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CONTRIBUTING.md

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+# Contributing
+
+The Open-Sora project welcomes any constructive contribution from the community and the team is more than willing to work on problems you have encountered to make it a better project.
+
+## Development Environment Setup
+
+To contribute to Open-Sora, we would like to first guide you to set up a proper development environment so that you can better implement your code. You can install this library from source with the `editable` flag (`-e`, for development mode) so that your change to the source code will be reflected in runtime without re-installation. 
+
+You can refer to the [Installation Section](./README.md#installation) and replace `pip install -v .` with `pip install -v -e .`.
+
+
+### Code Style
+
+We have some static checks when you commit your code change, please make sure you can pass all the tests and make sure the coding style meets our requirements. We use pre-commit hook to make sure the code is aligned with the writing standard. To set up the code style checking, you need to follow the steps below.
+
+```shell
+# these commands are executed under the Open-Sora directory
+pip install pre-commit
+pre-commit install
+```
+
+Code format checking will be automatically executed when you commit your changes.
+
+
+## Contribution Guide
+
+You need to follow these steps below to make contribution to the main repository via pull request. You can learn about the details of pull request [here](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests).
+
+### 1. Fork the Official Repository
+
+Firstly, you need to visit the [Open-Sora repository](https://github.com/hpcaitech/Open-Sora) and fork into your own account. The `fork` button is at the right top corner of the web page alongside with buttons such as `watch` and `star`.
+
+Now, you can clone your own forked repository into your local environment.
+
+```shell
+git clone https://github.com/<YOUR-USERNAME>/Open-Sora.git
+```
+
+### 2. Configure Git
+
+You need to set the official repository as your upstream so that you can synchronize with the latest update in the official repository. You can learn about upstream [here](https://www.atlassian.com/git/tutorials/git-forks-and-upstreams).
+
+Then add the original repository as upstream
+
+```shell
+cd Open-Sora
+git remote add upstream https://github.com/hpcaitech/Open-Sora.git
+```
+
+you can use the following command to verify that the remote is set. You should see both `origin` and `upstream` in the output.
+
+```shell
+git remote -v
+```
+
+### 3. Synchronize with Official Repository
+
+Before you make changes to the codebase, it is always good to fetch the latest updates in the official repository. In order to do so, you can use the commands below.
+
+```shell
+git fetch upstream
+git checkout main
+git merge upstream/main
+git push origin main
+```
+
+### 5. Create a New Branch
+
+You should not make changes to the `main` branch of your forked repository as this might make upstream synchronization difficult. You can create a new branch with the appropriate name. General branch name format should start with `hotfix/` and `feature/`. `hotfix` is for bug fix and `feature` is for addition of a new feature.
+
+
+```shell
+git checkout -b <NEW-BRANCH-NAME>
+```
+
+### 6. Implementation and Code Commit
+
+Now you can implement your code change in the source code. Remember that you installed the system in development, thus you do not need to uninstall and install to make the code take effect. The code change will be reflected in every new PyThon execution.
+You can commit and push the changes to your local repository. The changes should be kept logical, modular and atomic.
+
+```shell
+git add -A
+git commit -m "<COMMIT-MESSAGE>"
+git push -u origin <NEW-BRANCH-NAME>
+```
+
+### 7. Open a Pull Request
+
+You can now create a pull request on the GitHub webpage of your repository. The source branch is `<NEW-BRANCH-NAME>` of your repository and the target branch should be `main` of `hpcaitech/Open-Sora`. After creating this pull request, you should be able to see it [here](https://github.com/hpcaitech/Open-Sora/pulls).
+
+The Open-Sora team will review your code change and merge your code if applicable.

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+      conditions.
+
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+      failure to comply consented to unless expressly agreed to by the
+      Licensor.
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+      as a limitation upon, or waiver of, any privileges and immunities
+      that apply to the Licensor or You, including from the legal
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+   =======================================================================
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+   licenses. Notwithstanding, Creative Commons may elect to apply one of
+   its public licenses to material it publishes and in those instances
+   will be considered the “Licensor.” The text of the Creative Commons
+   public licenses is dedicated to the public domain under the CC0 Public
+   Domain Dedication. Except for the limited purpose of indicating that
+   material is shared under a Creative Commons public license or as
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+
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+   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.
+
+

README.md

@@ -1,12 +1,13 @@
 ---
-title: Chat Ui Template
-emoji: 🚀
-colorFrom: indigo
-colorTo: blue
+title: Open Sora
+emoji: 📚
+colorFrom: yellow
+colorTo: indigo
 sdk: gradio
-pinned: false
+sdk_version: 4.21.0
 app_file: app.py
-suggested_hardware: a10g-small
+pinned: false
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,25 +1,32 @@
 import gradio as gr
-from huggingface_hub import hf_hub_download
+from huggingface_hub import hf_hub_download, snapshot_download
 import subprocess
 import tempfile
 import shutil
 import os
 import spaces
-
+import importlib
 from transformers import T5ForConditionalGeneration, T5Tokenizer
 import os
-   
+
+def check_and_install(package_name):
+    if importlib.util.find_spec(package_name) is None:
+        print(f"{package_name} not installed, installing...")
+        subprocess.run(
+            f'pip install {package_name} --no-build-isolation',
+            env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"},
+            shell=True
+        )
+    else:
+        print(f"{package_name} is already installed.")
+        
+check_and_install('flash_attn')
 
 def download_t5_model(model_id, save_directory):
     # Modelin tokenizer'ını ve modeli indir
-    model = T5ForConditionalGeneration.from_pretrained(model_id)
-    tokenizer = T5Tokenizer.from_pretrained(model_id)
-
-    # Model ve tokenizer'ı belirtilen dizine kaydet
     if not os.path.exists(save_directory):
         os.makedirs(save_directory)
-    model.save_pretrained(save_directory)
-    tokenizer.save_pretrained(save_directory)
+    snapshot_download(repo_id="DeepFloyd/t5-v1_1-xxl",local_dir=save_directory, local_dir_use_symlinks=False)
 
 # Model ID ve kaydedilecek dizin
 model_id = "DeepFloyd/t5-v1_1-xxl"
@@ -40,9 +47,9 @@ def run_inference(model_name, prompt_text):
     
     # Map model names to their respective configuration files
     config_mapping = {
-        "OpenSora-v1-16x256x256.pth": "16x256x256.py",
-        "OpenSora-v1-HQ-16x256x256.pth": "16x512x512.py",
-        "OpenSora-v1-HQ-16x512x512.pth": "64x512x512.py"
+        "OpenSora-v1-16x256x256.pth": "configs/opensora/inference/16x256x256.py",
+        "OpenSora-v1-HQ-16x256x256.pth": "configs/opensora/inference/16x512x512.py",
+        "OpenSora-v1-HQ-16x512x512.pth": "configs/opensora/inference/64x512x512.py"
     }
     
     config_path = config_mapping[model_name]
@@ -82,22 +89,48 @@ def run_inference(model_name, prompt_text):
     os.remove(prompt_file.name)
 
 def main():
-    gr.Interface(
-        fn=run_inference,
-        inputs=[
-            gr.Dropdown(choices=[
-                "OpenSora-v1-16x256x256.pth",
-                "OpenSora-v1-HQ-16x256x256.pth",
-                "OpenSora-v1-HQ-16x512x512.pth"
-            ], 
-            value="OpenSora-v1-16x256x256.pth",
-            label="Model Selection"),
-            gr.Textbox(label="Prompt Text", value="Enter prompt text here")
-        ],
-        outputs=gr.Video(label="Output Video"),
-        title="Open-Sora Inference",
-        description="Run Open-Sora Inference with Custom Parameters",
-    ).launch()
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                gr.HTML(
+                """
+                <h1 style='text-align: center'>
+               Open-Sora: Democratizing Efficient Video Production for All
+                </h1>
+                """
+            )
+                gr.HTML(
+                    """
+                    <h3 style='text-align: center'>
+                    Follow me for more! 
+                    <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>
+                    </h3>
+                    """
+            )
+
+        with gr.Row():
+            with gr.Column():
+                model_dropdown = gr.Dropdown(
+                    choices=[
+                        "OpenSora-v1-16x256x256.pth",
+                        "OpenSora-v1-HQ-16x256x256.pth",
+                        "OpenSora-v1-HQ-16x512x512.pth"
+                    ], 
+                    value="OpenSora-v1-16x256x256.pth"
+                )
+                prompt_text = gr.Textbox(show_label=False, placeholder="Enter prompt text here", lines=4)
+                submit_button = gr.Button("Run Inference")
+
+            with gr.Column():
+                output_video = gr.Video()
+
+        submit_button.click(
+            fn=run_inference, 
+            inputs=[model_dropdown, prompt_text], 
+            outputs=output_video
+        )
+
+    demo.launch()
 
