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In no event and under no legal theory, +whether in tort (including negligence), contract, or otherwise, +unless required by applicable law (such as deliberate and grossly +negligent acts) or agreed to in writing, shall any Contributor be +liable to You for damages, including any direct, indirect, special, +incidental, or consequential damages of any character arising as a +result of this License or out of the use or inability to use the +Work (including but not limited to damages for loss of goodwill, +work stoppage, computer failure or malfunction, or any and all +other commercial damages or losses), even if such Contributor +has been advised of the possibility of such damages. + +9. Accepting Warranty or Additional Liability. While redistributing +the Work or Derivative Works thereof, You may choose to offer, +and charge a fee for, acceptance of support, warranty, indemnity, +or other liability obligations and/or rights consistent with this +License. However, in accepting such obligations, You may act only +on Your own behalf and on Your sole responsibility, not on behalf +of any other Contributor, and only if You agree to indemnify, +defend, and hold each Contributor harmless for any liability +incurred by, or claims asserted against, such Contributor by reason +of your accepting any such warranty or additional liability. + +END OF TERMS AND CONDITIONS + +APPENDIX: How to apply the Apache License to your work. + +To apply the Apache License to your work, attach the following +boilerplate notice, with the fields enclosed by brackets "[]" +replaced with your own identifying information. (Don't include +the brackets!) The text should be enclosed in the appropriate +comment syntax for the file format. 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/README.md b/README.md index c323968ca4f7b4cd647e17551975cb2b3628c5f1..7c27a49d055a351b47f874c192a1bec6795137b0 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,13 @@ --- -title: DynamiCrafter CIL -emoji: 🌖 -colorFrom: green -colorTo: indigo +title: DynamiCrafter +emoji: 🐨 +colorFrom: yellow +colorTo: purple sdk: gradio -sdk_version: 4.37.1 +sdk_version: 4.36.0 app_file: app.py pinned: false -license: mit +license: other --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..3e303df32505b9345130b350850b3821b10db44d --- /dev/null +++ b/app.py @@ -0,0 +1,166 @@ +# import spaces +import gradio as gr +import os +import sys +import argparse +import random +import time +from omegaconf import OmegaConf +import torch +import torchvision +from pytorch_lightning import seed_everything +from huggingface_hub import hf_hub_download +from einops import repeat +import torchvision.transforms as transforms +from utils.utils import instantiate_from_config +sys.path.insert(0, "scripts/evaluation") +from scripts.evaluation.funcs import ( + batch_ddim_sampling, + load_model_checkpoint, + get_latent_z, + save_videos +) + +def download_model(): + REPO_ID = 'GraceZhao/DynamiCrafter-CIL-512' + ckpt_dir = './checkpoints/dynamicrafter_512_cil/' + filename_list = ['timenoise.ckpt'] + if not os.path.exists(ckpt_dir): + os.makedirs(ckpt_dir) + for filename in filename_list: + local_file = os.path.join(ckpt_dir, filename) + if not os.path.exists(local_file): + hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir=ckpt_dir, force_download=True) + + + +download_model() +ckpt_path='checkpoints/dynamicrafter_512_cil/timenoise.ckpt' +config_file='configs/inference_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 = instantiate_from_config(model_config) +assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!" +model = load_model_checkpoint(model, ckpt_path) +model.eval() +model = model.cuda() + + + +@spaces.GPU(duration=300) +def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123, ddpm_from=1000): + resolution = (320, 512) + save_fps = 8 + seed_everything(seed) + transform = transforms.Compose([ + transforms.Resize(resolution), + ]) + torch.cuda.empty_cache() + print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))) + start = time.time() + if steps > 60: + steps = 60 + + batch_size=1 + channels = model.model.diffusion_model.out_channels + frames = model.temporal_length + h, w = resolution[0] // 8, resolution[1] // 8 + noise_shape = [batch_size, channels, frames, h, w] + + # text cond + with torch.no_grad(), torch.cuda.amp.autocast(): + text_emb = model.get_learned_conditioning([prompt]) + + # img cond + img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().to(model.device) + img_tensor = (img_tensor / 255. - 0.5) * 2 + + image_tensor_resized = transform(img_tensor) #3,256,256 + videos = image_tensor_resized.unsqueeze(0) # bchw + + z = get_latent_z(model, videos.unsqueeze(2)) #bc,1,hw + + img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames) + + cond_images = model.embedder(img_tensor.unsqueeze(0)) ## blc + img_emb = model.image_proj_model(cond_images) + + imtext_cond = torch.cat([text_emb, img_emb], dim=1) + + fs = torch.tensor([fs], dtype=torch.long, device=model.device) + cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]} + + ## inference + batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale, ddpm_from=ddpm_from) + ## b,samples,c,t,h,w + + video_path = './output.mp4' + save_videos(batch_samples, './', filenames=['output'], fps=save_fps) + return video_path + + +i2v_examples = [ + ['prompts/512/7.png', 'Donkeys in traditional attire gallop across a lush green meadow.', 50, 7.5, 1.0, 24, 123,900], + ['prompts/512/41.png', 'Rabbits playing in a river.', 50, 7.5, 1.0, 24, 123,900], + ['prompts/512/32.png', 'Mountains under the starlight.', 50, 7.5, 1.0, 24, 123,900], + ['prompts/512/14.png', 'A duck swimming in the lake.', 50, 7.5, 1.0, 24, 123,900], + ['prompts/512/30.png', 'A soldier riding a horse.', 50, 7.5, 1.0, 24, 123,900], + ['prompts/512/52.png', 'Fireworks exploding in the sky.', 50, 7.5, 1.0, 24, 123,900], +] + + + + +css = """#input_img {max-width: 1024px !important} #output_vid {max-width: 1024px; max-height: 576px}""" + +with gr.Blocks(analytics_enabled=False, css=css) as demo: + gr.Markdown("

DynamiCrafter-CIL

\ +

\ + Min Zhao, \ + Hongzhou Zhu, \ + Chendong Xiang, \ + Kaiwen Zheng, \ + Chongxuan Li,\ + Jun Zhu,\ +

\ + If DynamiCrafter is useful, please help star the \ + [Github Repo]\ + , which is important to Open-Source projects. Thanks!\ + [ArXiv] \ + [Project Page]
") + + with gr.Tab(label='ImageAnimation_576x1024'): + with gr.Column(): + with gr.Row(): + with gr.Column(): + with gr.Row(): + i2v_input_image = gr.Image(label="Input Image",elem_id="input_img") + with gr.Row(): + i2v_input_text = gr.Text(label='Prompts') + with gr.Row(): + i2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=10000, step=1, value=123) + i2v_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="i2v_eta") + i2v_cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.5, elem_id="i2v_cfg_scale") + with gr.Row(): + i2v_steps = gr.Slider(minimum=1, maximum=50, step=1, elem_id="i2v_steps", label="Sampling steps", value=30) + i2v_motion = gr.Slider(minimum=5, maximum=20, step=1, elem_id="i2v_motion", label="FPS", value=10) + i2v_ddpm_from = gr.Slider(minimum=840, maximum=1000, step=1, elem_id="i2v_motion", label="ddpm_from", value=900) + + i2v_end_btn = gr.Button("Generate") + # with gr.Tab(label='Result'): + with gr.Row(): + i2v_output_video = gr.Video(label="Generated Video",elem_id="output_vid",autoplay=True,show_share_button=True) + + gr.Examples(examples=i2v_examples, + inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed], + outputs=[i2v_output_video], + fn = infer, + cache_examples=True, + ) + i2v_end_btn.click(inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed, i2v_ddpm_from], + outputs=[i2v_output_video], + fn = infer + ) + +demo.queue(max_size=12).launch(show_api=True) \ No newline at end of file diff --git a/app_256.py b/app_256.py new file mode 100644 index 0000000000000000000000000000000000000000..3dcfce6141cf25ea4207c75ad899ffefb496fca0 --- /dev/null +++ b/app_256.py @@ -0,0 +1,155 @@ +import gradio as gr +import os +import sys +import argparse +import random +import time +from omegaconf import OmegaConf +import torch +import torchvision +from pytorch_lightning import seed_everything +from huggingface_hub import hf_hub_download +from einops import repeat +import torchvision.transforms as transforms +from utils.utils import instantiate_from_config +sys.path.insert(0, "scripts/evaluation") +from funcs import ( + batch_ddim_sampling, + load_model_checkpoint, + get_latent_z, + save_videos +) + +def download_model(): + REPO_ID = 'Doubiiu/DynamiCrafter' + filename_list = ['model.ckpt'] + if not os.path.exists('./checkpoints/dynamicrafter_256_v1/'): + os.makedirs('./checkpoints/dynamicrafter_256_v1/') + for filename in filename_list: + local_file = os.path.join('./checkpoints/dynamicrafter_256_v1/', filename) + if not os.path.exists(local_file): + hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_256_v1/', force_download=True) + + +def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123): + download_model() + ckpt_path='checkpoints/dynamicrafter_256_v1/model.ckpt' + config_file='configs/inference_256_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 = instantiate_from_config(model_config) + assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!" + model = load_model_checkpoint(model, ckpt_path) + model.eval() + model = model.cuda() + save_fps = 8 + + seed_everything(seed) + transform = transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(256), + ]) + torch.cuda.empty_cache() + print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))) + start = time.time() + if steps > 60: + steps = 60 + + batch_size=1 + channels = model.model.diffusion_model.out_channels + frames = model.temporal_length + h, w = 256 // 8, 256 // 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().to(model.device) + img_tensor = (img_tensor / 255. - 0.5) * 2 + + image_tensor_resized = transform(img_tensor) #3,256,256 + videos = image_tensor_resized.unsqueeze(0) # bchw + + z = get_latent_z(model, videos.unsqueeze(2)) #bc,1,hw + + img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames) + + cond_images = model.embedder(img_tensor.unsqueeze(0)) ## blc + img_emb = model.image_proj_model(cond_images) + + imtext_cond = torch.cat([text_emb, img_emb], dim=1) + + fs = torch.tensor([fs], dtype=torch.long, device=model.device) + cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]} + + ## 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 + + video_path = './output.mp4' + save_videos(batch_samples, './', filenames=['output'], fps=save_fps) + model = model.cpu() + return video_path + + + + + + +i2v_examples = [ + ['prompts/256/art.png', 'man fishing in a boat at sunset', 50, 7.5, 1.0, 3, 234], + ['prompts/256/boy.png', 'boy walking on the street', 50, 7.5, 1.0, 3, 125], + ['prompts/256/dance1.jpeg', 'two people dancing', 50, 7.5, 1.0, 3, 116], + ['prompts/256/fire_and_beach.jpg', 'a campfire on the beach and the ocean waves in the background', 50, 7.5, 1.0, 3, 111], + ['prompts/256/girl3.jpeg', 'girl talking and blinking', 50, 7.5, 1.0, 3, 111], + ['prompts/256/guitar0.jpeg', 'bear playing guitar happily, snowing', 50, 7.5, 1.0, 3, 122], +] +css = """#input_img {max-width: 256px !important} #output_vid {max-width: 256px; max-height: 256px}""" + +with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface: + gr.Markdown("

DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors

\ +

\ + Jinbo Xing, \ + Menghan Xia, Yong Zhang, \ + Haoxin Chen, Wangbo Yu,\ + Hanyuan Liu, Xintao Wang,\ + Tien-Tsin Wong,\ + Ying Shan\ +

\ + [ArXiv] \ + [Project Page] \ + [Github]
") + + with gr.Tab(label='ImageAnimation_256x256'): + with gr.Column(): + with gr.Row(): + with gr.Column(): + with gr.Row(): + i2v_input_image = gr.Image(label="Input Image",elem_id="input_img") + with gr.Row(): + i2v_input_text = gr.Text(label='Prompts') + with gr.Row(): + i2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=10000, step=1, value=123) + i2v_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="i2v_eta") + i2v_cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.5, elem_id="i2v_cfg_scale") + with gr.Row(): + i2v_steps = gr.Slider(minimum=1, maximum=60, step=1, elem_id="i2v_steps", label="Sampling steps", value=50) + i2v_motion = gr.Slider(minimum=1, maximum=4, step=1, elem_id="i2v_motion", label="Motion magnitude", value=3) + i2v_end_btn = gr.Button("Generate") + # with gr.Tab(label='Result'): + with gr.Row(): + i2v_output_video = gr.Video(label="Generated Video",elem_id="output_vid",autoplay=True,show_share_button=True) + + gr.Examples(examples=i2v_examples, + inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed], + outputs=[i2v_output_video], + fn = infer, + ) + i2v_end_btn.click(inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed], + outputs=[i2v_output_video], + fn = infer + ) + +dynamicrafter_iface.queue(max_size=12).launch(show_api=True) \ No newline at end of file diff --git a/app_512.py b/app_512.py new file mode 100644 index 0000000000000000000000000000000000000000..3c5c7b6ca9896efbeb22581d82d641d0ca4433ba --- /dev/null +++ b/app_512.py @@ -0,0 +1,158 @@ +import gradio as gr +import os +import sys +import argparse +import random +import time +from omegaconf import OmegaConf +import torch +import torchvision +from pytorch_lightning import seed_everything +from huggingface_hub import hf_hub_download +from einops import repeat +import torchvision.transforms as transforms +from utils.utils import instantiate_from_config +sys.path.insert(0, "scripts/evaluation") +from scripts.evaluation.funcs import ( + batch_ddim_sampling, + load_model_checkpoint, + get_latent_z, + save_videos +) + +def download_model(): + REPO_ID = 'Doubiiu/DynamiCrafter_512' + filename_list = ['model.ckpt'] + if not os.path.exists('./checkpoints/dynamicrafter_512_v1/'): + os.makedirs('./checkpoints/dynamicrafter_512_v1/') + for filename in filename_list: + local_file = os.path.join('./checkpoints/dynamicrafter_512_v1/', filename) + if not os.path.exists(local_file): + hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_512_v1/', force_download=True) + + +def infer(image, prompt, steps=50, cfg_scale=7.5, eta=1.0, fs=3, seed=123,ddpm_from=1000): + resolution = (320, 512) + download_model() + ckpt_path='checkpoints/dynamicrafter_512_v1/model.ckpt' + config_file='configs/inference_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 = instantiate_from_config(model_config) + assert os.path.exists(ckpt_path), "Error: checkpoint Not Found!" + model = load_model_checkpoint(model, ckpt_path) + model.eval() + model = model.cuda() + save_fps = 8 + + seed_everything(seed) + transform = transforms.Compose([ + transforms.Resize(min(resolution)), + transforms.CenterCrop(resolution), + ]) + torch.cuda.empty_cache() + print('start:', prompt, time.strftime('%Y-%m-%d %H:%M:%S',time.localtime(time.time()))) + start = time.time() + if steps > 60: + steps = 60 + + batch_size=1 + channels = model.model.diffusion_model.out_channels + frames = model.temporal_length + h, w = resolution[0] // 8, resolution[1] // 8 + noise_shape = [batch_size, channels, frames, h, w] + + # text cond + with torch.no_grad(), torch.cuda.amp.autocast(): + text_emb = model.get_learned_conditioning([prompt]) + + # img cond + img_tensor = torch.from_numpy(image).permute(2, 0, 1).float().to(model.device) + img_tensor = (img_tensor / 255. - 0.5) * 2 + + image_tensor_resized = transform(img_tensor) #3,256,256 + videos = image_tensor_resized.unsqueeze(0) # bchw + + z = get_latent_z(model, videos.unsqueeze(2)) #bc,1,hw + + img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames) + + cond_images = model.embedder(img_tensor.unsqueeze(0)) ## blc + img_emb = model.image_proj_model(cond_images) + + imtext_cond = torch.cat([text_emb, img_emb], dim=1) + + fs = torch.tensor([fs], dtype=torch.long, device=model.device) + cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]} + + ## inference + batch_samples = batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=steps, ddim_eta=eta, cfg_scale=cfg_scale, ddpm_from=ddpm_from) + ## b,samples,c,t,h,w + + video_path = './output.mp4' + save_videos(batch_samples, './', filenames=['output'], fps=save_fps) + model = model.cpu() + return video_path + + +i2v_examples = [ + ['prompts/512/7.png', 'Donkeys in traditional attire gallop across a lush green meadow.', 50, 7.5, 1.0, 24, 123,900], + ['prompts/512/41.png', 'Rabbits playing in a river.', 50, 7.5, 1.0, 24, 123,900], + ['prompts/512/32.png', 'Mountains under the starlight.', 50, 7.5, 1.0, 24, 123,900], + ['prompts/512/14.png', 'A duck swimming in the lake.', 50, 7.5, 1.0, 24, 123,900], + ['prompts/512/30.png', 'A soldier riding a horse.', 50, 7.5, 1.0, 24, 123,900], + ['prompts/512/52.png', 'Fireworks exploding in the sky.', 50, 7.5, 1.0, 24, 123,900], +] + + + + +css = """#input_img {max-width: 512px !important} #output_vid {max-width: 512px; max-height: 320px}""" + +with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface: + gr.Markdown("

DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors

\ +

\ + Jinbo Xing, \ + Menghan Xia, Yong Zhang, \ + Haoxin Chen, Wangbo Yu,\ + Hanyuan Liu, Xintao Wang,\ + Tien-Tsin Wong,\ + Ying Shan\ +

\ + [ArXiv] \ + [Project Page] \ + [Github]
") + + with gr.Tab(label='ImageAnimation_320x512'): + with gr.Column(): + with gr.Row(): + with gr.Column(): + with gr.Row(): + i2v_input_image = gr.Image(label="Input Image",elem_id="input_img") + with gr.Row(): + i2v_input_text = gr.Text(label='Prompts') + with gr.Row(): + i2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=10000, step=1, value=123) + i2v_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="i2v_eta") + i2v_cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.5, elem_id="i2v_cfg_scale") + with gr.Row(): + i2v_steps = gr.Slider(minimum=1, maximum=60, step=1, elem_id="i2v_steps", label="Sampling steps", value=50) + i2v_motion = gr.Slider(minimum=15, maximum=30, step=1, elem_id="i2v_motion", label="FPS", value=24) + i2v_ddpm_from = gr.Slider(minimum=840, maximum=1000, step=1, elem_id="i2v_motion", label="FPS", value=900) + i2v_end_btn = gr.Button("Generate") + # with gr.Tab(label='Result'): + with gr.Row(): + i2v_output_video = gr.Video(label="Generated Video",elem_id="output_vid",autoplay=True,show_share_button=True) + + gr.Examples(examples=i2v_examples, + inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed,i2v_ddpm_from], + outputs=[i2v_output_video], + fn = infer, + ) + i2v_end_btn.click(inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed,i2v_ddpm_from], + outputs=[i2v_output_video], + fn = infer + ) + +dynamicrafter_iface.queue(max_size=12).launch(show_api=True) \ No newline at end of file diff --git a/configs/inference_1024_v1.0.yaml b/configs/inference_1024_v1.0.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e830ebbc00a2b8b768aa9de22f8b6d1a01130526 --- /dev/null +++ b/configs/inference_1024_v1.0.yaml @@ -0,0 +1,103 @@ +model: + target: lvdm.models.ddpm3d.LatentVisualDiffusion + params: + rescale_betas_zero_snr: True + parameterization: "v" + 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: hybrid + image_size: [72, 128] + channels: 4 + scale_by_std: False + scale_factor: 0.18215 + use_ema: False + uncond_type: 'empty_seq' + use_dynamic_rescale: true + base_scale: 0.3 + fps_condition_type: 'fps' + perframe_ae: True + unet_config: + target: lvdm.modules.networks.openaimodel3d.UNetModel + params: + in_channels: 8 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + dropout: 0.1 + 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 + image_cross_attention: true + default_fs: 10 + fs_condition: 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: 256 + 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" + + img_cond_stage_config: + target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2 + params: + freeze: true + + image_proj_stage_config: + target: lvdm.modules.encoders.resampler.Resampler + params: + dim: 1024 + depth: 4 + dim_head: 64 + heads: 12 + num_queries: 16 + embedding_dim: 1280 + output_dim: 1024 + ff_mult: 4 + video_length: 16 + diff --git a/configs/inference_256_v1.0.yaml b/configs/inference_256_v1.0.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3f098796cf2e7487669b93ae3fa8e4ac2087e385 --- /dev/null +++ b/configs/inference_256_v1.0.yaml @@ -0,0 +1,98 @@ +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: hybrid + image_size: [32, 32] + 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: 8 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + dropout: 0.1 + 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 + image_cross_attention: true + image_cross_attention_scale_learnable: true + default_fs: 3 + fs_condition: 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: 256 + 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" + + img_cond_stage_config: + target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2 + params: + freeze: true + + image_proj_stage_config: + target: lvdm.modules.encoders.resampler.Resampler + params: + dim: 1024 + depth: 4 + dim_head: 64 + heads: 12 + num_queries: 16 + embedding_dim: 1280 + output_dim: 1024 + ff_mult: 4 + video_length: 16 + diff --git a/configs/inference_512_v1.0.yaml b/configs/inference_512_v1.0.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b3c58f10729bd550f7ccca72773a00d5d9b07e4c --- /dev/null +++ b/configs/inference_512_v1.0.yaml @@ -0,0 +1,102 @@ +model: + target: lvdm.models.ddpm3d.LatentVisualDiffusion + params: + rescale_betas_zero_snr: True + parameterization: "v" + 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: hybrid + image_size: [40, 64] + channels: 4 + scale_by_std: False + scale_factor: 0.18215 + use_ema: False + uncond_type: 'empty_seq' + use_dynamic_rescale: true + base_scale: 0.7 + fps_condition_type: 'fps' + unet_config: + target: lvdm.modules.networks.openaimodel3d.UNetModel + params: + in_channels: 8 + out_channels: 4 + model_channels: 320 + attention_resolutions: + - 4 + - 2 + - 1 + num_res_blocks: 2 + channel_mult: + - 1 + - 2 + - 4 + - 4 + dropout: 0.1 + 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 + image_cross_attention: true + default_fs: 24 + fs_condition: 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: 256 + 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" + + img_cond_stage_config: + target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2 + params: + freeze: true + + image_proj_stage_config: + target: lvdm.modules.encoders.resampler.Resampler + params: + dim: 1024 + depth: 4 + dim_head: 64 + heads: 12 + num_queries: 16 + embedding_dim: 1280 + output_dim: 1024 + ff_mult: 4 + video_length: 16 + diff --git a/lvdm/basics.py b/lvdm/basics.py new file mode 100644 index 0000000000000000000000000000000000000000..65c771d13a7f4a932ac370f08797a8b6ba9e85ff --- /dev/null +++ b/lvdm/basics.py @@ -0,0 +1,100 @@ +# adopted from +# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py +# and +# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +# and +# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py +# +# thanks! + +import torch.nn as nn +from utils.utils import instantiate_from_config + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def linear(*args, **kwargs): + """ + Create a linear module. + """ + return nn.Linear(*args, **kwargs) + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def nonlinearity(type='silu'): + if type == 'silu': + return nn.SiLU() + elif type == 'leaky_relu': + return nn.LeakyReLU() + + +class GroupNormSpecific(nn.GroupNorm): + def forward(self, x): + return super().forward(x.float()).type(x.dtype) + + +def normalization(channels, num_groups=32): + """ + Make a standard normalization layer. + :param channels: number of input channels. + :return: an nn.Module for normalization. + """ + return GroupNormSpecific(num_groups, channels) + + +class HybridConditioner(nn.Module): + + def __init__(self, c_concat_config, c_crossattn_config): + super().