metrics and fixes

example_prompts.txt

@@ -0,0 +1,200 @@
+A person reading a book with words that float off the pages and form pictures.
+A person diving into a pool of liquid crystal, creating ripples of light.
+A handheld shot chasing after a group of friends laughing and playing on the beach at sunset.
+A mysterious ancient temple hidden in the jungle.
+A high-speed train navigating a steep descent.
+a toy robot wearing blue jeans and a white t shirt taking a pleasant stroll in Antarctica during a winter storm
+A cheetah accelerating to full speed while chasing its prey.
+A serene orchard is in full bloom, with trees heavy with blossoms and bees buzzing around, darting from flower to flower in a display of natural harmony.
+A little child let out a big yawn
+Subtle reflections of a woman on the window of a train moving at hyper-speed in a Japanese city.
+A truck left along the edge of a cliff, revealing the stunning coastal landscape below with waves crashing against the rocks.
+A red bird transforms into a flag
+A zoom-out from a single leaf on a tree to reveal the entire forest, showcasing the vastness and diversity of the woodland.
+A slow-motion video of a liquid droplet bouncing on a water-repellent surface.
+Static camera shot. A dinasour running near some lions and chasing them away.
+an adorable kangaroo wearing purple overalls and cowboy boots taking a pleasant stroll in Mumbai India during a beautiful sunset
+A zoom-in on an artist's brush touching the canvas, highlighting the texture of the paint and the strokes being made.
+an old man wearing blue jeans and a white t shirt taking a pleasant stroll in Mumbai India during a colorful festival
+A woman is ascending to the sky from the ground
+View out a window of a giant strange creature walking in rundown city at night, one single street lamp dimly lighting the area.
+An arc shot around a lone tree in a vast, foggy field at dawn, revealing the changing light and shadows.
+A person sculpting a statue out of a waterfall, the water solidifying under their touch.
+The person's forehead creased with concentration as she worked on a challenging puzzle.
+The person's cheeks flushed with pleasure as she savored a delicious meal.
+Hand-drawn simple line art, a young kid looking up into space with a wondrous expression on his face.
+A crab made of different jewlery is walking on the beach. As it walks, it drops different jewelry pieces like diamonds, pearls, etc
+Gold coins are falling out when elevator door opens
+the scene transitions from huge waves into a snowy mountain at sunset
+a giant cathedral is completely filled with cats. there are cats everywhere you look. a man enters the cathedral and bows before the giant cat king sitting on a throne.
+A mother dog gently picks up a piece of meat and carefully places it in her puppy's bowl, her eyes filled with warmth and care as she watches her little one eat.
+A soap bubble floating in the air, displaying iridescent colors that shift and change as it moves through different angles of light.
+A truck left alongside a train moving through the countryside, matching its speed and revealing the changing landscape.
+An astronaut walking between stone buildings.
+A close-up shot of the person's face reveals his fear and desperation as he navigates the ship through the storm.
+A frozen lake slowly cracking and thawing as spring arrives, with sheets of ice breaking apart and drifting across the surface.
+A FPV shot zooming through a tunnel into a vibrant underwater space.
+a toy robot wearing blue jeans and a white t shirt taking a pleasant stroll in Mumbai India during a colorful festival
+A person sips on a smoothie, the cool and fruity flavors refreshing her mouth.
+In a vibrant theater, a magician in dazzling attire stands center stage, pulling a comically oversized rubber chicken from an ornate, old-fashioned box. His costume shimmers under the stage lights, adding to the spectacle. The crowd erupts in laughter and applause, their faces filled with joy and amazement. The magician's expression hints at mischievous delight as he holds up the rubber chicken, his performance bringing cheer to the audience.
+A hamster running on a spinning wheel.
+A quaint village nestled in a valley is surrounded by blooming cherry blossoms, with petals drifting through the air as villagers go about their daily activities, adding life to the scene.
+In a tranquil forest clearing, a sparkling waterfall cascades down into a clear pool, surrounded by lush greenery and flowers, with occasional birds fluttering by.
+A woman beamed with pride as she watched her child perform on stage.
+an adorable kangaroo wearing blue jeans and a white t shirt taking a pleasant stroll in Mumbai India during a winter storm
+A man is eating salad
+An Asian girl wearing a bright yellow T-shirt and white pants is Hip-Hop dancing
+nighttime footage of a hermit crab using an incandescent lightbulb as its shell
+a toy robot wearing a green dress and a sun hat taking a pleasant stroll in Antarctica during a beautiful sunset
+A goat operating a food truck, serving gourmet grilled cheese sandwiches to a line of animals.
+Macro shot. Man in an antique scuba helmet with dark glass walking out of a flower
+A bustling train station in the heart of a vibrant city.
+Light filtering through a canopy of autumn leaves, casting warm, dappled patterns of yellow, orange, and red onto the ground.
+Chimneys in the setting sun
+A longboarder accelerating downhill, carving through turns.
+A couple runs through a sudden downpour, laughing and splashing in puddles as they try to find shelter.
+A glass of iced coffee condensing water on the outside, with droplets forming and sliding down the glass in slow motion.
+macro shot of a leaf showing tiny trains moving through its veins
+A corgi wearing sunglasses walks on the beach of a tropical island
+Borneo wildlife on the Kinabatangan River
+A beautiful silhouette animation shows a wolf howling at the moon, feeling lonely, until it finds its pack.
+an adorable kangaroo wearing blue jeans and a white t shirt taking a pleasant stroll in Johannesburg South Africa during a colorful festival
+A green monster made of plants walks through an airport.
+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.
+A person on a scooter colliding with a park bench, the scooter tipping over.
+A tilt-up from a city street, ascending to show the skyline with its mix of modern and historic architecture.
+A chef tossing a pancake into the air and catching it.
+A woman whispering a secret into a friend's ear.
+A vulture circling high in the sky.
+A medieval castle overlooking a bustling renaissance fair.
+a toy robot wearing purple overalls and cowboy boots taking a pleasant stroll in Mumbai India during a beautiful sunset
+A man standing in front of a burning building giving the 'thumbs up' sign.
+The person's cheeks flushed with embarrassment as he told a funny story.
+Llamas and Emus are playing chess
+A woman sipping a steaming cup of tea.
+A tree root bursting through the seat of an ancient, weathered bench, intertwining with the wood.
+Smoke rises from the chimney of a cozy log cabin nestled in the woods, with soft light glowing from the windows, suggesting a warm and inviting atmosphere.
+A close-up of sparkling water being poured into a glass, capturing the detailed flow and bubbles.
+a woman wearing blue jeans and a white t shirt taking a pleasant stroll in Antarctica during a beautiful sunset
+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.
+A piece of elastic fabric being pulled and stretched, then returning to its original size when the tension is released.
+a woman wearing a green dress and a sun hat taking a pleasant stroll in Antarctica during a beautiful sunset
+A video of a water jet cutting through metal, showing the powerful and precise movement of water.
+Car mirrors and sunsets
+Giant Pandas are eating hot noodles in a Chinese restaurant
+A rally car taking a fast turn on a track
+a toy robot wearing purple overalls and cowboy boots taking a pleasant stroll in Mumbai India during a colorful festival
+A crystal-clear icicle slowly dripping as it melts in the warmth of the midday sun, each drop sparkling as it falls.
+A tilt-down from a chandelier in a grand hall, revealing the ornate decor and people mingling below.
+A man is playing the drums under the water
+A person playing an electric guitar made of lightning, with thunderous sound waves.
+A person floating in a bubble, drifting over a bustling cityscape.
+A tilt-down from a starry night sky, revealing a quiet forest clearing bathed in moonlight.
+A pan right through a dense jungle, moving past lush vegetation and exotic wildlife.
+Close-up of a man eating an apple.
+A low-angle shot of a dancer leaping gracefully into the air, making their movement appear even more dynamic and powerful.
+A woman is search her bag trying to find something.
+A bulldozer clears debris from a demolished building, making way for new construction.
+A man sighed in relief as the doctor delivered the good news.
+A tsunami coming through an alley in Bulgaria, dynamic movement.
+Blooming Flowers
+A push-in through a dense crowd at a festival, moving towards a performer on stage who is captivating the audience.
+A truck right through a tranquil garden, moving past blooming flowers, trees, and a small fountain.
+The person's eyes sparkled with excitement as he greeted a friend.
+A person playing chess with a robot on a floating platform above the ocean.
+A gentle breeze rustles the leaves as someone walks down a serene forest path, sunlight filtering through the trees and shifting patterns on the ground as branches sway.
+A rollercoaster ride from a city to a desert and then to an ice world
+A pan left across an ancient library, moving from shelf to shelf, showcasing rows of leather-bound books.
+A mother otter floating on her back in a river, cradling her pup on her stomach to keep it safe and warm in the gentle current.
+an adorable kangaroo wearing purple overalls and cowboy boots taking a pleasant stroll in Johannesburg South Africa during a colorful festival
+a woman wearing a green dress and a sun hat taking a pleasant stroll in Mumbai India during a colorful festival
+A delicate layer of morning frost melting off a flower petal, the tiny droplets glistening like diamonds in the light.
+A panda is cooking for her child, her child is next to her.
+Macro shot of a man wearing an antique diving helmet with dark glass and a jetpack walking on the veins of a leaf. Realistic style
+an old man wearing purple overalls and cowboy boots taking a pleasant stroll in Johannesburg South Africa during a beautiful sunset
+A girl is unfolding a birthday gift.
+A pencil drawing an architectural plan.
+A handheld camera following a dog running through a park, bouncing and tilting as it captures the dog's joyful exploration.
+A pan left across a serene beach at sunrise, moving from the darkened shore to the brightening horizon.
+A group of people are clapping to celebrate
+Vendors set up stalls at a bustling farmer’s market, displaying fresh fruits and vegetables, while people stroll through, selecting produce and enjoying the lively atmosphere.
+A police helicopter hovers above a high-speed chase, guiding officers on the ground to apprehend a suspect.
+A paper origami dragon riding a boat in waves. Realistic style.
+A close-up of a droplet of dew forming on a leaf, capturing the detailed surface tension.
+a toy robot wearing blue jeans and a white t shirt taking a pleasant stroll in Mumbai India during a beautiful sunset
+A dry rainbow rose is coming back to life.
+A glass falling off a table and shattering on the floor.
+A marathon runner crossing the finish line after a grueling race.
+A zoom-in on a drop of morning dew on a leaf, showing the reflection of the surrounding world within it.
+A child blowing on hot cocoa to cool it down.
+A squad of futsal players showcasing their skills on an indoor court.
+A princess is brushing her long golden hair in the garden.
+A close-up of a pair of eyes, revealing the subtle emotions and reflections within them.
+A tracking shot of a group of cyclists racing through a forest trail, with trees and foliage rushing by.
+A woman yawning widely at the end of a long day.
+an old man wearing a green dress and a sun hat taking a pleasant stroll in Johannesburg South Africa during a colorful festival
+Hidden within a garden, an ancient fountain trickles with water, surrounded by vibrant flowers and lush greenery that seem to whisper secrets of the past.
+A Chinese man sits at a table and eats noodles with chopsticks
+A pink pig running fast toward the camera in an alley in Tokyo.
+Strange creatures move through a mysterious, foggy marsh, their silhouettes barely visible through the dense mist as they navigate the eerie, otherworldly landscape.
+Tour of an art gallery with many beautiful works of art in different styles.
+FPV flying through a colorful coral lined streets of an underwater suburban neighborhood.
+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.
+Camera zoom out. A couple walking along the beach as the sun sets over the ocean.
+an extreme close up shot of a woman's eye, with her iris appearing as earth
+a woman wearing purple overalls and cowboy boots taking a pleasant stroll in Mumbai India during a colorful festival
+an old man wearing a green dress and a sun hat taking a pleasant stroll in Mumbai India during a winter storm
+an adorable kangaroo wearing blue jeans and a white t shirt taking a pleasant stroll in Antarctica during a winter storm
+A martial artist breaking a board with a powerful punch.
+People gather on a peaceful beach at sunset, a bonfire crackling as they sit around, enjoying the warmth and the sight of the sun dipping below the horizon.
+A close-up of a waterfall, showing the detailed movement of water as it crashes down.
+A child is blowing bubbles
+a woman wearing a green dress and a sun hat taking a pleasant stroll in Johannesburg South Africa during a winter storm
+A wide-angle perspective of a serene lake surrounded by mountains, reflecting the sky and creating a sense of infinite space.
+The person's eyebrows arched in skepticism as she listened to a dubious claim.
+an old man wearing blue jeans and a white t shirt taking a pleasant stroll in Mumbai India during a beautiful sunset
+a woman wearing a green dress and a sun hat taking a pleasant stroll in Johannesburg South Africa during a colorful festival
+a woman wearing purple overalls and cowboy boots taking a pleasant stroll in Antarctica during a colorful festival
+a toy robot wearing blue jeans and a white t shirt taking a pleasant stroll in Antarctica during a colorful festival
+A chef flips a pancake and puts cream on it.
+An astronaut runs on the surface of the moon, the low angle shot shows the vast background of the moon, the movement is smooth and appears lightweight
+A man's face lit up with happiness as he received a heartfelt compliment.
+A futuristic spaceport hums with activity as ships of various shapes and sizes take off and land on multiple platforms, their engines glowing with vibrant colors.
+A person knitting a scarf using beams of light instead of yarn.
+A pedestal up from the edge of a canyon, gradually revealing the expansive landscape and river below.
+a woman wearing purple overalls and cowboy boots taking a pleasant stroll in Johannesburg South Africa during a colorful festival
+an old man wearing blue jeans and a white t shirt taking a pleasant stroll in Johannesburg South Africa during a colorful festival
+A person walking up a staircase made of clouds leading to a floating castle.
+Monks meditate in a serene mountaintop temple, sitting in quiet reflection as the wind gently moves through the surrounding trees, creating a sense of peace and tranquility.
+An aerial shot of a bustling city intersection at rush hour, capturing the organized chaos of cars and pedestrians.
+A pair of hands skillfully knitting a colorful scarf, the yarn winding through their fingers with each stitch.
+Close-up, a Chinese child is eating dumplings
+A kite losing wind and falling to the ground.
+Bioluminescent waves gently wash ashore on a deserted beach, illuminating the sand with each cresting wave as a figure walks along the water's edge, leaving glowing footprints.
+A red panda taking a bite of a pizza
+A close-up shot of a young woman driving a car, looking thoughtful, blurred green forest visible through the rainy car window.
+A high-speed video of a splash created by a stone thrown into a pond.
+A metal rod being bent slightly by a force and then springing back to its original straight shape when the force is removed.
+A hedgehog in a knight's armor, riding a toy horse into a medieval castle.
+A bird made of fresh oranges rushes out of the orange
+A low altitude first person perspective camera tracking shot of a soccer player's feet dribbling the ball on the groud in a soccer field, Sports Videography, Motion Tracking camera shot
+A tranquil island retreat features swaying palm trees and hammocks strung between them, inviting guests to relax and enjoy the serene beauty of the surroundings.
+a spooky haunted mansion, with friendly jack o lanterns and ghost characters welcoming trick or treaters to the entrance, tilt shift photography
+A coconut tree made of dollar bills at sunset, with bills falling off like leaves.
+A motocross bike accelerating out of a tight turn on a dirt track.
+A tranquil Zen garden with a gently flowing stream and koi fish.
+A green monster made of leaves walks through the airport, carrying a suitcase.
+A time-lapse of a frost-covered leaf gradually thawing in the morning sunlight, with tiny water droplets forming and trickling down.
+A woman practicing her archery skills at a range.
+A slow-motion video of ink being injected into a tank of water, creating intricate and beautiful patterns.
+a woman wearing blue jeans and a white t shirt taking a pleasant stroll in Johannesburg South Africa during a winter storm
+The person's forehead creased with worry as he listened to bad news.
+An arc shot around a grand piano being played in an empty concert hall, the motion revealing the intricate details of the instrument.
+A person conducting a symphony of animals in a forest clearing.
+A truck right alongside a flowing river, capturing the movement of the water and the surrounding forest.
+A rocket blasting off from the launch pad, accelerating rapidly into the sky.
+Workers move through a picturesque vineyard during the harvest season, carefully picking grapes and placing them into baskets as the sun bathes the vines in a warm glow.
+A person is eating an ice cream.
+An over-the-shoulder perspective of a chef meticulously plating a dish in a bustling kitchen.
+A man looked away in shame when confronted with his wrongdoing.
+A person is savoring a slice of pizza at a pizzeria.