 if __name__ == "__main__":
-    main()
+    main()

assets/texts/imagenet_id.txt

@@ -0,0 +1,8 @@
+207
+360
+387
+974
+88
+979
+417
+279

assets/texts/imagenet_labels.txt

@@ -0,0 +1,8 @@
+golden retriever
+otter
+lesser panda
+geyser
+macaw
+valley
+balloon
+golden panda

assets/texts/t2i_samples.txt

@@ -0,0 +1,8 @@
+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

@@ -0,0 +1,7 @@
+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

@@ -0,0 +1,10 @@
+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

@@ -0,0 +1,48 @@
+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

@@ -0,0 +1,6 @@
+0
+1
+2
+3
+4
+5

assets/texts/ucf101_labels.txt

@@ -0,0 +1,6 @@
+Apply Eye Makeup
+Apply Lipstick
+Archery
+Baby Crawling
+Balance Beam
+Band Marching

configs/dit/inference/16x256x256.py

@@ -0,0 +1,31 @@
+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

@@ -0,0 +1,31 @@
+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

@@ -0,0 +1,32 @@
+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

@@ -0,0 +1,50 @@
+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

@@ -0,0 +1,50 @@
+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

@@ -0,0 +1,30 @@
+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

@@ -0,0 +1,31 @@
+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

@@ -0,0 +1,49 @@
+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

@@ -0,0 +1,34 @@
+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=False,
+    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

@@ -0,0 +1,35 @@
+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=False,
+    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

@@ -0,0 +1,35 @@
+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=False,
+    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

@@ -0,0 +1,53 @@
+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="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

@@ -0,0 +1,54 @@
+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

@@ -0,0 +1,55 @@
+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

configs/opensora/train/64x512x512-sp.py

@@ -0,0 +1,54 @@
+num_frames = 64
+frame_interval = 2
+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",
+)
+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 = 1
+lr = 2e-5
+grad_clip = 1.0

configs/opensora/train/64x512x512.py

@@ -0,0 +1,54 @@
+num_frames = 64
+frame_interval = 2
+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"
+sp_size = 1
+
+# 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,
+)
+vae = dict(
+    type="VideoAutoencoderKL",
+    from_pretrained="stabilityai/sd-vae-ft-ema",
+    micro_batch_size=64,
+)
+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 = 4
+lr = 2e-5
+grad_clip = 1.0

configs/pixart/inference/16x256x256.py

@@ -0,0 +1,32 @@
+num_frames = 16
+fps = 8
+image_size = (256, 256)
+
+# Define model
+model = dict(
+    type="PixArt-XL/2",
+    space_scale=0.5,
+    time_scale=1.0,
+    from_pretrained="outputs/098-F16S3-PixArt-XL-2/epoch7-global_step30000/model_ckpt.pt",
+)
+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="dpm-solver",
+    num_sampling_steps=20,
+    cfg_scale=7.0,
+)
+dtype = "fp16"
+
+# Others
+batch_size = 2
+seed = 42
+prompt_path = "./assets/texts/t2v_samples.txt"
+save_dir = "./outputs/samples/"

configs/pixart/inference/1x1024MS.py

@@ -0,0 +1,34 @@
+num_frames = 1
+fps = 1
+image_size = (1920, 512)
+multi_resolution = True
+
+# Define model
+model = dict(
+    type="PixArtMS-XL/2",
+    space_scale=2.0,
+    time_scale=1.0,
+    no_temporal_pos_emb=True,
+    from_pretrained="PixArt-XL-2-1024-MS.pth",
+)
+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="dpm-solver",
+    num_sampling_steps=20,
+    cfg_scale=7.0,
+)
+dtype = "fp16"
+
+# Others
+batch_size = 2
+seed = 42
+prompt_path = "./assets/texts/t2i_samples.txt"
+save_dir = "./outputs/samples/"

configs/pixart/inference/1x256x256.py

@@ -0,0 +1,33 @@
+num_frames = 1
+fps = 1
+image_size = (256, 256)
+
+# Define model
+model = dict(
+    type="PixArt-XL/2",
+    space_scale=1.0,
+    time_scale=1.0,
+    no_temporal_pos_emb=True,
+    from_pretrained="PixArt-XL-2-256x256.pth",
+)
+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="dpm-solver",
+    num_sampling_steps=20,
+    cfg_scale=7.0,
+)
+dtype = "fp16"
+
+# Others
+batch_size = 2
+seed = 42
+prompt_path = "./assets/texts/t2i_samples.txt"
+save_dir = "./outputs/samples/"

configs/pixart/inference/1x512x512.py

@@ -0,0 +1,33 @@
+num_frames = 1
+fps = 1
+image_size = (512, 512)
+
+# Define model
+model = dict(
+    type="PixArt-XL/2",
+    space_scale=1.0,
+    time_scale=1.0,
+    no_temporal_pos_emb=True,
+    from_pretrained="PixArt-XL-2-512x512.pth",
+)
+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="dpm-solver",
+    num_sampling_steps=20,
+    cfg_scale=7.0,
+)
+dtype = "fp16"
+
+# Others
+batch_size = 2
+seed = 42
+prompt_path = "./assets/texts/t2i_samples.txt"
+save_dir = "./outputs/samples/"

configs/pixart/train/16x256x256.py

@@ -0,0 +1,53 @@
+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="PixArt-XL/2",
+    space_scale=0.5,
+    time_scale=1.0,
+    from_pretrained="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/pixart/train/1x512x512.py

@@ -0,0 +1,54 @@
+num_frames = 1
+frame_interval = 1
+image_size = (512, 512)
+
+# Define dataset
+root = None
+data_path = "CSV_PATH"
+use_image_transform = True
+num_workers = 4
+
+# Define acceleration
+dtype = "bf16"
+grad_checkpoint = True
+plugin = "zero2"
+sp_size = 1
+
+# Define model
+model = dict(
+    type="PixArt-XL/2",
+    space_scale=1.0,
+    time_scale=1.0,
+    no_temporal_pos_emb=True,
+    from_pretrained="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 = 32
+lr = 2e-5
+grad_clip = 1.0

configs/pixart/train/64x512x512.py

@@ -0,0 +1,54 @@
+num_frames = 64
+frame_interval = 2
+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"
+sp_size = 1
+
+# Define model
+model = dict(
+    type="PixArt-XL/2",
+    space_scale=1.0,
+    time_scale=2 / 3,
+    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 = 250
+load = None
+
+batch_size = 4
+lr = 2e-5
+grad_clip = 1.0