__init__() + self.concat_conditioner = instantiate_from_config(c_concat_config) + self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) + + def forward(self, c_concat, c_crossattn): + c_concat = self.concat_conditioner(c_concat) + c_crossattn = self.crossattn_conditioner(c_crossattn) + return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} \ No newline at end of file diff --git a/lvdm/common.py b/lvdm/common.py new file mode 100644 index 0000000000000000000000000000000000000000..55a150b618e275f01d3a59ad9c7579176c4ea1b8 --- /dev/null +++ b/lvdm/common.py @@ -0,0 +1,94 @@ +import math +from inspect import isfunction +import torch +from torch import nn +import torch.distributed as dist + + +def gather_data(data, return_np=True): + ''' gather data from multiple processes to one list ''' + data_list = [torch.zeros_like(data) for _ in range(dist.get_world_size())] + dist.all_gather(data_list, data) # gather not supported with NCCL + if return_np: + data_list = [data.cpu().numpy() for data in data_list] + return data_list + +def autocast(f): + def do_autocast(*args, **kwargs): + with torch.cuda.amp.autocast(enabled=True, + dtype=torch.get_autocast_gpu_dtype(), + cache_enabled=torch.is_autocast_cache_enabled()): + return f(*args, **kwargs) + return do_autocast + + +def extract_into_tensor(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + +def exists(val): + return val is not None + +def identity(*args, **kwargs): + return nn.Identity() + +def uniq(arr): + return{el: True for el in arr}.keys() + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + +def ismap(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] > 3) + +def isimage(x): + if not isinstance(x,torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + +def shape_to_str(x): + shape_str = "x".join([str(x) for x in x.shape]) + return shape_str + +def init_(tensor): + dim = tensor.shape[-1] + std = 1 / math.sqrt(dim) + tensor.uniform_(-std, std) + return tensor + +ckpt = torch.utils.checkpoint.checkpoint +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + return ckpt(func, *inputs, use_reentrant=False) + else: + return func(*inputs) \ No newline at end of file diff --git a/lvdm/data/base.py b/lvdm/data/base.py new file mode 100644 index 0000000000000000000000000000000000000000..01aac7a81cf970c702d452aeaad7b6ff979d97d1 --- /dev/null +++ b/lvdm/data/base.py @@ -0,0 +1,23 @@ +from abc import abstractmethod +from torch.utils.data import IterableDataset + + +class Txt2ImgIterableBaseDataset(IterableDataset): + ''' + Define an interface to make the IterableDatasets for text2img data chainable + ''' + def __init__(self, num_records=0, valid_ids=None, size=256): + super().__init__() + self.num_records = num_records + self.valid_ids = valid_ids + self.sample_ids = valid_ids + self.size = size + + print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.') + + def __len__(self): + return self.num_records + + @abstractmethod + def __iter__(self): + pass \ No newline at end of file diff --git a/lvdm/data/webvid.py b/lvdm/data/webvid.py new file mode 100644 index 0000000000000000000000000000000000000000..0de7d15b179f502fd3349687105e181b85c99556 --- /dev/null +++ b/lvdm/data/webvid.py @@ -0,0 +1,191 @@ +import os +import random +from tqdm import tqdm +import pandas as pd +from decord import VideoReader, cpu +import csv +import torch +from torch.utils.data import Dataset +from torch.utils.data import DataLoader +from torchvision import transforms + + +class WebVid(Dataset): + """ + WebVid Dataset. + Assumes webvid data is structured as follows. + Webvid/ + videos/ + 000001_000050/ ($page_dir) + 1.mp4 (videoid.mp4) + ... + 5000.mp4 + ... + """ + def __init__(self, + file_path, + video_folder, + subsample=None, + video_length=16, + resolution=[256, 512], + frame_stride=1, + frame_stride_min=1, + spatial_transform=None, + crop_resolution=None, + fps_max=None, + load_raw_resolution=False, + fixed_fps=None, + random_fs=False, + ): + + with open(file_path, 'r') as csvfile: + reader = csv.DictReader(csvfile) + self.dataset = [video for video in reader] + csvfile.close() + self.video_folder = video_folder + self.length = len(self.dataset) + print(f"data scale: {self.length}") + self.subsample = subsample + self.video_length = video_length + self.resolution = [resolution, resolution] if isinstance(resolution, int) else resolution + self.fps_max = fps_max + self.frame_stride = frame_stride + self.frame_stride_min = frame_stride_min + self.fixed_fps = fixed_fps + self.load_raw_resolution = load_raw_resolution + self.random_fs = random_fs + if spatial_transform is not None: + if spatial_transform == "random_crop": + self.spatial_transform = transforms.RandomCrop(crop_resolution) + elif spatial_transform == "center_crop": + self.spatial_transform = transforms.Compose([ + transforms.CenterCrop(resolution), + ]) + elif spatial_transform == "resize_center_crop": + # assert(self.resolution[0] == self.resolution[1]) + self.spatial_transform = transforms.Compose([ + transforms.Resize(min(self.resolution)), + transforms.CenterCrop(self.resolution), + ]) + elif spatial_transform == "resize": + self.spatial_transform = transforms.Resize(self.resolution) + else: + raise NotImplementedError + else: + self.spatial_transform = None + + + def __getitem__(self, index): + if self.random_fs: + frame_stride = random.randint(self.frame_stride_min, self.frame_stride) + else: + frame_stride = self.frame_stride + + ## get frames until success + while True: + video_dict = self.dataset[index] + videoid = video_dict["videoid"] + video_path = os.path.join(self.video_folder, f"{videoid}.mp4") + caption = video_dict["name"] + + try: + if self.load_raw_resolution: + video_reader = VideoReader(video_path, ctx=cpu(0)) + else: + video_reader = VideoReader(video_path, ctx=cpu(0), width=530, height=300) + if len(video_reader) < self.video_length: + print(f"video length ({len(video_reader)}) is smaller than target length({self.video_length})") + index = random.randint(0, self.length - 1) + continue + else: + pass + except: + index = random.randint(0, self.length - 1) + print(f"Load video failed! path = {video_path}") + continue + + fps_ori = video_reader.get_avg_fps() + if self.fixed_fps is not None: + frame_stride = int(frame_stride * (1.0 * fps_ori / self.fixed_fps)) + + ## to avoid extreme cases when fixed_fps is used + frame_stride = max(frame_stride, 1) + + ## get valid range (adapting case by case) + required_frame_num = frame_stride * (self.video_length-1) + 1 + frame_num = len(video_reader) + if frame_num < required_frame_num: + ## drop extra samples if fixed fps is required + if self.fixed_fps is not None and frame_num < required_frame_num * 0.5: + index = random.randint(0, self.length - 1) + continue + else: + frame_stride = frame_num // self.video_length + required_frame_num = frame_stride * (self.video_length-1) + 1 + + ## select a random clip + random_range = frame_num - required_frame_num + start_idx = random.randint(0, random_range) if random_range > 0 else 0 + + ## calculate frame indices + frame_indices = [start_idx + frame_stride*i for i in range(self.video_length)] + try: + frames = video_reader.get_batch(frame_indices) + break + except: + print(f"Get frames failed! path = {video_path}; [max_ind vs frame_total:{max(frame_indices)} / {frame_num}]") + index = random.randint(0, self.length - 1) + continue + + ## process data + assert(frames.shape[0] == self.video_length),f'{len(frames)}, self.video_length={self.video_length}' + frames = torch.tensor(frames.asnumpy()).permute(3, 0, 1, 2).float() # [t,h,w,c] -> [c,t,h,w] + + if self.spatial_transform is not None: + frames = self.spatial_transform(frames) + + if self.resolution is not None: + assert (frames.shape[2], frames.shape[3]) == (self.resolution[0], self.resolution[1]), f'frames={frames.shape}, self.resolution={self.resolution}' + + ## turn frames tensors to [-1,1] + frames = (frames / 255 - 0.5) * 2 + fps_clip = fps_ori // frame_stride + if self.fps_max is not None and fps_clip > self.fps_max: + fps_clip = self.fps_max + + data = {'video': frames, 'caption': caption, 'path': video_path, 'fps': fps_clip, 'frame_stride': frame_stride} + return data + + def __len__(self): + return self.length + + +if __name__== "__main__": + meta_path = "" ## path to the meta file + data_dir = "" ## path to the data directory + save_dir = "" ## path to the save directory + dataset = WebVid(meta_path, + data_dir, + subsample=None, + video_length=16, + resolution=[256,448], + frame_stride=4, + spatial_transform="resize_center_crop", + crop_resolution=None, + fps_max=None, + load_raw_resolution=True + ) + dataloader = DataLoader(dataset, + batch_size=1, + num_workers=0, + shuffle=False) + + + import sys + sys.path.insert(1, os.path.join(sys.path[0], '..', '..')) + from utils.save_video import tensor_to_mp4 + for i, batch in tqdm(enumerate(dataloader), desc="Data Batch"): + video = batch['video'] + name = batch['path'][0].split('videos/')[-1].replace('/','_') + tensor_to_mp4(video, save_dir+'/'+name, fps=8) + diff --git a/lvdm/distributions.py b/lvdm/distributions.py new file mode 100644 index 0000000000000000000000000000000000000000..9a2a82ecace3ce27fb7816ddaf088e179c2d5ffd --- /dev/null +++ b/lvdm/distributions.py @@ -0,0 +1,95 @@ +import torch +import numpy as np + + +class AbstractDistribution: + def sample(self): + raise NotImplementedError() + + def mode(self): + raise NotImplementedError() + + +class DiracDistribution(AbstractDistribution): + def __init__(self, value): + self.value = value + + def sample(self): + return self.value + + def mode(self): + return self.value + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) + + def sample(self, noise=None): + if noise is None: + noise = torch.randn(self.mean.shape) + + x = self.mean + self.std * noise.to(device=self.parameters.device) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.]) + else: + if other is None: + return 0.5 * torch.sum(torch.pow(self.mean, 2) + + self.var - 1.0 - self.logvar, + dim=[1, 2, 3]) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var - 1.0 - self.logvar + other.logvar, + dim=[1, 2, 3]) + + def nll(self, sample, dims=[1,2,3]): + if self.deterministic: + return torch.Tensor([0.]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims) + + def mode(self): + return self.mean + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 + Compute the KL divergence between two gaussians. + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, torch.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for torch.exp(). + logvar1, logvar2 = [ + x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + torch.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) + ) \ No newline at end of file diff --git a/lvdm/ema.py b/lvdm/ema.py new file mode 100644 index 0000000000000000000000000000000000000000..cd2f8e3115f816b4cac674397238cd8c22de9bc2 --- /dev/null +++ b/lvdm/ema.py @@ -0,0 +1,76 @@ +import torch +from torch import nn + + +class LitEma(nn.Module): + def __init__(self, model, decay=0.9999, use_num_upates=True): + super().__init__() + if decay < 0.0 or decay > 1.0: + raise ValueError('Decay must be between 0 and 1') + + self.m_name2s_name = {} + self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) + self.register_buffer('num_updates', torch.tensor(0,dtype=torch.int) if use_num_upates + else torch.tensor(-1,dtype=torch.int)) + + for name, p in model.named_parameters(): + if p.requires_grad: + #remove as '.'-character is not allowed in buffers + s_name = name.replace('.','') + self.m_name2s_name.update({name:s_name}) + self.register_buffer(s_name,p.clone().detach().data) + + self.collected_params = [] + + def forward(self,model): + decay = self.decay + + if self.num_updates >= 0: + self.num_updates += 1 + decay = min(self.decay,(1 + self.num_updates) / (10 + self.num_updates)) + + one_minus_decay = 1.0 - decay + + with torch.no_grad(): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + + for key in m_param: + if m_param[key].requires_grad: + sname = self.m_name2s_name[key] + shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) + shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) + else: + assert not key in self.m_name2s_name + + def copy_to(self, model): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + for key in m_param: + if m_param[key].requires_grad: + m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) + else: + assert not key in self.m_name2s_name + + def store(self, parameters): + """ + Save the current parameters for restoring later. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + temporarily stored. + """ + self.collected_params = [param.clone() for param in parameters] + + def restore(self, parameters): + """ + Restore the parameters stored with the `store` method. + Useful to validate the model with EMA parameters without affecting the + original optimization process. Store the parameters before the + `copy_to` method. After validation (or model saving), use this to + restore the former parameters. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + updated with the stored parameters. + """ + for c_param, param in zip(self.collected_params, parameters): + param.data.copy_(c_param.data) \ No newline at end of file diff --git a/lvdm/models/autoencoder.py b/lvdm/models/autoencoder.py new file mode 100644 index 0000000000000000000000000000000000000000..d6880ff3ad6dc44447969586b807b81eccaecd35 --- /dev/null +++ b/lvdm/models/autoencoder.py @@ -0,0 +1,219 @@ +import os +from contextlib import contextmanager +import torch +import numpy as np +from einops import rearrange +import torch.nn.functional as F +import pytorch_lightning as pl +from lvdm.modules.networks.ae_modules import Encoder, Decoder +from lvdm.distributions import DiagonalGaussianDistribution +from utils.utils import instantiate_from_config + + +class AutoencoderKL(pl.LightningModule): + def __init__(self, + ddconfig, + lossconfig, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + test=False, + logdir=None, + input_dim=4, + test_args=None, + ): + super().__init__() + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.loss = instantiate_from_config(lossconfig) + assert ddconfig["double_z"] + self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + self.embed_dim = embed_dim + self.input_dim = input_dim + self.test = test + self.test_args = test_args + self.logdir = logdir + if colorize_nlabels is not None: + assert type(colorize_nlabels)==int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + if self.test: + self.init_test() + + def init_test(self,): + self.test = True + save_dir = os.path.join(self.logdir, "test") + if 'ckpt' in self.test_args: + ckpt_name = os.path.basename(self.test_args.ckpt).split('.ckpt')[0] + f'_epoch{self._cur_epoch}' + self.root = os.path.join(save_dir, ckpt_name) + else: + self.root = save_dir + if 'test_subdir' in self.test_args: + self.root = os.path.join(save_dir, self.test_args.test_subdir) + + self.root_zs = os.path.join(self.root, "zs") + self.root_dec = os.path.join(self.root, "reconstructions") + self.root_inputs = os.path.join(self.root, "inputs") + os.makedirs(self.root, exist_ok=True) + + if self.test_args.save_z: + os.makedirs(self.root_zs, exist_ok=True) + if self.test_args.save_reconstruction: + os.makedirs(self.root_dec, exist_ok=True) + if self.test_args.save_input: + os.makedirs(self.root_inputs, exist_ok=True) + assert(self.test_args is not None) + self.test_maximum = getattr(self.test_args, 'test_maximum', None) + self.count = 0 + self.eval_metrics = {} + self.decodes = [] + self.save_decode_samples = 2048 + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu") + try: + self._cur_epoch = sd['epoch'] + sd = sd["state_dict"] + except: + self._cur_epoch = 'null' + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + self.load_state_dict(sd, strict=False) + # self.load_state_dict(sd, strict=True) + print(f"Restored from {path}") + + def encode(self, x, **kwargs): + + h = self.encoder(x) + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + return posterior + + def decode(self, z, **kwargs): + z = self.post_quant_conv(z) + dec = self.decoder(z) + return dec + + def forward(self, input, sample_posterior=True): + posterior = self.encode(input) + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + dec = self.decode(z) + return dec, posterior + + def get_input(self, batch, k): + x = batch[k] + if x.dim() == 5 and self.input_dim == 4: + b,c,t,h,w = x.shape + self.b = b + self.t = t + x = rearrange(x, 'b c t h w -> (b t) c h w') + + return x + + def training_step(self, batch, batch_idx, optimizer_idx): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + + if optimizer_idx == 0: + # train encoder+decoder+logvar + aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return aeloss + + if optimizer_idx == 1: + # train the discriminator + discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, + last_layer=self.get_last_layer(), split="train") + + self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) + self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) + return discloss + + def validation_step(self, batch, batch_idx): + inputs = self.get_input(batch, self.image_key) + reconstructions, posterior = self(inputs) + aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step, + last_layer=self.get_last_layer(), split="val") + + discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step, + last_layer=self.get_last_layer(), split="val") + + self.log("val/rec_loss", log_dict_ae["val/rec_loss"]) + self.log_dict(log_dict_ae) + self.log_dict(log_dict_disc) + return self.log_dict + + def configure_optimizers(self): + lr = self.learning_rate + opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ + list(self.decoder.parameters())+ + list(self.quant_conv.parameters())+ + list(self.post_quant_conv.parameters()), + lr=lr, betas=(0.5, 0.9)) + opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), + lr=lr, betas=(0.5, 0.9)) + return [opt_ae, opt_disc], [] + + def get_last_layer(self): + return self.decoder.conv_out.weight + + @torch.no_grad() + def log_images(self, batch, only_inputs=False, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if not only_inputs: + xrec, posterior = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["samples"] = self.decode(torch.randn_like(posterior.sample())) + log["reconstructions"] = xrec + log["inputs"] = x + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.*(x-x.min())/(x.max()-x.min()) - 1. + return x + +class IdentityFirstStage(torch.nn.Module): + def __init__(self, *args, vq_interface=False, **kwargs): + self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff + super().__init__() + + def encode(self, x, *args, **kwargs): + return x + + def decode(self, x, *args, **kwargs): + return x + + def quantize(self, x, *args, **kwargs): + if self.vq_interface: + return x, None, [None, None, None] + return x + + def forward(self, x, *args, **kwargs): + return x \ No newline at end of file diff --git a/lvdm/models/ddpm3d.py b/lvdm/models/ddpm3d.py new file mode 100644 index 0000000000000000000000000000000000000000..4612995f6b1df67d9867b815dd29086ec3e27d70 --- /dev/null +++ b/lvdm/models/ddpm3d.py @@ -0,0 +1,762 @@ +""" +wild mixture of +https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py +https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +https://github.com/CompVis/taming-transformers +-- merci +""" + +from functools import partial +from contextlib import contextmanager +import numpy as np +from tqdm import tqdm +from einops import rearrange, repeat +import logging +mainlogger = logging.getLogger('mainlogger') +import torch +import torch.nn as nn +from torchvision.utils import make_grid +import pytorch_lightning as pl +from utils.utils import instantiate_from_config +from lvdm.ema import LitEma +from lvdm.distributions import DiagonalGaussianDistribution +from lvdm.models.utils_diffusion import make_beta_schedule, rescale_zero_terminal_snr +from lvdm.basics import disabled_train +from lvdm.common import ( + extract_into_tensor, + noise_like, + exists, + default +) + +__conditioning_keys__ = {'concat': 'c_concat', + 'crossattn': 'c_crossattn', + 'adm': 'y'} + +class DDPM(pl.LightningModule): + # classic DDPM with Gaussian diffusion, in image space + def __init__(self, + unet_config, + timesteps=1000, + beta_schedule="linear", + loss_type="l2", + ckpt_path=None, + ignore_keys=[], + load_only_unet=False, + monitor=None, + use_ema=True, + first_stage_key="image", + image_size=256, + channels=3, + log_every_t=100, + clip_denoised=True, + linear_start=1e-4, + linear_end=2e-2, + cosine_s=8e-3, + given_betas=None, + original_elbo_weight=0., + v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta + l_simple_weight=1., + conditioning_key=None, + parameterization="eps", # all assuming fixed variance schedules + scheduler_config=None, + use_positional_encodings=False, + learn_logvar=False, + logvar_init=0., + rescale_betas_zero_snr=False, + ): + super().__init__() + assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"' + self.parameterization = parameterization + mainlogger.info(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode") + self.cond_stage_model = None + self.clip_denoised = clip_denoised + self.log_every_t = log_every_t + self.first_stage_key = first_stage_key + self.channels = channels + self.temporal_length = unet_config.params.temporal_length + self.image_size = image_size # try conv? + if isinstance(self.image_size, int): + self.image_size = [self.image_size, self.image_size] + self.use_positional_encodings = use_positional_encodings + self.model = DiffusionWrapper(unet_config, conditioning_key) + #count_params(self.model, verbose=True) + self.use_ema = use_ema + self.rescale_betas_zero_snr = rescale_betas_zero_snr + if self.use_ema: + self.model_ema = LitEma(self.model) + mainlogger.