examples/inference/basic/basic.py

@@ -1,6 +1,10 @@
 from fastvideo import VideoGenerator
 
-# from fastvideo.configs.sample import SamplingParam
+from fastvideo.configs.sample import SamplingParam
+
+import os
+
+os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
 
 OUTPUT_PATH = "video_samples"
 def main():
@@ -8,30 +12,41 @@ def main():
     # model.
     # If a local path is provided, FastVideo will make a best effort
     # attempt to identify the optimal arguments.
+    model_name = "FastVideo/FastWan2.1-T2V-14B-Diffusers"
     generator = VideoGenerator.from_pretrained(
-        "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+        model_name,
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
         use_fsdp_inference=True,
         dit_cpu_offload=False,
         vae_cpu_offload=False,
-        text_encoder_cpu_offload=True,
+        text_encoder_cpu_offload=False,
         # Set pin_cpu_memory to false if CPU RAM is limited and there're no frequent CPU-GPU transfer
-        pin_cpu_memory=False,
+        pin_cpu_memory=True,
         # image_encoder_cpu_offload=False,
     )
 
-    # sampling_param = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
-    # sampling_param.num_frames = 45
-    # sampling_param.image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
+    sampling_param = SamplingParam.from_pretrained(model_name)
+    # sampling_param.image_path = "test.jpg"
+    # sampling_param.num_inference_steps = 0
     # Generate videos with the same simple API, regardless of GPU count
-    prompt = (
-        "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
-        "wide with interest. The playful yet serene atmosphere is complemented by soft "
-        "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
-    )
-    video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True)
+    i2v_prompt = "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot."
+    i2v_prompt = "A little girl is packing a suitcase and the contents starts flying out of the suitcase everywhere."
+    prompt = i2v_prompt
+    # prompt = (
+    #     "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
+    #     "wide with interest. The playful yet serene atmosphere is complemented by soft "
+    #     "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
+    # )
+    results = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, sampling_param=sampling_param)
+    stage_names = results["stage_names"]
+    stage_execution_times = results["stage_execution_times"]
+    # print(logging_info)
+    print(stage_names)
+    print(stage_execution_times)
+
     # video = generator.generate_video(prompt, sampling_param=sampling_param, output_path="wan_t2v_videos/")
+    return
 
     # Generate another video with a different prompt, without reloading the
     # model!

examples/inference/basic/basic_dmd.py

@@ -31,6 +31,7 @@ def main():
     prompt = (
         "A neon-lit alley in futuristic Tokyo during a heavy rainstorm at night. The puddles reflect glowing signs in kanji, advertising ramen, karaoke, and VR arcades. A woman in a translucent raincoat walks briskly with an LED umbrella. Steam rises from a street food cart, and a cat darts across the screen. Raindrops are visible on the camera lens, creating a cinematic bokeh effect."
     )
+    prompt = "A vintage train snakes through the mountains, its plume of white steam rising dramatically against the jagged peaks. The cars glint in the late afternoon sun, their deep crimson and gold accents lending a touch of elegance. The tracks carve a precarious path along the cliffside, revealing glimpses of a roaring river far below. Inside, passengers peer out the large windows, their faces lit with awe as the landscape unfolds."
     start_time = time.perf_counter()
     video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, sampling_param=sampling_param)
     end_time = time.perf_counter()
@@ -45,7 +46,7 @@ def main():
         "embodying the raw energy of the wild. Low angle, steady tracking shot, "
         "cinematic.")
     start_time = time.perf_counter()
-    video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True)
+    video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=False)
     end_time = time.perf_counter()
     gen_time2 = end_time - start_time
 

examples/inference/basic_i2v.py

@@ -0,0 +1,38 @@
+from fastvideo import VideoGenerator
+
+from fastvideo.configs.sample import SamplingParam
+
+OUTPUT_PATH = "video_samples"
+def main():
+    # FastVideo will automatically use the optimal default arguments for the
+    # model.
+    # If a local path is provided, FastVideo will make a best effort
+    # attempt to identify the optimal arguments.
+    generator = VideoGenerator.from_pretrained(
+        "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",
+        # FastVideo will automatically handle distributed setup
+        num_gpus=2,
+        use_fsdp_inference=True,
+        dit_cpu_offload=False,
+        vae_cpu_offload=False,
+        text_encoder_cpu_offload=False,
+        image_encoder_cpu_offload=False,
+    )
+
+    sampling_param = SamplingParam.from_pretrained("Wan-AI/Wan2.1-I2V-14B-480P-Diffusers")
+    sampling_param.num_frames = 61
+    sampling_param.num_inference_steps = 40
+    sampling_param.guidance_scale = 5.0
+    sampling_param.height = 448
+    sampling_param.width = 832
+    sampling_param.seed = 1024
+    sampling_param.image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
+    # Generate videos with the same simple API, regardless of GPU count
+    prompt = (
+        "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot."
+    )
+    video = generator.generate_video(prompt, sampling_param=sampling_param, output_path=OUTPUT_PATH, save_video=True)
+
+
+if __name__ == "__main__":
+    main()

