open_sora_inference

assets/texts/t2i_samples.txt

+A small cactus with a happy face in the Sahara desert.
+Bright scene, aerial view,ancient city, fantasy, gorgeous light, mirror reflection, high detail, wide angle lens.
+Nature vs human nature, surreal, UHD, 8k, hyper details, rich colors, photograph.
+Poster of a mechanical cat, techical Schematics viewed from front.
+Luffy from ONEPIECE, handsome face, fantasy.
+Real beautiful woman.
+A alpaca made of colorful building blocks, cyberpunk.
+artistic

assets/texts/t2v_latte.txt

+Yellow and black tropical fish dart through the sea.
+An epic tornado attacking above aglowing city at night.
+Slow pan upward of blazing oak fire in an indoor fireplace.
+a cat wearing sunglasses and working as a lifeguard at pool.
+Sunset over the sea.
+A dog in astronaut suit and sunglasses floating in space.
+A astronaut in flying in space, 4k, high resolution

assets/texts/t2v_samples.txt

+A soaring drone footage captures the majestic beauty of a coastal cliff, its red and yellow stratified rock faces rich in color and against the vibrant turquoise of the sea. Seabirds can be seen taking flight around the cliff's precipices. As the drone slowly moves from different angles, the changing sunlight casts shifting shadows that highlight the rugged textures of the cliff and the surrounding calm sea. The water gently laps at the rock base and the greenery that clings to the top of the cliff, and the scene gives a sense of peaceful isolation at the fringes of the ocean. The video captures the essence of pristine natural beauty untouched by human structures.
+The video captures the majestic beauty of a waterfall cascading down a cliff into a serene lake. The waterfall, with its powerful flow, is the central focus of the video. The surrounding landscape is lush and green, with trees and foliage adding to the natural beauty of the scene. The camera angle provides a bird's eye view of the waterfall, allowing viewers to appreciate the full height and grandeur of the waterfall. The video is a stunning representation of nature's power and beauty.
+A vibrant scene of a snowy mountain landscape. The sky is filled with a multitude of colorful hot air balloons, each floating at different heights, creating a dynamic and lively atmosphere. The balloons are scattered across the sky, some closer to the viewer, others further away, adding depth to the scene.  Below, the mountainous terrain is blanketed in a thick layer of snow, with a few patches of bare earth visible here and there. The snow-covered mountains provide a stark contrast to the colorful balloons, enhancing the visual appeal of the scene.  In the foreground, a few cars can be seen driving along a winding road that cuts through the mountains. The cars are small compared to the vastness of the landscape, emphasizing the grandeur of the surroundings.  The overall style of the video is a mix of adventure and tranquility, with the hot air balloons adding a touch of whimsy to the otherwise serene mountain landscape. The video is likely shot during the day, as the lighting is bright and even, casting soft shadows on the snow-covered mountains.