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + self.use_scheduler = scheduler_config is not None + if self.use_scheduler: + self.scheduler_config = scheduler_config + + self.v_posterior = v_posterior + self.original_elbo_weight = original_elbo_weight + self.l_simple_weight = l_simple_weight + + if monitor is not None: + self.monitor = monitor + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet) + + self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps, + linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) + + self.loss_type = loss_type + + self.learn_logvar = learn_logvar + self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,)) + if self.learn_logvar: + self.logvar = nn.Parameter(self.logvar, requires_grad=True) + + def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if exists(given_betas): + betas = given_betas + else: + betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, + cosine_s=cosine_s) + if self.rescale_betas_zero_snr: + betas = rescale_zero_terminal_snr(betas) + + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + + if self.parameterization != 'v': + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + else: + self.register_buffer('sqrt_recip_alphas_cumprod', torch.zeros_like(to_torch(alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', torch.zeros_like(to_torch(alphas_cumprod))) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / ( + 1. - alphas_cumprod) + self.v_posterior * betas + # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) + self.register_buffer('posterior_variance', to_torch(posterior_variance)) + # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain + self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) + self.register_buffer('posterior_mean_coef1', to_torch( + betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) + self.register_buffer('posterior_mean_coef2', to_torch( + (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) + + if self.parameterization == "eps": + lvlb_weights = self.betas ** 2 / ( + 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)) + elif self.parameterization == "x0": + lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod)) + elif self.parameterization == "v": + lvlb_weights = torch.ones_like(self.betas ** 2 / ( + 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))) + else: + raise NotImplementedError("mu not supported") + # TODO how to choose this term + lvlb_weights[0] = lvlb_weights[1] + self.register_buffer('lvlb_weights', lvlb_weights, persistent=False) + assert not torch.isnan(self.lvlb_weights).all() + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.model.parameters()) + self.model_ema.copy_to(self.model) + if context is not None: + mainlogger.info(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.model.parameters()) + if context is not None: + mainlogger.info(f"{context}: Restored training weights") + + def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): + sd = torch.load(path, map_location="cpu") + if "state_dict" in list(sd.keys()): + sd = sd["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + mainlogger.info("Deleting key {} from state_dict.".format(k)) + del sd[k] + missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict( + sd, strict=False) + mainlogger.info(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") + if len(missing) > 0: + mainlogger.info(f"Missing Keys: {missing}") + if len(unexpected) > 0: + mainlogger.info(f"Unexpected Keys: {unexpected}") + + def q_mean_variance(self, x_start, t): + """ + Get the distribution q(x_t | x_0). + :param x_start: the [N x C x ...] tensor of noiseless inputs. + :param t: the number of diffusion steps (minus 1). Here, 0 means one step. + :return: A tuple (mean, variance, log_variance), all of x_start's shape. + """ + mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start) + variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) + log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) + return mean, variance, log_variance + + def predict_start_from_noise(self, x_t, t, noise): + return ( + extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - + extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise + ) + + def predict_start_from_z_and_v(self, x_t, t, v): + # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t - + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v + ) + + def predict_eps_from_z_and_v(self, x_t, t, v): + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t + ) + + def q_posterior(self, x_start, x_t, t): + posterior_mean = ( + extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + + extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def p_mean_variance(self, x, t, clip_denoised: bool): + model_out = self.model(x, t) + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + if clip_denoised: + x_recon.clamp_(-1., 1.) + + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): + b, *_, device = *x.shape, x.device + model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised) + noise = noise_like(x.shape, device, repeat_noise) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def p_sample_loop(self, shape, return_intermediates=False): + device = self.betas.device + b = shape[0] + img = torch.randn(shape, device=device) + intermediates = [img] + for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps): + img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long), + clip_denoised=self.clip_denoised) + if i % self.log_every_t == 0 or i == self.num_timesteps - 1: + intermediates.append(img) + if return_intermediates: + return img, intermediates + return img + + @torch.no_grad() + def sample(self, batch_size=16, return_intermediates=False): + image_size = self.image_size + channels = self.channels + return self.p_sample_loop((batch_size, channels, image_size, image_size), + return_intermediates=return_intermediates) + + def q_sample(self, x_start, t, noise=None): + noise = default(noise, lambda: torch.randn_like(x_start)) + return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise) + + def get_v(self, x, noise, t): + return ( + extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise - + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x + ) + + def get_input(self, batch, k): + x = batch[k] + x = x.to(memory_format=torch.contiguous_format).float() + return x + + def _get_rows_from_list(self, samples): + n_imgs_per_row = len(samples) + denoise_grid = rearrange(samples, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row) + return denoise_grid + + @torch.no_grad() + def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): + log = dict() + x = self.get_input(batch, self.first_stage_key) + N = min(x.shape[0], N) + n_row = min(x.shape[0], n_row) + x = x.to(self.device)[:N] + log["inputs"] = x + + # get diffusion row + diffusion_row = list() + x_start = x[:n_row] + + for t in range(self.num_timesteps): + if t % self.log_every_t == 0 or t == self.num_timesteps - 1: + t = repeat(torch.tensor([t]), '1 -> b', b=n_row) + t = t.to(self.device).long() + noise = torch.randn_like(x_start) + x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) + diffusion_row.append(x_noisy) + + log["diffusion_row"] = self._get_rows_from_list(diffusion_row) + + if sample: + # get denoise row + with self.ema_scope("Plotting"): + samples, denoise_row = self.sample(batch_size=N, return_intermediates=True) + + log["samples"] = samples + log["denoise_row"] = self._get_rows_from_list(denoise_row) + + if return_keys: + if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0: + return log + else: + return {key: log[key] for key in return_keys} + return log + + +class LatentDiffusion(DDPM): + """main class""" + def __init__(self, + first_stage_config, + cond_stage_config, + num_timesteps_cond=None, + cond_stage_key="caption", + cond_stage_trainable=False, + cond_stage_forward=None, + conditioning_key=None, + uncond_prob=0.2, + uncond_type="empty_seq", + scale_factor=1.0, + scale_by_std=False, + encoder_type="2d", + only_model=False, + noise_strength=0, + use_dynamic_rescale=False, + base_scale=0.7, + turning_step=400, + loop_video=False, + fps_condition_type='fs', + perframe_ae=False, + *args, **kwargs): + self.num_timesteps_cond = default(num_timesteps_cond, 1) + self.scale_by_std = scale_by_std + assert self.num_timesteps_cond <= kwargs['timesteps'] + # for backwards compatibility after implementation of DiffusionWrapper + ckpt_path = kwargs.pop("ckpt_path", None) + ignore_keys = kwargs.pop("ignore_keys", []) + conditioning_key = default(conditioning_key, 'crossattn') + super().__init__(conditioning_key=conditioning_key, *args, **kwargs) + + self.cond_stage_trainable = cond_stage_trainable + self.cond_stage_key = cond_stage_key + self.noise_strength = noise_strength + self.use_dynamic_rescale = use_dynamic_rescale + self.loop_video = loop_video + self.fps_condition_type = fps_condition_type + self.perframe_ae = perframe_ae + try: + self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1 + except: + self.num_downs = 0 + if not scale_by_std: + self.scale_factor = scale_factor + else: + self.register_buffer('scale_factor', torch.tensor(scale_factor)) + + if use_dynamic_rescale: + scale_arr1 = np.linspace(1.0, base_scale, turning_step) + scale_arr2 = np.full(self.num_timesteps, base_scale) + scale_arr = np.concatenate((scale_arr1, scale_arr2)) + to_torch = partial(torch.tensor, dtype=torch.float32) + self.register_buffer('scale_arr', to_torch(scale_arr)) + + self.instantiate_first_stage(first_stage_config) + self.instantiate_cond_stage(cond_stage_config) + self.first_stage_config = first_stage_config + self.cond_stage_config = cond_stage_config + self.clip_denoised = False + + self.cond_stage_forward = cond_stage_forward + self.encoder_type = encoder_type + assert(encoder_type in ["2d", "3d"]) + self.uncond_prob = uncond_prob + self.classifier_free_guidance = True if uncond_prob > 0 else False + assert(uncond_type in ["zero_embed", "empty_seq"]) + self.uncond_type = uncond_type + + self.restarted_from_ckpt = False + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys, only_model=only_model) + self.restarted_from_ckpt = True + + + def make_cond_schedule(self, ): + self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long) + ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long() + self.cond_ids[:self.num_timesteps_cond] = ids + + def instantiate_first_stage(self, config): + model = instantiate_from_config(config) + self.first_stage_model = model.eval() + self.first_stage_model.train = disabled_train + for param in self.first_stage_model.parameters(): + param.requires_grad = False + + def instantiate_cond_stage(self, config): + if not self.cond_stage_trainable: + model = instantiate_from_config(config) + self.cond_stage_model = model.eval() + self.cond_stage_model.train = disabled_train + for param in self.cond_stage_model.parameters(): + param.requires_grad = False + else: + model = instantiate_from_config(config) + self.cond_stage_model = model + + def get_learned_conditioning(self, c): + if self.cond_stage_forward is None: + if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode): + c = self.cond_stage_model.encode(c) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + else: + c = self.cond_stage_model(c) + else: + assert hasattr(self.cond_stage_model, self.cond_stage_forward) + c = getattr(self.cond_stage_model, self.cond_stage_forward)(c) + return c + + def get_first_stage_encoding(self, encoder_posterior, noise=None): + if isinstance(encoder_posterior, DiagonalGaussianDistribution): + z = encoder_posterior.sample(noise=noise) + elif isinstance(encoder_posterior, torch.Tensor): + z = encoder_posterior + else: + raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") + return self.scale_factor * z + + @torch.no_grad() + def encode_first_stage(self, x): + if self.encoder_type == "2d" and x.dim() == 5: + b, _, t, _, _ = x.shape + x = rearrange(x, 'b c t h w -> (b t) c h w') + reshape_back = True + else: + reshape_back = False + + ## consume more GPU memory but faster + if not self.perframe_ae: + encoder_posterior = self.first_stage_model.encode(x) + results = self.get_first_stage_encoding(encoder_posterior).detach() + else: ## consume less GPU memory but slower + results = [] + for index in range(x.shape[0]): + frame_batch = self.first_stage_model.encode(x[index:index+1,:,:,:]) + frame_result = self.get_first_stage_encoding(frame_batch).detach() + results.append(frame_result) + results = torch.cat(results, dim=0) + + if reshape_back: + results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t) + + return results + + def decode_core(self, z, **kwargs): + if self.encoder_type == "2d" and z.dim() == 5: + b, _, t, _, _ = z.shape + z = rearrange(z, 'b c t h w -> (b t) c h w') + reshape_back = True + else: + reshape_back = False + + if not self.perframe_ae: + z = 1. / self.scale_factor * z + results = self.first_stage_model.decode(z, **kwargs) + else: + results = [] + for index in range(z.shape[0]): + frame_z = 1. / self.scale_factor * z[index:index+1,:,:,:] + frame_result = self.first_stage_model.decode(frame_z, **kwargs) + results.append(frame_result) + results = torch.cat(results, dim=0) + + if reshape_back: + results = rearrange(results, '(b t) c h w -> b c t h w', b=b,t=t) + return results + + @torch.no_grad() + def decode_first_stage(self, z, **kwargs): + return self.decode_core(z, **kwargs) + + # same as above but without decorator + def differentiable_decode_first_stage(self, z, **kwargs): + return self.decode_core(z, **kwargs) + + def forward(self, x, c, **kwargs): + t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() + if self.use_dynamic_rescale: + x = x * extract_into_tensor(self.scale_arr, t, x.shape) + return self.p_losses(x, c, t, **kwargs) + + def apply_model(self, x_noisy, t, cond, **kwargs): + if isinstance(cond, dict): + # hybrid case, cond is exptected to be a dict + pass + else: + if not isinstance(cond, list): + cond = [cond] + key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn' + cond = {key: cond} + + x_recon = self.model(x_noisy, t, **cond, **kwargs) + + if isinstance(x_recon, tuple): + return x_recon[0] + else: + return x_recon + + def _get_denoise_row_from_list(self, samples, desc=''): + denoise_row = [] + for zd in tqdm(samples, desc=desc): + denoise_row.append(self.decode_first_stage(zd.to(self.device))) + n_log_timesteps = len(denoise_row) + + denoise_row = torch.stack(denoise_row) # n_log_timesteps, b, C, H, W + + if denoise_row.dim() == 5: + denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w') + denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w') + denoise_grid = make_grid(denoise_grid, nrow=n_log_timesteps) + elif denoise_row.dim() == 6: + # video, grid_size=[n_log_timesteps*bs, t] + video_length = denoise_row.shape[3] + denoise_grid = rearrange(denoise_row, 'n b c t h w -> b n c t h w') + denoise_grid = rearrange(denoise_grid, 'b n c t h w -> (b n) c t h w') + denoise_grid = rearrange(denoise_grid, 'n c t h w -> (n t) c h w') + denoise_grid = make_grid(denoise_grid, nrow=video_length) + else: + raise ValueError + + return denoise_grid + + + def p_mean_variance(self, x, c, t, clip_denoised: bool, return_x0=False, score_corrector=None, corrector_kwargs=None, **kwargs): + t_in = t + model_out = self.apply_model(x, t_in, c, **kwargs) + + if score_corrector is not None: + assert self.parameterization == "eps" + model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs) + + if self.parameterization == "eps": + x_recon = self.predict_start_from_noise(x, t=t, noise=model_out) + elif self.parameterization == "x0": + x_recon = model_out + else: + raise NotImplementedError() + + if clip_denoised: + x_recon.clamp_(-1., 1.) + + model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) + + if return_x0: + return model_mean, posterior_variance, posterior_log_variance, x_recon + else: + return model_mean, posterior_variance, posterior_log_variance + + @torch.no_grad() + def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, return_x0=False, \ + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, **kwargs): + b, *_, device = *x.shape, x.device + outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised, return_x0=return_x0, \ + score_corrector=score_corrector, corrector_kwargs=corrector_kwargs, **kwargs) + if return_x0: + model_mean, _, model_log_variance, x0 = outputs + else: + model_mean, _, model_log_variance = outputs + + noise = noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + # no noise when t == 0 + nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) + + if return_x0: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0 + else: + return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise + + @torch.no_grad() + def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, \ + timesteps=None, mask=None, x0=None, img_callback=None, start_T=None, log_every_t=None, **kwargs): + + if not log_every_t: + log_every_t = self.log_every_t + device = self.betas.device + b = shape[0] + # sample an initial noise + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + + intermediates = [img] + if timesteps is None: + timesteps = self.num_timesteps + if start_T is not None: + timesteps = min(timesteps, start_T) + + iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(range(0, timesteps)) + + if mask is not None: + assert x0 is not None + assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match + + for i in iterator: + ts = torch.full((b,), i, device=device, dtype=torch.long) + if self.shorten_cond_schedule: + assert self.model.conditioning_key != 'hybrid' + tc = self.cond_ids[ts].to(cond.device) + cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond)) + + img = self.p_sample(img, cond, ts, clip_denoised=self.clip_denoised, **kwargs) + if mask is not None: + img_orig = self.q_sample(x0, ts) + img = img_orig * mask + (1. - mask) * img + + if i % log_every_t == 0 or i == timesteps - 1: + intermediates.append(img) + if callback: callback(i) + if img_callback: img_callback(img, i) + + if return_intermediates: + return img, intermediates + return img + + +class LatentVisualDiffusion(LatentDiffusion): + def __init__(self, img_cond_stage_config, image_proj_stage_config, freeze_embedder=True, *args, **kwargs): + super().__init__(*args, **kwargs) + self._init_embedder(img_cond_stage_config, freeze_embedder) + self.image_proj_model = instantiate_from_config(image_proj_stage_config) + + def _init_embedder(self, config, freeze=True): + embedder = instantiate_from_config(config) + if freeze: + self.embedder = embedder.eval() + self.embedder.train = disabled_train + for param in self.embedder.parameters(): + param.requires_grad = False + + +class DiffusionWrapper(pl.LightningModule): + def __init__(self, diff_model_config, conditioning_key): + super().__init__() + self.diffusion_model = instantiate_from_config(diff_model_config) + self.conditioning_key = conditioning_key + + def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, + c_adm=None, s=None, mask=None, **kwargs): + # temporal_context = fps is foNone + if self.conditioning_key is None: + out = self.diffusion_model(x, t) + elif self.conditioning_key == 'concat': + xc = torch.cat([x] + c_concat, dim=1) + out = self.diffusion_model(xc, t, **kwargs) + elif self.conditioning_key == 'crossattn': + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(x, t, context=cc, **kwargs) + elif self.conditioning_key == 'hybrid': + ## it is just right [b,c,t,h,w]: concatenate in channel dim + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc, **kwargs) + elif self.conditioning_key == 'resblockcond': + cc = c_crossattn[0] + out = self.diffusion_model(x, t, context=cc) + elif self.conditioning_key == 'adm': + cc = c_crossattn[0] + out = self.diffusion_model(x, t, y=cc) + elif self.conditioning_key == 'hybrid-adm': + assert c_adm is not None + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc, y=c_adm, **kwargs) + elif self.conditioning_key == 'hybrid-time': + assert s is not None + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc, s=s) + elif self.conditioning_key == 'concat-time-mask': + # assert s is not None + xc = torch.cat([x] + c_concat, dim=1) + out = self.diffusion_model(xc, t, context=None, s=s, mask=mask) + elif self.conditioning_key == 'concat-adm-mask': + # assert s is not None + if c_concat is not None: + xc = torch.cat([x] + c_concat, dim=1) + else: + xc = x + out = self.diffusion_model(xc, t, context=None, y=s, mask=mask) + elif self.conditioning_key == 'hybrid-adm-mask': + cc = torch.cat(c_crossattn, 1) + if c_concat is not None: + xc = torch.cat([x] + c_concat, dim=1) + else: + xc = x + out = self.diffusion_model(xc, t, context=cc, y=s, mask=mask) + elif self.conditioning_key == 'hybrid-time-adm': # adm means y, e.g., class index + # assert s is not None + assert c_adm is not None + xc = torch.cat([x] + c_concat, dim=1) + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(xc, t, context=cc, s=s, y=c_adm) + elif self.conditioning_key == 'crossattn-adm': + assert c_adm is not None + cc = torch.cat(c_crossattn, 1) + out = self.diffusion_model(x, t, context=cc, y=c_adm) + else: + raise NotImplementedError() + + return out \ No newline at end of file diff --git a/lvdm/models/samplers/ddim.py b/lvdm/models/samplers/ddim.py new file mode 100644 index 0000000000000000000000000000000000000000..996e693f004061c62a21387a170041346b4f5294 --- /dev/null +++ b/lvdm/models/samplers/ddim.py @@ -0,0 +1,322 @@ +import numpy as np +from tqdm import tqdm +import torch +from lvdm.models.utils_diffusion import make_ddim_sampling_parameters, make_ddim_timesteps, rescale_noise_cfg +from lvdm.common import noise_like +from lvdm.common import extract_into_tensor +import copy + + +class DDIMSampler(object): + def __init__(self, model, schedule="linear", **kwargs): + super().__init__() + self.model = model + self.ddpm_num_timesteps = model.num_timesteps + self.schedule = schedule + self.counter = 0 + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != torch.device("cuda"): + attr = attr.to(torch.device("cuda")) + setattr(self, name, attr) + + def make_schedule(self, ddim_num_steps, ddim_discretize="linear", ddim_eta=0., verbose=True,ddpm_from=1000): + self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=ddpm_from,verbose=verbose) + alphas_cumprod = self.model.alphas_cumprod + assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' + to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) + + if self.model.use_dynamic_rescale: + self.ddim_scale_arr = self.model.scale_arr[self.ddim_timesteps] + self.ddim_scale_arr_prev = torch.cat([self.ddim_scale_arr[0:1], self.ddim_scale_arr[:-1]]) + + self.register_buffer('betas', to_torch(self.model.betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) + + # ddim sampling parameters + ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), + ddim_timesteps=self.ddim_timesteps, + eta=ddim_eta,verbose=verbose) + self.register_buffer('ddim_sigmas', ddim_sigmas) + self.register_buffer('ddim_alphas', ddim_alphas) + self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) + self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) + sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( + (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( + 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) + self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) + + @torch.no_grad() + def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + schedule_verbose=False, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + precision=None, + fs=None, + timestep_spacing='uniform_trailing',# for starting from last timestep + guidance_rescale=0.0, + ddpm_from=1000, + **kwargs + ): + + # check condition bs + if conditioning is not None: + if isinstance(conditioning, dict): + try: + cbs = conditioning[list(conditioning.keys())[0]].shape[0] + except: + cbs = conditioning[list(conditioning.keys())[0]][0].shape[0] + + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + self.make_schedule(ddim_num_steps=S, ddim_discretize=timestep_spacing, ddim_eta=eta, verbose=schedule_verbose,ddpm_from=ddpm_from) + + # make shape + if len(shape) == 3: + C, H, W = shape + size = (batch_size, C, H, W) + elif len(shape) == 4: + C, T, H, W = shape + size = (batch_size, C, T, H, W) + + samples, intermediates = self.ddim_sampling(conditioning, size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + verbose=verbose, + precision=precision, + fs=fs, + guidance_rescale=guidance_rescale, + **kwargs) + return samples, intermediates + + @torch.no_grad() + def ddim_sampling(self, cond, shape, + x_T=None, ddim_use_original_steps=False, + callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, log_every_t=100, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, verbose=True,precision=None,fs=None,guidance_rescale=0.0, + **kwargs): + device = self.model.betas.device + b = shape[0] + + if x_T is None: + img = torch.randn(shape, device=device) + else: + img=x_T + if precision is not None: + if precision == 16: + img = img.to(dtype=torch.float16) + + if timesteps is None: + timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps + elif timesteps is not None and not ddim_use_original_steps: + subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 + timesteps = self.ddim_timesteps[:subset_end] + + intermediates = {'x_inter': [img], 'pred_x0': [img]} + time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps) + total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] + + if verbose: + iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) + else: + iterator = time_range + + clean_cond = kwargs.pop("clean_cond", False) + # cond_copy, unconditional_conditioning_copy = copy.deepcopy(cond), copy.deepcopy(unconditional_conditioning) + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + + ## use mask to blend noised original latent (img_orig) & new sampled latent (img) + if mask is not None: + assert x0 is not None + if clean_cond: + img_orig = x0 + else: + img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? + img = img_orig * mask + (1. - mask) * img # keep original & modify use img + + + + + outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, temperature=temperature, + noise_dropout=noise_dropout, score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + mask=mask,x0=x0,fs=fs,guidance_rescale=guidance_rescale, + **kwargs) + + + img, pred_x0 = outs + if callback: callback(i) + if img_callback: img_callback(pred_x0, i) + + if index % log_every_t == 0 or index == total_steps - 1: + intermediates['x_inter'].append(img) + intermediates['pred_x0'].append(pred_x0) + + return img, intermediates['pred_x0'] + + @torch.no_grad() + def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, + uc_type=None, conditional_guidance_scale_temporal=None,mask=None,x0=None,guidance_rescale=0.0,**kwargs): + + + b, *_, device = *x.shape, x.device + if x.dim() == 5: + is_video = True + else: + is_video = False + + if unconditional_conditioning is None or unconditional_guidance_scale == 1.: + model_output = self.model.apply_model(x, t, c, **kwargs) # unet denoiser + else: + ### do_classifier_free_guidance + if isinstance(c, torch.Tensor) or isinstance(c, dict): + e_t_cond = self.model.apply_model(x, t, c, **kwargs) + e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs) + else: + raise NotImplementedError + + model_output = e_t_uncond + unconditional_guidance_scale * (e_t_cond - e_t_uncond) + + if guidance_rescale > 0.0: + model_output = rescale_noise_cfg(model_output, e_t_cond, guidance_rescale=guidance_rescale) + + + if self.model.parameterization == "v": + e_t = self.model.predict_eps_from_z_and_v(x, t, model_output) + else: + e_t = model_output + + if score_corrector is not None: + assert self.model.parameterization == "eps", 'not implemented' + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + # sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + sigmas = self.