examples/inference/gradio/backend_test.py

@@ -0,0 +1,501 @@
+import argparse
+import os
+import time
+import json
+import statistics
+import asyncio
+import aiohttp
+from copy import deepcopy
+from typing import List, Dict, Any
+import threading
+
+import torch
+
+# All the prompts for stress testing
+STRESS_TEST_PROMPTS = [
+    "A person reading a book with words that float off the pages and form pictures.",
+    "A person diving into a pool of liquid crystal, creating ripples of light.",
+    "A handheld shot chasing after a group of friends laughing and playing on the beach at sunset.",
+    "A mysterious ancient temple hidden in the jungle.",
+    "A high-speed train navigating a steep descent.",
+    "a toy robot wearing blue jeans and a white t shirt taking a pleasant stroll in Antarctica during a winter storm",
+    "A cheetah accelerating to full speed while chasing its prey.",
+    "A serene orchard is in full bloom, with trees heavy with blossoms and bees buzzing around, darting from flower to flower in a display of natural harmony.",
+    "A little child let out a big yawn",
+    "Subtle reflections of a woman on the window of a train moving at hyper-speed in a Japanese city.",
+    "A truck left along the edge of a cliff, revealing the stunning coastal landscape below with waves crashing against the rocks.",
+    "A red bird transforms into a flag",
+    "A zoom-out from a single leaf on a tree to reveal the entire forest, showcasing the vastness and diversity of the woodland.",
+    "A slow-motion video of a liquid droplet bouncing on a water-repellent surface.",
+    "Static camera shot. A dinasour running near some lions and chasing them away.",
+    "an adorable kangaroo wearing purple overalls and cowboy boots taking a pleasant stroll in Mumbai India during a beautiful sunset",
+    "A zoom-in on an artist's brush touching the canvas, highlighting the texture of the paint and the strokes being made.",
+    "an old man wearing blue jeans and a white t shirt taking a pleasant stroll in Mumbai India during a colorful festival",
+    "A woman is ascending to the sky from the ground",
+    "View out a window of a giant strange creature walking in rundown city at night, one single street lamp dimly lighting the area.",
+    "An arc shot around a lone tree in a vast, foggy field at dawn, revealing the changing light and shadows.",
+    "A person sculpting a statue out of a waterfall, the water solidifying under their touch.",
+    "The person's forehead creased with concentration as she worked on a challenging puzzle.",
+    "The person's cheeks flushed with pleasure as she savored a delicious meal.",
+    "Hand-drawn simple line art, a young kid looking up into space with a wondrous expression on his face.",
+    "A crab made of different jewlery is walking on the beach. As it walks, it drops different jewelry pieces like diamonds, pearls, etc",
+    "Gold coins are falling out when elevator door opens",
+    "the scene transitions from huge waves into a snowy mountain at sunset",
+    "a giant cathedral is completely filled with cats. there are cats everywhere you look. a man enters the cathedral and bows before the giant cat king sitting on a throne.",
+    "A mother dog gently picks up a piece of meat and carefully places it in her puppy's bowl, her eyes filled with warmth and care as she watches her little one eat.",
+    "A soap bubble floating in the air, displaying iridescent colors that shift and change as it moves through different angles of light.",
+    "A truck left alongside a train moving through the countryside, matching its speed and revealing the changing landscape.",
+    "An astronaut walking between stone buildings.",
+    "A close-up shot of the person's face reveals his fear and desperation as he navigates the ship through the storm.",
+    "A frozen lake slowly cracking and thawing as spring arrives, with sheets of ice breaking apart and drifting across the surface.",
+    "A FPV shot zooming through a tunnel into a vibrant underwater space.",
+    "a toy robot wearing blue jeans and a white t shirt taking a pleasant stroll in Mumbai India during a colorful festival",
+    "A person sips on a smoothie, the cool and fruity flavors refreshing her mouth.",
+    "In a vibrant theater, a magician in dazzling attire stands center stage, pulling a comically oversized rubber chicken from an ornate, old-fashioned box. His costume shimmers under the stage lights, adding to the spectacle. The crowd erupts in laughter and applause, their faces filled with joy and amazement. The magician's expression hints at mischievous delight as he holds up the rubber chicken, his performance bringing cheer to the audience.",
+    "A hamster running on a spinning wheel.",
+    "A quaint village nestled in a valley is surrounded by blooming cherry blossoms, with petals drifting through the air as villagers go about their daily activities, adding life to the scene.",
+    "In a tranquil forest clearing, a sparkling waterfall cascades down into a clear pool, surrounded by lush greenery and flowers, with occasional birds fluttering by.",
+    "A woman beamed with pride as she watched her child perform on stage.",
+    "an adorable kangaroo wearing blue jeans and a white t shirt taking a pleasant stroll in Mumbai India during a winter storm",
+    "A man is eating salad",
+    "An Asian girl wearing a bright yellow T-shirt and white pants is Hip-Hop dancing",
+    "nighttime footage of a hermit crab using an incandescent lightbulb as its shell",
+    "a toy robot wearing a green dress and a sun hat taking a pleasant stroll in Antarctica during a beautiful sunset",
+    "A goat operating a food truck, serving gourmet grilled cheese sandwiches to a line of animals.",
+    "Macro shot. Man in an antique scuba helmet with dark glass walking out of a flower",
+    "A bustling train station in the heart of a vibrant city.",
+    "Light filtering through a canopy of autumn leaves, casting warm, dappled patterns of yellow, orange, and red onto the ground.",
+    "Chimneys in the setting sun",
+    "A longboarder accelerating downhill, carving through turns.",
+    "A couple runs through a sudden downpour, laughing and splashing in puddles as they try to find shelter.",
+    "A glass of iced coffee condensing water on the outside, with droplets forming and sliding down the glass in slow motion.",
+    "macro shot of a leaf showing tiny trains moving through its veins",
+    "A corgi wearing sunglasses walks on the beach of a tropical island",
+    "Borneo wildlife on the Kinabatangan River",
+    "A beautiful silhouette animation shows a wolf howling at the moon, feeling lonely, until it finds its pack.",
+    "an adorable kangaroo wearing blue jeans and a white t shirt taking a pleasant stroll in Johannesburg South Africa during a colorful festival",
+    "A green monster made of plants walks through an airport.",
+    "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.",
+    "A person on a scooter colliding with a park bench, the scooter tipping over.",
+    "A tilt-up from a city street, ascending to show the skyline with its mix of modern and historic architecture.",
+    "A chef tossing a pancake into the air and catching it.",
+    "A woman whispering a secret into a friend's ear.",
+    "A vulture circling high in the sky.",
+    "A medieval castle overlooking a bustling renaissance fair.",
+    "a toy robot wearing purple overalls and cowboy boots taking a pleasant stroll in Mumbai India during a beautiful sunset",
+    "A man standing in front of a burning building giving the 'thumbs up' sign.",
+    "The person's cheeks flushed with embarrassment as he told a funny story.",
+    "Llamas and Emus are playing chess",
+    "A woman sipping a steaming cup of tea.",
+    "A tree root bursting through the seat of an ancient, weathered bench, intertwining with the wood.",
+    "Smoke rises from the chimney of a cozy log cabin nestled in the woods, with soft light glowing from the windows, suggesting a warm and inviting atmosphere.",
+    "A close-up of sparkling water being poured into a glass, capturing the detailed flow and bubbles.",
+    "a woman wearing blue jeans and a white t shirt taking a pleasant stroll in Antarctica during a beautiful sunset",
+    "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.",
+    "A piece of elastic fabric being pulled and stretched, then returning to its original size when the tension is released.",
+    "a woman wearing a green dress and a sun hat taking a pleasant stroll in Antarctica during a beautiful sunset",
+    "A video of a water jet cutting through metal, showing the powerful and precise movement of water.",
+    "Car mirrors and sunsets",
+    "Giant Pandas are eating hot noodles in a Chinese restaurant",
+    "A rally car taking a fast turn on a track",
+    "a toy robot wearing purple overalls and cowboy boots taking a pleasant stroll in Mumbai India during a colorful festival",
+    "A crystal-clear icicle slowly dripping as it melts in the warmth of the midday sun, each drop sparkling as it falls.",
+    "A tilt-down from a chandelier in a grand hall, revealing the ornate decor and people mingling below.",
+    "A man is playing the drums under the water",
+    "A person playing an electric guitar made of lightning, with thunderous sound waves.",
+    "A person floating in a bubble, drifting over a bustling cityscape.",
+    "A tilt-down from a starry night sky, revealing a quiet forest clearing bathed in moonlight.",
+    "A pan right through a dense jungle, moving past lush vegetation and exotic wildlife.",
+    "Close-up of a man eating an apple.",
+    "A low-angle shot of a dancer leaping gracefully into the air, making their movement appear even more dynamic and powerful.",
+    "A woman is search her bag trying to find something.",
+    "A bulldozer clears debris from a demolished building, making way for new construction.",
+    "A man sighed in relief as the doctor delivered the good news.",
+    "A tsunami coming through an alley in Bulgaria, dynamic movement.",
+    "Blooming Flowers",
+    "A push-in through a dense crowd at a festival, moving towards a performer on stage who is captivating the audience.",
+    "A truck right through a tranquil garden, moving past blooming flowers, trees, and a small fountain.",
+    "The person's eyes sparkled with excitement as he greeted a friend.",
+    "A person playing chess with a robot on a floating platform above the ocean.",
+    "A gentle breeze rustles the leaves as someone walks down a serene forest path, sunlight filtering through the trees and shifting patterns on the ground as branches sway.",
+    "A rollercoaster ride from a city to a desert and then to an ice world",
+    "A pan left across an ancient library, moving from shelf to shelf, showcasing rows of leather-bound books.",
+    "A mother otter floating on her back in a river, cradling her pup on her stomach to keep it safe and warm in the gentle current.",