+The vibrant beauty of a sunflower field. The sunflowers, with their bright yellow petals and dark brown centers, are in full bloom, creating a stunning contrast against the green leaves and stems. The sunflowers are arranged in neat rows, creating a sense of order and symmetry. The sun is shining brightly, casting a warm glow on the flowers and highlighting their intricate details. The video is shot from a low angle, looking up at the sunflowers, which adds a sense of grandeur and awe to the scene. The sunflowers are the main focus of the video, with no other objects or people present. The video is a celebration of nature's beauty and the simple joy of a sunny day in the countryside.
+A serene underwater scene featuring a sea turtle swimming through a coral reef. The turtle, with its greenish-brown shell, is the main focus of the video, swimming gracefully towards the right side of the frame. The coral reef, teeming with life, is visible in the background, providing a vibrant and colorful backdrop to the turtle's journey. Several small fish, darting around the turtle, add a sense of movement and dynamism to the scene. The video is shot from a slightly elevated angle, providing a comprehensive view of the turtle's surroundings. The overall style of the video is calm and peaceful, capturing the beauty and tranquility of the underwater world.
+A vibrant underwater scene. A group of blue fish, with yellow fins, are swimming around a coral reef. The coral reef is a mix of brown and green, providing a natural habitat for the fish. The water is a deep blue, indicating a depth of around 30 feet. The fish are swimming in a circular pattern around the coral reef, indicating a sense of motion and activity. The overall scene is a beautiful representation of marine life.
+A bustling city street at night, filled with the glow of car headlights and the ambient light of streetlights. The scene is a blur of motion, with cars speeding by and pedestrians navigating the crosswalks. The cityscape is a mix of towering buildings and illuminated signs, creating a vibrant and dynamic atmosphere. The perspective of the video is from a high angle, providing a bird's eye view of the street and its surroundings. The overall style of the video is dynamic and energetic, capturing the essence of urban life at night.
+A snowy forest landscape with a dirt road running through it. The road is flanked by trees covered in snow, and the ground is also covered in snow. The sun is shining, creating a bright and serene atmosphere. The road appears to be empty, and there are no people or animals visible in the video. The style of the video is a natural landscape shot, with a focus on the beauty of the snowy forest and the peacefulness of the road.
+The dynamic movement of tall, wispy grasses swaying in the wind. The sky above is filled with clouds, creating a dramatic backdrop. The sunlight pierces through the clouds, casting a warm glow on the scene. The grasses are a mix of green and brown, indicating a change in seasons. The overall style of the video is naturalistic, capturing the beauty of the landscape in a realistic manner. The focus is on the grasses and their movement, with the sky serving as a secondary element. The video does not contain any human or animal elements.
+A serene night scene in a forested area. The first frame shows a tranquil lake reflecting the star-filled sky above. The second frame reveals a beautiful sunset, casting a warm glow over the landscape. The third frame showcases the night sky, filled with stars and a vibrant Milky Way galaxy. The video is a time-lapse, capturing the transition from day to night, with the lake and forest serving as a constant backdrop. The style of the video is naturalistic, emphasizing the beauty of the night sky and the peacefulness of the forest.