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + # select parameters corresponding to the currently considered timestep + + if is_video: + size = (b, 1, 1, 1, 1) + else: + size = (b, 1, 1, 1) + a_t = torch.full(size, alphas[index], device=device) + a_prev = torch.full(size, alphas_prev[index], device=device) + sigma_t = torch.full(size, sigmas[index], device=device) + sqrt_one_minus_at = torch.full(size, sqrt_one_minus_alphas[index],device=device) + + # current prediction for x_0 + if self.model.parameterization != "v": + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + else: + pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output) + + if self.model.use_dynamic_rescale: + scale_t = torch.full(size, self.ddim_scale_arr[index], device=device) + prev_scale_t = torch.full(size, self.ddim_scale_arr_prev[index], device=device) + rescale = (prev_scale_t / scale_t) + pred_x0 *= rescale + + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + # direction pointing to x_t + dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + + return x_prev, pred_x0 + + @torch.no_grad() + def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, + use_original_steps=False, callback=None): + + timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps + timesteps = timesteps[:t_start] + + time_range = np.flip(timesteps) + total_steps = timesteps.shape[0] + print(f"Running DDIM Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc='Decoding image', total=total_steps) + x_dec = x_latent + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) + x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning) + if callback: callback(i) + return x_dec + + @torch.no_grad() + def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): + # fast, but does not allow for exact reconstruction + # t serves as an index to gather the correct alphas + if use_original_steps: + sqrt_alphas_cumprod = self.sqrt_alphas_cumprod + sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod + else: + sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) + sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas + + if noise is None: + noise = torch.randn_like(x0) + return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise) diff --git a/lvdm/models/samplers/ddim_multiplecond.py b/lvdm/models/samplers/ddim_multiplecond.py new file mode 100644 index 0000000000000000000000000000000000000000..31c3d89aa2df1ad72dbc8533622ba75d8b5feb16 --- /dev/null +++ b/lvdm/models/samplers/ddim_multiplecond.py @@ -0,0 +1,323 @@ +import numpy as np +from tqdm import tqdm +import torch +from lvdm.models.utils_diffusion import make_ddim_sampling_parameters, make_ddim_timesteps, rescale_noise_cfg +from lvdm.common import noise_like +from lvdm.common import extract_into_tensor +import copy + + +class DDIMSampler(object): + def __init__(self, model, schedule="linear", **kwargs): + super().__init__() + self.model = model + self.ddpm_num_timesteps = model.num_timesteps + self.schedule = schedule + self.counter = 0 + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != torch.device("cuda"): + attr = attr.to(torch.device("cuda")) + setattr(self, name, attr) + + def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True): + self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps, + num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose) + alphas_cumprod = self.model.alphas_cumprod + assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep' + to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device) + + if self.model.use_dynamic_rescale: + self.ddim_scale_arr = self.model.scale_arr[self.ddim_timesteps] + self.ddim_scale_arr_prev = torch.cat([self.ddim_scale_arr[0:1], self.ddim_scale_arr[:-1]]) + + self.register_buffer('betas', to_torch(self.model.betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu()))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) + + # ddim sampling parameters + ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(), + ddim_timesteps=self.ddim_timesteps, + eta=ddim_eta,verbose=verbose) + self.register_buffer('ddim_sigmas', ddim_sigmas) + self.register_buffer('ddim_alphas', ddim_alphas) + self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) + self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas)) + sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt( + (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * ( + 1 - self.alphas_cumprod / self.alphas_cumprod_prev)) + self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps) + + @torch.no_grad() + def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + schedule_verbose=False, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + precision=None, + fs=None, + timestep_spacing='uniform', #uniform_trailing for starting from last timestep + guidance_rescale=0.0, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs + ): + + # check condition bs + if conditioning is not None: + if isinstance(conditioning, dict): + try: + cbs = conditioning[list(conditioning.keys())[0]].shape[0] + except: + cbs = conditioning[list(conditioning.keys())[0]][0].shape[0] + + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + # print('==> timestep_spacing: ', timestep_spacing, guidance_rescale) + self.make_schedule(ddim_num_steps=S, ddim_discretize=timestep_spacing, ddim_eta=eta, verbose=schedule_verbose) + + # make shape + if len(shape) == 3: + C, H, W = shape + size = (batch_size, C, H, W) + elif len(shape) == 4: + C, T, H, W = shape + size = (batch_size, C, T, H, W) + # print(f'Data shape for DDIM sampling is {size}, eta {eta}') + + samples, intermediates = self.ddim_sampling(conditioning, size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + verbose=verbose, + precision=precision, + fs=fs, + guidance_rescale=guidance_rescale, + **kwargs) + return samples, intermediates + + @torch.no_grad() + def ddim_sampling(self, cond, shape, + x_T=None, ddim_use_original_steps=False, + callback=None, timesteps=None, quantize_denoised=False, + mask=None, x0=None, img_callback=None, log_every_t=100, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, verbose=True,precision=None,fs=None,guidance_rescale=0.0, + **kwargs): + device = self.model.betas.device + b = shape[0] + if x_T is None: + img = torch.randn(shape, device=device) + else: + img = x_T + if precision is not None: + if precision == 16: + img = img.to(dtype=torch.float16) + + + if timesteps is None: + timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps + elif timesteps is not None and not ddim_use_original_steps: + subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1 + timesteps = self.ddim_timesteps[:subset_end] + + intermediates = {'x_inter': [img], 'pred_x0': [img]} + time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps) + total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0] + if verbose: + iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) + else: + iterator = time_range + + clean_cond = kwargs.pop("clean_cond", False) + + # cond_copy, unconditional_conditioning_copy = copy.deepcopy(cond), copy.deepcopy(unconditional_conditioning) + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((b,), step, device=device, dtype=torch.long) + + ## use mask to blend noised original latent (img_orig) & new sampled latent (img) + if mask is not None: + assert x0 is not None + if clean_cond: + img_orig = x0 + else: + img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass? + img = img_orig * mask + (1. - mask) * img # keep original & modify use img + + + + + outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, + quantize_denoised=quantize_denoised, temperature=temperature, + noise_dropout=noise_dropout, score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + mask=mask,x0=x0,fs=fs,guidance_rescale=guidance_rescale, + **kwargs) + + + + img, pred_x0 = outs + if callback: callback(i) + if img_callback: img_callback(pred_x0, i) + + if index % log_every_t == 0 or index == total_steps - 1: + intermediates['x_inter'].append(img) + intermediates['pred_x0'].append(pred_x0) + + return img, intermediates + + @torch.no_grad() + def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, + uc_type=None, cfg_img=None,mask=None,x0=None,guidance_rescale=0.0, **kwargs): + b, *_, device = *x.shape, x.device + if x.dim() == 5: + is_video = True + else: + is_video = False + if cfg_img is None: + cfg_img = unconditional_guidance_scale + + unconditional_conditioning_img_nonetext = kwargs['unconditional_conditioning_img_nonetext'] + + + if unconditional_conditioning is None or unconditional_guidance_scale == 1.: + model_output = self.model.apply_model(x, t, c, **kwargs) # unet denoiser + else: + ### with unconditional condition + e_t_cond = self.model.apply_model(x, t, c, **kwargs) + e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs) + e_t_uncond_img = self.model.apply_model(x, t, unconditional_conditioning_img_nonetext, **kwargs) + # text cfg + model_output = e_t_uncond + cfg_img * (e_t_uncond_img - e_t_uncond) + unconditional_guidance_scale * (e_t_cond - e_t_uncond_img) + if guidance_rescale > 0.0: + model_output = rescale_noise_cfg(model_output, e_t_cond, guidance_rescale=guidance_rescale) + + if self.model.parameterization == "v": + e_t = self.model.predict_eps_from_z_and_v(x, t, model_output) + else: + e_t = model_output + + if score_corrector is not None: + assert self.model.parameterization == "eps", 'not implemented' + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + sigmas = self.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + # select parameters corresponding to the currently considered timestep + + if is_video: + size = (b, 1, 1, 1, 1) + else: + size = (b, 1, 1, 1) + a_t = torch.full(size, alphas[index], device=device) + a_prev = torch.full(size, alphas_prev[index], device=device) + sigma_t = torch.full(size, sigmas[index], device=device) + sqrt_one_minus_at = torch.full(size, sqrt_one_minus_alphas[index],device=device) + + # current prediction for x_0 + if self.model.parameterization != "v": + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + else: + pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output) + + if self.model.use_dynamic_rescale: + scale_t = torch.full(size, self.ddim_scale_arr[index], device=device) + prev_scale_t = torch.full(size, self.ddim_scale_arr_prev[index], device=device) + rescale = (prev_scale_t / scale_t) + pred_x0 *= rescale + + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + # direction pointing to x_t + dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + + return x_prev, pred_x0 + + @torch.no_grad() + def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None, + use_original_steps=False, callback=None): + + timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps + timesteps = timesteps[:t_start] + + time_range = np.flip(timesteps) + total_steps = timesteps.shape[0] + print(f"Running DDIM Sampling with {total_steps} timesteps") + + iterator = tqdm(time_range, desc='Decoding image', total=total_steps) + x_dec = x_latent + for i, step in enumerate(iterator): + index = total_steps - i - 1 + ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long) + x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning) + if callback: callback(i) + return x_dec + + @torch.no_grad() + def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): + # fast, but does not allow for exact reconstruction + # t serves as an index to gather the correct alphas + if use_original_steps: + sqrt_alphas_cumprod = self.sqrt_alphas_cumprod + sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod + else: + sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas) + sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas + + if noise is None: + noise = torch.randn_like(x0) + return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + + extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise) \ No newline at end of file diff --git a/lvdm/models/utils_diffusion.py b/lvdm/models/utils_diffusion.py new file mode 100644 index 0000000000000000000000000000000000000000..30043d3cbf91ce08f9206c00e2df6955221e8cb5 --- /dev/null +++ b/lvdm/models/utils_diffusion.py @@ -0,0 +1,158 @@ +import math +import numpy as np +import torch +import torch.nn.functional as F +from einops import repeat + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + else: + embedding = repeat(timesteps, 'b -> b d', d=dim) + return embedding + + +def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if schedule == "linear": + betas = ( + torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 + ) + + elif schedule == "cosine": + timesteps = ( + torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s + ) + alphas = timesteps / (1 + cosine_s) * np.pi / 2 + alphas = torch.cos(alphas).pow(2) + alphas = alphas / alphas[0] + betas = 1 - alphas[1:] / alphas[:-1] + betas = np.clip(betas, a_min=0, a_max=0.999) + + elif schedule == "sqrt_linear": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) + elif schedule == "sqrt": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 + else: + raise ValueError(f"schedule '{schedule}' unknown.") + return betas.numpy() + + +def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): + if ddim_discr_method == 'uniform': + c = num_ddpm_timesteps // num_ddim_timesteps + ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) + steps_out = ddim_timesteps + 1 + elif ddim_discr_method == 'uniform_trailing': + c = num_ddpm_timesteps / num_ddim_timesteps + ddim_timesteps = np.flip(np.round(np.arange(num_ddpm_timesteps, 0, -c))).astype(np.int64) + steps_out = ddim_timesteps - 1 + elif ddim_discr_method == 'quad': + ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) + steps_out = ddim_timesteps + 1 + else: + raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') + + # assert ddim_timesteps.shape[0] == num_ddim_timesteps + # add one to get the final alpha values right (the ones from first scale to data during sampling) + # steps_out = ddim_timesteps + 1 + if verbose: + print(f'Selected timesteps for ddim sampler: {steps_out}') + return steps_out + + +def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): + # select alphas for computing the variance schedule + # print(f'ddim_timesteps={ddim_timesteps}, len_alphacums={len(alphacums)}') + alphas = alphacums[ddim_timesteps] + alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) + + # according the the formula provided in https://arxiv.org/abs/2010.02502 + sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) + if verbose: + print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') + print(f'For the chosen value of eta, which is {eta}, ' + f'this results in the following sigma_t schedule for ddim sampler {sigmas}') + return sigmas, alphas, alphas_prev + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, + which defines the cumulative product of (1-beta) over time from t = [0,1]. + :param num_diffusion_timesteps: the number of betas to produce. + :param alpha_bar: a lambda that takes an argument t from 0 to 1 and + produces the cumulative product of (1-beta) up to that + part of the diffusion process. + :param max_beta: the maximum beta to use; use values lower than 1 to + prevent singularities. + """ + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) + +def rescale_zero_terminal_snr(betas): + """ + Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1) + + Args: + betas (`numpy.ndarray`): + the betas that the scheduler is being initialized with. + + Returns: + `numpy.ndarray`: rescaled betas with zero terminal SNR + """ + # Convert betas to alphas_bar_sqrt + alphas = 1.0 - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_bar_sqrt = np.sqrt(alphas_cumprod) + + # Store old values. + alphas_bar_sqrt_0 = alphas_bar_sqrt[0].copy() + alphas_bar_sqrt_T = alphas_bar_sqrt[-1].copy() + + # Shift so the last timestep is zero. + alphas_bar_sqrt -= alphas_bar_sqrt_T + + # Scale so the first timestep is back to the old value. + alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) + + # Convert alphas_bar_sqrt to betas + alphas_bar = alphas_bar_sqrt**2 # Revert sqrt + alphas = alphas_bar[1:] / alphas_bar[:-1] # Revert cumprod + alphas = np.concatenate([alphas_bar[0:1], alphas]) + betas = 1 - alphas + + return betas + + +def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): + """ + Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 + """ + std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) + std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) + # rescale the results from guidance (fixes overexposure) + noise_pred_rescaled = noise_cfg * (std_text / std_cfg) + # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images + noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg + return noise_cfg \ No newline at end of file diff --git a/lvdm/modules/attention.py b/lvdm/modules/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..176885d9ff6f5675c413523a38b78845ce04bd97 --- /dev/null +++ b/lvdm/modules/attention.py @@ -0,0 +1,514 @@ +import torch +from torch import nn, einsum +import torch.nn.functional as F +from einops import rearrange, repeat +from functools import partial +try: + import xformers + import xformers.ops + XFORMERS_IS_AVAILBLE = True +except: + XFORMERS_IS_AVAILBLE = False +from lvdm.common import ( + checkpoint, + exists, + default, +) +from lvdm.basics import zero_module + + +class RelativePosition(nn.Module): + """ https://github.com/evelinehong/Transformer_Relative_Position_PyTorch/blob/master/relative_position.py """ + + def __init__(self, num_units, max_relative_position): + super().__init__() + self.num_units = num_units + self.max_relative_position = max_relative_position + self.embeddings_table = nn.Parameter(torch.Tensor(max_relative_position * 2 + 1, num_units)) + nn.init.xavier_uniform_(self.embeddings_table) + + def forward(self, length_q, length_k): + device = self.embeddings_table.device + range_vec_q = torch.arange(length_q, device=device) + range_vec_k = torch.arange(length_k, device=device) + distance_mat = range_vec_k[None, :] - range_vec_q[:, None] + distance_mat_clipped = torch.clamp(distance_mat, -self.max_relative_position, self.max_relative_position) + final_mat = distance_mat_clipped + self.max_relative_position + final_mat = final_mat.long() + embeddings = self.embeddings_table[final_mat] + return embeddings + + +class CrossAttention(nn.Module): + + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., + relative_position=False, temporal_length=None, video_length=None, image_cross_attention=False, image_cross_attention_scale=1.0, image_cross_attention_scale_learnable=False, text_context_len=77): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.scale = dim_head**-0.5 + self.heads = heads + self.dim_head = dim_head + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)) + + self.relative_position = relative_position + if self.relative_position: + assert(temporal_length is not None) + self.relative_position_k = RelativePosition(num_units=dim_head, max_relative_position=temporal_length) + self.relative_position_v = RelativePosition(num_units=dim_head, max_relative_position=temporal_length) + else: + ## only used for spatial attention, while NOT for temporal attention + if XFORMERS_IS_AVAILBLE and temporal_length is None: + self.forward = self.efficient_forward + + self.video_length = video_length + self.image_cross_attention = image_cross_attention + self.image_cross_attention_scale = image_cross_attention_scale + self.text_context_len = text_context_len + self.image_cross_attention_scale_learnable = image_cross_attention_scale_learnable + if self.image_cross_attention: + self.to_k_ip = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v_ip = nn.Linear(context_dim, inner_dim, bias=False) + if image_cross_attention_scale_learnable: + self.register_parameter('alpha', nn.Parameter(torch.tensor(0.)) ) + + + def forward(self, x, context=None, mask=None): + spatial_self_attn = (context is None) + k_ip, v_ip, out_ip = None, None, None + + h = self.heads + q = self.to_q(x) + context = default(context, x) + + if self.image_cross_attention and not spatial_self_attn: + context, context_image = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:] + k = self.to_k(context) + v = self.to_v(context) + k_ip = self.to_k_ip(context_image) + v_ip = self.to_v_ip(context_image) + else: + if not spatial_self_attn: + context = context[:,:self.text_context_len,:] + k = self.to_k(context) + v = self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + + sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale + if self.relative_position: + len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1] + k2 = self.relative_position_k(len_q, len_k) + sim2 = einsum('b t d, t s d -> b t s', q, k2) * self.scale # TODO check + sim += sim2 + del k + + if exists(mask): + ## feasible for causal attention mask only + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b i j -> (b h) i j', h=h) + sim.masked_fill_(~(mask>0.5), max_neg_value) + + # attention, what we cannot get enough of + sim = sim.softmax(dim=-1) + + out = torch.einsum('b i j, b j d -> b i d', sim, v) + if self.relative_position: + v2 = self.relative_position_v(len_q, len_v) + out2 = einsum('b t s, t s d -> b t d', sim, v2) # TODO check + out += out2 + out = rearrange(out, '(b h) n d -> b n (h d)', h=h) + + + ## for image cross-attention + if k_ip is not None: + k_ip, v_ip = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (k_ip, v_ip)) + sim_ip = torch.einsum('b i d, b j d -> b i j', q, k_ip) * self.scale + del k_ip + sim_ip = sim_ip.softmax(dim=-1) + out_ip = torch.einsum('b i j, b j d -> b i d', sim_ip, v_ip) + out_ip = rearrange(out_ip, '(b h) n d -> b n (h d)', h=h) + + + if out_ip is not None: + if self.image_cross_attention_scale_learnable: + out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha)+1) + else: + out = out + self.image_cross_attention_scale * out_ip + + return self.to_out(out) + + def efficient_forward(self, x, context=None, mask=None): + spatial_self_attn = (context is None) + k_ip, v_ip, out_ip = None, None, None + + q = self.to_q(x) + context = default(context, x) + + if self.image_cross_attention and not spatial_self_attn: + context, context_image = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:] + k = self.to_k(context) + v = self.to_v(context) + k_ip = self.to_k_ip(context_image) + v_ip = self.to_v_ip(context_image) + else: + if not spatial_self_attn: + context = context[:,:self.text_context_len,:] + k = self.to_k(context) + v = self.to_v(context) + + b, _, _ = q.shape + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (q, k, v), + ) + # actually compute the attention, what we cannot get enough of + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=None) + + ## for image cross-attention + if k_ip is not None: + k_ip, v_ip = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (k_ip, v_ip), + ) + out_ip = xformers.ops.memory_efficient_attention(q, k_ip, v_ip, attn_bias=None, op=None) + out_ip = ( + out_ip.unsqueeze(0) + .reshape(b, self.heads, out.shape[1], self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b, out.shape[1], self.heads * self.dim_head) + ) + + if exists(mask): + raise NotImplementedError + out = ( + out.unsqueeze(0) + .reshape(b, self.heads, out.shape[1], self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b, out.shape[1], self.heads * self.dim_head) + ) + if out_ip is not None: + if self.image_cross_attention_scale_learnable: + out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha)+1) + else: + out = out + self.image_cross_attention_scale * out_ip + + return self.to_out(out) + + +class BasicTransformerBlock(nn.Module): + + def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, + disable_self_attn=False, attention_cls=None, video_length=None, image_cross_attention=False, image_cross_attention_scale=1.0, image_cross_attention_scale_learnable=False, text_context_len=77): + super().