+    "an adorable kangaroo wearing purple overalls and cowboy boots taking a pleasant stroll in Johannesburg South Africa during a colorful festival",
+    "a woman wearing a green dress and a sun hat taking a pleasant stroll in Mumbai India during a colorful festival",
+    "A delicate layer of morning frost melting off a flower petal, the tiny droplets glistening like diamonds in the light.",
+    "A panda is cooking for her child, her child is next to her.",
+    "Macro shot of a man wearing an antique diving helmet with dark glass and a jetpack walking on the veins of a leaf. Realistic style",
+    "an old man wearing purple overalls and cowboy boots taking a pleasant stroll in Johannesburg South Africa during a beautiful sunset",
+    "A girl is unfolding a birthday gift.",
+    "A pencil drawing an architectural plan.",
+    "A handheld camera following a dog running through a park, bouncing and tilting as it captures the dog's joyful exploration.",
+    "A pan left across a serene beach at sunrise, moving from the darkened shore to the brightening horizon.",
+    "A group of people are clapping to celebrate",
+    "Vendors set up stalls at a bustling farmer's market, displaying fresh fruits and vegetables, while people stroll through, selecting produce and enjoying the lively atmosphere.",
+    "A police helicopter hovers above a high-speed chase, guiding officers on the ground to apprehend a suspect.",
+    "A paper origami dragon riding a boat in waves. Realistic style.",
+    "A close-up of a droplet of dew forming on a leaf, capturing the detailed surface tension.",
+    "a toy robot wearing blue jeans and a white t shirt taking a pleasant stroll in Mumbai India during a beautiful sunset",
+    "A dry rainbow rose is coming back to life.",
+    "A glass falling off a table and shattering on the floor.",
+    "A marathon runner crossing the finish line after a grueling race.",
+    "A zoom-in on a drop of morning dew on a leaf, showing the reflection of the surrounding world within it.",
+    "A child blowing on hot cocoa to cool it down.",
+    "A squad of futsal players showcasing their skills on an indoor court.",
+    "A princess is brushing her long golden hair in the garden.",
+    "A close-up of a pair of eyes, revealing the subtle emotions and reflections within them.",
+    "A tracking shot of a group of cyclists racing through a forest trail, with trees and foliage rushing by.",
+    "A woman yawning widely at the end of a long day.",
+    "an old man wearing a green dress and a sun hat taking a pleasant stroll in Johannesburg South Africa during a colorful festival",
+    "Hidden within a garden, an ancient fountain trickles with water, surrounded by vibrant flowers and lush greenery that seem to whisper secrets of the past.",
+    "A Chinese man sits at a table and eats noodles with chopsticks",
+    "A pink pig running fast toward the camera in an alley in Tokyo.",
+    "Strange creatures move through a mysterious, foggy marsh, their silhouettes barely visible through the dense mist as they navigate the eerie, otherworldly landscape.",
+    "Tour of an art gallery with many beautiful works of art in different styles.",
+    "FPV flying through a colorful coral lined streets of an underwater suburban neighborhood.",
+    "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.",
+    "Camera zoom out. A couple walking along the beach as the sun sets over the ocean.",
+    "an extreme close up shot of a woman's eye, with her iris appearing as earth",
+    "a woman wearing purple overalls and cowboy boots taking a pleasant stroll in Mumbai India during a colorful festival",
+    "an old man wearing a green dress and a sun hat taking a pleasant stroll in Mumbai India during a winter storm",
+    "an adorable kangaroo wearing blue jeans and a white t shirt taking a pleasant stroll in Antarctica during a winter storm",
+    "A martial artist breaking a board with a powerful punch.",
+    "People gather on a peaceful beach at sunset, a bonfire crackling as they sit around, enjoying the warmth and the sight of the sun dipping below the horizon.",
+    "A close-up of a waterfall, showing the detailed movement of water as it crashes down.",
+    "A child is blowing bubbles",
+    "a woman wearing a green dress and a sun hat taking a pleasant stroll in Johannesburg South Africa during a winter storm",
+    "A wide-angle perspective of a serene lake surrounded by mountains, reflecting the sky and creating a sense of infinite space.",
+    "The person's eyebrows arched in skepticism as she listened to a dubious claim.",
+    "an old man wearing blue jeans and a white t shirt taking a pleasant stroll in Mumbai India during a beautiful sunset",
+    "a woman wearing a green dress and a sun hat taking a pleasant stroll in Johannesburg South Africa during a colorful festival",
+    "a woman wearing purple overalls and cowboy boots taking a pleasant stroll in Antarctica during a colorful festival",
+    "a toy robot wearing blue jeans and a white t shirt taking a pleasant stroll in Antarctica during a colorful festival",
+    "A chef flips a pancake and puts cream on it.",
+    "An astronaut runs on the surface of the moon, the low angle shot shows the vast background of the moon, the movement is smooth and appears lightweight",
+    "A man's face lit up with happiness as he received a heartfelt compliment.",
+    "A futuristic spaceport hums with activity as ships of various shapes and sizes take off and land on multiple platforms, their engines glowing with vibrant colors.",
+    "A person knitting a scarf using beams of light instead of yarn.",
+    "A pedestal up from the edge of a canyon, gradually revealing the expansive landscape and river below.",
+    "a woman wearing purple overalls and cowboy boots taking a pleasant stroll in Johannesburg South Africa during a colorful festival",
+    "an old man wearing blue jeans and a white t shirt taking a pleasant stroll in Johannesburg South Africa during a colorful festival",
+    "A person walking up a staircase made of clouds leading to a floating castle.",
+    "Monks meditate in a serene mountaintop temple, sitting in quiet reflection as the wind gently moves through the surrounding trees, creating a sense of peace and tranquility.",
+    "An aerial shot of a bustling city intersection at rush hour, capturing the organized chaos of cars and pedestrians.",
+    "A pair of hands skillfully knitting a colorful scarf, the yarn winding through their fingers with each stitch.",
+    "Close-up, a Chinese child is eating dumplings",
+    "A kite losing wind and falling to the ground.",
+    "Bioluminescent waves gently wash ashore on a deserted beach, illuminating the sand with each cresting wave as a figure walks along the water's edge, leaving glowing footprints.",
+    "A red panda taking a bite of a pizza",
+    "A close-up shot of a young woman driving a car, looking thoughtful, blurred green forest visible through the rainy car window.",
+    "A high-speed video of a splash created by a stone thrown into a pond.",
+    "A metal rod being bent slightly by a force and then springing back to its original straight shape when the force is removed.",
+    "A hedgehog in a knight's armor, riding a toy horse into a medieval castle.",
+    "A bird made of fresh oranges rushes out of the orange",
+    "A low altitude first person perspective camera tracking shot of a soccer player's feet dribbling the ball on the groud in a soccer field, Sports Videography, Motion Tracking camera shot",
+    "A tranquil island retreat features swaying palm trees and hammocks strung between them, inviting guests to relax and enjoy the serene beauty of the surroundings.",
+    "a spooky haunted mansion, with friendly jack o lanterns and ghost characters welcoming trick or treaters to the entrance, tilt shift photography",
+    "A coconut tree made of dollar bills at sunset, with bills falling off like leaves.",
+    "A motocross bike accelerating out of a tight turn on a dirt track.",
+    "A tranquil Zen garden with a gently flowing stream and koi fish.",
+    "A green monster made of leaves walks through the airport, carrying a suitcase.",
+    "A time-lapse of a frost-covered leaf gradually thawing in the morning sunlight, with tiny water droplets forming and trickling down.",
+    "A woman practicing her archery skills at a range.",
+    "A slow-motion video of ink being injected into a tank of water, creating intricate and beautiful patterns.",
+    "a woman wearing blue jeans and a white t shirt taking a pleasant stroll in Johannesburg South Africa during a winter storm",
+    "The person's forehead creased with worry as he listened to bad news.",
+    "An arc shot around a grand piano being played in an empty concert hall, the motion revealing the intricate details of the instrument.",
+    "A person conducting a symphony of animals in a forest clearing.",
+    "A truck right alongside a flowing river, capturing the movement of the water and the surrounding forest.",
+    "A rocket blasting off from the launch pad, accelerating rapidly into the sky.",
+    "Workers move through a picturesque vineyard during the harvest season, carefully picking grapes and placing them into baskets as the sun bathes the vines in a warm glow.",
+    "A person is eating an ice cream.",
+    "An over-the-shoulder perspective of a chef meticulously plating a dish in a bustling kitchen.",
+    "A man looked away in shame when confronted with his wrongdoing.",
+    "A person is savoring a slice of pizza at a pizzeria."
+]
+
+class BackendStressTest:
+    def __init__(self, output_path: str, 
+                 server_url: str = "http://localhost:8000", max_concurrent: int = 50):
+        self.output_path = output_path
+        self.server_url = server_url
+        self.max_concurrent = max_concurrent
+        
+        # Results storage
+        self.results = []
+        self.lock = threading.Lock()
+        
+    async def check_health(self) -> bool:
+        """Check if the Ray Serve backend is healthy"""
+        try:
+            async with aiohttp.ClientSession() as session:
+                async with session.get(f"{self.server_url}/health", timeout=aiohttp.ClientTimeout(total=5)) as response:
+                    return response.status == 200
+        except Exception:
+            return False
+        
+    def build_request_params(self, prompt: str, **kwargs) -> Dict[str, Any]:
+        """Build request parameters for Ray Serve backend"""
+        # Default parameters matching the Ray Serve backend
+        default_params = {
+            'prompt': prompt,
+            'negative_prompt': None,
+            'use_negative_prompt': False,
+            'seed': 42,
+            'guidance_scale': 7.5,
+            'num_frames': 21,
+            'height': 448,
+            'width': 832,
+            'num_inference_steps': 20,
+            'randomize_seed': True,
+            'return_frames': False  # Don't return frames for stress testing to reduce overhead
+        }
+        
+        # Override with any provided kwargs