docs/README_zh.md

@@ -0,0 +1,206 @@
+<p align="center">
+    <img src="../assets/readme/icon.png" width="250"/>
+<p>
+
+<div align="center">
+    <a href="https://github.com/hpcaitech/Open-Sora/stargazers"><img src="https://img.shields.io/github/stars/hpcaitech/Open-Sora?style=social"></a>
+    <a href="https://hpcaitech.github.io/Open-Sora/"><img src="https://img.shields.io/badge/Gallery-View-orange?logo=&amp"></a>
+    <a href="https://discord.gg/shpbperhGs"><img src="https://img.shields.io/badge/Discord-join-blueviolet?logo=discord&amp"></a>
+    <a href="https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-247ipg9fk-KRRYmUl~u2ll2637WRURVA"><img src="https://img.shields.io/badge/Slack-ColossalAI-blueviolet?logo=slack&amp"></a>
+    <a href="https://twitter.com/yangyou1991/status/1769411544083996787?s=61&t=jT0Dsx2d-MS5vS9rNM5e5g"><img src="https://img.shields.io/badge/Twitter-Discuss-blue?logo=twitter&amp"></a>
+    <a href="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png"><img src="https://img.shields.io/badge/微信-小助手加群-green?logo=wechat&amp"></a>
+</div>
+
+## Open-Sora： 完全开源的高效复现类Sora视频生成方案
+**Open-Sora**项目是一项致力于**高效**制作高质量视频，并使所有人都能使用其模型、工具和内容的计划。
+通过采用**开源**原则，Open-Sora 不仅实现了先进视频生成技术的低成本普及，还提供了一个精简且用户友好的方案，简化了视频制作的复杂性。
+通过 Open-Sora，我们希望更多开发者一起探索内容创作领域的创新、创造和包容。
+ [[English]](/README.md)
+
+## 📰 资讯
+
+* **[2024.03.18]** 🔥 我们发布了**Open-Sora 1.0**，这是一个完全开源的视频生成项目。
+* Open-Sora 1.0 支持视频数据预处理、<a href="https://github.com/hpcaitech/ColossalAI"><img src="../assets/readme/colossal_ai.png" width="8%" ></a> 加速训练、推理等全套流程。
+* 我们提供的[模型权重](#model-weights)只需 3 天的训练就能生成 2~5 秒的 512x512 视频。
+* **[2024.03.04]** Open-Sora：开源Sora复现方案，成本降低46%，序列扩充至近百万
+
+## 🎥 最新视频
+
+| **2s 512×512**                                                                                                                                                                 | **2s 512×512**                                                                                                                                                              | **2s 512×512**                                                                                                                                    |
+| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
+| [<img src="/assets/readme/sample_0.gif" width="">](https://github.com/hpcaitech/Open-Sora/assets/99191637/de1963d3-b43b-4e68-a670-bb821ebb6f80)                                 | [<img src="/assets/readme/sample_1.gif" width="">](https://github.com/hpcaitech/Open-Sora/assets/99191637/13f8338f-3d42-4b71-8142-d234fbd746cc)                              | [<img src="/assets/readme/sample_2.gif" width="">](https://github.com/hpcaitech/Open-Sora/assets/99191637/fa6a65a6-e32a-4d64-9a9e-eabb0ebb8c16)    |
+| A serene night scene in a forested area. [...] The video is a time-lapse, capturing the transition from day to night, with the lake and forest serving as a constant backdrop. | A soaring drone footage captures the majestic beauty of a coastal cliff, [...] The water gently laps at the rock base and the greenery that clings to the top of the cliff. | The majestic beauty of a waterfall cascading down a cliff into a serene lake. [...] The camera angle provides a bird's eye view of the waterfall. |
+| [<img src="/assets/readme/sample_3.gif" width="">](https://github.com/hpcaitech/Open-Sora/assets/99191637/64232f84-1b36-4750-a6c0-3e610fa9aa94) | [<img src="/assets/readme/sample_4.gif" width="">](https://github.com/hpcaitech/Open-Sora/assets/99191637/983a1965-a374-41a7-a76b-c07941a6c1e9) | [<img src="/assets/readme/sample_5.gif" width="">](https://github.com/hpcaitech/Open-Sora/assets/99191637/ec10c879-9767-4c31-865f-2e8d6cf11e65) |
+| A bustling city street at night, filled with the glow of car headlights and the ambient light of streetlights. [...]                                                           | The vibrant beauty of a sunflower field. The sunflowers are arranged in neat rows, creating a sense of order and symmetry. [...]                                            | A serene underwater scene featuring a sea turtle swimming through a coral reef. The turtle, with its greenish-brown shell [...]                   |
+
+视频经过降采样处理为`.gif`格式，以便显示。点击查看原始视频。为便于显示，文字经过修剪，全文请参见 [此处](/assets/texts/t2v_samples.txt)。在我们的[图片库](https://hpcaitech.github.io/Open-Sora/)中查看更多样本。
+
+## 🔆 新功能
+
+* 📍Open-Sora-v1 已发布。[这里](#model-weights)提供了模型权重。只需 400K 视频片段和在单卡 H800 上训200天（类比Stable Video Diffusion 的 152M 样本），我们就能生成 2 秒的 512×512 视频。
+* ✅ 从图像扩散模型到视频扩散模型的三阶段训练。我们提供每个阶段的权重。
+* ✅ 支持训练加速，包括加速变压器、更快的 T5 和 VAE 以及序列并行。在对 64x512x512 视频进行训练时，Open-Sora 可将训练速度提高**55%**。详细信息请参见[加速训练](docs/acceleration.md)。
+* ✅ 我们提供用于数据预处理的视频切割和字幕工具。有关说明请点击[此处](tools/data/README.md)，我们的数据收集计划请点击 [数据集](docs/datasets.md)。
+* ✅ 我们发现来自[VideoGPT](https://wilson1yan.github.io/videogpt/index.html)的 VQ-VAE 质量较低，因此采用了来自[Stability-AI](https://huggingface.co/stabilityai/sd-vae-ft-mse-original) 的更好的 VAE。我们还发现在时间维度上进行修补会降低质量。更多讨论，请参阅我们的 **[报告](docs/report_v1.md)**。
+* ✅ 我们研究了不同的架构，包括 DiT、Latte 和我们提出的 **STDiT**。我们的STDiT在质量和速度之间实现了更好的权衡。更多讨论，请参阅我们的 **[报告](docs/report_v1.md)**。
+* ✅ 支持剪辑和 T5 文本调节。
+* ✅ 通过将图像视为单帧视频，我们的项目支持在图像和视频（如 ImageNet 和 UCF101）上训练 DiT。更多说明请参见 [指令解析](docs/command.md)。
+* ✅ 利用[DiT](https://github.com/facebookresearch/DiT)、[Latte](https://github.com/Vchitect/Latte) 和 [PixArt](https://pixart-alpha.github.io/) 的官方权重支持推理。
+
+<details>
+<summary>查看更多</summary>
+
+* ✅ 重构代码库。请参阅[结构](docs/structure.md)，了解项目结构以及如何使用配置文件。
+
+</details>
+
+### 下一步计划【按优先级排序】
+
+* [ ] 完成数据处理管道（包括密集光流、美学评分、文本图像相似性、重复数据删除等）。更多信息请参见[数据集](/docs/datasets.md)。**[项目进行中］**
+* [ ] 训练视频-VAE。 **[项目进行中]**
+
+<details>
+<summary>查看更多</summary>
+
+* [ ] 支持图像和视频调节。
+* [ ] 评估流程。
