parser.add_argument("--fp16", action="store_true", help="use float16 for training") + parser.add_argument("--vram_O", action="store_true", help="optimization for low VRAM usage") + parser.add_argument("--use_rgb", action="store_true", help="use rgb") + + return parser + +class VideoCrafter2(nn.Module): + def __init__( + self, + model_config, + ckpt_path, + device=torch.device("cuda"), + weights_dtype=torch.float32 + ): + super().__init__() + + self.model = instantiate_from_config(model_config).to(weights_dtype) + self.device = device + self.weights_dtype = weights_dtype + if ckpt_path: + self.model = load_model_checkpoint(self.model, ckpt_path).to(weights_dtype) + self.model.model.diffusion_model.dtype = weights_dtype + self.model.to(device) + self.model.eval() + print(f"{self.model.parameterization} {self.model.dtype} {self.model.model.diffusion_model.dtype}") + self._init_train() + + def _init_train(self, t_range=[0.02, 0.98]): + total_steps = self.model.num_timesteps + self.min_step = int(total_steps * t_range[0]) + self.max_step = int(total_steps * t_range[1]) + self.alphas = self.model.alphas_cumprod.to(self.weights_dtype).to(self.device) + self.sigmas = ((1 - self.model.alphas_cumprod) ** 0.5).to(self.weights_dtype).to(self.device) + + def train_step(self, rgbs, cond, un_cond, cfg=10.0, cfg_temporal=0.0, as_latent=False): + batch_size = rgbs.shape[0] + t = torch.randint(self.min_step, self.max_step + 1, (batch_size,), dtype=torch.long, device=self.device) + rgbs = rgbs.to(self.weights_dtype) + if as_latent: + rgbs_latent = rgbs + else: + rgbs_latent = self.model.encode_first_stage(rgbs) + with torch.no_grad(): + noise = torch.randn_like(rgbs_latent).to(self.weights_dtype) + rgb_noisy = self.model.q_sample(x_start=rgbs_latent, t=t, noise=noise).to(self.weights_dtype) + noise_pred_text= self.model.apply_model(rgb_noisy, t.to(self.weights_dtype), cond) + noise_pred_uncond= self.model.apply_model(rgb_noisy, t.to(self.weights_dtype), un_cond) + + if cfg_temporal: + noise_pred_static = self.model.apply_model(rgb_noisy, t.to(self.weights_dtype), cond, no_temporal_attn=True).to(self.weights_dtype) + + # perform guidance (high scale from paper!) + noise_pred_cond = noise_pred_text + cfg * ( + noise_pred_text - noise_pred_uncond + ) + if cfg_temporal: + noise_pred_cond += cfg_temporal * (noise_pred_text - noise_pred_static) + + weight = (1 - self.alphas[t]).view(-1, 1, 1, 1, 1) + + # print(noise_pred_cond.shape, noise.shape) + grad = weight * (noise_pred_cond - noise) + target = (rgbs_latent - grad).detach() + loss_sds = 0.5 * F.mse_loss(rgbs_latent.float(), target, reduction='sum') / rgbs_latent.shape[0] + # print(f"loss_sds {loss_sds}") + + # latents_1step_orig = ( + # 1 + # / self.alphas[t].view(-1, 1, 1, 1) + # * (rgb_noisy - self.sigmas[t].view(-1, 1, 1, 1) * noise_pred_cond) + # ).detach() + # with torch.no_grad(): + # # rgb_target = self.model.decode_first_stage(target.to(self.weights_dtype)) + # image_denoised_pretrain = self.model.decode_first_stage(latents_1step_orig) + # grad_img = ( + # weight + # * (rgbs - image_denoised_pretrain) + # * self.alphas[t].view(-1, 1, 1, 1) + # / self.sigmas[t].view(-1, 1, 1, 1) + # ) + # target_img = (rgbs - grad_img).detach() + # recon_loss = F.mse_loss(rgbs.float(), target_img.detach().float(), reduction="sum") / target_img.shape[0] + # loss_sds += 0.01 * torch.nan_to_num(recon_loss) + # print(f"recon_loss {recon_loss}") + + return loss_sds + + def decode_latent(self, rgbs_latent): + return self.model.decode_first_stage(rgbs_latent) + +if __name__ == "__main__": + @torch.no_grad() + def save_results(results, filename, fps=10): + # print('results.shape :', results.shape) + video = results.permute(2, 0, 1, 3, 4) # [t, sample_num, c, h, w] + frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(video.shape[1])) for framesheet in video] #[3, 1*h, n*w] + grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, n*h, w] + # already in [0,1] + grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1) + # torchvision.io.write_video(filename, grid, fps=fps, video_codec='h264', options={'crf': '10'}) + imageio.mimwrite(filename, grid, format='gif') + # imageio.mimwrite(filename, grid, format='mp4', fps=8) + + + parser = get_parser() + opt = parser.parse_args() + seed_everything(opt.seed) + device = torch.device("cuda") + + weights_dtype = torch.float16 if opt.fp16 else torch.float32 + + ## step 1: model config + ## ----------------------------------------------------------------- + config = OmegaConf.load(opt.config) + model_config = config.pop("model", OmegaConf.create()) + # model = instantiate_from_config(model_config) + vc2 = VideoCrafter2(model_config, ckpt_path=opt.ckpt_path, weights_dtype=weights_dtype, device=device) + ## saving folders + os.makedirs(opt.savedir, exist_ok=True) + + ## step 2: load data + ## ----------------------------------------------------------------- + assert os.path.exists(opt.prompt_file), "Error: prompt file NOT Found!" + prompt_list = load_prompts(opt.prompt_file) + num_samples = len(prompt_list) + filename_list = [f"{id+1:04d}" for id in range(num_samples)] + + ## step 3: run over samples + ## ----------------------------------------------------------------- + for prompts, filename in zip(prompt_list, filename_list): + if isinstance(prompts, str): + prompts = [prompts] + with torch.no_grad(): + text_emb = vc2.model.get_learned_conditioning(prompts) + neg_prompt_emb = vc2.model.get_learned_conditioning(["text, watermark, copyright, blurry, nsfw"]) + + ## sample shape + assert (opt.height % 16 == 0) and (opt.width % 16 == 0), "Error: image size [h,w] should be multiples of 16!" + frames = vc2.model.temporal_length if opt.frames < 0 else opt.frames + batch_size = 1 + fps = torch.tensor([opt.fps]*batch_size).to(vc2.model.device).long() + + if opt.use_rgb: + # rgbs = torch.ones(batch_size, 3, frames, opt.height, opt.width).to(weights_dtype).to(device) + rgbs = torch.randn(batch_size, 3, 1, opt.height, opt.width).repeat(1, 1, frames, 1, 1).clamp(0, 1).to(weights_dtype).to(device) # works better + rgbs.requires_grad = True + + optimizer = torch.optim.Adam([rgbs], lr=opt.lr) + cond = {"c_crossattn": [text_emb], "fps": fps} + un_cond = {"c_crossattn": [neg_prompt_emb], "fps": fps} + for step in tqdm.tqdm(range(1001)): + optimizer.zero_grad() + loss_sds = vc2.train_step(rgbs, cond, un_cond, cfg=opt.cfg, cfg_temporal=opt.cfg_temporal, as_latent=False) + loss_sds.backward() + optimizer.step() + + if step % 100 == 0: + tqdm.tqdm.write(f"step: {step}, loss_sds: {loss_sds.item()}") + video_path = os.path.join(opt.savedir, f"{filename}_sds_{step}.gif") + out = rgbs.detach().float().clamp(0, 1) + save_results(out.data.cpu(), video_path, fps=opt.savefps) + + else: + ## latent noise shape + h, w = opt.height // 8, opt.width // 8 + latent_channels = vc2.model.channels + rgbs_latent = torch.randn(batch_size, latent_channels, 1, h, w).repeat(1, 1, frames, 1, 1).to(device) + rgbs_latent.requires_grad = True + + optimizer = torch.optim.Adam([rgbs_latent], lr=opt.lr) + cond = {"c_crossattn": [text_emb], "fps": fps} + un_cond = {"c_crossattn": [neg_prompt_emb], "fps": fps} + for step in tqdm.tqdm(range(1001)): + optimizer.zero_grad() + loss_sds = vc2.train_step(rgbs_latent, cond, un_cond, cfg=opt.cfg, cfg_temporal=opt.cfg_temporal, as_latent=True) + loss_sds.backward() + optimizer.step() + if step % 100 == 0: + tqdm.tqdm.write(f"step: {step}, loss_sds: {loss_sds.item()}") + # print(f"step: {step}, loss_sds: {loss_sds.item()}") + with torch.no_grad(): + if step % 100 == 0: + video_path = os.path.join(opt.savedir, f"{filename}_sds_{step}.gif") + # out = model.decode_first_stage_2DAE(rgbs_latent.detach()) + out = vc2.decode_latent(rgbs_latent.detach()) + out = out.float() + out = (out / 2 + 0.5).clamp(0, 1) + save_results(out.data.cpu(), video_path, fps=opt.savefps) + diff --git a/videocrafter/scripts/gradio/i2v_test.py b/videocrafter/scripts/gradio/i2v_test.py new file mode 100644 index 0000000..7059273 --- /dev/null +++ b/videocrafter/scripts/gradio/i2v_test.py @@ -0,0 +1,83 @@ +import os +import time +from omegaconf import OmegaConf +import torch +from scripts.evaluation.funcs import load_model_checkpoint, load_image_batch, save_videos, batch_ddim_sampling +from utils.utils import instantiate_from_config +from huggingface_hub import hf_hub_download + +class Image2Video(): + def __init__(self,result_dir='./tmp/',gpu_num=1) -> None: + self.download_model() + self.result_dir = result_dir + if not os.path.exists(self.result_dir): + os.mkdir(self.result_dir) + ckpt_path='checkpoints/i2v_512_v1/model.ckpt' + config_file='configs/inference_i2v_512_v1.0.yaml' + config = OmegaConf.load(config_file) + model_config = config.pop("model", OmegaConf.create()) + model_config['params']['unet_config']['params']['use_checkpoint']=False + model_list = [] + for gpu_id in range(gpu_num): + model = instantiate_from_config(model_config) + # model = model.cuda(gpu_id) + assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!" + model = load_model_checkpoint(model, ckpt_path) + model.eval() + model_list.append(model) + self.model_list = model_list + self.save_fps = 8 + + def get_image(self, image, prompt, steps=50, cfg_scale=12.0, eta=1.0, fps=16): + torch.cuda.empty_cache() + print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))) + start = time.time() + gpu_id=0 + if steps > 60: + steps = 60 + model = self.model_list[gpu_id] + model = model.cuda() + batch_size=1 + channels = model.model.diffusion_model.in_channels + frames = model.temporal_length + h, w = 320 // 8, 512 // 8 + noise_shape = [batch_size, channels, frames, h, w] + + # text cond + text_emb = model.get_learned_conditioning([prompt]) + + # img cond + img_tensor = torch.from_numpy(image).permute(2, 0, 1).float() + img_tensor = (img_tensor / 255. - 0.5) * 2 + img_tensor = img_tensor.unsqueeze(0) + cond_images = img_tensor.to(model.device) + img_emb = model.get_image_embeds(cond_images) + imtext_cond = torch.cat([text_emb, img_emb], dim=1) + cond = {"c_crossattn": [imtext_cond], "fps": fps} + + ## inference + batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale) + ## b,samples,c,t,h,w + prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt + prompt_str = prompt_str.replace(" ", "_") if " " in prompt else prompt_str + prompt_str=prompt_str[:30] + + save_videos(batch_samples, self.result_dir, filenames=[prompt_str], fps=self.save_fps) + print(f"Saved in {prompt_str}. Time used: {(time.time() - start):.2f} seconds") + model = model.cpu() + return os.path.join(self.result_dir, f"{prompt_str}.mp4") + + def download_model(self): + REPO_ID = 'VideoCrafter/Image2Video-512' + filename_list = ['model.ckpt'] + if not os.path.exists('./checkpoints/i2v_512_v1/'): + os.makedirs('./checkpoints/i2v_512_v1/') + for filename in filename_list: + local_file = os.path.join('./checkpoints/i2v_512_v1/', filename) + if not os.path.exists(local_file): + hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/i2v_512_v1/', local_dir_use_symlinks=False) + +if __name__ == '__main__': + i2v = Image2Video() + video_path = i2v.get_image('prompts/i2v_prompts/horse.png','horses are walking on the grassland') + print('done', video_path) \ No newline at end of file diff --git a/videocrafter/scripts/gradio/t2v_test.py b/videocrafter/scripts/gradio/t2v_test.py new file mode 100644 index 0000000..3c94f25 --- /dev/null +++ b/videocrafter/scripts/gradio/t2v_test.py @@ -0,0 +1,77 @@ +import os +import time +from omegaconf import OmegaConf +import torch +from scripts.evaluation.funcs import load_model_checkpoint, save_videos, batch_ddim_sampling +from utils.utils import instantiate_from_config +from huggingface_hub import hf_hub_download + +class Text2Video(): + def __init__(self,result_dir='./tmp/',gpu_num=1) -> None: + self.download_model() + self.result_dir = result_dir + if not os.path.exists(self.result_dir): + os.mkdir(self.result_dir) + ckpt_path='checkpoints/base_512_v2/model.ckpt' + config_file='configs/inference_t2v_512_v2.0.yaml' + config = OmegaConf.load(config_file) + model_config = config.pop("model", OmegaConf.create()) + model_config['params']['unet_config']['params']['use_checkpoint']=False + model_list = [] + for gpu_id in range(gpu_num): + model = instantiate_from_config(model_config) + # model = model.cuda(gpu_id) + assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!" + model = load_model_checkpoint(model, ckpt_path) + model.eval() + model_list.append(model) + self.model_list = model_list + self.save_fps = 8 + + def get_prompt(self, prompt, steps=50, cfg_scale=12.0, eta=1.0, fps=16): + torch.cuda.empty_cache() + print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))) + start = time.time() + gpu_id=0 + if steps > 60: + steps = 60 + model = self.model_list[gpu_id] + model = model.cuda() + batch_size=1 + channels = model.model.diffusion_model.in_channels + frames = model.temporal_length + h, w = 320 // 8, 512 // 8 + noise_shape = [batch_size, channels, frames, h, w] + + # text cond + text_emb = model.get_learned_conditioning([prompt]) + cond = {"c_crossattn": [text_emb], "fps": fps} + + ## inference + batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale) + ## b,samples,c,t,h,w + prompt_str = prompt.replace("/", "_slash_") if "/" in prompt else prompt + prompt_str = prompt_str.replace(" ", "_") if " " in prompt else prompt_str + prompt_str=prompt_str[:30] + + save_videos(batch_samples, self.result_dir, filenames=[prompt_str], fps=self.save_fps) + print(f"Saved in {prompt_str}. Time used: {(time.time() - start):.2f} seconds") + model=model.cpu() + return os.path.join(self.result_dir, f"{prompt_str}.mp4") + + def download_model(self): + REPO_ID = 'VideoCrafter/VideoCrafter2' + filename_list = ['model.ckpt'] + if not os.path.exists('./checkpoints/base_512_v2/'): + os.makedirs('./checkpoints/base_512_v2/') + for filename in filename_list: + local_file = os.path.join('./checkpoints/base_512_v2/', filename) + + if not os.path.exists(local_file): + hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/base_512_v2/', local_dir_use_symlinks=False) + + +if __name__ == '__main__': + t2v = Text2Video() + video_path = t2v.get_prompt('a black swan swims on the pond') + print('done', video_path) \ No newline at end of file diff --git a/videocrafter/scripts/run_image2video.sh b/videocrafter/scripts/run_image2video.sh new file mode 100644 index 0000000..70e8746 --- /dev/null +++ b/videocrafter/scripts/run_image2video.sh @@ -0,0 +1,24 @@ +name="i2v_512_test" + +ckpt='checkpoints/i2v_512_v1/model.ckpt' +config='configs/inference_i2v_512_v1.0.yaml' + +prompt_file="prompts/i2v_prompts/test_prompts.txt" +condimage_dir="prompts/i2v_prompts" +res_dir="results" + +python3 scripts/evaluation/inference.py \ +--seed 123 \ +--mode 'i2v' \ +--ckpt_path $ckpt \ +--config $config \ +--savedir $res_dir/$name \ +--n_samples 1 \ +--bs 1 --height 320 --width 512 \ +--unconditional_guidance_scale 12.0 \ +--ddim_steps 50 \ +--ddim_eta 1.0 \ +--prompt_file $prompt_file \ +--cond_input $condimage_dir \ +--fps 8 + diff --git a/videocrafter/scripts/run_sds.sh b/videocrafter/scripts/run_sds.sh new file mode 100644 index 0000000..7f16a93 --- /dev/null +++ b/videocrafter/scripts/run_sds.sh @@ -0,0 +1,18 @@ +ckpt='checkpoints/base_512_v2/model.ckpt' +config='configs/inference_t2v_512_v2.0.yaml' + +prompt_file="prompts/test_prompts.txt" +res_dir="results" +export CUDA_VISIBLE_DEVICES=1 + +name="cfg10_lr1_t20" +python scripts/evaluation/videocrafter2_utils.py \ + --seed 123 \ + --mode 'base' \ + --ckpt_path $ckpt \ + --config $config \ + --savedir $res_dir/$name \ + --n_samples 1 \ + --bs 1 --height 320 --width 512 \ + --prompt_file $prompt_file \ + --cfg 10.0 --lr 0.1 --cfg_temporal 20.0 \ diff --git a/videocrafter/scripts/run_text2video.sh b/videocrafter/scripts/run_text2video.sh new file mode 100644 index 0000000..68205fb --- /dev/null +++ b/videocrafter/scripts/run_text2video.sh @@ -0,0 +1,21 @@ +name="base_512_v2" + +ckpt='checkpoints/base_512_v2/model.ckpt' +config='configs/inference_t2v_512_v2.0.yaml' + +prompt_file="prompts/test_prompts.txt" +res_dir="results" +export CUDA_VISIBLE_DEVICES=1 +python scripts/evaluation/inference.py \ +--seed 123 \ +--mode 'base' \ +--ckpt_path $ckpt \ +--config $config \ +--savedir $res_dir/$name \ +--n_samples 1 \ +--bs 1 --height 320 --width 512 \ +--unconditional_guidance_scale 12.0 \ +--ddim_steps 50 \ +--ddim_eta 1.0 \ +--prompt_file $prompt_file \ +--fps 28