__init__() + attn_cls = CrossAttention if attention_cls is None else attention_cls + self.disable_self_attn = disable_self_attn + self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, + context_dim=context_dim if self.disable_self_attn else None) + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim, heads=n_heads, dim_head=d_head, dropout=dropout, video_length=video_length, image_cross_attention=image_cross_attention, image_cross_attention_scale=image_cross_attention_scale, image_cross_attention_scale_learnable=image_cross_attention_scale_learnable,text_context_len=text_context_len) + self.image_cross_attention = image_cross_attention + + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + self.checkpoint = checkpoint + + + def forward(self, x, context=None, mask=None, **kwargs): + ## implementation tricks: because checkpointing doesn't support non-tensor (e.g. None or scalar) arguments + input_tuple = (x,) ## should not be (x), otherwise *input_tuple will decouple x into multiple arguments + if context is not None: + input_tuple = (x, context) + if mask is not None: + forward_mask = partial(self._forward, mask=mask) + return checkpoint(forward_mask, (x,), self.parameters(), self.checkpoint) + return checkpoint(self._forward, input_tuple, self.parameters(), self.checkpoint) + + + def _forward(self, x, context=None, mask=None): + x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None, mask=mask) + x + x = self.attn2(self.norm2(x), context=context, mask=mask) + x + x = self.ff(self.norm3(x)) + x + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data in spatial axis. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + NEW: use_linear for more efficiency instead of the 1x1 convs + """ + + def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, + use_checkpoint=True, disable_self_attn=False, use_linear=False, video_length=None, + image_cross_attention=False, image_cross_attention_scale_learnable=False): + super().__init__() + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + if not use_linear: + self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + else: + self.proj_in = nn.Linear(in_channels, inner_dim) + + attention_cls = None + self.transformer_blocks = nn.ModuleList([ + BasicTransformerBlock( + inner_dim, + n_heads, + d_head, + dropout=dropout, + context_dim=context_dim, + disable_self_attn=disable_self_attn, + checkpoint=use_checkpoint, + attention_cls=attention_cls, + video_length=video_length, + image_cross_attention=image_cross_attention, + image_cross_attention_scale_learnable=image_cross_attention_scale_learnable, + ) for d in range(depth) + ]) + if not use_linear: + self.proj_out = zero_module(nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)) + else: + self.proj_out = zero_module(nn.Linear(inner_dim, in_channels)) + self.use_linear = use_linear + + + def forward(self, x, context=None, **kwargs): + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, 'b c h w -> b (h w) c').contiguous() + if self.use_linear: + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + x = block(x, context=context, **kwargs) + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous() + if not self.use_linear: + x = self.proj_out(x) + return x + x_in + + +class TemporalTransformer(nn.Module): + """ + Transformer block for image-like data in temporal axis. + First, reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + """ + def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, + use_checkpoint=True, use_linear=False, only_self_att=True, causal_attention=False, causal_block_size=1, + relative_position=False, temporal_length=None): + super().__init__() + self.only_self_att = only_self_att + self.relative_position = relative_position + self.causal_attention = causal_attention + self.causal_block_size = causal_block_size + + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + if not use_linear: + self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + else: + self.proj_in = nn.Linear(in_channels, inner_dim) + + if relative_position: + assert(temporal_length is not None) + attention_cls = partial(CrossAttention, relative_position=True, temporal_length=temporal_length) + else: + attention_cls = partial(CrossAttention, temporal_length=temporal_length) + if self.causal_attention: + assert(temporal_length is not None) + self.mask = torch.tril(torch.ones([1, temporal_length, temporal_length])) + + if self.only_self_att: + context_dim = None + self.transformer_blocks = nn.ModuleList([ + BasicTransformerBlock( + inner_dim, + n_heads, + d_head, + dropout=dropout, + context_dim=context_dim, + attention_cls=attention_cls, + checkpoint=use_checkpoint) for d in range(depth) + ]) + if not use_linear: + self.proj_out = zero_module(nn.Conv1d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)) + else: + self.proj_out = zero_module(nn.Linear(inner_dim, in_channels)) + self.use_linear = use_linear + + def forward(self, x, context=None): + b, c, t, h, w = x.shape + x_in = x + x = self.norm(x) + x = rearrange(x, 'b c t h w -> (b h w) c t').contiguous() + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, 'bhw c t -> bhw t c').contiguous() + if self.use_linear: + x = self.proj_in(x) + + temp_mask = None + if self.causal_attention: + # slice the from mask map + temp_mask = self.mask[:,:t,:t].to(x.device) + + if temp_mask is not None: + mask = temp_mask.to(x.device) + mask = repeat(mask, 'l i j -> (l bhw) i j', bhw=b*h*w) + else: + mask = None + + if self.only_self_att: + ## note: if no context is given, cross-attention defaults to self-attention + for i, block in enumerate(self.transformer_blocks): + x = block(x, mask=mask) + x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous() + else: + x = rearrange(x, '(b hw) t c -> b hw t c', b=b).contiguous() + context = rearrange(context, '(b t) l con -> b t l con', t=t).contiguous() + for i, block in enumerate(self.transformer_blocks): + # calculate each batch one by one (since number in shape could not greater then 65,535 for some package) + for j in range(b): + context_j = repeat( + context[j], + 't l con -> (t r) l con', r=(h * w) // t, t=t).contiguous() + ## note: causal mask will not applied in cross-attention case + x[j] = block(x[j], context=context_j) + + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, 'b (h w) t c -> b c t h w', h=h, w=w).contiguous() + if not self.use_linear: + x = rearrange(x, 'b hw t c -> (b hw) c t').contiguous() + x = self.proj_out(x) + x = rearrange(x, '(b h w) c t -> b c t h w', b=b, h=h, w=w).contiguous() + + return x + x_in + + +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +class LinearAttention(nn.Module): + def __init__(self, dim, heads=4, dim_head=32): + super().__init__() + self.heads = heads + hidden_dim = dim_head * heads + self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False) + self.to_out = nn.Conv2d(hidden_dim, dim, 1) + + def forward(self, x): + b, c, h, w = x.shape + qkv = self.to_qkv(x) + q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3) + k = k.softmax(dim=-1) + context = torch.einsum('bhdn,bhen->bhde', k, v) + out = torch.einsum('bhde,bhdn->bhen', context, q) + out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w) + return self.to_out(out) + + +class SpatialSelfAttention(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = rearrange(q, 'b c h w -> b (h w) c') + k = rearrange(k, 'b c h w -> b c (h w)') + w_ = torch.einsum('bij,bjk->bik', q, k) + + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = rearrange(v, 'b c h w -> b c (h w)') + w_ = rearrange(w_, 'b i j -> b j i') + h_ = torch.einsum('bij,bjk->bik', v, w_) + h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h) + h_ = self.proj_out(h_) + + return x+h_ diff --git a/lvdm/modules/encoders/condition.py b/lvdm/modules/encoders/condition.py new file mode 100644 index 0000000000000000000000000000000000000000..443ce9a9e7e015dad5c05db737a189da83dbbb4f --- /dev/null +++ b/lvdm/modules/encoders/condition.py @@ -0,0 +1,389 @@ +import torch +import torch.nn as nn +import kornia +import open_clip +from torch.utils.checkpoint import checkpoint +from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel +from lvdm.common import autocast +from utils.utils import count_params + + +class AbstractEncoder(nn.Module): + def __init__(self): + super().__init__() + + def encode(self, *args, **kwargs): + raise NotImplementedError + + +class IdentityEncoder(AbstractEncoder): + def encode(self, x): + return x + + +class ClassEmbedder(nn.Module): + def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1): + super().__init__() + self.key = key + self.embedding = nn.Embedding(n_classes, embed_dim) + self.n_classes = n_classes + self.ucg_rate = ucg_rate + + def forward(self, batch, key=None, disable_dropout=False): + if key is None: + key = self.key + # this is for use in crossattn + c = batch[key][:, None] + if self.ucg_rate > 0. and not disable_dropout: + mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate) + c = mask * c + (1 - mask) * torch.ones_like(c) * (self.n_classes - 1) + c = c.long() + c = self.embedding(c) + return c + + def get_unconditional_conditioning(self, bs, device="cuda"): + uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000) + uc = torch.ones((bs,), device=device) * uc_class + uc = {self.key: uc} + return uc + + +def disabled_train(self, mode=True): + """Overwrite model.train with this function to make sure train/eval mode + does not change anymore.""" + return self + + +class FrozenT5Embedder(AbstractEncoder): + """Uses the T5 transformer encoder for text""" + + def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, + freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl + super().__init__() + self.tokenizer = T5Tokenizer.from_pretrained(version) + self.transformer = T5EncoderModel.from_pretrained(version) + self.device = device + self.max_length = max_length # TODO: typical value? + if freeze: + self.freeze() + + def freeze(self): + self.transformer = self.transformer.eval() + # self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, + return_overflowing_tokens=False, padding="max_length", return_tensors="pt") + tokens = batch_encoding["input_ids"].to(self.device) + outputs = self.transformer(input_ids=tokens) + + z = outputs.last_hidden_state + return z + + def encode(self, text): + return self(text) + + +class FrozenCLIPEmbedder(AbstractEncoder): + """Uses the CLIP transformer encoder for text (from huggingface)""" + LAYERS = [ + "last", + "pooled", + "hidden" + ] + + def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77, + freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32 + super().__init__() + assert layer in self.LAYERS + self.tokenizer = CLIPTokenizer.from_pretrained(version) + self.transformer = CLIPTextModel.from_pretrained(version) + self.device = device + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + self.layer_idx = layer_idx + if layer == "hidden": + assert layer_idx is not None + assert 0 <= abs(layer_idx) <= 12 + + def freeze(self): + self.transformer = self.transformer.eval() + # self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, + return_overflowing_tokens=False, padding="max_length", return_tensors="pt") + tokens = batch_encoding["input_ids"].to(self.device) + outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer == "hidden") + if self.layer == "last": + z = outputs.last_hidden_state + elif self.layer == "pooled": + z = outputs.pooler_output[:, None, :] + else: + z = outputs.hidden_states[self.layer_idx] + return z + + def encode(self, text): + return self(text) + + +class ClipImageEmbedder(nn.Module): + def __init__( + self, + model, + jit=False, + device='cuda' if torch.cuda.is_available() else 'cpu', + antialias=True, + ucg_rate=0. + ): + super().__init__() + from clip import load as load_clip + self.model, _ = load_clip(name=model, device=device, jit=jit) + + self.antialias = antialias + + self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) + self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) + self.ucg_rate = ucg_rate + + def preprocess(self, x): + # normalize to [0,1] + x = kornia.geometry.resize(x, (224, 224), + interpolation='bicubic', align_corners=True, + antialias=self.antialias) + x = (x + 1.) / 2. + # re-normalize according to clip + x = kornia.enhance.normalize(x, self.mean, self.std) + return x + + def forward(self, x, no_dropout=False): + # x is assumed to be in range [-1,1] + out = self.model.encode_image(self.preprocess(x)) + out = out.to(x.dtype) + if self.ucg_rate > 0. and not no_dropout: + out = torch.bernoulli((1. - self.ucg_rate) * torch.ones(out.shape[0], device=out.device))[:, None] * out + return out + + +class FrozenOpenCLIPEmbedder(AbstractEncoder): + """ + Uses the OpenCLIP transformer encoder for text + """ + LAYERS = [ + # "pooled", + "last", + "penultimate" + ] + + def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77, + freeze=True, layer="last"): + super().__init__() + assert layer in self.LAYERS + model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version) + del model.visual + self.model = model + + self.device = device + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + if self.layer == "last": + self.layer_idx = 0 + elif self.layer == "penultimate": + self.layer_idx = 1 + else: + raise NotImplementedError() + + def freeze(self): + self.model = self.model.eval() + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + tokens = open_clip.tokenize(text) ## all clip models use 77 as context length + z = self.encode_with_transformer(tokens.to(self.device)) + return z + + def encode_with_transformer(self, text): + x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model] + x = x + self.model.positional_embedding + x = x.permute(1, 0, 2) # NLD -> LND + x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.model.ln_final(x) + return x + + def text_transformer_forward(self, x: torch.Tensor, attn_mask=None): + for i, r in enumerate(self.model.transformer.resblocks): + if i == len(self.model.transformer.resblocks) - self.layer_idx: + break + if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint(r, x, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + def encode(self, text): + return self(text) + + +class FrozenOpenCLIPImageEmbedder(AbstractEncoder): + """ + Uses the OpenCLIP vision transformer encoder for images + """ + + def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77, + freeze=True, layer="pooled", antialias=True, ucg_rate=0.): + super().__init__() + model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), + pretrained=version, ) + del model.transformer + self.model = model + # self.mapper = torch.nn.Linear(1280, 1024) + self.device = device + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + if self.layer == "penultimate": + raise NotImplementedError() + self.layer_idx = 1 + + self.antialias = antialias + + self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) + self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) + self.ucg_rate = ucg_rate + + def preprocess(self, x): + # normalize to [0,1] + x = kornia.geometry.resize(x, (224, 224), + interpolation='bicubic', align_corners=True, + antialias=self.antialias) + x = (x + 1.) / 2. + # renormalize according to clip + x = kornia.enhance.normalize(x, self.mean, self.std) + return x + + def freeze(self): + self.model = self.model.eval() + for param in self.model.parameters(): + param.requires_grad = False + + @autocast + def forward(self, image, no_dropout=False): + z = self.encode_with_vision_transformer(image) + if self.ucg_rate > 0. and not no_dropout: + z = torch.bernoulli((1. - self.ucg_rate) * torch.ones(z.shape[0], device=z.device))[:, None] * z + return z + + def encode_with_vision_transformer(self, img): + img = self.preprocess(img) + x = self.model.visual(img) + return x + + def encode(self, text): + return self(text) + +class FrozenOpenCLIPImageEmbedderV2(AbstractEncoder): + """ + Uses the OpenCLIP vision transformer encoder for images + """ + + def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", + freeze=True, layer="pooled", antialias=True): + super().__init__() + model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), + pretrained=version, ) + del model.transformer + self.model = model + self.device = device + + if freeze: + self.freeze() + self.layer = layer + if self.layer == "penultimate": + raise NotImplementedError() + self.layer_idx = 1 + + self.antialias = antialias + + self.register_buffer('mean', torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False) + self.register_buffer('std', torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False) + + + def preprocess(self, x): + # normalize to [0,1] + x = kornia.geometry.resize(x, (224, 224), + interpolation='bicubic', align_corners=True, + antialias=self.antialias) + x = (x + 1.) / 2. + # renormalize according to clip + x = kornia.enhance.normalize(x, self.mean, self.std) + return x + + def freeze(self): + self.model = self.model.eval() + for param in self.model.parameters(): + param.requires_grad = False + + def forward(self, image, no_dropout=False): + ## image: b c h w + z = self.encode_with_vision_transformer(image) + return z + + def encode_with_vision_transformer(self, x): + x = self.preprocess(x) + + # to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1 + if self.model.visual.input_patchnorm: + # einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)') + x = x.reshape(x.shape[0], x.shape[1], self.model.visual.grid_size[0], self.model.visual.patch_size[0], self.model.visual.grid_size[1], self.model.visual.patch_size[1]) + x = x.permute(0, 2, 4, 1, 3, 5) + x = x.reshape(x.shape[0], self.model.visual.grid_size[0] * self.model.visual.grid_size[1], -1) + x = self.model.visual.patchnorm_pre_ln(x) + x = self.model.visual.conv1(x) + else: + x = self.model.visual.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + + # class embeddings and positional embeddings + x = torch.cat( + [self.model.visual.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), + x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + self.model.visual.positional_embedding.to(x.dtype) + + # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in + x = self.model.visual.patch_dropout(x) + x = self.model.visual.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.model.visual.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + + return x + +class FrozenCLIPT5Encoder(AbstractEncoder): + def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda", + clip_max_length=77, t5_max_length=77): + super().__init__() + self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length) + self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length) + print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, " + f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params.") + + def encode(self, text): + return self(text) + + def forward(self, text): + clip_z = self.clip_encoder.encode(text) + t5_z = self.t5_encoder.encode(text) + return [clip_z, t5_z] diff --git a/lvdm/modules/encoders/resampler.py b/lvdm/modules/encoders/resampler.py new file mode 100644 index 0000000000000000000000000000000000000000..d9abd23f681510d757acdf4dbb31794fe49ab5b1 --- /dev/null +++ b/lvdm/modules/encoders/resampler.py @@ -0,0 +1,145 @@ +# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py +# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py +# and https://github.com/tencent-ailab/IP-Adapter/blob/main/ip_adapter/resampler.py +import math +import torch +import torch.nn as nn + + +class ImageProjModel(nn.Module): + """Projection Model""" + def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4): + super().__init__() + self.cross_attention_dim = cross_attention_dim + self.clip_extra_context_tokens = clip_extra_context_tokens + self.proj = nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim) + self.norm = nn.LayerNorm(cross_attention_dim) + + def forward(self, image_embeds): + #embeds = image_embeds + embeds = image_embeds.type(list(self.proj.parameters())[0].dtype) + clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim) + clip_extra_context_tokens = self.norm(clip_extra_context_tokens) + return clip_extra_context_tokens + + +# FFN +def FeedForward(dim, mult=4): + inner_dim = int(dim * mult) + return nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, inner_dim, bias=False), + nn.GELU(), + nn.Linear(inner_dim, dim, bias=False), + ) + + +def reshape_tensor(x, heads): + bs, length, width = x.shape + #(bs, length, width) --> (bs, length, n_heads, dim_per_head) + x = x.view(bs, length, heads, -1) + # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) + x = x.transpose(1, 2) + # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) + x = x.reshape(bs, heads, length, -1) + return x + + +class PerceiverAttention(nn.Module): + def __init__(self, *, dim, dim_head=64, heads=8): + super().__init__() + self.scale = dim_head**-0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + + def forward(self, x, latents): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, l, _ = latents.shape + + q = self.to_q(latents) + kv_input = torch.cat((x, latents), dim=-2) + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, l, -1) + + return self.to_out(out) + + +class Resampler(nn.Module): + def __init__( + self, + dim=1024, + depth=8, + dim_head=64, + heads=16, + num_queries=8, + embedding_dim=768, + output_dim=1024, + ff_mult=4, + video_length=None, # using frame-wise version or not + ): + super().__init__() + ## queries for a single frame / image + self.num_queries = num_queries + self.video_length = video_length + + ## queries for each frame + if video_length is not None: + num_queries = num_queries * video_length + + self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5) + self.proj_in = nn.Linear(embedding_dim, dim) + self.proj_out = nn.Linear(dim, output_dim) + self.norm_out = nn.LayerNorm(output_dim) + + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), + FeedForward(dim=dim, mult=ff_mult), + ] + ) + ) + + def forward(self, x): + latents = self.latents.repeat(x.size(0), 1, 1) ## B (T L) C + x = self.proj_in(x) + + for attn, ff in self.layers: + latents = attn(x, latents) + latents + latents = ff(latents) + latents + + latents = self.proj_out(latents) + latents = self.norm_out(latents) # B L C or B (T L) C + + return latents \ No newline at end of file diff --git a/lvdm/modules/networks/ae_modules.py b/lvdm/modules/networks/ae_modules.py new file mode 100644 index 0000000000000000000000000000000000000000..8565997210d89422ac09feb1bf3e71d172f6f639 --- /dev/null +++ b/lvdm/modules/networks/ae_modules.py @@ -0,0 +1,844 @@ +# pytorch_diffusion + derived encoder decoder +import math +import torch +import numpy as np +import torch.nn as nn +from einops import rearrange +from utils.utils import instantiate_from_config +from lvdm.modules.attention import LinearAttention + +def nonlinearity(x): + # swish + return x*torch.sigmoid(x) + + +def Normalize(in_channels, num_groups=32): + return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) + + + +class LinAttnBlock(LinearAttention): + """to match AttnBlock usage""" + def __init__(self, in_channels): + super().