+* [ ] 加入更好的调度程序，如 SD3 中的整流程序。
+* [ ] 支持可变长宽比、分辨率和持续时间。
+* [ ] 发布后支持 SD3。
+
+</details>
+
+## 目录
+
+* [安装](#installation)
+* [模型权重](/#model-weights)
+* [推理](/#inference)
+* [数据处理](/#data-processing)
+* [训练](/#training)
+* [贡献](/#contribution)
+* [声明](/#acknowledgement)
+* [引用](/#citation)
+
+## Installation
+
+```bash
+# create a virtual env
+conda create -n opensora python=3.10
+
+# install torch
+# the command below is for CUDA 12.1, choose install commands from 
+# https://pytorch.org/get-started/locally/ based on your own CUDA version
+pip3 install torch torchvision
+
+# install flash attention (optional)
+pip install packaging ninja
+pip install flash-attn --no-build-isolation
+
+# install apex (optional)
+pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" git+https://github.com/NVIDIA/apex.git
+
+# install xformers
+pip3 install -U xformers --index-url https://download.pytorch.org/whl/cu121
+
+# install this project
+git clone https://github.com/hpcaitech/Open-Sora
+cd Open-Sora
+pip install -v .
+```
+
+安装完成后，建议阅读[结构](docs/structure.md)，了解项目结构以及如何使用配置文件。
+
+## 模型权重
+
+| 分辨率  | 数据   | 迭代次数 | 批量大小 | GPU 天数 (H800) | 网址       |
+| ---------- | ------ | ----------- | ---------- | --------------- | ---------- |
+| 16×256×256 | 366K   | 80k         | 8×64       | 117             | [:link:]() |
+| 16×256×256 | 20K HQ | 24k         | 8×64       | 45              | [:link:]() |
+| 16×512×512 | 20K HQ | 20k         | 2×64       | 35              | [:link:]() |
+| 64×512×512 | 50K HQ |             |            |                 | TBD        |
+
+我们模型的权重部分由[PixArt-α](https://github.com/PixArt-alpha/PixArt-alpha) 初始化。参数数量为 724M。有关训练的更多信息，请参阅我们的 **[报告](/docs/report_v1.md)**。有关数据集的更多信息，请参阅[数据](/docs/dataset.md)。HQ 表示高质量。
+:warning: **局限性**：我们的模型是在有限的预算内训练出来的。质量和文本对齐度相对较差。特别是在生成人类时，模型表现很差，无法遵循详细的指令。我们正在努力改进质量和文本对齐。
+
+## 推理
+
+要使用我们提供的权重进行推理，首先要将[T5](https://huggingface.co/DeepFloyd/t5-v1_1-xxl/tree/main)权重下载到pretrained_models/t5_ckpts/t5-v1_1-xxl 中。然后下载模型权重。运行以下命令生成样本。请参阅[此处](docs/structure.md#inference-config-demos)自定义配置。
+
+```bash
+# Sample 16x256x256 (5s/sample)
+torchrun --standalone --nproc_per_node 1 scripts/inference.py configs/opensora/inference/16x256x256.py --ckpt-path ./path/to/your/ckpt.pth
+
+# Sample 16x512x512 (20s/sample, 100 time steps)
+torchrun --standalone --nproc_per_node 1 scripts/inference.py configs/opensora/inference/16x512x512.py --ckpt-path ./path/to/your/ckpt.pth
+
+# Sample 64x512x512 (40s/sample, 100 time steps)
+torchrun --standalone --nproc_per_node 1 scripts/inference.py configs/opensora/inference/64x512x512.py --ckpt-path ./path/to/your/ckpt.pth
+
+# Sample 64x512x512 with sequence parallelism (30s/sample, 100 time steps)
+# sequence parallelism is enabled automatically when nproc_per_node is larger than 1
+torchrun --standalone --nproc_per_node 2 scripts/inference.py configs/opensora/inference/64x512x512.py --ckpt-path ./path/to/your/ckpt.pth
+```
+
+我们在 H800 GPU 上进行了速度测试。如需使用其他模型进行推理，请参阅[此处](docs/commands.md)获取更多说明。
+
+## 数据处理
+
+高质量数据是高质量模型的关键。[这里](/docs/datasets.md)有我们使用过的数据集和数据收集计划。我们提供处理视频数据的工具。目前，我们的数据处理流程包括以下步骤：
+
+1. 下载数据集。[[文件](/tools/datasets/README.md)]
+2. 将视频分割成片段。 [[文件](/tools/scenedetect/README.md)]
+3. 生成视频字幕。 [[文件](/tools/caption/README.md)]
+
+## 训练
+
+要启动训练，首先要将[T5](https://huggingface.co/DeepFloyd/t5-v1_1-xxl/tree/main)权重下载到pretrained_models/t5_ckpts/t5-v1_1-xxl 中。然后运行以下命令在单个节点上启动训练。
+
+```bash
+# 1 GPU, 16x256x256
+torchrun --nnodes=1 --nproc_per_node=1 scripts/train.py configs/opensora/train/16x256x512.py --data-path YOUR_CSV_PATH
+# 8 GPUs, 64x512x512
+torchrun --nnodes=1 --nproc_per_node=8 scripts/train.py configs/opensora/train/64x512x512.py --data-path YOUR_CSV_PATH --ckpt-path YOUR_PRETRAINED_CKPT
+```
+
+要在多个节点上启动训练，请根据[ColossalAI](https://colossalai.org/docs/basics/launch_colossalai/#launch-with-colossal-ai-cli) 准备一个主机文件，并运行以下命令。
+
+```bash
+colossalai run --nproc_per_node 8 --hostfile hostfile scripts/train.py configs/opensora/train/64x512x512.py --data-path YOUR_CSV_PATH --ckpt-path YOUR_PRETRAINED_CKPT
+```
+
+有关其他型号的培训和高级使用方法，请参阅[此处](docs/commands.md)获取更多说明。
+
+## 贡献
+
+如果您希望为该项目做出贡献，可以参考 [贡献指南](./CONTRIBUTING.md).
+
+## 声明
+
+* [DiT](https://github.com/facebookresearch/DiT): Scalable Diffusion Models with Transformers.
+* [OpenDiT](https://github.com/NUS-HPC-AI-Lab/OpenDiT): An acceleration for DiT training. We adopt valuable acceleration strategies for training progress from OpenDiT.
+* [PixArt](https://github.com/PixArt-alpha/PixArt-alpha): An open-source DiT-based text-to-image model.
+* [Latte](https://github.com/Vchitect/Latte): An attempt to efficiently train DiT for video.
+* [StabilityAI VAE](https://huggingface.co/stabilityai/sd-vae-ft-mse-original): A powerful image VAE model.
+* [CLIP](https://github.com/openai/CLIP): A powerful text-image embedding model.
+* [T5](https://github.com/google-research/text-to-text-transfer-transformer): A powerful text encoder.
+* [LLaVA](https://github.com/haotian-liu/LLaVA): A powerful image captioning model based on [Yi-34B](https://huggingface.co/01-ai/Yi-34B).
+
+我们对他们的出色工作和对开源的慷慨贡献表示感谢。
+
+## 引用
+
+```bibtex
+@software{opensora,
+  author = {Zangwei Zheng and Xiangyu Peng and Yang You},
+  title = {Open-Sora: Democratizing Efficient Video Production for All},
+  month = {March},
+  year = {2024},
+  url = {https://github.com/hpcaitech/Open-Sora}
+}
+```
+
+[Zangwei Zheng](https://github.com/zhengzangw) and [Xiangyu Peng](https://github.com/xyupeng) equally contributed to this work during their internship at [HPC-AI Tech](https://hpc-ai.com/).
+
+## Star 走势
+
+[![Star History Chart](https://api.star-history.com/svg?repos=hpcaitech/Open-Sora&type=Date)](https://star-history.com/#hpcaitech/Open-Sora&Date)