assets/texts/t2v_sora.txt

+A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about.
+Several giant wooly mammoths approach treading through a snowy meadow, their long wooly fur lightly blows in the wind as they walk, snow covered trees and dramatic snow capped mountains in the distance, mid afternoon light with wispy clouds and a sun high in the distance creates a warm glow, the low camera view is stunning capturing the large furry mammal with beautiful photography, depth of field.
+A movie trailer featuring the adventures of the 30 year old space man wearing a red wool knitted motorcycle helmet, blue sky, salt desert, cinematic style, shot on 35mm film, vivid colors.
+Drone view of waves crashing against the rugged cliffs along Big Sur’s garay point beach. The crashing blue waters create white-tipped waves, while the golden light of the setting sun illuminates the rocky shore. A small island with a lighthouse sits in the distance, and green shrubbery covers the cliff’s edge. The steep drop from the road down to the beach is a dramatic feat, with the cliff’s edges jutting out over the sea. This is a view that captures the raw beauty of the coast and the rugged landscape of the Pacific Coast Highway.
+Animated scene features a close-up of a short fluffy monster kneeling beside a melting red candle. The art style is 3D and realistic, with a focus on lighting and texture. The mood of the painting is one of wonder and curiosity, as the monster gazes at the flame with wide eyes and open mouth. Its pose and expression convey a sense of innocence and playfulness, as if it is exploring the world around it for the first time. The use of warm colors and dramatic lighting further enhances the cozy atmosphere of the image.
+A gorgeously rendered papercraft world of a coral reef, rife with colorful fish and sea creatures.
+This close-up shot of a Victoria crowned pigeon showcases its striking blue plumage and red chest. Its crest is made of delicate, lacy feathers, while its eye is a striking red color. The bird’s head is tilted slightly to the side, giving the impression of it looking regal and majestic. The background is blurred, drawing attention to the bird’s striking appearance.
+Photorealistic closeup video of two pirate ships battling each other as they sail inside a cup of coffee.
+A young man at his 20s is sitting on a piece of cloud in the sky, reading a book.
+Historical footage of California during the gold rush.
+A close up view of a glass sphere that has a zen garden within it. There is a small dwarf in the sphere who is raking the zen garden and creating patterns in the sand.
+Extreme close up of a 24 year old woman’s eye blinking, standing in Marrakech during magic hour, cinematic film shot in 70mm, depth of field, vivid colors, cinematic
+A cartoon kangaroo disco dances.
+A beautiful homemade video showing the people of Lagos, Nigeria in the year 2056. Shot with a mobile phone camera.
+A petri dish with a bamboo forest growing within it that has tiny red pandas running around.
+The camera rotates around a large stack of vintage televisions all showing different programs — 1950s sci-fi movies, horror movies, news, static, a 1970s sitcom, etc, set inside a large New York museum gallery.
+3D animation of a small, round, fluffy creature with big, expressive eyes explores a vibrant, enchanted forest. The creature, a whimsical blend of a rabbit and a squirrel, has soft blue fur and a bushy, striped tail. It hops along a sparkling stream, its eyes wide with wonder. The forest is alive with magical elements: flowers that glow and change colors, trees with leaves in shades of purple and silver, and small floating lights that resemble fireflies. The creature stops to interact playfully with a group of tiny, fairy-like beings dancing around a mushroom ring. The creature looks up in awe at a large, glowing tree that seems to be the heart of the forest.
+The camera follows behind a white vintage SUV with a black roof rack as it speeds up a steep dirt road surrounded by pine trees on a steep mountain slope, dust kicks up from it’s tires, the sunlight shines on the SUV as it speeds along the dirt road, casting a warm glow over the scene. The dirt road curves gently into the distance, with no other cars or vehicles in sight. The trees on either side of the road are redwoods, with patches of greenery scattered throughout. The car is seen from the rear following the curve with ease, making it seem as if it is on a rugged drive through the rugged terrain. The dirt road itself is surrounded by steep hills and mountains, with a clear blue sky above with wispy clouds.
+Reflections in the window of a train traveling through the Tokyo suburbs.
+A drone camera circles around a beautiful historic church built on a rocky outcropping along the Amalfi Coast, the view showcases historic and magnificent architectural details and tiered pathways and patios, waves are seen crashing against the rocks below as the view overlooks the horizon of the coastal waters and hilly landscapes of the Amalfi Coast Italy, several distant people are seen walking and enjoying vistas on patios of the dramatic ocean views, the warm glow of the afternoon sun creates a magical and romantic feeling to the scene, the view is stunning captured with beautiful photography.