__init__(dim=in_channels, heads=1, dim_head=in_channels) + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = q.reshape(b,c,h*w) # bcl + q = q.permute(0,2,1) # bcl -> blc l=hw + k = k.reshape(b,c,h*w) # bcl + + w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b,c,h*w) + w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q) + h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] + h_ = h_.reshape(b,c,h,w) + + h_ = self.proj_out(h_) + + return x+h_ + +def make_attn(in_channels, attn_type="vanilla"): + assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown' + #print(f"making attention of type '{attn_type}' with {in_channels} in_channels") + if attn_type == "vanilla": + return AttnBlock(in_channels) + elif attn_type == "none": + return nn.Identity(in_channels) + else: + return LinAttnBlock(in_channels) + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + self.in_channels = in_channels + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=2, + padding=0) + def forward(self, x): + if self.with_conv: + pad = (0,1,0,1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + self.in_channels = in_channels + if self.with_conv: + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0,1,0,0)) + return emb + + + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, + dropout, temb_channels=512): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, + out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + else: + self.nin_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x+h + +class Model(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = self.ch*4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList([ + torch.nn.Linear(self.ch, + self.temb_ch), + torch.nn.Linear(self.temb_ch, + self.temb_ch), + ]) + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + skip_in = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + if i_block == self.num_res_blocks: + skip_in = ch*in_ch_mult[i_level] + block.append(ResnetBlock(in_channels=block_in+skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x, t=None, context=None): + #assert x.shape[2] == x.shape[3] == self.resolution + if context is not None: + # assume aligned context, cat along channel axis + x = torch.cat((x, context), dim=1) + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + def get_last_layer(self): + return self.conv_out.weight + + +class Encoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", + **ignore_kwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + 2*z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # timestep embedding + temb = None + + # print(f'encoder-input={x.shape}') + # downsampling + hs = [self.conv_in(x)] + # print(f'encoder-conv in feat={hs[0].shape}') + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + # print(f'encoder-down feat={h.shape}') + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + # print(f'encoder-downsample (input)={hs[-1].shape}') + hs.append(self.down[i_level].downsample(hs[-1])) + # print(f'encoder-downsample (output)={hs[-1].shape}') + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + # print(f'encoder-mid1 feat={h.shape}') + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + # print(f'encoder-mid2 feat={h.shape}') + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + # print(f'end feat={h.shape}') + return h + + +class Decoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, + attn_type="vanilla", **ignorekwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,)+tuple(ch_mult) + block_in = ch*ch_mult[self.num_resolutions-1] + curr_res = resolution // 2**(self.num_resolutions-1) + self.z_shape = (1,z_channels,curr_res,curr_res) + print("AE working on z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z): + #assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # print(f'decoder-input={z.shape}') + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + # print(f'decoder-conv in feat={h.shape}') + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + # print(f'decoder-mid feat={h.shape}') + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + # print(f'decoder-up feat={h.shape}') + if i_level != 0: + h = self.up[i_level].upsample(h) + # print(f'decoder-upsample feat={h.shape}') + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + # print(f'decoder-conv_out feat={h.shape}') + if self.tanh_out: + h = torch.tanh(h) + return h + + +class SimpleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, *args, **kwargs): + super().__init__() + self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1), + ResnetBlock(in_channels=in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=2 * in_channels, + out_channels=4 * in_channels, + temb_channels=0, dropout=0.0), + ResnetBlock(in_channels=4 * in_channels, + out_channels=2 * in_channels, + temb_channels=0, dropout=0.0), + nn.Conv2d(2*in_channels, in_channels, 1), + Upsample(in_channels, with_conv=True)]) + # end + self.norm_out = Normalize(in_channels) + self.conv_out = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + for i, layer in enumerate(self.model): + if i in [1,2,3]: + x = layer(x, None) + else: + x = layer(x) + + h = self.norm_out(x) + h = nonlinearity(h) + x = self.conv_out(h) + return x + + +class UpsampleDecoder(nn.Module): + def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution, + ch_mult=(2,2), dropout=0.0): + super().__init__() + # upsampling + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + block_in = in_channels + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.res_blocks = nn.ModuleList() + self.upsample_blocks = nn.ModuleList() + for i_level in range(self.num_resolutions): + res_block = [] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + res_block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + self.res_blocks.append(nn.ModuleList(res_block)) + if i_level != self.num_resolutions - 1: + self.upsample_blocks.append(Upsample(block_in, True)) + curr_res = curr_res * 2 + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # upsampling + h = x + for k, i_level in enumerate(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.res_blocks[i_level][i_block](h, None) + if i_level != self.num_resolutions - 1: + h = self.upsample_blocks[k](h) + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class LatentRescaler(nn.Module): + def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2): + super().__init__() + # residual block, interpolate, residual block + self.factor = factor + self.conv_in = nn.Conv2d(in_channels, + mid_channels, + kernel_size=3, + stride=1, + padding=1) + self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0) for _ in range(depth)]) + self.attn = AttnBlock(mid_channels) + self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels, + out_channels=mid_channels, + temb_channels=0, + dropout=0.0) for _ in range(depth)]) + + self.conv_out = nn.Conv2d(mid_channels, + out_channels, + kernel_size=1, + ) + + def forward(self, x): + x = self.conv_in(x) + for block in self.res_block1: + x = block(x, None) + x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor)))) + x = self.attn(x) + for block in self.res_block2: + x = block(x, None) + x = self.conv_out(x) + return x + + +class MergedRescaleEncoder(nn.Module): + def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, + ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1): + super().__init__() + intermediate_chn = ch * ch_mult[-1] + self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult, + z_channels=intermediate_chn, double_z=False, resolution=resolution, + attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv, + out_ch=None) + self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn, + mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth) + + def forward(self, x): + x = self.encoder(x) + x = self.rescaler(x) + return x + + +class MergedRescaleDecoder(nn.Module): + def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8), + dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1): + super().__init__() + tmp_chn = z_channels*ch_mult[-1] + self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout, + resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks, + ch_mult=ch_mult, resolution=resolution, ch=ch) + self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn, + out_channels=tmp_chn, depth=rescale_module_depth) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Upsampler(nn.Module): + def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2): + super().__init__() + assert out_size >= in_size + num_blocks = int(np.log2(out_size//in_size))+1 + factor_up = 1.+ (out_size % in_size) + print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}") + self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels, + out_channels=in_channels) + self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2, + attn_resolutions=[], in_channels=None, ch=in_channels, + ch_mult=[ch_mult for _ in range(num_blocks)]) + + def forward(self, x): + x = self.rescaler(x) + x = self.decoder(x) + return x + + +class Resize(nn.Module): + def __init__(self, in_channels=None, learned=False, mode="bilinear"): + super().__init__() + self.with_conv = learned + self.mode = mode + if self.with_conv: + print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode") + raise NotImplementedError() + assert in_channels is not None + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=4, + stride=2, + padding=1) + + def forward(self, x, scale_factor=1.0): + if scale_factor==1.0: + return x + else: + x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor) + return x + +class FirstStagePostProcessor(nn.Module): + + def __init__(self, ch_mult:list, in_channels, + pretrained_model:nn.Module=None, + reshape=False, + n_channels=None, + dropout=0., + pretrained_config=None): + super().__init__() + if pretrained_config is None: + assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' + self.pretrained_model = pretrained_model + else: + assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None' + self.instantiate_pretrained(pretrained_config) + + self.do_reshape = reshape + + if n_channels is None: + n_channels = self.pretrained_model.encoder.ch + + self.proj_norm = Normalize(in_channels,num_groups=in_channels//2) + self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3, + stride=1,padding=1) + + blocks = [] + downs = [] + ch_in = n_channels + for m in ch_mult: + blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout)) + ch_in = m * n_channels + downs.append(Downsample(ch_in, with_conv=False)) + + self.model = nn.ModuleList(blocks) + self.downsampler = nn.ModuleList(downs) + + + def instantiate_pretrained(self, config): + model = instantiate_from_config(config) + self.pretrained_model = model.eval() + # self.pretrained_model.train = False + for param in self.pretrained_model.parameters(): + param.requires_grad = False + + + @torch.no_grad() + def encode_with_pretrained(self,x): + c = self.pretrained_model.encode(x) + if isinstance(c, DiagonalGaussianDistribution): + c = c.mode() + return c + + def forward(self,x): + z_fs = self.encode_with_pretrained(x) + z = self.proj_norm(z_fs) + z = self.proj(z) + z = nonlinearity(z) + + for submodel, downmodel in zip(self.model,self.downsampler): + z = submodel(z,temb=None) + z = downmodel(z) + + if self.do_reshape: + z = rearrange(z,'b c h w -> b (h w) c') + return z \ No newline at end of file diff --git a/lvdm/modules/networks/openaimodel3d.py b/lvdm/modules/networks/openaimodel3d.py new file mode 100644 index 0000000000000000000000000000000000000000..49245da8ff896d938cf13c6cf6cb23548383c6dc --- /dev/null +++ b/lvdm/modules/networks/openaimodel3d.py @@ -0,0 +1,603 @@ +from functools import partial +from abc import abstractmethod +import torch +import torch.nn as nn +from einops import rearrange +import torch.nn.functional as F +from lvdm.models.utils_diffusion import timestep_embedding +from lvdm.common import checkpoint +from lvdm.basics import ( + zero_module, + conv_nd, + linear, + avg_pool_nd, + normalization +) +from lvdm.modules.attention import SpatialTransformer, TemporalTransformer + + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, x, emb, context=None, batch_size=None): + for layer in self: + if isinstance(layer, TimestepBlock): + x = layer(x, emb, batch_size=batch_size) + elif isinstance(layer, SpatialTransformer): + x = layer(x, context) + elif isinstance(layer, TemporalTransformer): + x = rearrange(x, '(b f) c h w -> b c f h w', b=batch_size) + x = layer(x, context) + x = rearrange(x, 'b c f h w -> (b f) c h w') + else: + x = layer(x) + return x + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=padding + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding) + + def forward(self, x): + assert x.shape[1] == self.channels + if self.dims == 3: + x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode='nearest') + else: + x = F.interpolate(x, scale_factor=2, mode='nearest') + if self.use_conv: + x = self.conv(x) + return x + + +class ResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + :param use_temporal_conv: if True, use the temporal convolution. + :param use_image_dataset: if True, the temporal parameters will not be optimized. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + use_conv=False, + up=False, + down=False, + use_temporal_conv=False, + tempspatial_aware=False + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + self.use_temporal_conv = use_temporal_conv + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + conv_nd(dims, channels, self.out_channels, 3, padding=1), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.emb_layers = nn.Sequential( + nn.SiLU(), + nn.Linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, + ), + ) + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module(nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1)), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 3, padding=1) + else: + self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) + + if self.use_temporal_conv: + self.temopral_conv = TemporalConvBlock( + self.out_channels, + self.out_channels, + dropout=0.1, + spatial_aware=tempspatial_aware + ) + + def forward(self, x, emb, batch_size=None): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + input_tuple = (x, emb) + if batch_size: + forward_batchsize = partial(self._forward, batch_size=batch_size) + return checkpoint(forward_batchsize, input_tuple, self.parameters(), self.use_checkpoint) + return checkpoint(self._forward, input_tuple, self.parameters(), self.use_checkpoint) + + def _forward(self, x, emb, batch_size=None): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = torch.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + h = self.skip_connection(x) + h + + if self.use_temporal_conv and batch_size: + h = rearrange(h, '(b t) c h w -> b c t h w', b=batch_size) + h = self.temopral_conv(h) + h = rearrange(h, 'b c t h w -> (b t) c h w') + return h + + +class TemporalConvBlock(nn.Module): + """ + Adapted from modelscope: https://github.com/modelscope/modelscope/blob/master/modelscope/models/multi_modal/video_synthesis/unet_sd.py + """ + def __init__(self, in_channels, out_channels=None, dropout=0.0, spatial_aware=False): + super(TemporalConvBlock, self).__init__() + if out_channels is None: + out_channels = in_channels + self.in_channels = in_channels + self.out_channels = out_channels + th_kernel_shape = (3, 1, 1) if not spatial_aware else (3, 3, 1) + th_padding_shape = (1, 0, 0) if not spatial_aware else (1, 1, 0) + tw_kernel_shape = (3, 1, 1) if not spatial_aware else (3, 1, 3) + tw_padding_shape = (1, 0, 0) if not spatial_aware else (1, 0, 1) + + # conv layers + self.conv1 = nn.Sequential( + nn.GroupNorm(32, in_channels), nn.SiLU(), + nn.Conv3d(in_channels, out_channels, th_kernel_shape, padding=th_padding_shape)) + self.conv2 = nn.Sequential( + nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout), + nn.Conv3d(out_channels, in_channels, tw_kernel_shape, padding=tw_padding_shape)) + self.conv3 = nn.Sequential( + nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout), + nn.Conv3d(out_channels, in_channels, th_kernel_shape, padding=th_padding_shape)) + self.conv4 = nn.Sequential( + nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout), + nn.Conv3d(out_channels, in_channels, tw_kernel_shape, padding=tw_padding_shape)) + + # zero out the last layer params,so the conv block is identity + nn.init.zeros_(self.conv4[-1].weight) + nn.init.zeros_(self.conv4[-1].bias) + + def forward(self, x): + identity = x + x = self.conv1(x) + x = self.conv2(x) + x = self.conv3(x) + x = self.conv4(x) + + return identity + x + +class UNetModel(nn.Module): + """ + The full UNet model with attention and timestep embedding. + :param in_channels: in_channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param use_checkpoint: use gradient checkpointing to reduce memory usage. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + """ + + def __init__(self, + in_channels, + model_channels, + out_channels, + num_res_blocks, + attention_resolutions, + dropout=0.0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + context_dim=None, + use_scale_shift_norm=False, + resblock_updown=False, + num_heads=-1, + num_head_channels=-1, + transformer_depth=1, + use_linear=False, + use_checkpoint=False, + temporal_conv=False, + tempspatial_aware=False, + temporal_attention=True, + use_relative_position=True, + use_causal_attention=False, + temporal_length=None, + use_fp16=False, + addition_attention=False, + temporal_selfatt_only=True, + image_cross_attention=False, + image_cross_attention_scale_learnable=False, + default_fs=4, + fs_condition=False, + ): + super(UNetModel, self).__init__() + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.num_res_blocks = num_res_blocks + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.temporal_attention = temporal_attention + time_embed_dim = model_channels * 4 + self.use_checkpoint = use_checkpoint + self.dtype = torch.float16 if use_fp16 else torch.float32 + temporal_self_att_only = True + self.addition_attention = addition_attention + self.temporal_length = temporal_length + self.image_cross_attention = image_cross_attention + self.image_cross_attention_scale_learnable = image_cross_attention_scale_learnable + self.default_fs = default_fs + self.fs_condition = fs_condition + + ## Time embedding blocks + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + if fs_condition: + self.fps_embedding = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + nn.init.zeros_(self.fps_embedding[-1].weight) + nn.init.zeros_(self.fps_embedding[-1].bias) + ## Input Block + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential(conv_nd(dims, in_channels, model_channels, 3, padding=1)) + ] + ) + if self.addition_attention: + self.init_attn=TimestepEmbedSequential( + TemporalTransformer( + model_channels, + n_heads=8, + d_head=num_head_channels, + depth=transformer_depth, + context_dim=context_dim, + use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only, + causal_attention=False, relative_position=use_relative_position, + temporal_length=temporal_length)) + + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for _ in range(num_res_blocks): + layers = [ + ResBlock(ch, time_embed_dim, dropout, + out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, + use_temporal_conv=temporal_conv + ) + ] + ch = mult * model_channels + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + layers.append( + SpatialTransformer(ch, num_heads, dim_head, + depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, disable_self_attn=False, + video_length=temporal_length, image_cross_attention=self.image_cross_attention, + image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable, + ) + ) + if self.temporal_attention: + layers.append( + TemporalTransformer(ch, num_heads, dim_head, + depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only, + causal_attention=use_causal_attention, relative_position=use_relative_position, + temporal_length=temporal_length + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock(ch, time_embed_dim, dropout, + out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True + ) + if resblock_updown + else Downsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + layers = [ + ResBlock(ch, time_embed_dim, dropout, + dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, + use_temporal_conv=temporal_conv + ), + SpatialTransformer(ch, num_heads, dim_head, + depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, disable_self_attn=False, video_length=temporal_length, + image_cross_attention=self.image_cross_attention,image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable + ) + ] + if self.temporal_attention: + layers.append( + TemporalTransformer(ch, num_heads, dim_head, + depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only, + causal_attention=use_causal_attention, relative_position=use_relative_position, + temporal_length=temporal_length + ) + ) + layers.append( + ResBlock(ch, time_embed_dim, dropout, + dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, + use_temporal_conv=temporal_conv + ) + ) + + ## Middle Block + self.middle_block = TimestepEmbedSequential(*layers) + + ## Output Block + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(num_res_blocks + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock(ch + ich, time_embed_dim, dropout, + out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, + use_temporal_conv=temporal_conv + ) + ] + ch = model_channels * mult + if ds in attention_resolutions: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + layers.append( + SpatialTransformer(ch, num_heads, dim_head, + depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, disable_self_attn=False, video_length=temporal_length, + image_cross_attention=self.image_cross_attention,image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable + ) + ) + if self.temporal_attention: + layers.append( + TemporalTransformer(ch, num_heads, dim_head, + depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, + use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only, + causal_attention=use_causal_attention, relative_position=use_relative_position, + temporal_length=temporal_length + ) + ) + if level and i == num_res_blocks: + out_ch = ch + layers.append( + ResBlock(ch, time_embed_dim, dropout, + out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + up=True + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), + ) + + def forward(self, x, timesteps, context=None, features_adapter=None, fs=None, **kwargs): + b,_,t,_,_ = x.shape + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).type(x.dtype) + emb = self.time_embed(t_emb) + + ## repeat t times for context [(b t) 77 768] & time embedding + ## check if we use per-frame image conditioning + _, l_context, _ = context.shape + if l_context == 77 + t*16: ## !!! HARD CODE here + context_text, context_img = context[:,:77,:], context[:,77:,:] + context_text = context_text.repeat_interleave(repeats=t, dim=0) + context_img = rearrange(context_img, 'b (t l) c -> (b t) l c', t=t) + context = torch.cat([context_text, context_img], dim=1) + else: + context = context.repeat_interleave(repeats=t, dim=0) + emb = emb.repeat_interleave(repeats=t, dim=0) + + ## always in shape (b t) c h w, except for temporal layer + x = rearrange(x, 'b c t h w -> (b t) c h w') + + ## combine emb + if self.fs_condition: + if fs is None: + fs = torch.tensor( + [self.default_fs] * b, dtype=torch.long, device=x.device) + fs_emb = timestep_embedding(fs, self.model_channels, repeat_only=False).type(x.dtype) + + fs_embed = self.fps_embedding(fs_emb) + fs_embed = fs_embed.repeat_interleave(repeats=t, dim=0) + emb = emb + fs_embed + + h = x.type(self.dtype) + adapter_idx = 0 + hs = [] + for id, module in enumerate(self.input_blocks): + h = module(h, emb, context=context, batch_size=b) + if id ==0 and self.addition_attention: + h = self.init_attn(h, emb, context=context, batch_size=b) + ## plug-in adapter features + if ((id+1)%3 == 0) and features_adapter is not None: + h = h + features_adapter[adapter_idx] + adapter_idx += 1 + hs.append(h) + if features_adapter is not None: + assert len(features_adapter)==adapter_idx, 'Wrong features_adapter' + + h = self.middle_block(h, emb, context=context, batch_size=b) + for module in self.output_blocks: + h = torch.cat([h, hs.pop()], dim=1) + h = module(h, emb, context=context, batch_size=b) + h = h.type(x.dtype) + y = self.out(h) + + # reshape back to (b c t h w) + y = rearrange(y, '(b t) c h w -> b c t h w', b=b) + return y \ No newline at end of file diff --git a/lvdm/modules/x_transformer.py b/lvdm/modules/x_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..5321012f860a8fb06850c1ddf495db934addecae --- /dev/null +++ b/lvdm/modules/x_transformer.py @@ -0,0 +1,639 @@ +"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers""" +from functools import partial +from inspect import isfunction +from collections import namedtuple +from einops import rearrange, repeat +import torch +from torch import nn, einsum +import torch.nn.functional as F + +# constants +DEFAULT_DIM_HEAD = 64 + +Intermediates = namedtuple('Intermediates', [ + 'pre_softmax_attn', + 'post_softmax_attn' +]) + +LayerIntermediates = namedtuple('Intermediates', [ + 'hiddens', + 'attn_intermediates' +]) + + +class AbsolutePositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + self.emb = nn.Embedding(max_seq_len, dim) + self.init_() + + def init_(self): + nn.init.normal_(self.emb.weight, std=0.02) + + def forward(self, x): + n = torch.arange(x.shape[1], device=x.device) + return self.emb(n)[None, :, :] + + +class FixedPositionalEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer('inv_freq', inv_freq) + + def forward(self, x, seq_dim=1, offset=0): + t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset + sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq) + emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1) + return emb[None, :, :] + + +# helpers + +def exists(val): + return val is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def always(val): + def inner(*args, **kwargs): + return val + return inner + + +def not_equals(val): + def inner(x): + return x != val + return inner + + +def equals(val): + def inner(x): + return x == val + return inner + + +def max_neg_value(tensor): + return -torch.finfo(tensor.dtype).max + + +# keyword argument helpers + +def pick_and_pop(keys, d): + values = list(map(lambda key: d.pop(key), keys)) + return dict(zip(keys, values)) + + +def group_dict_by_key(cond, d): + return_val = [dict(), dict()] + for key in d.keys(): + match = bool(cond(key)) + ind = int(not match) + return_val[ind][key] = d[key] + return (*return_val,) + + +def string_begins_with(prefix, str): + return str.startswith(prefix) + + +def group_by_key_prefix(prefix, d): + return group_dict_by_key(partial(string_begins_with, prefix), d) + + +def groupby_prefix_and_trim(prefix, d): + kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d) + kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items()))) + return kwargs_without_prefix, kwargs + + +# classes +class Scale(nn.Module): + def __init__(self, value, fn): + super().