docs/acceleration.md

@@ -0,0 +1,57 @@
+# Acceleration
+
+Open-Sora aims to provide a high-speed training framework for diffusion models. We can achieve **55%** training speed acceleration when training on **64 frames 512x512 videos**. Our framework support training **1min 1080p videos**.
+
+## Accelerated Transformer
+
+Open-Sora boosts the training speed by:
+
+- Kernal optimization including [flash attention](https://github.com/Dao-AILab/flash-attention), fused layernorm kernal, and the ones compiled by colossalAI.
+- Hybrid parallelism including ZeRO.
+- Gradient checkpointing for larger batch size.
+
+Our training speed on images is comparable to [OpenDiT](https://github.com/NUS-HPC-AI-Lab/OpenDiT), an project to accelerate DiT training. The training speed is measured on 8 H800 GPUs with batch size 128, image size 256x256.
+
+| Model    | Throughput (img/s/GPU) | Throughput (tokens/s/GPU) |
+| -------- | ---------------------- | ------------------------- |
+| DiT      | 100                    | 26k                       |
+| OpenDiT  | 175                    | 45k                       |
+| OpenSora | 175                    | 45k                       |
+
+## Efficient STDiT
+
+Our STDiT adopts spatial-temporal attention to model the video data. Compared with directly applying full attention on DiT, our STDiT is more efficient as the number of frames increases. Our current framework only supports sequence parallelism for very long sequence.
+
+The training speed is measured on 8 H800 GPUs with acceleration techniques applied, GC means gradient checkpointing. Both with T5 conditioning like PixArt.
+
+| Model            | Setting        | Throughput (sample/s/GPU) | Throughput (tokens/s/GPU) |
+| ---------------- | -------------- | ------------------------- | ------------------------- |
+| DiT              | 16x256  (4k)   | 7.20                      | 29k                       |
+| STDiT            | 16x256  (4k)   | 7.00                      | 28k                       |
+| DiT              | 16x512  (16k)  | 0.85                      | 14k                       |
+| STDiT            | 16x512  (16k)  | 1.45                      | 23k                       |
+| DiT (GC)         | 64x512  (65k)  | 0.08                      | 5k                        |
+| STDiT (GC)       | 64x512  (65k)  | 0.40                      | 25k                       |
+| STDiT (GC, sp=2) | 360x512 (370k) | 0.10                      | 18k                       |
+
+With a 4x downsampling in the temporal dimension with Video-VAE, an 24fps video has 450 frames. The gap between the speed of STDiT (28k tokens/s) and DiT on images (up to 45k tokens/s) mainly comes from the T5 and VAE encoding, and temperal attention.
+
+## Accelerated Encoder (T5, VAE)
+
+During training, texts are encoded by T5, and videos are encoded by VAE. Typically there are two ways to accelerate the training:
+
+1. Preprocess text and video data in advance and save them to disk.
+2. Encode text and video data during training, and accelerate the encoding process.
+
+For option 1, 120 tokens for one sample require 1M disk space, and a 64x64x64 latent requires 4M. Considering a training dataset with 10M video clips, the total disk space required is 50TB. Our storage system is not ready at this time for this scale of data.
+
+For option 2, we boost T5 speed and memory requirement. According to [OpenDiT](https://github.com/NUS-HPC-AI-Lab/OpenDiT), we find VAE consumes a large number of GPU memory. Thus we split batch size into smaller ones for VAE encoding. With both techniques, we can greatly accelerated the training speed.
+
+The training speed is measured on 8 H800 GPUs with STDiT.
+
+| Acceleration | Setting       | Throughput (img/s/GPU) | Throughput (tokens/s/GPU) |
+| ------------ | ------------- | ---------------------- | ------------------------- |
+| Baseline     | 16x256  (4k)  | 6.16                   | 25k                       |
+| w. faster T5 | 16x256  (4k)  | 7.00                   | 29k                       |
+| Baseline     | 64x512  (65k) | 0.94                   | 15k                       |
+| w. both      | 64x512  (65k) | 1.45                   | 23k                       |

docs/commands.md

@@ -0,0 +1,91 @@
+# Commands
+
+## Inference
+
+You can modify corresponding config files to change the inference settings. See more details [here](/docs/structure.md#inference-config-demos).
+
+### Inference with DiT pretrained on ImageNet
+
+The following command automatically downloads the pretrained weights on ImageNet and runs inference.
+
+```bash
+python scripts/inference.py configs/dit/inference/1x256x256-class.py --ckpt-path DiT-XL-2-256x256.pt
+```
+
+### Inference with Latte pretrained on UCF101
+
+The following command automatically downloads the pretrained weights on UCF101 and runs inference.
+
+```bash
+python scripts/inference.py configs/latte/inference/16x256x256-class.py --ckpt-path Latte-XL-2-256x256-ucf101.pt
+```
+
+### Inference with PixArt-α pretrained weights
+
+Download T5 into `./pretrained_models` and run the following command.
+
+```bash
+# 256x256
+torchrun --standalone --nproc_per_node 1 scripts/inference.py configs/pixart/inference/1x256x256.py --ckpt-path PixArt-XL-2-256x256.pth
+
+# 512x512
+torchrun --standalone --nproc_per_node 1 scripts/inference.py configs/pixart/inference/1x512x512.py --ckpt-path PixArt-XL-2-512x512.pth
+
+# 1024 multi-scale
+torchrun --standalone --nproc_per_node 1 scripts/inference.py configs/pixart/inference/1x1024MS.py --ckpt-path PixArt-XL-2-1024MS.pth
+```
+
+### Inference with checkpoints saved during training
+
+During training, an experiment logging folder is created in `outputs` directory. Under each checpoint folder, e.g. `epoch12-global_step2000`, there is a `ema.pt` and the shared `model` folder. Run the following command to perform inference.
+
+```bash
+# inference with ema model
+torchrun --standalone --nproc_per_node 1 scripts/inference.py configs/opensora/inference/16x256x256.py --ckpt-path outputs/001-STDiT-XL-2/epoch12-global_step2000/ema.pt
+
+# inference with model
+torchrun --standalone --nproc_per_node 1 scripts/inference.py configs/opensora/inference/16x256x256.py --ckpt-path outputs/001-STDiT-XL-2/epoch12-global_step2000
+
+# inference with sequence parallelism
+# sequence parallelism is enabled automatically when nproc_per_node is larger than 1
+torchrun --standalone --nproc_per_node 2 scripts/inference.py configs/opensora/inference/16x256x256.py --ckpt-path outputs/001-STDiT-XL-2/epoch12-global_step2000
+```
+
+The second command will automatically generate a `model_ckpt.pt` file in the checkpoint folder.
+
+### Inference Hyperparameters
+
+1. DPM-solver is good at fast inference for images. However, the video result is not satisfactory. You can use it for fast demo purpose.
+
+```python
+type="dmp-solver"
+num_sampling_steps=20
+```
+
+1. You can use [SVD](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt)'s finetuned VAE decoder on videos for inference (consumes more memory). However, we do not see significant improvement in the video result. To use it, download [the pretrained weights](https://huggingface.co/maxin-cn/Latte/tree/main/t2v_required_models/vae_temporal_decoder) into `./pretrained_models/vae_temporal_decoder` and modify the config file as follows.
+
+```python
+vae = dict(
+    type="VideoAutoencoderKLTemporalDecoder",
+    from_pretrained="pretrained_models/vae_temporal_decoder",
+)
+
+## Training
+
+To resume training, run the following command. ``--load`` different from ``--ckpt-path`` as it loads the optimizer and dataloader states.
+
+```bash
+torchrun --nnodes=1 --nproc_per_node=8 scripts/train.py configs/opensora/train/64x512x512.py --data-path YOUR_CSV_PATH --load YOUR_PRETRAINED_CKPT
+```
+
+To enable wandb logging, add `--wandb` to the command.
+
+```bash
+WANDB_API_KEY=YOUR_WANDB_API_KEY torchrun --nnodes=1 --nproc_per_node=8 scripts/train.py configs/opensora/train/64x512x512.py --data-path YOUR_CSV_PATH --wandb True
+```
+
+You can modify corresponding config files to change the training settings. See more details [here](/docs/structure.md#training-config-demos).
+
+### Training Hyperparameters
+
+1. `dtype` is the data type for training. Only `fp16` and `bf16` are supported. ColossalAI automatically enables the mixed precision training for `fp16` and `bf16`. During training, we find `bf16` more stable.