+A large orange octopus is seen resting on the bottom of the ocean floor, blending in with the sandy and rocky terrain. Its tentacles are spread out around its body, and its eyes are closed. The octopus is unaware of a king crab that is crawling towards it from behind a rock, its claws raised and ready to attack. The crab is brown and spiny, with long legs and antennae. The scene is captured from a wide angle, showing the vastness and depth of the ocean. The water is clear and blue, with rays of sunlight filtering through. The shot is sharp and crisp, with a high dynamic range. The octopus and the crab are in focus, while the background is slightly blurred, creating a depth of field effect.
+A flock of paper airplanes flutters through a dense jungle, weaving around trees as if they were migrating birds.
+A cat waking up its sleeping owner demanding breakfast. The owner tries to ignore the cat, but the cat tries new tactics and finally the owner pulls out a secret stash of treats from under the pillow to hold the cat off a little longer.
+Borneo wildlife on the Kinabatangan River
+A Chinese Lunar New Year celebration video with Chinese Dragon.
+Tour of an art gallery with many beautiful works of art in different styles.
+Beautiful, snowy Tokyo city is bustling. The camera moves through the bustling city street, following several people enjoying the beautiful snowy weather and shopping at nearby stalls. Gorgeous sakura petals are flying through the wind along with snowflakes.
+A stop motion animation of a flower growing out of the windowsill of a suburban house.
+The story of a robot’s life in a cyberpunk setting.
+An extreme close-up of an gray-haired man with a beard in his 60s, he is deep in thought pondering the history of the universe as he sits at a cafe in Paris, his eyes focus on people offscreen as they walk as he sits mostly motionless, he is dressed in a wool coat suit coat with a button-down shirt , he wears a brown beret and glasses and has a very professorial appearance, and the end he offers a subtle closed-mouth smile as if he found the answer to the mystery of life, the lighting is very cinematic with the golden light and the Parisian streets and city in the background, depth of field, cinematic 35mm film.
+A beautiful silhouette animation shows a wolf howling at the moon, feeling lonely, until it finds its pack.
+New York City submerged like Atlantis. Fish, whales, sea turtles and sharks swim through the streets of New York.
+A litter of golden retriever puppies playing in the snow. Their heads pop out of the snow, covered in.
+Step-printing scene of a person running, cinematic film shot in 35mm.
+Five gray wolf pups frolicking and chasing each other around a remote gravel road, surrounded by grass. The pups run and leap, chasing each other, and nipping at each other, playing.
+Basketball through hoop then explodes.
+Archeologists discover a generic plastic chair in the desert, excavating and dusting it with great care.
+A grandmother with neatly combed grey hair stands behind a colorful birthday cake with numerous candles at a wood dining room table, expression is one of pure joy and happiness, with a happy glow in her eye. She leans forward and blows out the candles with a gentle puff, the cake has pink frosting and sprinkles and the candles cease to flicker, the grandmother wears a light blue blouse adorned with floral patterns, several happy friends and family sitting at the table can be seen celebrating, out of focus. The scene is beautifully captured, cinematic, showing a 3/4 view of the grandmother and the dining room. Warm color tones and soft lighting enhance the mood.
+The camera directly faces colorful buildings in Burano Italy. An adorable dalmation looks through a window on a building on the ground floor. Many people are walking and cycling along the canal streets in front of the buildings.
+An adorable happy otter confidently stands on a surfboard wearing a yellow lifejacket, riding along turquoise tropical waters near lush tropical islands, 3D digital render art style.
+This close-up shot of a chameleon showcases its striking color changing capabilities. The background is blurred, drawing attention to the animal’s striking appearance.
+A corgi vlogging itself in tropical Maui.
+A white and orange tabby cat is seen happily darting through a dense garden, as if chasing something. Its eyes are wide and happy as it jogs forward, scanning the branches, flowers, and leaves as it walks. The path is narrow as it makes its way between all the plants. the scene is captured from a ground-level angle, following the cat closely, giving a low and intimate perspective. The image is cinematic with warm tones and a grainy texture. The scattered daylight between the leaves and plants above creates a warm contrast, accentuating the cat’s orange fur. The shot is clear and sharp, with a shallow depth of field.
+Aerial view of Santorini during the blue hour, showcasing the stunning architecture of white Cycladic buildings with blue domes. The caldera views are breathtaking, and the lighting creates a beautiful, serene atmosphere.
+Tiltshift of a construction site filled with workers, equipment, and heavy machinery.
+A giant, towering cloud in the shape of a man looms over the earth. The cloud man shoots lighting bolts down to the earth.
+A Samoyed and a Golden Retriever dog are playfully romping through a futuristic neon city at night. The neon lights emitted from the nearby buildings glistens off of their fur.
+The Glenfinnan Viaduct is a historic railway bridge in Scotland, UK, that crosses over the west highland line between the towns of Mallaig and Fort William. It is a stunning sight as a steam train leaves the bridge, traveling over the arch-covered viaduct. The landscape is dotted with lush greenery and rocky mountains, creating a picturesque backdrop for the train journey. The sky is blue and the sun is shining, making for a beautiful day to explore this majestic spot.