+        for key, value in kwargs.items():
+            if key in default_params:
+                default_params[key] = value
+        
+        # Randomize seed if requested
+        if default_params.get('randomize_seed', True):
+            default_params['seed'] = torch.randint(0, 1000000, (1,)).item()
+            
+        # Handle negative prompt
+        if not default_params.get('use_negative_prompt', False):
+            default_params['negative_prompt'] = None
+        
+        # NEW: Remove keys with None values to avoid sending nulls that may break validation
+        clean_params = {k: v for k, v in default_params.items() if v is not None}
+        return clean_params
+    
+    async def test_single_request(self, session: aiohttp.ClientSession, prompt: str, request_id: int) -> Dict[str, Any]:
+        """Test a single request and measure latency"""
+        start_time = time.time()
+        
+        try:
+            # Build request parameters
+            request_params = self.build_request_params(prompt)
+            
+            # Make request to Ray Serve backend
+            async with session.post(
+                f"{self.server_url}/generate_video",
+                json=request_params,
+                timeout=aiohttp.ClientTimeout(total=900)  # 15 minute timeout for video generation
+            ) as response:
+                
+                end_time = time.time()
+                latency = end_time - start_time
+                
+                if response.status == 200:
+                    response_data = await response.json()
+                    if response_data.get('success', False):
+                        result = {
+                            'request_id': request_id,
+                            'prompt': prompt,
+                            'latency': latency,
+                            'status': 'success',
+                            'response_time': latency,  # Use our own timing
+                            'timestamp': start_time,
+                            'output_path': response_data.get('output_path', ''),
+                            'used_seed': response_data.get('seed', request_params['seed'])
+                        }
+                    else:
+                        result = {
+                            'request_id': request_id,
+                            'prompt': prompt,
+                            'latency': latency,
+                            'status': 'error',
+                            'error': response_data.get('error_message', 'Unknown backend error'),
+                            'timestamp': start_time
+                        }
+                else:
+                    response_text = await response.text()
+                    result = {
+                        'request_id': request_id,
+                        'prompt': prompt,
+                        'latency': latency,
+                        'status': 'error',
+                        'error': f"HTTP {response.status}: {response_text}",
+                        'timestamp': start_time
+                    }
+                    
+        except Exception as e:
+            end_time = time.time()
+            latency = end_time - start_time
+            result = {
+                'request_id': request_id,
+                'prompt': prompt,
+                'latency': latency,
+                'status': 'error',
+                'error': str(e),
+                'timestamp': start_time
+            }
+        
+        # Thread-safe result storage
+        with self.lock:
+            self.results.append(result)
+            
+        return result
+    
+    async def run_stress_test(self, num_iterations: int = 1, concurrent_requests: int = None):
+        """Run the stress test with multiple iterations and concurrent requests"""
+        if concurrent_requests is None:
+            concurrent_requests = self.max_concurrent
+            
+        # Check backend health before starting
+        print(f"Testing Ray Serve backend at {self.server_url}...")
+        if not await self.check_health():
+            print(f"❌ Backend is not healthy at {self.server_url}")
+            print("Make sure the Ray Serve backend is running with:")
+            print("python ray_serve_backend.py")
+            return
+        print("✅ Backend is healthy and ready for stress testing")
+        
+        print(f"\nStarting stress test with {len(STRESS_TEST_PROMPTS)} prompts")
+        print(f"Running {num_iterations} iteration(s) with {concurrent_requests} concurrent requests")
+        print(f"Total requests: {len(STRESS_TEST_PROMPTS) * num_iterations}")
+        print(f"Backend URL: {self.server_url}")
+        print("-" * 80)
+        
+        all_prompts = STRESS_TEST_PROMPTS * num_iterations
+        request_id = 0
+        
+        # Create semaphore to limit concurrent requests
+        semaphore = asyncio.Semaphore(concurrent_requests)
+        
+        async def limited_request(session: aiohttp.ClientSession, prompt: str, req_id: int):
+            async with semaphore:
+                return await self.test_single_request(session, prompt, req_id)
+        
+        # Run concurrent requests using asyncio
+        async with aiohttp.ClientSession() as session:
+            # Create all tasks
+            tasks = [
+                limited_request(session, prompt, request_id + i)
+                for i, prompt in enumerate(all_prompts)
+            ]
+            
+            # Process completed requests as they finish
+            completed = 0
+            for coro in asyncio.as_completed(tasks):
+                try:
+                    result = await coro
+                    completed += 1
+                    prompt = result['prompt']
+                    status_icon = "✅" if result['status'] == 'success' else "❌"
+                    output_info = f" -> {result.get('output_path', 'N/A')}" if result['status'] == 'success' else ""
+                    print(f"{status_icon} [{completed}/{len(all_prompts)}] {result['latency']:.2f}s - {prompt[:50]}...{output_info}")
+                except Exception as e:
+                    completed += 1
+                    print(f"❌ [{completed}/{len(all_prompts)}] Exception: {e}")
+        
+        self.analyze_results()
+    
+    def analyze_results(self):
+        """Analyze and print test results"""
+        print("\n" + "=" * 80)
+        print("STRESS TEST RESULTS")
+        print("=" * 80)
+        
+        successful_requests = [r for r in self.results if r['status'] == 'success']
+        failed_requests = [r for r in self.results if r['status'] == 'error']
+        
+        print(f"Total Requests: {len(self.results)}")
+        print(f"Successful: {len(successful_requests)}")
+        print(f"Failed: {len(failed_requests)}")
+        print(f"Success Rate: {len(successful_requests)/len(self.results)*100:.1f}%")
+        
+        if successful_requests:
+            latencies = [r['latency'] for r in successful_requests]
+            print(f"\nLatency Statistics (seconds):")
+            print(f"  Min: {min(latencies):.2f}")
+            print(f"  Max: {max(latencies):.2f}")
+            print(f"  Mean: {statistics.mean(latencies):.2f}")
+            print(f"  Median: {statistics.median(latencies):.2f}")
+            print(f"  Std Dev: {statistics.stdev(latencies):.2f}")
+            
+            # Percentiles
+            sorted_latencies = sorted(latencies)
+            p50 = sorted_latencies[int(len(sorted_latencies) * 0.5)]
+            p90 = sorted_latencies[int(len(sorted_latencies) * 0.9)]
+            p95 = sorted_latencies[int(len(sorted_latencies) * 0.95)]
+            p99 = sorted_latencies[int(len(sorted_latencies) * 0.99)]
+            
+            print(f"  P50: {p50:.2f}")
+            print(f"  P90: {p90:.2f}")
+            print(f"  P95: {p95:.2f}")
+            print(f"  P99: {p99:.2f}")
+        
+        if failed_requests:
+            print(f"\nFailed Requests ({len(failed_requests)}):")
+            for req in failed_requests[:5]:  # Show first 5 failures
+                print(f"  - {req['error']}")
+            if len(failed_requests) > 5:
+                print(f"  ... and {len(failed_requests) - 5} more")
+        
+        # Save detailed results
+        results_file = os.path.join(self.output_path, "stress_test_results.json")
+        os.makedirs(self.output_path, exist_ok=True)
+        
+        with open(results_file, 'w') as f:
+            json.dump({
+                'summary': {
+                    'total_requests': len(self.results),
+                    'successful_requests': len(successful_requests),
+                    'failed_requests': len(failed_requests),
+                    'success_rate': len(successful_requests)/len(self.results)*100 if self.results else 0
+                },
+                'latency_stats': {
+                    'min': min(latencies) if successful_requests else 0,
+                    'max': max(latencies) if successful_requests else 0,
+                    'mean': statistics.mean(latencies) if successful_requests else 0,
+                    'median': statistics.median(latencies) if successful_requests else 0,
+                    'std_dev': statistics.stdev(latencies) if len(successful_requests) > 1 else 0
+                },
+                'detailed_results': self.results
+            }, f, indent=2)
+        
+        print(f"\nDetailed results saved to: {results_file}")
+
+async def main():
+    parser = argparse.ArgumentParser(description="FastVideo Ray Serve Backend Stress Test")
+    parser.add_argument("--output_path",
+                        type=str,
+                        default="outputs",
+                        help="Path to save test results")
+    parser.add_argument("--server_url",
+                        type=str,
+                        default="http://localhost:8000",
+                        help="Ray Serve backend URL")
+    parser.add_argument("--max_concurrent",
+                        type=int,
+                        default=50,
+                        help="Maximum concurrent requests")
+    parser.add_argument("--iterations",
+                        type=int,
+                        default=1,
+                        help="Number of iterations through all prompts")
+    parser.add_argument("--concurrent_requests",
+                        type=int,
+                        default=None,
+                        help="Number of concurrent requests (overrides max_concurrent)")
+    
+    args = parser.parse_args()
+    
+    # Create stress test instance
+    stress_test = BackendStressTest(
+        output_path=args.output_path,
+        server_url=args.server_url,
+        max_concurrent=args.max_concurrent
+    )
+    
+    # Run the stress test
+    await stress_test.run_stress_test(
+        num_iterations=args.iterations,
+        concurrent_requests=args.concurrent_requests
+    )
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/inference/gradio/gradio_frontend.py