docs/datasets.md

@@ -0,0 +1,28 @@
+# Datasets
+
+## Datasets used for now
+
+### HD-VG-130M
+
+[HD-VG-130M](https://github.com/daooshee/HD-VG-130M?tab=readme-ov-file) comprises 130M text-video pairs. The caption is generated by BLIP-2. We find the cut and the text quality are relatively poor. It contains 20 splits. For OpenSora 1.0, we use the first split. We plan to use the whole dataset and re-process it.
+
+### Inter4k
+
+[Inter4k](https://github.com/alexandrosstergiou/Inter4K) is a dataset containing 1k video clips with 4K resolution. The dataset is proposed for super-resolution tasks. We use the dataset for HQ training. The videos are processed as mentioned [here](/README.md#data-processing).
+
+### Pexels.com
+
+[Pexels.com](https://www.pexels.com/) is a website that provides free stock photos and videos. We collect 19K video clips from this website for HQ training. The videos are processed as mentioned [here](/README.md#data-processing).
+
+## Datasets watching list
+
+We are also watching the following datasets and considering using them in the future, which depends on our disk space and the quality of the dataset.
+
+| Name              | Size         | Description                   |
+| ----------------- | ------------ | ----------------------------- |
+| Panda-70M         | 70M videos   | High quality video-text pairs |
+| WebVid-10M        | 10M videos   | Low quality                   |
+| InternVid-10M-FLT | 10M videos   |                               |
+| EGO4D             | 3670 hours   |                               |
+| OpenDV-YouTube    | 1700 hours   |                               |
+| VidProM           | 6.69M videos |                               |

docs/report_v1.md

@@ -0,0 +1,47 @@
+# Open-Sora v1 Report
+
+OpenAI's Sora is amazing at generating one minutes high quality videos. However, it reveals almost no information about its details. To make AI more "open", we are dedicated to build an open-source version of Sora. This report describes our first attempt to train a transformer-based video diffusion model.
+
+## Efficiency in choosing the architecture
+
+To lower the computational cost, we want to utilize existing VAE models. Sora uses spatial-temporal VAE to reduce the temporal dimensions. However, we found that there is no open-source high-quality spatial-temporal VAE model. [MAGVIT](https://github.com/google-research/magvit)'s 4x4x4 VAE is not open-sourced, while [VideoGPT](https://wilson1yan.github.io/videogpt/index.html)'s 2x4x4 VAE has a low quality in our experiments. Thus, we decided to use a 2D VAE (from [Stability-AI](https://huggingface.co/stabilityai/sd-vae-ft-mse-original)) in our first version.
+
+The video training involves a large amount of tokens. Considering 24fps 1min videos, we have 1440 frames. With VAE downsampling 4x and patch size downsampling 2x, we have 1440x1024≈1.5M tokens. Full attention on 1.5M tokens leads to a huge computational cost. Thus, we use spatial-temporal attention to reduce the cost following [Latte](https://github.com/Vchitect/Latte).
+
+As shown in the figure, we insert a temporal attention right after each spatial attention in STDiT (ST stands for spatial-temporal). This is similar to variant 3 in Latte's paper. However, we do not control a similar number of parameters for these variants. While Latte's paper claims their variant is better than variant 3, our experiments on 16x256x256 videos show that with same number of iterations, the performance ranks as: DiT (full) > STDiT (Sequential) > STDiT (Parallel) ≈ Latte. Thus, we choose STDiT (Sequential) out of efficiency. Speed benchmark is provided [here](/docs/acceleration.md#efficient-stdit).
+
+![Architecture Comparison](https://i0.imgs.ovh/2024/03/15/eLk9D.png)
+
+To focus on video generation, we hope to train the model based on a powerful image generation model. [PixArt-α](https://github.com/PixArt-alpha/PixArt-alpha) is an efficiently trained high-quality image generation model with T5-conditioned DiT structure. We initialize our model with PixArt-α and initialize the projection layer of inserted temporal attention with zero. This initialization preserves model's ability of image generation at beginning, while Latte's architecture cannot. The inserted attention increases the number of parameter from 580M to 724M.
+
+![Architecture](https://i0.imgs.ovh/2024/03/16/erC1d.png)
+
+Drawing from the success of PixArt-α and Stable Video Diffusion, we also adopt a progressive training strategy: 16x256x256 on 366K pretraining datasets, and then 16x256x256, 16x512x512, and 64x512x512 on 20K datasets. With scaled position embedding, this strategy greatly reduces the computational cost.
+
+We also try to use a 3D patch embedder in DiT. However, with 2x downsampling on temporal dimension, the generated videos have a low quality. Thus, we leave the downsampling to temporal VAE in our next version. For now, we sample at every 3 frames with 16 frames training and every 2 frames with 64 frames training.
+
+## Data is the key to high quality
+
+We find that the number and quality of data have a great impact on the quality of generated videos, even larger than the model architecture and training strategy. At this time, we only prepared the first split (366K video clips) from [HD-VG-130M](https://github.com/daooshee/HD-VG-130M). The quality of these videos varies greatly, and the captions are not that accurate. Thus, we further collect 20k relatively high quality videos from [Pexels](https://www.pexels.com/), which provides free license videos. We label the video with LLaVA, an image captioning model, with three frames and a designed prompt. With designed prompt, LLaVA can generate good quality of captions.
+
+![Caption](https://i0.imgs.ovh/2024/03/16/eXdvC.png)
+
+As we lay more emphasis on the quality of data, we prepare to collect more data and build a video preprocessing pipeline in our next version.
+
+## Training Details
+
+With a limited training budgets, we made only a few exploration. We find learning rate 1e-4 is too large and scales down to 2e-5. When training with a large batch size, we find `fp16` less stable than `bf16` and may lead to generation failure. Thus, we switch to `bf16` for training on 64x512x512. For other hyper-parameters, we follow previous works.
+
+## Loss curves
+
+16x256x256 Pretraining Loss Curve
+
+![16x256x256 Pretraining Loss Curve](https://i0.imgs.ovh/2024/03/16/erXQj.png)
+
+16x256x256 HQ Training Loss Curve
+
+![16x256x256 HQ Training Loss Curve](https://i0.imgs.ovh/2024/03/16/ernXv.png)
+
+16x512x512 HQ Training Loss Curve
+
+![16x512x512 HQ Training Loss Curve](https://i0.imgs.ovh/2024/03/16/erHBe.png)