__init__() + self.value = value + self.fn = fn + + def forward(self, x, **kwargs): + x, *rest = self.fn(x, **kwargs) + return (x * self.value, *rest) + + +class Rezero(nn.Module): + def __init__(self, fn): + super().__init__() + self.fn = fn + self.g = nn.Parameter(torch.zeros(1)) + + def forward(self, x, **kwargs): + x, *rest = self.fn(x, **kwargs) + return (x * self.g, *rest) + + +class ScaleNorm(nn.Module): + def __init__(self, dim, eps=1e-5): + super().__init__() + self.scale = dim ** -0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(1)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g + + +class RMSNorm(nn.Module): + def __init__(self, dim, eps=1e-8): + super().__init__() + self.scale = dim ** -0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(dim)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g + + +class Residual(nn.Module): + def forward(self, x, residual): + return x + residual + + +class GRUGating(nn.Module): + def __init__(self, dim): + super().__init__() + self.gru = nn.GRUCell(dim, dim) + + def forward(self, x, residual): + gated_output = self.gru( + rearrange(x, 'b n d -> (b n) d'), + rearrange(residual, 'b n d -> (b n) d') + ) + + return gated_output.reshape_as(x) + + +# feedforward + +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +# attention. +class Attention(nn.Module): + def __init__( + self, + dim, + dim_head=DEFAULT_DIM_HEAD, + heads=8, + causal=False, + mask=None, + talking_heads=False, + sparse_topk=None, + use_entmax15=False, + num_mem_kv=0, + dropout=0., + on_attn=False + ): + super().__init__() + if use_entmax15: + raise NotImplementedError("Check out entmax activation instead of softmax activation!") + self.scale = dim_head ** -0.5 + self.heads = heads + self.causal = causal + self.mask = mask + + inner_dim = dim_head * heads + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_k = nn.Linear(dim, inner_dim, bias=False) + self.to_v = nn.Linear(dim, inner_dim, bias=False) + self.dropout = nn.Dropout(dropout) + + # talking heads + self.talking_heads = talking_heads + if talking_heads: + self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads)) + self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads)) + + # explicit topk sparse attention + self.sparse_topk = sparse_topk + + # entmax + #self.attn_fn = entmax15 if use_entmax15 else F.softmax + self.attn_fn = F.softmax + + # add memory key / values + self.num_mem_kv = num_mem_kv + if num_mem_kv > 0: + self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) + self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) + + # attention on attention + self.attn_on_attn = on_attn + self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim) + + def forward( + self, + x, + context=None, + mask=None, + context_mask=None, + rel_pos=None, + sinusoidal_emb=None, + prev_attn=None, + mem=None + ): + b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device + kv_input = default(context, x) + + q_input = x + k_input = kv_input + v_input = kv_input + + if exists(mem): + k_input = torch.cat((mem, k_input), dim=-2) + v_input = torch.cat((mem, v_input), dim=-2) + + if exists(sinusoidal_emb): + # in shortformer, the query would start at a position offset depending on the past cached memory + offset = k_input.shape[-2] - q_input.shape[-2] + q_input = q_input + sinusoidal_emb(q_input, offset=offset) + k_input = k_input + sinusoidal_emb(k_input) + + q = self.to_q(q_input) + k = self.to_k(k_input) + v = self.to_v(v_input) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v)) + + input_mask = None + if any(map(exists, (mask, context_mask))): + q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool()) + k_mask = q_mask if not exists(context) else context_mask + k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool()) + q_mask = rearrange(q_mask, 'b i -> b () i ()') + k_mask = rearrange(k_mask, 'b j -> b () () j') + input_mask = q_mask * k_mask + + if self.num_mem_kv > 0: + mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v)) + k = torch.cat((mem_k, k), dim=-2) + v = torch.cat((mem_v, v), dim=-2) + if exists(input_mask): + input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True) + + dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale + mask_value = max_neg_value(dots) + + if exists(prev_attn): + dots = dots + prev_attn + + pre_softmax_attn = dots + + if talking_heads: + dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous() + + if exists(rel_pos): + dots = rel_pos(dots) + + if exists(input_mask): + dots.masked_fill_(~input_mask, mask_value) + del input_mask + + if self.causal: + i, j = dots.shape[-2:] + r = torch.arange(i, device=device) + mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j') + mask = F.pad(mask, (j - i, 0), value=False) + dots.masked_fill_(mask, mask_value) + del mask + + if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]: + top, _ = dots.topk(self.sparse_topk, dim=-1) + vk = top[..., -1].unsqueeze(-1).expand_as(dots) + mask = dots < vk + dots.masked_fill_(mask, mask_value) + del mask + + attn = self.attn_fn(dots, dim=-1) + post_softmax_attn = attn + + attn = self.dropout(attn) + + if talking_heads: + attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous() + + out = einsum('b h i j, b h j d -> b h i d', attn, v) + out = rearrange(out, 'b h n d -> b n (h d)') + + intermediates = Intermediates( + pre_softmax_attn=pre_softmax_attn, + post_softmax_attn=post_softmax_attn + ) + + return self.to_out(out), intermediates + + +class AttentionLayers(nn.Module): + def __init__( + self, + dim, + depth, + heads=8, + causal=False, + cross_attend=False, + only_cross=False, + use_scalenorm=False, + use_rmsnorm=False, + use_rezero=False, + rel_pos_num_buckets=32, + rel_pos_max_distance=128, + position_infused_attn=False, + custom_layers=None, + sandwich_coef=None, + par_ratio=None, + residual_attn=False, + cross_residual_attn=False, + macaron=False, + pre_norm=True, + gate_residual=False, + **kwargs + ): + super().__init__() + ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs) + attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs) + + dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD) + + self.dim = dim + self.depth = depth + self.layers = nn.ModuleList([]) + + self.has_pos_emb = position_infused_attn + self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None + self.rotary_pos_emb = always(None) + + assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance' + self.rel_pos = None + + self.pre_norm = pre_norm + + self.residual_attn = residual_attn + self.cross_residual_attn = cross_residual_attn + + norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm + norm_class = RMSNorm if use_rmsnorm else norm_class + norm_fn = partial(norm_class, dim) + + norm_fn = nn.Identity if use_rezero else norm_fn + branch_fn = Rezero if use_rezero else None + + if cross_attend and not only_cross: + default_block = ('a', 'c', 'f') + elif cross_attend and only_cross: + default_block = ('c', 'f') + else: + default_block = ('a', 'f') + + if macaron: + default_block = ('f',) + default_block + + if exists(custom_layers): + layer_types = custom_layers + elif exists(par_ratio): + par_depth = depth * len(default_block) + assert 1 < par_ratio <= par_depth, 'par ratio out of range' + default_block = tuple(filter(not_equals('f'), default_block)) + par_attn = par_depth // par_ratio + depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper + par_width = (depth_cut + depth_cut // par_attn) // par_attn + assert len(default_block) <= par_width, 'default block is too large for par_ratio' + par_block = default_block + ('f',) * (par_width - len(default_block)) + par_head = par_block * par_attn + layer_types = par_head + ('f',) * (par_depth - len(par_head)) + elif exists(sandwich_coef): + assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth' + layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef + else: + layer_types = default_block * depth + + self.layer_types = layer_types + self.num_attn_layers = len(list(filter(equals('a'), layer_types))) + + for layer_type in self.layer_types: + if layer_type == 'a': + layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs) + elif layer_type == 'c': + layer = Attention(dim, heads=heads, **attn_kwargs) + elif layer_type == 'f': + layer = FeedForward(dim, **ff_kwargs) + layer = layer if not macaron else Scale(0.5, layer) + else: + raise Exception(f'invalid layer type {layer_type}') + + if isinstance(layer, Attention) and exists(branch_fn): + layer = branch_fn(layer) + + if gate_residual: + residual_fn = GRUGating(dim) + else: + residual_fn = Residual() + + self.layers.append(nn.ModuleList([ + norm_fn(), + layer, + residual_fn + ])) + + def forward( + self, + x, + context=None, + mask=None, + context_mask=None, + mems=None, + return_hiddens=False + ): + hiddens = [] + intermediates = [] + prev_attn = None + prev_cross_attn = None + + mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers + + for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)): + is_last = ind == (len(self.layers) - 1) + + if layer_type == 'a': + hiddens.append(x) + layer_mem = mems.pop(0) + + residual = x + + if self.pre_norm: + x = norm(x) + + if layer_type == 'a': + out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos, + prev_attn=prev_attn, mem=layer_mem) + elif layer_type == 'c': + out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn) + elif layer_type == 'f': + out = block(x) + + x = residual_fn(out, residual) + + if layer_type in ('a', 'c'): + intermediates.append(inter) + + if layer_type == 'a' and self.residual_attn: + prev_attn = inter.pre_softmax_attn + elif layer_type == 'c' and self.cross_residual_attn: + prev_cross_attn = inter.pre_softmax_attn + + if not self.pre_norm and not is_last: + x = norm(x) + + if return_hiddens: + intermediates = LayerIntermediates( + hiddens=hiddens, + attn_intermediates=intermediates + ) + + return x, intermediates + + return x + + +class Encoder(AttentionLayers): + def __init__(self, **kwargs): + assert 'causal' not in kwargs, 'cannot set causality on encoder' + super().__init__(causal=False, **kwargs) + + + +class TransformerWrapper(nn.Module): + def __init__( + self, + *, + num_tokens, + max_seq_len, + attn_layers, + emb_dim=None, + max_mem_len=0., + emb_dropout=0., + num_memory_tokens=None, + tie_embedding=False, + use_pos_emb=True + ): + super().__init__() + assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder' + + dim = attn_layers.dim + emb_dim = default(emb_dim, dim) + + self.max_seq_len = max_seq_len + self.max_mem_len = max_mem_len + self.num_tokens = num_tokens + + self.token_emb = nn.Embedding(num_tokens, emb_dim) + self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if ( + use_pos_emb and not attn_layers.has_pos_emb) else always(0) + self.emb_dropout = nn.Dropout(emb_dropout) + + self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity() + self.attn_layers = attn_layers + self.norm = nn.LayerNorm(dim) + + self.init_() + + self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t() + + # memory tokens (like [cls]) from Memory Transformers paper + num_memory_tokens = default(num_memory_tokens, 0) + self.num_memory_tokens = num_memory_tokens + if num_memory_tokens > 0: + self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim)) + + # let funnel encoder know number of memory tokens, if specified + if hasattr(attn_layers, 'num_memory_tokens'): + attn_layers.num_memory_tokens = num_memory_tokens + + def init_(self): + nn.init.normal_(self.token_emb.weight, std=0.02) + + def forward( + self, + x, + return_embeddings=False, + mask=None, + return_mems=False, + return_attn=False, + mems=None, + **kwargs + ): + b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens + x = self.token_emb(x) + x += self.pos_emb(x) + x = self.emb_dropout(x) + + x = self.project_emb(x) + + if num_mem > 0: + mem = repeat(self.memory_tokens, 'n d -> b n d', b=b) + x = torch.cat((mem, x), dim=1) + + # auto-handle masking after appending memory tokens + if exists(mask): + mask = F.pad(mask, (num_mem, 0), value=True) + + x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs) + x = self.norm(x) + + mem, x = x[:, :num_mem], x[:, num_mem:] + + out = self.to_logits(x) if not return_embeddings else x + + if return_mems: + hiddens = intermediates.hiddens + new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens + new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems)) + return out, new_mems + + if return_attn: + attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates)) + return out, 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0000000000000000000000000000000000000000..5b632be8879c85d0867fd39902bdc042653c15d6 --- /dev/null +++ b/prompts/512/65.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e075791c706f9742c4f520abf01d527c2d0c3da077a0bac4a42f1973466f2f8e +size 630298 diff --git a/prompts/512/7.png b/prompts/512/7.png new file mode 100644 index 0000000000000000000000000000000000000000..3b21e7597b2fb1c3abb2aebb9c88d79c43db1e43 --- /dev/null +++ b/prompts/512/7.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3cbea2d47f1646793d25ad924f913416ad9d7e035b1f399512ec2148632c963a +size 716811 diff --git a/prompts/512/A girl with long curly blonde hair and sunglasses, camera pan from left to right..png b/prompts/512/A girl with long curly blonde hair and sunglasses, camera pan from left to right..png new file mode 100644 index 0000000000000000000000000000000000000000..39fd80fd2f5c706645a97f353359bacdd3700cf7 --- /dev/null +++ b/prompts/512/A girl with long curly blonde hair and sunglasses, camera pan from left to right..png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce4e10cf85b86e51878faba53344c64d478f43bc099685b354bcc4d5e281561b +size 803902 diff --git a/prompts/512/A panda wearing sunglasses walking in slow-motion under water, in photorealistic style..png b/prompts/512/A panda wearing sunglasses walking in slow-motion under water, in photorealistic style..png new file mode 100644 index 0000000000000000000000000000000000000000..ce2592ab9f72ed8193a4aa30fdf559f01df267c0 --- /dev/null +++ b/prompts/512/A panda wearing sunglasses walking in slow-motion under water, in photorealistic style..png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c7ccbbc4fb8ca8ba85be837509194a65fecfd4c43646de82def63722de4f2e92 +size 854783 diff --git a/prompts/512/a car parked in a parking lot with palm trees nearby,calm seas and skies..png b/prompts/512/a car parked in a parking lot with palm trees nearby,calm seas and skies..png new file mode 100644 index 0000000000000000000000000000000000000000..424e8376f5083d09bcaad9eeae9ccfa3b64711a4 --- /dev/null +++ b/prompts/512/a car parked in a parking lot with palm trees nearby,calm seas and skies..png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d1e31d03228a4fa95a9773891c488a3864f0b896a05468426f09e2c850e448b5 +size 727910 diff --git a/prompts/512/test_prompts.txt b/prompts/512/test_prompts.txt new file mode 100644 index 0000000000000000000000000000000000000000..5215ae3ee3cf8b112c52cdc252547a8682cd8819 --- /dev/null +++ b/prompts/512/test_prompts.txt @@ -0,0 +1,22 @@ +A duck swimming in the lake. +A duck swimming in the lake. +A man riding motor on a mountain road. +A couple hugging a cat. +Mystical hills with a glowing blue portal. +Two tigers fighting in the snow. +A soldier riding a horse. +Mountains under the starlight. +A duck swimming in the lake. +A girl walks up the steps of a palace. +A woman with flowing, curly silver hair and dark eyes. +Rabbits playing in a river. +Sailing of boats on the water surface. +A plate full of food, with camera spinning. +A cartoon girl with brown curly hair splashes joyfully in a bubble-filled bathtub. +Fireworks exploding in the sky. +A kitten lying on the bed. +A duck swimming in the lake. +Donkeys in traditional attire gallop across a lush green meadow. +A girl with long curly blonde hair and sunglasses, camera pan from left to right. +A panda wearing sunglasses walking in slow-motion under water, in photorealistic style. +a car parked in a parking lot with palm trees nearby,calm seas and skies. diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..3d38d6f4b95d3f9cb3447aafc4dbd9c6e879e506 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,22 @@ +decord==0.6.0 +einops==0.3.0 +imageio==2.9.0 +numpy==1.24.2 +omegaconf==2.1.1 +opencv_python +pandas==2.0.0 +Pillow==9.5.0 +pytorch_lightning==1.8.3 +PyYAML==6.0 +setuptools==65.6.3 +torch==2.0.0 +torchvision +tqdm==4.65.0 +transformers==4.25.1 +moviepy +av +xformers +gradio +scikit-learn +open_clip_torch==2.22.0 +kornia \ No newline at end of file diff --git a/scripts/evaluation/__pycache__/funcs.cpython-39.pyc b/scripts/evaluation/__pycache__/funcs.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f5479de63188269b72ebec6a8f88183766a2b107 Binary files /dev/null and b/scripts/evaluation/__pycache__/funcs.cpython-39.pyc differ diff --git a/scripts/evaluation/__pycache__/inference.cpython-39.pyc b/scripts/evaluation/__pycache__/inference.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b38a8a9067fddaba000ee427b194002b5a8aa16d Binary files /dev/null and b/scripts/evaluation/__pycache__/inference.cpython-39.pyc differ diff --git a/scripts/evaluation/ddp_wrapper.py b/scripts/evaluation/ddp_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..528e273511f359bb6cec5503038ba550a96ee7e4 --- /dev/null +++ b/scripts/evaluation/ddp_wrapper.py @@ -0,0 +1,47 @@ +import datetime +import argparse, importlib +from pytorch_lightning import seed_everything + +import torch +import torch.distributed as dist + +def setup_dist(local_rank): + if dist.is_initialized(): + return + torch.cuda.set_device(local_rank) + torch.distributed.init_process_group('nccl', init_method='env://') + + +def get_dist_info(): + if dist.is_available(): + initialized = dist.is_initialized() + else: + initialized = False + if initialized: + rank = dist.get_rank() + world_size = dist.get_world_size() + else: + rank = 0 + world_size = 1 + return rank, world_size + + +if __name__ == '__main__': + now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + parser = argparse.ArgumentParser() + parser.add_argument("--module", type=str, help="module name", default="inference") + parser.add_argument("--local_rank", type=int, nargs="?", help="for ddp", default=0) + args, unknown = parser.parse_known_args() + inference_api = importlib.import_module(args.module, package=None) + + inference_parser = inference_api.get_parser() + inference_args, unknown = inference_parser.parse_known_args() + + seed_everything(inference_args.seed) + setup_dist(args.local_rank) + torch.backends.cudnn.benchmark = True + rank, gpu_num = get_dist_info() + + # inference_args.savedir = inference_args.savedir+str('_seed')+str(inference_args.seed) + print("@DynamiCrafter Inference [rank%d]: %s"%(rank, now)) + inference_api.run_inference(inference_args, gpu_num, rank) \ No newline at end of file diff --git a/scripts/evaluation/funcs.py b/scripts/evaluation/funcs.py new file mode 100644 index 0000000000000000000000000000000000000000..a689dcd6c110d2498844c354e0a59fbc6647f970 --- /dev/null +++ b/scripts/evaluation/funcs.py @@ -0,0 +1,227 @@ +import os, sys, glob +import numpy as np +from collections import OrderedDict +from decord import VideoReader, cpu +import cv2 + +import torch +import torchvision +sys.path.insert(1, os.path.join(sys.path[0], '..', '..')) +from lvdm.models.samplers.ddim import DDIMSampler +from einops import rearrange + + +def batch_ddim_sampling(model, cond, noise_shape, n_samples=1, ddim_steps=50, ddim_eta=1.0,\ + cfg_scale=1.0, temporal_cfg_scale=None,ddpm_from=1000, **kwargs): + ddim_sampler = DDIMSampler(model) + uncond_type = model.uncond_type + batch_size = noise_shape[0] + fs = cond["fs"] + del cond["fs"] + if noise_shape[-1] == 32: + timestep_spacing = "uniform" + guidance_rescale = 0.0 + else: + timestep_spacing = "uniform_trailing" + guidance_rescale = 0.7 + ## construct unconditional guidance + if cfg_scale != 1.0: + if uncond_type == "empty_seq": + prompts = batch_size * [""] + #prompts = N * T * [""] ## if is_imgbatch=True + uc_emb = model.get_learned_conditioning(prompts) + elif uncond_type == "zero_embed": + c_emb = cond["c_crossattn"][0] if isinstance(cond, dict) else cond + uc_emb = torch.zeros_like(c_emb) + + ## process image embedding token + if hasattr(model, 'embedder'): + uc_img = torch.zeros(noise_shape[0],3,224,224).to(model.device) + ## img: b c h w >> b l c + uc_img = model.embedder(uc_img) + uc_img = model.image_proj_model(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 = [] + + for _ in range(n_samples): + if ddim_sampler is not None: + kwargs.update({"clean_cond": True}) + 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, + fs=fs, + timestep_spacing=timestep_spacing, + guidance_rescale=guidance_rescale, + ddpm_from=ddpm_from, + **kwargs + ) + ## reconstruct from latent to pixel space + batch_images = model.decode_first_stage(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") + if "state_dict" in list(state_dict.keys()): + state_dict = state_dict["state_dict"] + try: + model.load_state_dict(state_dict, strict=full_strict) + except: + ## rename the keys for 256x256 model + new_pl_sd = OrderedDict() + for k,v in state_dict.items(): + new_pl_sd[k] = v + + for k in list(new_pl_sd.keys()): + if "framestride_embed" in k: + new_key = k.replace("framestride_embed", "fps_embedding") + new_pl_sd[new_key] = new_pl_sd[k] + del new_pl_sd[k] + model.load_state_dict(new_pl_sd, strict=full_strict) + else: + ## 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) + + 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'}) + + +def get_latent_z(model, videos): + b, c, t, h, w = videos.shape + x = rearrange(videos, 'b c t h w -> (b t) c h w') + z = model.encode_first_stage(x) + z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t) + return z \ No newline at end of file diff --git a/scripts/evaluation/inference.py b/scripts/evaluation/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..a006734cc61dc440c022da379d769498fcce8a13 --- /dev/null +++ b/scripts/evaluation/inference.py @@ -0,0 +1,346 @@ +import argparse, os, sys, glob +import datetime, time +from omegaconf import OmegaConf +from tqdm import tqdm +from einops import rearrange, repeat +from collections import OrderedDict + +import torch +import torchvision +import torchvision.transforms as transforms +from pytorch_lightning import seed_everything +from PIL import Image +sys.path.insert(1, os.path.join(sys.path[0], '..', '..')) +from lvdm.models.samplers.ddim import DDIMSampler +from lvdm.models.samplers.ddim_multiplecond import DDIMSampler as DDIMSampler_multicond +from utils.utils import instantiate_from_config + + +def get_filelist(data_dir, postfixes): + patterns = [os.path.join(data_dir, f"*.