@@ -0,0 +1,936 @@
+import argparse
+import os
+import requests
+import json
+import base64
+import io
+from typing import Optional
+
+import gradio as gr
+import torch
+import imageio
+from PIL import Image
+import numpy as np
+import time
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+class RayServeClient:
+    def __init__(self, backend_url: str):
+        self.backend_url = backend_url
+        self.session = requests.Session()
+    
+    def check_health(self) -> bool:
+        """Check if the backend is healthy"""
+        try:
+            response = self.session.get(f"{self.backend_url}/health", timeout=5)
+            return response.status_code == 200
+        except requests.exceptions.RequestException:
+            return False
+    
+    def generate_video(self, request_data: dict) -> dict:
+        """Generate video using the backend API"""
+        start_time = time.time()
+        
+        try:
+            headers = {"Content-Type": "application/json"}
+            
+            response = self.session.post(
+                f"{self.backend_url}/generate_video",
+                json=request_data,
+                headers=headers,
+                timeout=300  # 5 minutes timeout
+            )
+            
+            end_time = time.time()
+            round_trip_time = end_time - start_time
+            
+            if response.status_code == 200:
+                result = response.json()
+                # Calculate network time by subtracting backend total time
+                backend_total = result.get("total_time", 0)
+                network_time = round_trip_time - backend_total
+                result["network_time"] = network_time
+                return result
+            else:
+                return {"success": False, "error_message": f"HTTP {response.status_code}: {response.text}"}
+                
+        except requests.exceptions.RequestException as e:
+            return {"success": False, "error_message": f"Request failed: {str(e)}"}
+    
+
+
+
+def save_video_from_base64(video_data: str, output_dir: str, prompt: str) -> str:
+    """Save base64-encoded video data to a file"""
+    if not video_data:
+        return "No video data to save"
+    
+    try:
+        # Remove the data URL prefix if present
+        if video_data.startswith('data:video/'):
+            video_data = video_data.split(',')[1]
+        
+        # Decode base64 to bytes
+        video_bytes = base64.b64decode(video_data)
+        
+        # Create safe filename from prompt
+        safe_prompt = prompt[:50].replace(' ', '_').replace('/', '_').replace('\\', '_')
+        video_filename = f"{safe_prompt}.mp4"
+        video_path = os.path.join(output_dir, video_filename)
+        
+        # Ensure output directory exists
+        os.makedirs(output_dir, exist_ok=True)
+        
+        # Save video bytes to file
+        with open(video_path, 'wb') as f:
+            f.write(video_bytes)
+        
+        return f"Saved video to: {video_path}", video_path
+        
+    except Exception as e:
+        return f"Failed to save video: {str(e)}", ""
+
+
+def create_gradio_interface(backend_url: str, default_params: dict[str, SamplingParam]):
+    """Create the Gradio interface"""
+    
+    # Initialize the Ray Serve client
+    client = RayServeClient(backend_url)
+    
+    def generate_video(
+        prompt,
+        negative_prompt,
+        use_negative_prompt,
+        seed,
+        guidance_scale,
+        num_frames,
+        height,
+        width,
+        randomize_seed=False,
+        input_image=None,
+        model_selection="FastVideo/FastWan2.1-T2V-1.3B-Diffusers (Text-to-Video)",
+        progress=None,
+        request: gr.Request = None,
+    ):
+        # Check backend health first
+        if not client.check_health():
+            return None, f"Backend is not available. Please check if Ray Serve is running at {backend_url}", ""
+        
+        # Validate video dimensions
+        max_pixels = 720 * 1280
+        if height * width > max_pixels:
+            return None, f"Video dimensions too large. Maximum allowed: 720x1280 pixels. Current: {height}x{width} = {height*width} pixels", ""
+        
+        # Update progress
+        if progress:
+            progress(0.1, desc="Checking backend health...")
+        
+        # Determine if this is I2V based on model selection - I2V functionality commented out
+        # is_i2v = "I2V" in model_selection or "Image-to-Video" in model_selection
+        
+        # Handle input image for I2V - I2V functionality commented out
+        # image_path = None
+        # if is_i2v and input_image is not None:
+        #     if progress:
+        #         progress(0.2, desc="Processing input image...")
+        #     try:
+        #         # Save the uploaded image to a temporary file
+        #         import tempfile
+        #         temp_dir = "temp_images"
+        #         os.makedirs(temp_dir, exist_ok=True)
+        #         
+        #         # Generate a unique filename with appropriate extension
+        #         import uuid
+        #         # Determine the best format to preserve quality
+        #         if hasattr(input_image, 'format') and input_image.format:
+        #             # Use original format if available
+        #             ext = input_image.format.lower()
+        #             if ext == 'jpeg':
+        #                 ext = 'jpg'
+        #         else:
+        #             # Default to PNG for lossless quality
+        #             ext = 'png'
+        #         
+        #         image_filename = f"input_image_{uuid.uuid4().hex[:8]}.{ext}"
+        #         image_path = os.path.abspath(os.path.join(temp_dir, image_filename))
+        # 
+        #         # Save the image preserving original quality
+        #         if ext == 'png':
+        #             # Use PNG for lossless compression
+        #             input_image.save(image_path, "PNG", optimize=False)
+        #         elif ext == 'jpg':
+        #             # Use high quality JPEG with minimal compression
+        #             input_image.convert("RGB").save(image_path, "JPEG", quality=95, optimize=False)
+        #         else:
+        #             # For other formats, save as PNG to preserve quality
+        #             input_image.save(image_path, "PNG", optimize=False)
+        #         
+        #         print(f"Saved input image to: {image_path}")
+        #     except Exception as e:
+        #         print(f"Warning: Failed to save input image: {e}")
+        #         image_path = None
+        
+        # Prepare request data
+        if progress:
+            progress(0.3, desc="Preparing request...")
+        
+        # Map clean model names to full paths
+        model_path_mapping = {
+            "FastWan2.1-T2V-1.3B": "FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
+            "FastWan2.1-T2V-14B": "FastVideo/FastWan2.1-T2V-14B-Diffusers",
+            "FastWan2.2-TI2V-5B": "FastVideo/FastWan2.2-TI2V-5B-Diffusers"
+        }
+        
+        request_data = {
+            "prompt": prompt,
+            "negative_prompt": negative_prompt,
+            "use_negative_prompt": use_negative_prompt,
+            "seed": seed,
+            "guidance_scale": guidance_scale,
+            "num_frames": num_frames,
+            "height": height,
+            "width": width,
+            # "num_inference_steps": 20,  # Use default value
+            "randomize_seed": randomize_seed,
+            "return_frames": False,  # We'll get video data directly
+            "image_path": None, # For T2V, we pass None as input_image
+            # "model_type": "i2v" if "I2V" in model_selection or "Image-to-Video" in model_selection else "t2v",  # Use model type selection
+            "model_type": model_path_mapping[model_selection],
+            "model_path": model_selection.split(" (")[0] if model_selection else None  # Extract model path from selection
+        }
+        
+        # Send request to backend
+        if progress:
+            progress(0.4, desc="Sending request to backend...")
+        
+        response = client.generate_video(request_data)
+        
+        if progress:
+            progress(0.8, desc="Processing response...")
+        
+        # Clean up temporary image file after processing
+        # if image_path and os.path.exists(image_path):
+        #     try:
+        #         os.remove(image_path)
+        #         print(f"Cleaned up temporary image: {image_path}")
+        #     except Exception as e:
+        #         print(f"Warning: Failed to clean up temporary image {image_path}: {e}")
+        
+        if response.get("success", False):
+            video_data = response.get("video_data", "")
+            used_seed = response.get("seed", seed)
+            generation_time = response.get("generation_time", 0.0)
+            inference_time = response.get("inference_time", 0.0)
+            encoding_time = response.get("encoding_time", 0.0)
+            total_time = response.get("total_time", 0.0)
+            network_time = response.get("network_time", 0.0)
+            stage_names = response.get("stage_names", [])
+            stage_execution_times = response.get("stage_execution_times", [])
+            
+            print(f"Used seed: {used_seed}")
+            print(f"Inference time: {inference_time:.2f}s")
+            print(f"Encoding time: {encoding_time:.2f}s")
+            print(f"Network transfer: {network_time:.2f}s")
+            print(f"Total time: {total_time:.2f}s")
+            print(f"Stage names: {stage_names}")
+            print(f"Stage execution times: {stage_execution_times}")
+            
+            # Create detailed timing message with all cards in a single row
+            timing_details = f"""
+            <div style="margin: 10px 0;">
+                <h3 style="text-align: center; margin-bottom: 10px;">⏱️ Timing Breakdown</h3>
+                <div style="display: grid; grid-template-columns: repeat(4, 1fr); gap: 10px; margin-bottom: 10px;">
+                    <div class="timing-card timing-card-highlight">
+                        <div style="font-size: 20px;">🧠</div>
+                        <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Model Inference</div>
+                        <div style="font-size: 16px; color: #2563eb; font-weight: bold;">{inference_time:.2f}s</div>
+                    </div>
+                    <div class="timing-card">
+                        <div style="font-size: 20px;">🎬</div>
+                        <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Video Encoding</div>
+                        <div style="font-size: 16px; color: #dc2626;">{encoding_time:.2f}s</div>
+                    </div>
+                    <div class="timing-card">
+                        <div style="font-size: 20px;">🌐</div>
+                        <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Network Transfer</div>
+                        <div style="font-size: 16px; color: #059669;">{network_time:.2f}s</div>
+                    </div>
+                    <div class="timing-card">
+                        <div style="font-size: 20px;">📊</div>
+                        <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Total Processing</div>
+                        <div style="font-size: 18px; color: #0277bd;">{total_time:.2f}s</div>
+                    </div>
+                </div>"""
+            
+            # timing_details += f"""
+            #     <div style="margin-top: 15px;">
+            #         <h4 style="text-align: center; margin-bottom: 10px;">🔄 Processing Stages</h4>
+            #         <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 8px;">
+            # """
+            # 
+            # # Add individual stage timing cards
+            # for stage_name, stage_time in zip(stage_names, stage_execution_times):
+            #     if stage_name.strip() and stage_time > 0:  # Only show non-empty stages with valid times
+            #         timing_details += f"""
+            #             <div class="stage-card">
+            #                 <div style="font-weight: bold; font-size: 14px; margin-bottom: 5px;">{stage_name.strip()}</div>
+            #                 <div style="font-size: 16px; color: #7c3aed; font-weight: bold;">{stage_time:.2f}s</div>
+            #             </div>
+            #         """
+            # 
+            # timing_details += """
+            #         </div>
+            #     </div>
+            # """
+            
+            # Add performance insights
+            if inference_time > 0:
+                fps = num_frames / inference_time
+                timing_details += f"""
+                <div class="performance-card" style="margin-top: 15px;">
+                    <span style="font-weight: bold;">Generation Speed: </span>
+                    <span style="font-size: 18px; color: #6366f1; font-weight: bold;">{fps:.1f} frames/second</span>
+                </div>
+                """
+            
+            timing_details += "</div>"
+            
+            # Save video data to file for Gradio to display
+            if video_data:
+                try:
+                    if progress:
+                        progress(0.9, desc="Saving video...")
+                    
+                    output_dir = "outputs"
+                    save_status, video_path = save_video_from_base64(video_data, output_dir, prompt)
+                    print(f"Video save status: {save_status}")
+                    