docs/structure.md

@@ -0,0 +1,178 @@
+# Repo & Config Structure
+
+## Repo Structure
+
+```plaintext
+Open-Sora
+├── README.md
+├── docs
+│   ├── acceleration.md            -> Acceleration & Speed benchmark
+│   ├── command.md                 -> Commands for training & inference
+│   ├── datasets.md                -> Datasets used in this project
+│   ├── structure.md               -> This file
+│   └── report_v1.md               -> Report for Open-Sora v1
+├── scripts
+│   ├── train.py                   -> diffusion training script
+│   └── inference.py               -> Report for Open-Sora v1
+├── configs                        -> Configs for training & inference
+├── opensora
+│   ├── __init__.py
+│   ├── registry.py                -> Registry helper
+│   ├── acceleration               -> Acceleration related code
+│   ├── dataset                    -> Dataset related code
+│   ├── models
+│   │   ├── layers                 -> Common layers
+│   │   ├── vae                    -> VAE as image encoder
+│   │   ├── text_encoder           -> Text encoder
+│   │   │   ├── classes.py         -> Class id encoder (inference only)
+│   │   │   ├── clip.py            -> CLIP encoder
+│   │   │   └── t5.py              -> T5 encoder
+│   │   ├── dit
+│   │   ├── latte
+│   │   ├── pixart
+│   │   └── stdit                  -> Our STDiT related code
+│   ├── schedulers                 -> Diffusion shedulers
+│   │   ├── iddpm                  -> IDDPM for training and inference
+│   │   └── dpms                   -> DPM-Solver for fast inference
+│   └── utils
+└── tools                          -> Tools for data processing and more
+```
+
+## Configs
+
+Our config files follows [MMEgine](https://github.com/open-mmlab/mmengine). MMEngine will reads the config file (a `.py` file) and parse it into a dictionary-like object.
+
+```plaintext
+Open-Sora
+└── configs                        -> Configs for training & inference
+    ├── opensora                   -> STDiT related configs
+    │   ├── inference
+    │   │   ├── 16x256x256.py      -> Sample videos 16 frames 256x256
+    │   │   ├── 16x512x512.py      -> Sample videos 16 frames 512x512
+    │   │   └── 64x512x512.py      -> Sample videos 64 frames 512x512
+    │   └── train
+    │       ├── 16x256x256.py      -> Train on videos 16 frames 256x256
+    │       ├── 16x256x256.py      -> Train on videos 16 frames 256x256
+    │       └── 64x512x512.py      -> Train on videos 64 frames 512x512
+    ├── dit                        -> DiT related configs
+    │   ├── inference
+    │   │   ├── 1x256x256-class.py -> Sample images with ckpts from DiT
+    │   │   ├── 1x256x256.py       -> Sample images with clip condition
+    │   │   └── 16x256x256.py      -> Sample videos
+    │   └── train
+    │       ├── 1x256x256.py       -> Train on images with clip condition
+    │       └── 16x256x256.py      -> Train on videos
+    ├── latte                      -> Latte related configs
+    └── pixart                     -> PixArt related configs
+```
+
+## Inference config demos
+
+To change the inference settings, you can directly modify the corresponding config file. Or you can pass arguments to overwrite the config file ([config_utils.py](/opensora/utils/config_utils.py)). To change sampling prompts, you should modify the `.txt` file passed to the `--prompt_path` argument.
+
+```plaintext
+--prompt_path ./assets/texts/t2v_samples.txt  -> prompt_path
+--ckpt-path ./path/to/your/ckpt.pth           -> model["from_pretrained"]
+```
+
+The explanation of each field is provided below.
+
+```python
+# Define sampling size
+num_frames = 64               # number of frames
+fps = 24 // 2                 # frames per second (divided by 2 for frame_interval=2)
+image_size = (512, 512)       # image size (height, width)
+
+# Define model
+model = dict(
+    type="STDiT-XL/2",        # Select model type (STDiT-XL/2, DiT-XL/2, etc.)
+    space_scale=1.0,          # (Optional) Space positional encoding scale (new height / old height)
+    time_scale=2 / 3,         # (Optional) Time positional encoding scale (new frame_interval / old frame_interval)
+    enable_flashattn=True,    # (Optional) Speed up training and inference with flash attention
+    enable_layernorm_kernel=True, # (Optional) Speed up training and inference with fused kernel
+    from_pretrained="PRETRAINED_MODEL",  # (Optional) Load from pretrained model
+    no_temporal_pos_emb=True,  # (Optional) Disable temporal positional encoding (for image)
+)
+vae = dict(
+    type="VideoAutoencoderKL", # Select VAE type
+    from_pretrained="stabilityai/sd-vae-ft-ema", # Load from pretrained VAE
+    micro_batch_size=128,      # VAE with micro batch size to save memory
+)
+text_encoder = dict(
+    type="t5",                 # Select text encoder type (t5, clip)
+    from_pretrained="./pretrained_models/t5_ckpts", # Load from pretrained text encoder
+    model_max_length=120,      # Maximum length of input text
+)
+scheduler = dict(
+    type="iddpm",              # Select scheduler type (iddpm, dpm-solver)
+    num_sampling_steps=100,    # Number of sampling steps
+    cfg_scale=7.0,             # hyper-parameter for classifier-free diffusion
+)
+dtype = "fp16"                 # Computation type (fp16, fp32, bf16)
+
+# Other settings
+batch_size = 1                 # batch size
+seed = 42                      # random seed
+prompt_path = "./assets/texts/t2v_samples.txt"  # path to prompt file
+save_dir = "./samples"         # path to save samples
+```
+
+## Training config demos
+
+```python
+# Define sampling size
+num_frames = 64
+frame_interval = 2             # sample every 2 frames
+image_size = (512, 512)
+
+# Define dataset
+root = None                    # root path to the dataset
+data_path = "CSV_PATH"         # path to the csv file
+use_image_transform = False    # True if training on images
+num_workers = 4                # number of workers for dataloader
+
+# Define acceleration
+dtype = "bf16"                 # Computation type (fp16, bf16)
+grad_checkpoint = True         # Use gradient checkpointing
+plugin = "zero2"               # Plugin for distributed training (zero2, zero2-seq)
+sp_size = 1                    # Sequence parallelism size (1 for no sequence parallelism)
+
+# Define model
+model = dict(
+    type="STDiT-XL/2",
+    space_scale=1.0,
+    time_scale=2 / 3,
+    from_pretrained="YOUR_PRETRAINED_MODEL",
+    enable_flashattn=True,        # Enable flash attention
+    enable_layernorm_kernel=True, # Enable layernorm kernel
+)
+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,           # Enable shardformer for T5 acceleration
+)
+scheduler = dict(
+    type="iddpm",
+    timestep_respacing="",      # Default 1000 timesteps
+)
+
+# Others
+seed = 42
+outputs = "outputs"             # path to save checkpoints
+wandb = False                   # Use wandb for logging
+
+epochs = 1000                   # number of epochs (just large enough, kill when satisfied)
+log_every = 10
+ckpt_every = 250
+load = None                     # path to resume training
+
+batch_size = 4
+lr = 2e-5
+grad_clip = 1.0                 # gradient clipping
+```

opensora/__init__.py

@@ -0,0 +1,4 @@
+from .acceleration import *
+from .datasets import *
+from .models import *
+from .registry import *

opensora/acceleration/checkpoint.py

@@ -0,0 +1,24 @@
+from collections.abc import Iterable
+
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint, checkpoint_sequential
+
+
+def set_grad_checkpoint(model, use_fp32_attention=False, gc_step=1):
+    assert isinstance(model, nn.Module)
+
+    def set_attr(module):
+        module.grad_checkpointing = True
+        module.fp32_attention = use_fp32_attention
+        module.grad_checkpointing_step = gc_step
+
+    model.apply(set_attr)
+
+
+def auto_grad_checkpoint(module, *args, **kwargs):
+    if getattr(module, "grad_checkpointing", False):
+        if not isinstance(module, Iterable):
+            return checkpoint(module, *args, **kwargs)
+        gc_step = module[0].grad_checkpointing_step
+        return checkpoint_sequential(module, gc_step, *args, **kwargs)
+    return module(*args, **kwargs)