assets/texts/ucf101_id.txt

+0
+1
+2
+3
+4
+5

assets/texts/ucf101_labels.txt

+Apply Eye Makeup
+Apply Lipstick
+Archery
+Baby Crawling
+Balance Beam
+Band Marching

configs/dit/inference/16x256x256.py

+num_frames = 16
+fps = 8
+image_size = (256, 256)
+
+# Define model
+model = dict(
+    type="DiT-XL/2",
+    condition="text",
+    from_pretrained="PRETRAINED_MODEL",
+)
+vae = dict(
+    type="VideoAutoencoderKL",
+    from_pretrained="stabilityai/sd-vae-ft-ema",
+)
+text_encoder = dict(
+    type="clip",
+    from_pretrained="openai/clip-vit-base-patch32",
+    model_max_length=77,
+)
+scheduler = dict(
+    type="dpm-solver",
+    num_sampling_steps=20,
+    cfg_scale=4.0,
+)
+dtype = "fp16"
+
+# Others
+batch_size = 2
+seed = 42
+prompt_path = "./assets/texts/ucf101_labels.txt"
+save_dir = "./outputs/samples/"

configs/dit/inference/1x256x256-class.py

+num_frames = 1
+fps = 1
+image_size = (256, 256)
+
+# Define model
+model = dict(
+    type="DiT-XL/2",
+    no_temporal_pos_emb=True,
+    condition="label_1000",
+    from_pretrained="DiT-XL-2-256x256.pt",
+)
+vae = dict(
+    type="VideoAutoencoderKL",
+    from_pretrained="stabilityai/sd-vae-ft-ema",
+)
+text_encoder = dict(
+    type="classes",
+    num_classes=1000,
+)
+scheduler = dict(
+    type="dpm-solver",
+    num_sampling_steps=20,
+    cfg_scale=4.0,
+)
+dtype = "fp16"
+
+# Others
+batch_size = 2
+seed = 42
+prompt_path = "./assets/texts/imagenet_id.txt"
+save_dir = "./outputs/samples/"

configs/dit/inference/1x256x256.py

+num_frames = 1
+fps = 1
+image_size = (256, 256)
+
+# Define model
+model = dict(
+    type="DiT-XL/2",
+    no_temporal_pos_emb=True,
+    condition="text",
+    from_pretrained="PRETRAINED_MODEL",
+)
+vae = dict(
+    type="VideoAutoencoderKL",
+    from_pretrained="stabilityai/sd-vae-ft-ema",
+)
+text_encoder = dict(
+    type="clip",
+    from_pretrained="openai/clip-vit-base-patch32",
+    model_max_length=77,
+)
+scheduler = dict(
+    type="dpm-solver",
+    num_sampling_steps=20,
+    cfg_scale=4.0,
+)
+dtype = "fp16"
+
+# Others
+batch_size = 2
+seed = 42
+prompt_path = "./assets/texts/imagenet_labels.txt"
+save_dir = "./outputs/samples/"

configs/dit/train/16x256x256.py

+num_frames = 16
+frame_interval = 3
+image_size = (256, 256)
+
+# Define dataset
+root = None
+data_path = "CSV_PATH"
+use_image_transform = False
+num_workers = 4
+
+# Define acceleration
+dtype = "bf16"
+grad_checkpoint = False
+plugin = "zero2"
+sp_size = 1
+
+# Define model
+model = dict(
+    type="DiT-XL/2",
+    from_pretrained="DiT-XL-2-256x256.pt",
+    enable_flashattn=True,
+    enable_layernorm_kernel=True,
+)
+vae = dict(
+    type="VideoAutoencoderKL",
+    from_pretrained="stabilityai/sd-vae-ft-ema",
+)
+text_encoder = dict(
+    type="clip",
+    from_pretrained="openai/clip-vit-base-patch32",
+    model_max_length=77,
+)
+scheduler = dict(
+    type="iddpm",
+    timestep_respacing="",
+)
+
+# Others
+seed = 42
+outputs = "outputs"
+wandb = False
+
+epochs = 1000
+log_every = 10
+ckpt_every = 1000
+load = None
+
+batch_size = 8
+lr = 2e-5
+grad_clip = 1.0