{postfix}") for postfix in postfixes] + file_list = [] + for pattern in patterns: + file_list.extend(glob.glob(pattern)) + file_list.sort() + return file_list + +def load_model_checkpoint(model, ckpt): + state_dict = torch.load(ckpt, map_location="cpu") + if "state_dict" in list(state_dict.keys()): + state_dict = state_dict["state_dict"] + try: + model.load_state_dict(state_dict, strict=True) + except: + ## rename the keys for 256x256 model + new_pl_sd = OrderedDict() + for k,v in state_dict.items(): + new_pl_sd[k] = v + + for k in list(new_pl_sd.keys()): + if "framestride_embed" in k: + new_key = k.replace("framestride_embed", "fps_embedding") + new_pl_sd[new_key] = new_pl_sd[k] + del new_pl_sd[k] + model.load_state_dict(new_pl_sd, strict=True) + else: + # 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) + 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_data_prompts(data_dir, video_size=(256,256), video_frames=16, gfi=False): + transform = transforms.Compose([ + transforms.Resize(video_size), + transforms.ToTensor(), + transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))]) + ## load prompts + prompt_file = get_filelist(data_dir, ['txt']) + assert len(prompt_file) > 0, "Error: found NO prompt file!" + ###### default prompt + default_idx = 0 + default_idx = min(default_idx, len(prompt_file)-1) + if len(prompt_file) > 1: + print(f"Warning: multiple prompt files exist. The one {os.path.split(prompt_file[default_idx])[1]} is used.") + ## only use the first one (sorted by name) if multiple exist + + ## load video + file_list = get_filelist(data_dir, ['jpg', 'png', 'jpeg', 'JPEG', 'PNG']) + # assert len(file_list) == n_samples, "Error: data and prompts are NOT paired!" + data_list = [] + filename_list = [] + prompt_list = load_prompts(prompt_file[default_idx]) + n_samples = len(prompt_list) + for idx in range(n_samples): + image = Image.open(file_list[idx]).convert('RGB') + image_tensor = transform(image).unsqueeze(1) # [c,1,h,w] + frame_tensor = repeat(image_tensor, 'c t h w -> c (repeat t) h w', repeat=video_frames) + + data_list.append(frame_tensor) + _, filename = os.path.split(file_list[idx]) + filename_list.append(filename) + + return filename_list, data_list, prompt_list + + +def save_results(prompt, samples, filename, fakedir, fps=8, loop=False): + filename = filename.split('.')[0]+'.mp4' + prompt = prompt[0] if isinstance(prompt, list) else prompt + + ## save video + videos = [samples] + savedirs = [fakedir] + for idx, video in enumerate(videos): + if video is None: + continue + # b,c,t,h,w + video = video.detach().cpu() + video = torch.clamp(video.float(), -1., 1.) + n = video.shape[0] + video = video.permute(2, 0, 1, 3, 4) # t,n,c,h,w + if loop: + video = video[:-1,...] + + frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(n), padding=0) for framesheet in video] #[3, 1*h, n*w] + grid = torch.stack(frame_grids, dim=0) # stack in temporal dim [t, 3, h, n*w] + grid = (grid + 1.0) / 2.0 + grid = (grid * 255).to(torch.uint8).permute(0, 2, 3, 1) + path = os.path.join(savedirs[idx], filename) + torchvision.io.write_video(path, grid, fps=fps, video_codec='h264', options={'crf': '10'}) ## crf indicates the quality + + +def save_results_seperate(prompt, samples, filename, fakedir, fps=10, loop=False): + prompt = prompt[0] if isinstance(prompt, list) else prompt + + ## save video + videos = [samples] + savedirs = [fakedir] + for idx, video in enumerate(videos): + if video is None: + continue + # b,c,t,h,w + video = video.detach().cpu() + if loop: # remove the last frame + video = video[:,:,:-1,...] + video = torch.clamp(video.float(), -1., 1.) + n = video.shape[0] + for i in range(n): + grid = video[i,...] + grid = (grid + 1.0) / 2.0 + grid = (grid * 255).to(torch.uint8).permute(1, 2, 3, 0) #thwc + path = os.path.join(savedirs[idx].replace('samples', 'samples_separate'), f'{filename.split(".")[0]}_sample{i}.mp4') + torchvision.io.write_video(path, grid, fps=fps, video_codec='h264', options={'crf': '10'}) + +def get_latent_z(model, videos): + b, c, t, h, w = videos.shape + x = rearrange(videos, 'b c t h w -> (b t) c h w') + z = model.encode_first_stage(x) + z = rearrange(z, '(b t) c h w -> b c t h w', b=b, t=t) + return z + + +def image_guided_synthesis(model, prompts, videos, noise_shape, n_samples=1, ddim_steps=50, ddim_eta=1., \ + unconditional_guidance_scale=1.0, cfg_img=None, fs=None, text_input=False, multiple_cond_cfg=False, loop=False, gfi=False, timestep_spacing='uniform', guidance_rescale=0.0, **kwargs): + ddim_sampler = DDIMSampler(model) if not multiple_cond_cfg else DDIMSampler_multicond(model) + batch_size = noise_shape[0] + fs = torch.tensor([fs] * batch_size, dtype=torch.long, device=model.device) + + if not text_input: + prompts = [""]*batch_size + + img = videos[:,:,0] #bchw + img_emb = model.embedder(img) ## blc + img_emb = model.image_proj_model(img_emb) + + cond_emb = model.get_learned_conditioning(prompts) + cond = {"c_crossattn": [torch.cat([cond_emb,img_emb], dim=1)]} + if model.model.conditioning_key == 'hybrid': + z = get_latent_z(model, videos) # b c t h w + if loop or gfi: + img_cat_cond = torch.zeros_like(z) + img_cat_cond[:,:,0,:,:] = z[:,:,0,:,:] + img_cat_cond[:,:,-1,:,:] = z[:,:,-1,:,:] + else: + img_cat_cond = z[:,:,:1,:,:] + img_cat_cond = repeat(img_cat_cond, 'b c t h w -> b c (repeat t) h w', repeat=z.shape[2]) + cond["c_concat"] = [img_cat_cond] # b c 1 h w + + if unconditional_guidance_scale != 1.0: + if model.uncond_type == "empty_seq": + prompts = batch_size * [""] + uc_emb = model.get_learned_conditioning(prompts) + elif model.uncond_type == "zero_embed": + uc_emb = torch.zeros_like(cond_emb) + uc_img_emb = model.embedder(torch.zeros_like(img)) ## b l c + uc_img_emb = model.image_proj_model(uc_img_emb) + uc = {"c_crossattn": [torch.cat([uc_emb,uc_img_emb],dim=1)]} + if model.model.conditioning_key == 'hybrid': + uc["c_concat"] = [img_cat_cond] + else: + uc = None + + ## we need one more unconditioning image=yes, text="" + if multiple_cond_cfg and cfg_img != 1.0: + uc_2 = {"c_crossattn": [torch.cat([uc_emb,img_emb],dim=1)]} + if model.model.conditioning_key == 'hybrid': + uc_2["c_concat"] = [img_cat_cond] + kwargs.update({"unconditional_conditioning_img_nonetext": uc_2}) + else: + kwargs.update({"unconditional_conditioning_img_nonetext": None}) + + z0 = None + cond_mask = None + + batch_variants = [] + for _ in range(n_samples): + + if z0 is not None: + cond_z0 = z0.clone() + kwargs.update({"clean_cond": True}) + else: + cond_z0 = None + if ddim_sampler is not None: + + samples, _ = ddim_sampler.sample(S=ddim_steps, + conditioning=cond, + batch_size=batch_size, + shape=noise_shape[1:], + verbose=False, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=uc, + eta=ddim_eta, + cfg_img=cfg_img, + mask=cond_mask, + x0=cond_z0, + fs=fs, + timestep_spacing=timestep_spacing, + guidance_rescale=guidance_rescale, + **kwargs + ) + + ## reconstruct from latent to pixel space + batch_images = model.decode_first_stage(samples) + batch_variants.append(batch_images) + ## variants, batch, c, t, h, w + batch_variants = torch.stack(batch_variants) + return batch_variants.permute(1, 0, 2, 3, 4, 5) + + +def run_inference(args, gpu_num, gpu_no): + ## model config + config = OmegaConf.load(args.config) + model_config = config.pop("model", OmegaConf.create()) + + ## set use_checkpoint as False as when using deepspeed, it encounters an error "deepspeed backend not set" + model_config['params']['unet_config']['params']['use_checkpoint'] = False + model = instantiate_from_config(model_config) + model = model.cuda(gpu_no) + model.perframe_ae = args.perframe_ae + assert os.path.exists(args.ckpt_path), "Error: checkpoint Not Found!" + model = load_model_checkpoint(model, args.ckpt_path) + model.eval() + + ## run over data + assert (args.height % 16 == 0) and (args.width % 16 == 0), "Error: image size [h,w] should be multiples of 16!" + assert args.bs == 1, "Current implementation only support [batch size = 1]!" + ## latent noise shape + h, w = args.height // 8, args.width // 8 + channels = model.model.diffusion_model.out_channels + n_frames = args.video_length + print(f'Inference with {n_frames} frames') + noise_shape = [args.bs, channels, n_frames, h, w] + + fakedir = os.path.join(args.savedir, "samples") + fakedir_separate = os.path.join(args.savedir, "samples_separate") + + # os.makedirs(fakedir, exist_ok=True) + os.makedirs(fakedir_separate, exist_ok=True) + + ## prompt file setting + assert os.path.exists(args.prompt_dir), "Error: prompt file Not Found!" + filename_list, data_list, prompt_list = load_data_prompts(args.prompt_dir, video_size=(args.height, args.width), video_frames=n_frames, gfi=args.gfi) + num_samples = len(prompt_list) + samples_split = num_samples // gpu_num + print('Prompts testing [rank:%d] %d/%d samples loaded.'%(gpu_no, samples_split, num_samples)) + #indices = random.choices(list(range(0, num_samples)), k=samples_per_device) + indices = list(range(samples_split*gpu_no, samples_split*(gpu_no+1))) + prompt_list_rank = [prompt_list[i] for i in indices] + data_list_rank = [data_list[i] for i in indices] + filename_list_rank = [filename_list[i] for i in indices] + + start = time.time() + with torch.no_grad(), torch.cuda.amp.autocast(): + for idx, indice in tqdm(enumerate(range(0, len(prompt_list_rank), args.bs)), desc='Sample Batch'): + prompts = prompt_list_rank[indice:indice+args.bs] + videos = data_list_rank[indice:indice+args.bs] + filenames = filename_list_rank[indice:indice+args.bs] + if isinstance(videos, list): + videos = torch.stack(videos, dim=0).to("cuda") + else: + videos = videos.unsqueeze(0).to("cuda") + + batch_samples = image_guided_synthesis(model, prompts, videos, noise_shape, args.n_samples, args.ddim_steps, args.ddim_eta, \ + args.unconditional_guidance_scale, args.cfg_img, args.frame_stride, args.text_input, args.multiple_cond_cfg, args.loop, args.gfi, args.timestep_spacing, args.guidance_rescale) + + ## save each example individually + for nn, samples in enumerate(batch_samples): + ## samples : [n_samples,c,t,h,w] + prompt = prompts[nn] + filename = filenames[nn] + # save_results(prompt, samples, filename, fakedir, fps=8, loop=args.loop) + save_results_seperate(prompt, samples, filename, fakedir, fps=8, loop=args.loop) + + print(f"Saved in {args.savedir}. Time used: {(time.time() - start):.2f} seconds") + + +def get_parser(): + parser = argparse.ArgumentParser() + parser.add_argument("--savedir", type=str, default=None, help="results saving path") + 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_dir", type=str, default=None, help="a data dir containing videos and prompts") + 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, should be one") + 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("--frame_stride", type=int, default=3, help="frame stride control for 256 model (larger->larger motion), FPS control for 512 or 1024 model (smaller->larger motion)") + parser.add_argument("--unconditional_guidance_scale", type=float, default=1.0, help="prompt classifier-free guidance") + parser.add_argument("--seed", type=int, default=123, help="seed for seed_everything") + parser.add_argument("--video_length", type=int, default=16, help="inference video length") + parser.add_argument("--negative_prompt", action='store_true', default=False, help="negative prompt") + parser.add_argument("--text_input", action='store_true', default=False, help="input text to I2V model or not") + parser.add_argument("--multiple_cond_cfg", action='store_true', default=False, help="use multi-condition cfg or not") + parser.add_argument("--cfg_img", type=float, default=None, help="guidance scale for image conditioning") + parser.add_argument("--timestep_spacing", type=str, default="uniform", help="The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.") + parser.add_argument("--guidance_rescale", type=float, default=0.0, help="guidance rescale in [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891)") + parser.add_argument("--perframe_ae", action='store_true', default=False, help="if we use per-frame AE decoding, set it to True to save GPU memory, especially for the model of 576x1024") + + ## currently not support looping video and generative frame interpolation + parser.add_argument("--loop", action='store_true', default=False, help="generate looping videos or not") + parser.add_argument("--gfi", action='store_true', default=False, help="generate generative frame interpolation (gfi) or not") + return parser + + +if __name__ == '__main__': + now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + print("@DynamiCrafter cond-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/scripts/gradio/__pycache__/i2v_test.cpython-39.pyc b/scripts/gradio/__pycache__/i2v_test.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1f5c1f1d4d63dcfdffb0025a6e21933f371a58ab Binary files /dev/null and b/scripts/gradio/__pycache__/i2v_test.cpython-39.pyc differ diff --git a/scripts/gradio/i2v_test.py b/scripts/gradio/i2v_test.py new file mode 100644 index 0000000000000000000000000000000000000000..848f3166c3788a284fe785a322a0b8c38e090c6d --- /dev/null +++ b/scripts/gradio/i2v_test.py @@ -0,0 +1,106 @@ +import os +import time +from omegaconf import OmegaConf +import torch +from scripts.evaluation.funcs import load_model_checkpoint, save_videos, batch_ddim_sampling, get_latent_z +from utils.utils import instantiate_from_config +from huggingface_hub import hf_hub_download +from einops import repeat +import torchvision.transforms as transforms +from pytorch_lightning import seed_everything + + +class Image2Video(): + def __init__(self,result_dir='./tmp/',gpu_num=1,resolution='256_256') -> None: + self.resolution = (int(resolution.split('_')[0]), int(resolution.split('_')[1])) #hw + self.download_model() + + self.result_dir = result_dir + if not os.path.exists(self.result_dir): + os.mkdir(self.result_dir) + ckpt_path='checkpoints/dynamicrafter_'+resolution.split('_')[1]+'_v1/model.ckpt' + config_file='configs/inference_'+resolution.split('_')[1]+'_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=7.5, eta=1.0, fs=3, seed=123): + seed_everything(seed) + transform = transforms.Compose([ + transforms.Resize(min(self.resolution)), + transforms.CenterCrop(self.resolution), + ]) + 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.out_channels + frames = model.temporal_length + h, w = self.resolution[0] // 8, self.resolution[1] // 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().to(model.device) + img_tensor = (img_tensor / 255. - 0.5) * 2 + + image_tensor_resized = transform(img_tensor) #3,h,w + videos = image_tensor_resized.unsqueeze(0) # bchw + + z = get_latent_z(model, videos.unsqueeze(2)) #bc,1,hw + + img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames) + + cond_images = model.embedder(img_tensor.unsqueeze(0)) ## blc + img_emb = model.image_proj_model(cond_images) + + imtext_cond = torch.cat([text_emb, img_emb], dim=1) + + fs = torch.tensor([fs], dtype=torch.long, device=model.device) + cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat]} + + ## 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[:40] + if len(prompt_str) == 0: + prompt_str = 'empty_prompt' + + 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 = 'Doubiiu/DynamiCrafter' + filename_list = ['model.ckpt'] + if not os.path.exists('./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/'): + os.makedirs('./dynamicrafter_'+str(self.resolution[1])+'_v1/') + for filename in filename_list: + local_file = os.path.join('./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/', filename) + if not os.path.exists(local_file): + hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir='./checkpoints/dynamicrafter_'+str(self.resolution[1])+'_v1/', local_dir_use_symlinks=False) + +if __name__ == '__main__': + i2v = Image2Video() + video_path = i2v.get_image('prompts/art.png','man fishing in a boat at sunset') + print('done', video_path) \ No newline at end of file diff --git a/scripts/run.sh b/scripts/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..bc9b0e463eb5ca4a0b1a87eb33da7ae605911b71 --- /dev/null +++ b/scripts/run.sh @@ -0,0 +1,61 @@ +version=$1 ##1024, 512, 256 +seed=123 +name=dynamicrafter_$1_seed${seed} + +ckpt=checkpoints/dynamicrafter_$1_v1/model.ckpt +config=configs/inference_$1_v1.0.yaml + +prompt_dir=prompts/$1/ +res_dir="results" + +if [ "$1" == "256" ]; then + H=256 + FS=3 ## This model adopts frame stride=3, range recommended: 1-6 (larger value -> larger motion) +elif [ "$1" == "512" ]; then + H=320 + FS=24 ## This model adopts FPS=24, range recommended: 15-30 (smaller value -> larger motion) +elif [ "$1" == "1024" ]; then + H=576 + FS=10 ## This model adopts FPS=10, range recommended: 15-5 (smaller value -> larger motion) +else + echo "Invalid input. Please enter 256, 512, or 1024." + exit 1 +fi + +if [ "$1" == "256" ]; then +CUDA_VISIBLE_DEVICES=2 python3 scripts/evaluation/inference.py \ +--seed ${seed} \ +--ckpt_path $ckpt \ +--config $config \ +--savedir $res_dir/$name \ +--n_samples 1 \ +--bs 1 --height ${H} --width $1 \ +--unconditional_guidance_scale 7.5 \ +--ddim_steps 50 \ +--ddim_eta 1.0 \ +--prompt_dir $prompt_dir \ +--text_input \ +--video_length 16 \ +--frame_stride ${FS} +else +CUDA_VISIBLE_DEVICES=2 python3 scripts/evaluation/inference.py \ +--seed ${seed} \ +--ckpt_path $ckpt \ +--config $config \ +--savedir $res_dir/$name \ +--n_samples 1 \ +--bs 1 --height ${H} --width $1 \ +--unconditional_guidance_scale 7.5 \ +--ddim_steps 50 \ +--ddim_eta 1.0 \ +--prompt_dir $prompt_dir \ +--text_input \ +--video_length 16 \ +--frame_stride ${FS} \ +--timestep_spacing 'uniform_trailing' --guidance_rescale 0.7 --perframe_ae +fi + + +## multi-cond CFG: the is s_txt, is s_img +#--multiple_cond_cfg --cfg_img 7.5 +#--loop \ No newline at end of file diff --git a/scripts/run_mp.sh b/scripts/run_mp.sh new file mode 100644 index 0000000000000000000000000000000000000000..2ae2001f40f3cc613049aa95cecfc1e975773dd3 --- /dev/null +++ b/scripts/run_mp.sh @@ -0,0 +1,102 @@ +version=$1 ##1024, 512, 256 +seed=123 + +name=dynamicrafter_$1_mp_seed${seed} + +ckpt=checkpoints/dynamicrafter_$1_v1/model.ckpt +config=configs/inference_$1_v1.0.yaml + +prompt_dir=prompts/$1/ +res_dir="results" + +if [ "$1" == "256" ]; then + H=256 + FS=3 ## This model adopts frame stride=3 +elif [ "$1" == "512" ]; then + H=320 + FS=24 ## This model adopts FPS=24 +elif [ "$1" == "1024" ]; then + H=576 + FS=10 ## This model adopts FPS=10 +else + echo "Invalid input. Please enter 256, 512, or 1024." + exit 1 +fi + +# if [ "$1" == "256" ]; then +# CUDA_VISIBLE_DEVICES=2 python3 scripts/evaluation/inference.py \ +# --seed 123 \ +# --ckpt_path $ckpt \ +# --config $config \ +# --savedir $res_dir/$name \ +# --n_samples 1 \ +# --bs 1 --height ${H} --width $1 \ +# --unconditional_guidance_scale 7.5 \ +# --ddim_steps 50 \ +# --ddim_eta 1.0 \ +# --prompt_dir $prompt_dir \ +# --text_input \ +# --video_length 16 \ +# --frame_stride ${FS} +# else +# CUDA_VISIBLE_DEVICES=2 python3 scripts/evaluation/inference.py \ +# --seed 123 \ +# --ckpt_path $ckpt \ +# --config $config \ +# --savedir $res_dir/$name \ +# --n_samples 1 \ +# --bs 1 --height ${H} --width $1 \ +# --unconditional_guidance_scale 7.5 \ +# --ddim_steps 50 \ +# --ddim_eta 1.0 \ +# --prompt_dir $prompt_dir \ +# --text_input \ +# --video_length 16 \ +# --frame_stride ${FS} \ +# --timestep_spacing 'uniform_trailing' --guidance_rescale 0.7 +# fi + + +## multi-cond CFG: the is s_txt, is s_img +#--multiple_cond_cfg --cfg_img 7.5 +#--loop + +## inference using single node with multi-GPUs: +if [ "$1" == "256" ]; then +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 -m torch.distributed.launch \ +--nproc_per_node=8 --nnodes=1 --master_addr=127.0.0.1 --master_port=23456 --node_rank=0 \ +scripts/evaluation/ddp_wrapper.py \ +--module 'inference' \ +--seed ${seed} \ +--ckpt_path $ckpt \ +--config $config \ +--savedir $res_dir/$name \ +--n_samples 1 \ +--bs 1 --height ${H} --width $1 \ +--unconditional_guidance_scale 7.5 \ +--ddim_steps 50 \ +--ddim_eta 1.0 \ +--prompt_dir $prompt_dir \ +--text_input \ +--video_length 16 \ +--frame_stride ${FS} +else +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 -m torch.distributed.launch \ +--nproc_per_node=8 --nnodes=1 --master_addr=127.0.0.1 --master_port=23456 --node_rank=0 \ +scripts/evaluation/ddp_wrapper.py \ +--module 'inference' \ +--seed ${seed} \ +--ckpt_path $ckpt \ +--config $config \ +--savedir $res_dir/$name \ +--n_samples 1 \ +--bs 1 --height ${H} --width $1 \ +--unconditional_guidance_scale 7.5 \ +--ddim_steps 50 \ +--ddim_eta 1.0 \ +--prompt_dir $prompt_dir \ +--text_input \ +--video_length 16 \ +--frame_stride ${FS} \ +--timestep_spacing 'uniform_trailing' --guidance_rescale 0.7 --perframe_ae +fi \ No newline at end of file diff --git a/utils/__pycache__/utils.cpython-39.pyc b/utils/__pycache__/utils.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b1e28105d38250b218e943a22cc16351637ff045 Binary files /dev/null and b/utils/__pycache__/utils.cpython-39.pyc differ diff --git a/utils/utils.py b/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ef116e56f6f61dce0cf4942986ac659bf93fcd24 --- /dev/null +++ b/utils/utils.py @@ -0,0 +1,77 @@ +import importlib +import numpy as np +import cv2 +import torch +import torch.distributed as dist + + +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 load_npz_from_dir(data_dir): + data = [np.load(os.path.join(data_dir, data_name))['arr_0'] for data_name in os.listdir(data_dir)] + data = np.concatenate(data, axis=0) + return data + + +def load_npz_from_paths(data_paths): + data = [np.load(data_path)['arr_0'] for data_path in data_paths] + data = np.concatenate(data, axis=0) + return data + + +def resize_numpy_image(image, max_resolution=512 * 512, resize_short_edge=None): + h, w = image.shape[:2] + if resize_short_edge is not None: + k = resize_short_edge / min(h, w) + else: + k = max_resolution / (h * w) + k = k**0.5 + h = int(np.round(h * k / 64)) * 64 + w = int(np.round(w * k / 64)) * 64 + image = cv2.resize(image, (w, h), interpolation=cv2.INTER_LANCZOS4) + return image + + +def setup_dist(args): + if dist.is_initialized(): + return + torch.cuda.set_device(args.local_rank) + torch.distributed.init_process_group( + 'nccl', + init_method='env://' + ) \ No newline at end of file