opensora/acceleration/communications.py

@@ -0,0 +1,188 @@
+import torch
+import torch.distributed as dist
+
+
+# ====================
+# All-To-All
+# ====================
+def _all_to_all(
+    input_: torch.Tensor,
+    world_size: int,
+    group: dist.ProcessGroup,
+    scatter_dim: int,
+    gather_dim: int,
+):
+    input_list = [t.contiguous() for t in torch.tensor_split(input_, world_size, scatter_dim)]
+    output_list = [torch.empty_like(input_list[0]) for _ in range(world_size)]
+    dist.all_to_all(output_list, input_list, group=group)
+    return torch.cat(output_list, dim=gather_dim).contiguous()
+
+
+class _AllToAll(torch.autograd.Function):
+    """All-to-all communication.
+
+    Args:
+        input_: input matrix
+        process_group: communication group
+        scatter_dim: scatter dimension
+        gather_dim: gather dimension
+    """
+
+    @staticmethod
+    def forward(ctx, input_, process_group, scatter_dim, gather_dim):
+        ctx.process_group = process_group
+        ctx.scatter_dim = scatter_dim
+        ctx.gather_dim = gather_dim
+        ctx.world_size = dist.get_world_size(process_group)
+        output = _all_to_all(input_, ctx.world_size, process_group, scatter_dim, gather_dim)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        grad_output = _all_to_all(
+            grad_output,
+            ctx.world_size,
+            ctx.process_group,
+            ctx.gather_dim,
+            ctx.scatter_dim,
+        )
+        return (
+            grad_output,
+            None,
+            None,
+            None,
+        )
+
+
+def all_to_all(
+    input_: torch.Tensor,
+    process_group: dist.ProcessGroup,
+    scatter_dim: int = 2,
+    gather_dim: int = 1,
+):
+    return _AllToAll.apply(input_, process_group, scatter_dim, gather_dim)
+
+
+def _gather(
+    input_: torch.Tensor,
+    world_size: int,
+    group: dist.ProcessGroup,
+    gather_dim: int,
+):
+    if gather_list is None:
+        gather_list = [torch.empty_like(input_) for _ in range(world_size)]
+    dist.gather(input_, gather_list, group=group, gather_dim=gather_dim)
+    return gather_list
+
+
+# ====================
+# Gather-Split
+# ====================
+
+
+def _split(input_, pg: dist.ProcessGroup, dim=-1):
+    # skip if only one rank involved
+    world_size = dist.get_world_size(pg)
+    rank = dist.get_rank(pg)
+    if world_size == 1:
+        return input_
+
+    # Split along last dimension.
+    dim_size = input_.size(dim)
+    assert dim_size % world_size == 0, (
+        f"The dimension to split ({dim_size}) is not a multiple of world size ({world_size}), "
+        f"cannot split tensor evenly"
+    )
+
+    tensor_list = torch.split(input_, dim_size // world_size, dim=dim)
+    output = tensor_list[rank].contiguous()
+
+    return output
+
+
+def _gather(input_, pg: dist.ProcessGroup, dim=-1):
+    # skip if only one rank involved
+    input_ = input_.contiguous()
+    world_size = dist.get_world_size(pg)
+    dist.get_rank(pg)
+
+    if world_size == 1:
+        return input_
+
+    # all gather
+    tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
+    assert input_.device.type == "cuda"
+    torch.distributed.all_gather(tensor_list, input_, group=pg)
+
+    # concat
+    output = torch.cat(tensor_list, dim=dim).contiguous()
+
+    return output
+
+
+class _GatherForwardSplitBackward(torch.autograd.Function):
+    """Gather the input from model parallel region and concatenate.
+
+    Args:
+        input_: input matrix.
+        process_group: parallel mode.
+        dim: dimension
+    """
+
+    @staticmethod
+    def symbolic(graph, input_):
+        return _gather(input_)
+
+    @staticmethod
+    def forward(ctx, input_, process_group, dim, grad_scale):
+        ctx.mode = process_group
+        ctx.dim = dim
+        ctx.grad_scale = grad_scale
+        return _gather(input_, process_group, dim)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        if ctx.grad_scale == "up":
+            grad_output = grad_output * dist.get_world_size(ctx.mode)
+        elif ctx.grad_scale == "down":
+            grad_output = grad_output / dist.get_world_size(ctx.mode)
+
+        return _split(grad_output, ctx.mode, ctx.dim), None, None, None
+
+
+class _SplitForwardGatherBackward(torch.autograd.Function):
+    """
+    Split the input and keep only the corresponding chuck to the rank.
+
+    Args:
+        input_: input matrix.
+        process_group: parallel mode.
+        dim: dimension
+    """
+
+    @staticmethod
+    def symbolic(graph, input_):
+        return _split(input_)
+
+    @staticmethod
+    def forward(ctx, input_, process_group, dim, grad_scale):
+        ctx.mode = process_group
+        ctx.dim = dim
+        ctx.grad_scale = grad_scale
+        return _split(input_, process_group, dim)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        if ctx.grad_scale == "up":
+            grad_output = grad_output * dist.get_world_size(ctx.mode)
+        elif ctx.grad_scale == "down":
+            grad_output = grad_output / dist.get_world_size(ctx.mode)
+        return _gather(grad_output, ctx.mode, ctx.dim), None, None, None
+
+
+def split_forward_gather_backward(input_, process_group, dim, grad_scale=1.0):
+    return _SplitForwardGatherBackward.apply(input_, process_group, dim, grad_scale)
+
+
+def gather_forward_split_backward(input_, process_group, dim, grad_scale=None):
+    return _GatherForwardSplitBackward.apply(input_, process_group, dim, grad_scale)

opensora/acceleration/parallel_states.py

@@ -0,0 +1,19 @@
+import torch.distributed as dist
+
+_GLOBAL_PARALLEL_GROUPS = dict()
+
+
+def set_data_parallel_group(group: dist.ProcessGroup):
+    _GLOBAL_PARALLEL_GROUPS["data"] = group
+
+
+def get_data_parallel_group():
+    return _GLOBAL_PARALLEL_GROUPS.get("data", None)
+
+
+def set_sequence_parallel_group(group: dist.ProcessGroup):
+    _GLOBAL_PARALLEL_GROUPS["sequence"] = group
+
+
+def get_sequence_parallel_group():
+    return _GLOBAL_PARALLEL_GROUPS.get("sequence", None)

opensora/acceleration/plugin.py

@@ -0,0 +1,100 @@
+import random
+from typing import Optional
+
+import numpy as np
+import torch
+from colossalai.booster.plugin import LowLevelZeroPlugin
+from colossalai.cluster import ProcessGroupMesh
+from torch.utils.data import DataLoader
+from torch.utils.data.distributed import DistributedSampler
+
+DP_AXIS, SP_AXIS = 0, 1
+
+
+class ZeroSeqParallelPlugin(LowLevelZeroPlugin):
+    def __init__(
+        self,
+        sp_size: int = 1,
+        stage: int = 2,
+        precision: str = "fp16",
+        initial_scale: float = 2**32,
+        min_scale: float = 1,
+        growth_factor: float = 2,
+        backoff_factor: float = 0.5,
+        growth_interval: int = 1000,
+        hysteresis: int = 2,
+        max_scale: float = 2**32,
+        max_norm: float = 0.0,
+        norm_type: float = 2.0,
+        reduce_bucket_size_in_m: int = 12,
+        communication_dtype: Optional[torch.dtype] = None,
+        overlap_communication: bool = True,
+        cpu_offload: bool = False,
+        master_weights: bool = True,
+        verbose: bool = False,
+    ) -> None:
+        super().__init__(
+            stage=stage,
+            precision=precision,
+            initial_scale=initial_scale,
+            min_scale=min_scale,
+            growth_factor=growth_factor,
+            backoff_factor=backoff_factor,
+            growth_interval=growth_interval,
+            hysteresis=hysteresis,
+            max_scale=max_scale,
+            max_norm=max_norm,
+            norm_type=norm_type,
+            reduce_bucket_size_in_m=reduce_bucket_size_in_m,
+            communication_dtype=communication_dtype,
+            overlap_communication=overlap_communication,
+            cpu_offload=cpu_offload,
+            master_weights=master_weights,
+            verbose=verbose,
+        )
+        self.sp_size = sp_size
+        assert self.world_size % sp_size == 0, "world_size must be divisible by sp_size"
+        self.dp_size = self.world_size // sp_size
+        self.pg_mesh = ProcessGroupMesh(self.dp_size, self.sp_size)
+        self.dp_group = self.pg_mesh.get_group_along_axis(DP_AXIS)
+        self.sp_group = self.pg_mesh.get_group_along_axis(SP_AXIS)
+        self.dp_rank = self.pg_mesh.coordinate(DP_AXIS)
+        self.sp_rank = self.pg_mesh.coordinate(SP_AXIS)
+
+    def __del__(self):
+        """Destroy the prcess groups in ProcessGroupMesh"""
+        self.pg_mesh.destroy_mesh_process_groups()
+
+    def prepare_dataloader(
+        self,
+        dataset,
+        batch_size,
+        shuffle=False,
+        seed=1024,
+        drop_last=False,
+        pin_memory=False,
+        num_workers=0,
+        distributed_sampler_cls=None,
+        **kwargs,
+    ):
+        _kwargs = kwargs.copy()
+        distributed_sampler_cls = distributed_sampler_cls or DistributedSampler
+        sampler = distributed_sampler_cls(dataset, num_replicas=self.dp_size, rank=self.dp_rank, shuffle=shuffle)
+
+        # Deterministic dataloader
+        def seed_worker(worker_id):
+            worker_seed = seed
+            np.random.seed(worker_seed)
+            torch.manual_seed(worker_seed)
+            random.seed(worker_seed)
+
+        return DataLoader(
+            dataset,
+            batch_size=batch_size,
+            sampler=sampler,
+            worker_init_fn=seed_worker,
+            drop_last=drop_last,
+            pin_memory=pin_memory,
+            num_workers=num_workers,
+            **_kwargs,
+        )