+                    if progress:
+                        progress(1.0, desc="Generation complete!")
+                    
+                    if video_path and os.path.exists(video_path):
+                        return video_path, used_seed, timing_details
+                    else:
+                        return None, f"Video generated but failed to save: {save_status}", ""
+                except Exception as e:
+                    return None, f"Failed to save video: {str(e)}", ""
+            else:
+                return None, "No video data received from backend", ""
+        else:
+            error_msg = response.get("error_message", "Unknown error occurred")
+            return None, f"Generation failed: {error_msg}", ""
+    
+    # Example prompts
+    examples = []
+    example_labels = []
+    
+    def contains_chinese(text):
+        """Check if text contains Chinese characters"""
+        for char in text:
+            if '\u4e00' <= char <= '\u9fff':  # CJK Unified Ideographs (Chinese characters)
+                return True
+        return False
+    
+    # Load prompts from all text files in the prompts directory
+    prompts_dir = "prompts"
+    if os.path.exists(prompts_dir):
+        for filename in os.listdir(prompts_dir):
+            if filename.endswith('.txt'):
+                filepath = os.path.join(prompts_dir, filename)
+                try:
+                    with open(filepath, "r", encoding='utf-8') as f:
+                        for line_num, line in enumerate(f, 1):
+                            line = line.strip()
+                            if line and not contains_chinese(line):  # Skip empty lines and lines with Chinese text
+                                # Create a label from the first 100 characters
+                                label = line[:100] + "..." if len(line) > 100 else line
+                                example_labels.append(label)
+                                examples.append(line)
+                except Exception as e:
+                    print(f"Warning: Could not read {filepath}: {e}")
+    
+    # Fallback to example_prompts.txt if prompts directory is empty or doesn't exist
+    if not examples:
+        try:
+            with open("example_prompts.txt", "r") as f:
+                for line in f:
+                    line = line.strip()
+                    if line:
+                        example_labels.append(line[:100])
+                        examples.append(line)
+        except Exception as e:
+            print(f"Warning: Could not read example_prompts.txt: {e}")
+            # Add a default example if all else fails
+            examples = ["A crowded rooftop bar buzzes with energy, the city skyline twinkling like a field of stars in the background. Strings of fairy lights hang above, casting a warm, golden glow over the scene. Groups of people gather around high tables, their laughter blending with the soft rhythm of live jazz. The aroma of freshly mixed cocktails and charred appetizers wafts through the air, mingling with the cool night breeze."]
+            example_labels = ["Crowded rooftop bar at night"]
+    
+    # Create a custom theme with blue styling to match the logo
+    theme = gr.themes.Base().set(
+        button_primary_background_fill="#2563eb",  # Blue color
+        button_primary_background_fill_hover="#1d4ed8",  # Darker blue on hover
+        button_primary_text_color="white",
+        slider_color="#2563eb",  # Blue slider
+        checkbox_background_color_selected="#2563eb",  # Blue checkbox when selected
+    )
+    
+    def get_default_values_for_model(model_selection_value):
+        """Get default parameter values for the specified model"""
+        model_path = model_selection_value.split(" (")[0] if model_selection_value else None
+        if model_path and model_path in default_params:
+            params = default_params[model_path]
+            return {
+                'height': params.height,
+                'width': params.width,
+                'num_frames': params.num_frames,
+                'guidance_scale': params.guidance_scale,
+                'seed': params.seed,
+            }
+        else:
+            # Fallback defaults if model not found
+            return {
+                'height': 448,
+                'width': 832,
+                'num_frames': 61,
+                'guidance_scale': 3.0,
+                'seed': 1024,
+            }
+    
+    # Get initial values for the default model
+    default_model = "FastVideo/FastWan2.1-T2V-1.3B-Diffusers (Text-to-Video)"
+    initial_values = get_default_values_for_model(default_model)
+    
+    # Create Gradio interface
+    with gr.Blocks(title="FastWan", theme=theme) as demo:
+        
+        # Logo using Gradio's Image component
+        gr.Image("fastvideo-logos/main/png/full.png", show_label=False, container=False, height=80)
+        gr.HTML("""
+        <div style="text-align: center; margin-bottom: 10px;">
+            <p style="font-size: 18px;"> Make Video Generation Go Blurrrrrrr </p>
+            <p style="font-size: 18px;"> Twitter | <a href="https://github.com/hao-ai-lab/FastVideo/tree/main" target="_blank">Code</a> | Blog | <a href="https://hao-ai-lab.github.io/FastVideo/" target="_blank">Docs</a>  </p>
+        </div>
+        """)
+        
+        # What is FastVideo accordion
+        with gr.Accordion("🎥 What Is FastVideo?", open=False):
+            gr.HTML("""
+            <div style="padding: 20px; line-height: 1.6;">
+                <p style="font-size: 16px; margin-bottom: 15px;">
+                    It features a clean, consistent API that works across popular video models, making it easier for developers to author new models and incorporate system- or kernel-level optimizations. With FastVideo's optimizations, you can achieve more than 3x inference improvement compared to other systems.
+                </p>
+            </div>
+            """)
+        
+        # Backend status indicator
+        # status_text = gr.Text(
+        #     label="Backend Status",
+        #     value="Checking backend status...",
+        #     interactive=False
+        # )
+        
+        def update_status():
+            if client.check_health():
+                return "✅ Backend is healthy and ready"
+            else:
+                return "❌ Backend is not available"
+        
+        # Model selection dropdown
+        with gr.Row():
+            model_selection = gr.Dropdown(
+                choices=[
+                    "FastWan2.1-T2V-1.3B",
+                    "FastWan2.1-T2V-14B",
+                    "FastWan2.2-TI2V-5B",
+                    # "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers (Image-to-Video)"  # I2V functionality commented out
+                ],
+                value="FastWan2.1-T2V-1.3B",
+                label="Select Model",
+                interactive=True
+            )
+
+        # Examples dropdown
+        with gr.Row():
+            example_dropdown = gr.Dropdown(
+                choices=example_labels,
+                label="Example Prompts",
+                value=None,
+                interactive=True,
+                allow_custom_value=False
+            )
+        
+        # Main interface
+        with gr.Row():
+            with gr.Column(scale=6):
+                prompt = gr.Text(
+                    label="Prompt",
+                    show_label=False,
+                    max_lines=3,
+                    placeholder="Describe your scene...",
+                    container=False,
+                    lines=3,
+                    autofocus=True,
+                )
+            with gr.Column(scale=1, min_width=120, elem_classes="center-button"):
+                run_button = gr.Button("Run", variant="primary", size="lg")
+        
+        # Status and timing information
+        with gr.Row():
+            with gr.Column():
+                error_output = gr.Text(label="Error", visible=False)
+                # frames_output = gr.Text(label="Generation Status", visible=False)
+                timing_display = gr.Markdown(label="Timing Breakdown", visible=False)
+
+        # Two-column layout: Advanced options on left, Video on right
+        with gr.Row(equal_height=True, elem_classes="main-content-row"):
+            # Left column - Advanced options
+            with gr.Column(scale=1, elem_classes="advanced-options-column"):
+                with gr.Group():
+                    gr.HTML("<div style='margin: 0 0 15px 0; text-align: center; font-size: 16px;'>Advanced Options</div>")
+                with gr.Row():
+                    height = gr.Slider(
+                        label="Height",
+                        minimum=256,
+                        maximum=1280,
+                        step=32,
+                        value=initial_values['height'],
+                    )
+                    width = gr.Slider(
+                        label="Width",
+                        minimum=256,
+                        maximum=1280,
+                        step=32,
+                        value=initial_values['width']
+                    )
+                
+                with gr.Row():
+                    num_frames = gr.Slider(
+                        label="Number of Frames",
+                        minimum=16,
+                        maximum=121,
+                        step=16,
+                        value=initial_values['num_frames'],
+                    )
+                    guidance_scale = gr.Slider(
+                        label="Guidance Scale",
+                        minimum=1,
+                        maximum=12,
+                        value=initial_values['guidance_scale'],
+                    )
+                
+                with gr.Row():
+                    use_negative_prompt = gr.Checkbox(
+                        label="Use negative prompt", value=False)
+                    negative_prompt = gr.Text(
+                        label="Negative prompt",
+                        max_lines=3,
+                        lines=3,
+                        placeholder="Enter a negative prompt",
+                        visible=False,
+                    )
+
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=1000000,
+                    step=1,
+                    value=initial_values['seed'],
+                )
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
+                seed_output = gr.Number(label="Used Seed")
+        
+            # Right column - Video result
+            with gr.Column(scale=1, elem_classes="video-column"):
+                result = gr.Video(
+                    label="Generated Video", 
+                    show_label=True,
+                    height=436,  # Adjusted height for better vertical alignment
+                    width=600,   # Limit video width
+                    container=True
+                )
+        
+        # Add CSS to position the button and constrain width
+        gr.HTML("""
+        <style>
+        .center-button {
+            display: flex !important;
+            justify-content: center !important;
+            height: 100% !important;
+            padding-top: 1.4em !important;
+        }
+        
+        /* Constrain overall width */
+        .gradio-container {
+            max-width: 1200px !important;
+            margin: 0 auto !important;
+        }
+        
+        .main {
+            max-width: 1200px !important;
+            margin: 0 auto !important;
+        }
+        
+        /* Constrain individual components */
+        .gr-form, .gr-box, .gr-group {
+            max-width: 1200px !important;
+        }
+        
+        /* Make video component smaller */
+        .gr-video {
+            max-width: 500px !important;
+            margin: 0 auto !important;
+        }
+        
+        /* Ensure equal height columns */
+        .main-content-row {
+            display: flex !important;
+            align-items: flex-start !important;
+            min-height: 500px !important;
+        }
+        
+        .advanced-options-column,
+        .video-column {
+            display: flex !important;
+            flex-direction: column !important;
+            flex: 1 !important;
+            min-height: 400px !important;
+        }
+        
+        /* Force equal heights regardless of content */
+        .advanced-options-column > *:last-child,
+        .video-column > *:last-child {
+            flex-grow: 0 !important;
+        }
+        
+        /* Responsive alignment for split screen */
+        @media (max-width: 1400px) {
+            .main-content-row {
+                min-height: 600px !important;
+            }
+            
+            .advanced-options-column,
+            .video-column {
+                min-height: 600px !important;
+            }
+        }
+        
+        @media (max-width: 1200px) {
+            .main-content-row {
+                flex-direction: column !important;
+                align-items: stretch !important;
+            }
+            
+            .advanced-options-column,
+            .video-column {
+                min-height: auto !important;
+                width: 100% !important;
+            }
+        }
+        
+        /* Theme-agnostic timing cards */
+        .timing-card {
+            background: var(--background-fill-secondary) !important;
+            border: 1px solid var(--border-color-primary) !important;
+            color: var(--body-text-color) !important;
+            padding: 10px;
+            border-radius: 8px;