configs/dit/train/1x256x256.py

+num_frames = 1
+frame_interval = 1
+image_size = (256, 256)
+
+# Define dataset
+root = None
+data_path = "CSV_PATH"
+use_image_transform = True
+num_workers = 4
+
+# Define acceleration
+dtype = "bf16"
+grad_checkpoint = False
+plugin = "zero2"
+sp_size = 1
+
+# Define model
+model = dict(
+    type="DiT-XL/2",
+    no_temporal_pos_emb=True,
+    enable_flashattn=True,
+    enable_layernorm_kernel=True,
+)
+vae = dict(
+    type="VideoAutoencoderKL",
+    from_pretrained="stabilityai/sd-vae-ft-ema",
+)
+text_encoder = dict(
+    type="clip",
+    from_pretrained="openai/clip-vit-base-patch32",
+    model_max_length=77,
+)
+scheduler = dict(
+    type="iddpm",
+    timestep_respacing="",
+)
+
+# Others
+seed = 42
+outputs = "outputs"
+wandb = False
+
+epochs = 1000
+log_every = 10
+ckpt_every = 1000
+load = None
+
+batch_size = 128
+lr = 1e-4  # according to DiT repo
+grad_clip = 1.0

configs/latte/inference/16x256x256-class.py

+num_frames = 16
+fps = 8
+image_size = (256, 256)
+
+# Define model
+model = dict(
+    type="Latte-XL/2",
+    condition="label_101",
+    from_pretrained="Latte-XL-2-256x256-ucf101.pt",
+)
+vae = dict(
+    type="VideoAutoencoderKL",
+    from_pretrained="stabilityai/sd-vae-ft-ema",
+)
+text_encoder = dict(
+    type="classes",
+    num_classes=101,
+)
+scheduler = dict(
+    type="dpm-solver",
+    num_sampling_steps=20,
+    cfg_scale=4.0,
+)
+dtype = "fp16"
+
+# Others
+batch_size = 2
+seed = 42
+prompt_path = "./assets/texts/ucf101_id.txt"
+save_dir = "./outputs/samples/"

configs/latte/inference/16x256x256.py

+num_frames = 16
+fps = 8
+image_size = (256, 256)
+
+# Define model
+model = dict(
+    type="Latte-XL/2",
+    condition="text",
+    from_pretrained="PRETRAINED_MODEL",
+)
+vae = dict(
+    type="VideoAutoencoderKL",
+    from_pretrained="stabilityai/sd-vae-ft-ema",
+)
+text_encoder = dict(
+    type="clip",
+    from_pretrained="openai/clip-vit-base-patch32",
+    model_max_length=77,
+)
+scheduler = dict(
+    type="dpm-solver",
+    num_sampling_steps=20,
+    cfg_scale=4.0,
+)
+dtype = "fp16"
+
+# Others
+batch_size = 2
+seed = 42
+prompt_path = "./assets/texts/ucf101_labels.txt"
+save_dir = "./outputs/samples/"

configs/latte/train/16x256x256.py

+num_frames = 16
+frame_interval = 3
+image_size = (256, 256)
+
+# Define dataset
+root = None
+data_path = "CSV_PATH"
+use_image_transform = False
+num_workers = 4
+
+# Define acceleration
+dtype = "bf16"
+grad_checkpoint = True
+plugin = "zero2"
+sp_size = 1
+
+# Define model
+model = dict(
+    type="Latte-XL/2",
+    enable_flashattn=True,
+    enable_layernorm_kernel=True,
+)
+vae = dict(
+    type="VideoAutoencoderKL",
+    from_pretrained="stabilityai/sd-vae-ft-ema",
+)
+text_encoder = dict(
+    type="clip",
+    from_pretrained="openai/clip-vit-base-patch32",
+    model_max_length=77,
+)
+scheduler = dict(
+    type="iddpm",
+    timestep_respacing="",
+)
+
+# Others
+seed = 42
+outputs = "outputs"
+wandb = False
+
+epochs = 1000
+log_every = 10
+ckpt_every = 1000
+load = None
+
+batch_size = 8
+lr = 2e-5
+grad_clip = 1.0

configs/opensora/inference/16x256x256.py

+num_frames = 16
+fps = 24 // 3
+image_size = (256, 256)
+
+# Define model
+model = dict(
+    type="STDiT-XL/2",
+    space_scale=0.5,
+    time_scale=1.0,
+    enable_flashattn=True,
+    enable_layernorm_kernel=True,
+    from_pretrained="PRETRAINED_MODEL",
+)
+vae = dict(
+    type="VideoAutoencoderKL",
+    from_pretrained="stabilityai/sd-vae-ft-ema",
+)
+text_encoder = dict(
+    type="t5",
+    from_pretrained="./pretrained_models/t5_ckpts",
+    model_max_length=120,
+)
+scheduler = dict(
+    type="iddpm",
+    num_sampling_steps=100,
+    cfg_scale=7.0,
+)
+dtype = "fp16"
+
+# Others
+batch_size = 2
+seed = 42
+prompt_path = "./assets/texts/t2v_samples.txt"
+save_dir = "./outputs/samples/"

configs/opensora/inference/16x512x512.py

+num_frames = 16
+fps = 24 // 3
+image_size = (512, 512)
+
+# Define model
+model = dict(
+    type="STDiT-XL/2",
+    space_scale=1.0,
+    time_scale=1.0,
+    enable_flashattn=True,
+    enable_layernorm_kernel=True,
+    from_pretrained="PRETRAINED_MODEL"
+)
+vae = dict(
+    type="VideoAutoencoderKL",
+    from_pretrained="stabilityai/sd-vae-ft-ema",
+    micro_batch_size=128,
+)
+text_encoder = dict(
+    type="t5",
+    from_pretrained="./pretrained_models/t5_ckpts",
+    model_max_length=120,
+)
+scheduler = dict(
+    type="iddpm",
+    num_sampling_steps=100,
+    cfg_scale=7.0,
+)
+dtype = "fp16"
+
+# Others
+batch_size = 2
+seed = 42
+prompt_path = "./assets/texts/t2v_samples.txt"
+save_dir = "./outputs/samples/"

configs/opensora/inference/64x512x512.py

+num_frames = 64
+fps = 24 // 2
+image_size = (512, 512)
+
+# Define model
+model = dict(
+    type="STDiT-XL/2",
+    space_scale=1.0,
+    time_scale=2 / 3,
+    enable_flashattn=True,
+    enable_layernorm_kernel=True,
+    from_pretrained="PRETRAINED_MODEL",
+)
+vae = dict(
+    type="VideoAutoencoderKL",
+    from_pretrained="stabilityai/sd-vae-ft-ema",
+    micro_batch_size=128,
+)
+text_encoder = dict(
+    type="t5",
+    from_pretrained="./pretrained_models/t5_ckpts",
+    model_max_length=120,
+)
+scheduler = dict(
+    type="iddpm",
+    num_sampling_steps=100,
+    cfg_scale=7.0,
+)
+dtype = "fp16"
+
+# Others
+batch_size = 1
+seed = 42
+prompt_path = "./assets/texts/t2v_samples.txt"
+save_dir = "./outputs/samples/"

configs/opensora/train/16x256x256.py

+num_frames = 16
+frame_interval = 3
+image_size = (256, 256)
+
+# Define dataset
+root = None
+data_path = "CSV_PATH"
+use_image_transform = False
+num_workers = 4
+
+# Define acceleration
+dtype = "bf16"
+grad_checkpoint = True
+plugin = "zero2"
+sp_size = 1
+
+# Define model
+model = dict(
+    type="STDiT-XL/2",
+    space_scale=0.5,
+    time_scale=1.0,
+    from_pretrained="./pretrained_models/pixart-alpha/PixArt-XL-2-512x512.pth",
+    enable_flashattn=True,
+    enable_layernorm_kernel=True,
+)
+vae = dict(
+    type="VideoAutoencoderKL",
+    from_pretrained="stabilityai/sd-vae-ft-ema",
+)
+text_encoder = dict(
+    type="t5",
+    from_pretrained="./pretrained_models/t5_ckpts",
+    model_max_length=120,
+    shardformer=True,
+)
+scheduler = dict(
+    type="iddpm",
+    timestep_respacing="",
+)
+
+# Others
+seed = 42
+outputs = "outputs"
+wandb = False
+
+epochs = 1000
+log_every = 10
+ckpt_every = 1000
+load = None
+
+batch_size = 8
+lr = 2e-5
+grad_clip = 1.0

configs/opensora/train/16x512x512.py

+num_frames = 16
+frame_interval = 3
+image_size = (512, 512)
+
+# Define dataset
+root = None
+data_path = "CSV_PATH"
+use_image_transform = False
+num_workers = 4
+
+# Define acceleration
+dtype = "bf16"
+grad_checkpoint = False
+plugin = "zero2"
+sp_size = 1
+
+# Define model
+model = dict(
+    type="STDiT-XL/2",
+    space_scale=1.0,
+    time_scale=1.0,
+    from_pretrained=None,
+    enable_flashattn=True,
+    enable_layernorm_kernel=True,
+)
+vae = dict(
+    type="VideoAutoencoderKL",
+    from_pretrained="stabilityai/sd-vae-ft-ema",
+    micro_batch_size=128,
+)
+text_encoder = dict(
+    type="t5",
+    from_pretrained="./pretrained_models/t5_ckpts",
+    model_max_length=120,
+    shardformer=True,
+)
+scheduler = dict(
+    type="iddpm",
+    timestep_respacing="",
+)
+
+# Others
+seed = 42
+outputs = "outputs"
+wandb = False
+
+epochs = 1000
+log_every = 10
+ckpt_every = 500
+load = None
+
+batch_size = 8
+lr = 2e-5
+grad_clip = 1.0

configs/opensora/train/360x512x512.py

+num_frames = 360
+frame_interval = 1
+image_size = (512, 512)
+
+# Define dataset
+root = None
+data_path = "CSV_PATH"
+use_image_transform = False
+num_workers = 4
+
+# Define acceleration
+dtype = "bf16"
+grad_checkpoint = True
+plugin = "zero2-seq"
+sp_size = 2
+
+# Define model
+model = dict(
+    type="STDiT-XL/2",
+    space_scale=1.0,
+    time_scale=2 / 3,
+    from_pretrained=None,
+    enable_flashattn=True,
+    enable_layernorm_kernel=True,
+    enable_sequence_parallelism=True,  # enable sq here
+)
+vae = dict(
+    type="VideoAutoencoderKL",
+    from_pretrained="stabilityai/sd-vae-ft-ema",
+    micro_batch_size=128,
+)
+text_encoder = dict(
+    type="t5",
+    from_pretrained="./pretrained_models/t5_ckpts",
+    model_max_length=120,
+    shardformer=True,
+)
+scheduler = dict(
+    type="iddpm",
+    timestep_respacing="",
+)
+
+# Others
+seed = 42
+outputs = "outputs"
+wandb = False
+
+epochs = 1000
+log_every = 10
+ckpt_every = 250
+load = None
+
+batch_size = 1
+lr = 2e-5
+grad_clip = 1.0