+            text-align: center;
+            min-height: 80px;
+            display: flex;
+            flex-direction: column;
+            justify-content: center;
+        }
+        
+        .timing-card-highlight {
+            background: var(--background-fill-primary) !important;
+            border: 2px solid var(--color-accent) !important;
+        }
+        
+        .stage-card {
+            background: var(--background-fill-secondary) !important;
+            border: 1px solid var(--border-color-primary) !important;
+            color: var(--body-text-color) !important;
+            padding: 10px;
+            border-radius: 6px;
+            text-align: center;
+        }
+        
+        .performance-card {
+            background: var(--background-fill-secondary) !important;
+            border: 1px solid var(--border-color-primary) !important;
+            color: var(--body-text-color) !important;
+            padding: 10px;
+            border-radius: 6px;
+            text-align: center;
+        }
+        
+        /* Dark mode support */
+        .dark .timing-card {
+            background: var(--background-fill-secondary) !important;
+            border-color: var(--border-color-primary) !important;
+            color: var(--body-text-color) !important;
+        }
+        
+        .dark .timing-card-highlight {
+            background: var(--background-fill-primary) !important;
+            border-color: var(--color-accent) !important;
+        }
+        
+        .dark .stage-card,
+        .dark .performance-card {
+            background: var(--background-fill-secondary) !important;
+            border-color: var(--border-color-primary) !important;
+            color: var(--body-text-color) !important;
+        }
+        </style>
+        """)
+        
+        # Function to update prompt when example is selected
+        def on_example_select(example_label):
+            if example_label and example_label in example_labels:
+                index = example_labels.index(example_label)
+                return examples[index]
+            return ""
+        
+        example_dropdown.change(
+            fn=on_example_select,
+            inputs=example_dropdown,
+            outputs=prompt,
+        )
+        
+        # Disclaimer text
+        gr.HTML("""
+        <div style="text-align: center; margin-top: 10px; margin-bottom: 15px;">
+            <p style="font-size: 16px; margin: 0;">The compute for this demo is generously provided by <a href="https://www.gmicloud.ai/" target="_blank">GMI Cloud</a>. Note that this demo is meant to showcase FastWan2.1's quality and that under a large number of requests, generation speed may be affected.</p>
+        </div>
+        """)
+        
+        # Event handlers
+        use_negative_prompt.change(
+            fn=lambda x: gr.update(visible=x),
+            inputs=use_negative_prompt,
+            outputs=negative_prompt,
+        )
+        
+        # Model selection change handler - I2V functionality commented out
+        # def on_model_selection_change(model_selection):
+        #     is_i2v = "I2V" in model_selection or "Image-to-Video" in model_selection
+        #     prompt_placeholder = "Describe how the image should animate" if is_i2v else "Enter your prompt"
+        #     return gr.update(visible=is_i2v), gr.update(placeholder=prompt_placeholder)
+        # 
+        # model_selection.change(
+        #     fn=on_model_selection_change,
+        #     inputs=model_selection,
+        #     outputs=[input_image, prompt],
+        # )
+        
+        def on_model_selection_change(selected_model):
+            """Update advanced options based on selected model's default parameters"""
+            if not selected_model:
+                return {}, {}, {}, {}, {}  # Return empty updates if no model selected
+            
+            # Extract model path from selection (remove the description part)
+            model_path = selected_model.split(" ")[0] if selected_model else None
+            
+            if model_path and model_path in default_params:
+                params = default_params[model_path]
+                
+                # Update each component with the model's default values
+                return (
+                    gr.update(value=params.height),  # height
+                    gr.update(value=params.width),   # width  
+                    gr.update(value=params.num_frames),  # num_frames
+                    gr.update(value=params.guidance_scale),  # guidance_scale
+                    gr.update(value=params.seed),  # seed
+                )
+            else:
+                # If model not found in default_params, return current values (no change)
+                return {}, {}, {}, {}, {}
+        
+        model_selection.change(
+            fn=on_model_selection_change,
+            inputs=model_selection,
+            outputs=[height, width, num_frames, guidance_scale, seed],
+        )
+        
+        def handle_generation(*args, progress=None, request: gr.Request = None):
+            # Extract model selection and input image from args - I2V functionality commented out
+            model_selection, prompt, negative_prompt, use_negative_prompt, seed, guidance_scale, num_frames, height, width, randomize_seed = args
+            
+            # Determine if this is I2V based on model selection - I2V functionality commented out
+            # is_i2v = "I2V" in model_selection or "Image-to-Video" in model_selection
+            
+            # For T2V, we pass None as input_image - I2V functionality commented out
+            # if not is_i2v:
+            #     input_image = None
+            
+            # Call the generate_video function with progress tracking
+            result_path, seed_or_error, timing_details = generate_video(
+                prompt, negative_prompt, use_negative_prompt, seed, guidance_scale, 
+                num_frames, height, width, randomize_seed, None, model_selection, progress, request
+            )
+            
+            if result_path and os.path.exists(result_path):
+                return (
+                    result_path, 
+                    seed_or_error, 
+                    gr.update(visible=False),  # error_output
+                    # gr.update(visible=True, value="Generation completed successfully!"),  # frames_output
+                    gr.update(visible=True, value=timing_details),  # timing_display
+                )
+            else:
+                return (
+                    None, 
+                    seed_or_error, 
+                    gr.update(visible=True, value=seed_or_error),  # error_output
+                    # gr.update(visible=False),  # frames_output
+                    gr.update(visible=False),  # timing_display
+                )
+        
+        # Unified event handler
+        run_button.click(
+            fn=handle_generation,
+            inputs=[
+                model_selection,
+                prompt,
+                negative_prompt,
+                use_negative_prompt,
+                seed,
+                guidance_scale,
+                num_frames,
+                height,
+                width,
+                randomize_seed,
+                # input_image, # Removed input_image from inputs
+            ],
+            outputs=[result, seed_output, error_output, timing_display],
+            concurrency_limit=20,
+        )
+        
+        # Update status periodically
+        # demo.load(update_status, outputs=status_text)
+    
+    return demo
+
+
+def main():
+    parser = argparse.ArgumentParser(description="FastVideo Gradio Frontend")
+    parser.add_argument("--backend_url",
+                        type=str,
+                        default="http://localhost:8000",
+                        help="URL of the Ray Serve backend")
+    parser.add_argument("--t2v_model_paths",
+                        type=str,
+                        default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers,FastVideo/FastWan2.1-T2V-14B-Diffusers",
+                        help="Comma separated list of paths to the T2V model(s)")
+    # parser.add_argument("--t2v_model_replicas", type=int,
+    #                     default="4,4",
+    #                     help="Comma separated list of number of replicas for the T2V model(s)")
+    # parser.add_argument("--i2v_model_path",  # I2V functionality commented out
+    #                     type=str,
+    #                     default="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",
+    #                     help="Path to the I2V model (for default parameters)")
+    parser.add_argument("--host",
+                        type=str,
+                        default="0.0.0.0",
+                        help="Host to bind to")
+    parser.add_argument("--port",
+                        type=int,
+                        default=7860,
+                        help="Port to bind to")
+    
+    args = parser.parse_args()
+    
+    # Load default parameters from the models
+    # try:
+    default_params = {}
+    model_paths = args.t2v_model_paths.split(",")
+    for model_path in model_paths:
+        default_params[model_path] = SamplingParam.from_pretrained(model_path)
+    # except Exception as e:
+    #     print(f"Warning: Could not load default parameters from {args.t2v_model_path}: {e}")
+    #     print("Using fallback default parameters...")
+    #     # Create fallback default parameters
+    # default_params = SamplingParam()
+    # default_params.height = 448
+    # default_params.width = 832
+    # default_params.num_frames = 21
+    # default_params.guidance_scale = 7.5
+    # default_params.num_inference_steps = 20
+    # default_params.seed = 1024
+    
+    # Create and launch the interface
+    demo = create_gradio_interface(args.backend_url, default_params)
+    
+    print(f"Starting Gradio frontend at http://{args.host}:{args.port}")
+    print(f"Backend URL: {args.backend_url}")
+    print(f"T2V Models: {args.t2v_model_paths}")
+    # print(f"T2V Model Replicas: {args.t2v_model_replicas}")
+    # print(f"I2V Model: {args.i2v_model_path}") # I2V functionality commented out
+    
+    # Use FastAPI to serve custom HTML with proper Open Graph metadata
+    from fastapi import FastAPI
+    from fastapi.responses import HTMLResponse, FileResponse
+    import uvicorn
+    import os
+    
+    app = FastAPI()
+    
+    @app.get("/logo.png")
+    async def get_logo():
+        return FileResponse("fastvideo-logos/main/png/full.png", media_type="image/png")
+    
+    @app.get("/", response_class=HTMLResponse)
+    def index():
+        return """
+        <!DOCTYPE html>
+        <html lang="en">
+        <head>
+            <meta charset="UTF-8" />
+            <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+            <meta property="og:title" content="FastWan" />
+            <meta property="og:description" content="Make video generation go blurrrrrrr" />
+                         <meta property="og:image" content="https://fastwan.fastvideo.org/logo.png" />
+             <meta property="og:url" content="https://fastwan.fastvideo.org/" />
+             <meta property="og:type" content="website" />
+             <meta name="twitter:card" content="summary_large_image" />
+             <meta name="twitter:title" content="FastWan" />
+             <meta name="twitter:description" content="Make video generation go blurrrrrrr" />
+             <meta name="twitter:image" content="https://fastwan.fastvideo.org/logo.png" />
+            <title>FastWan</title>
+            <link rel="icon" type="image/png" href="/gradio/file/fastvideo-logos/main/png/icon-simple.png">
+            <style>
+                body, html {
+                    margin: 0;
+                    padding: 0;
+                    height: 100%;
+                    overflow: hidden;
+                }
+                iframe {
+                    width: 100%;
+                    height: 100vh;
+                    border: none;
+                }
+            </style>
+        </head>
+        <body>
+            <iframe src="/gradio" width="100%" height="100%" style="border: none;"></iframe>
+        </body>
+        </html>
+        """
+    
+    # Mount Gradio app under /gradio
+    app = gr.mount_gradio_app(
+        app, 
+        demo, 
+        path="/gradio",
+        allowed_paths=[os.path.abspath("outputs"), os.path.abspath("temp_images"), os.path.abspath("fastvideo-logos")]
+    )
+    
+    # Run the FastAPI server
+    uvicorn.run(app, host=args.host, port=args.port)
+
+
+if __name__ == "__main__":
+    main() 

examples/inference/gradio/multi_frontend_launcher.py

@@ -0,0 +1,182 @@
+import argparse
+import os
+import subprocess
+import sys
+import time
+import threading
+import signal
+import requests
+from pathlib import Path
+
+# Add the project root to the Python path
+project_root = Path(__file__).parent.parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+
+def check_frontend_health(frontend_url: str, max_retries: int = 30) -> bool:
+    """Check if the frontend is healthy"""
+    for i in range(max_retries):
+        try:
+            response = requests.get(frontend_url, timeout=5)
+            if response.status_code == 200:
+                print(f"✅ Frontend is healthy at {frontend_url}")
+                return True
+        except requests.exceptions.RequestException:
+            pass
+        
+        if i < max_retries - 1:
+            print(f"⏳ Waiting for frontend to start... ({i+1}/{max_retries})")
+            time.sleep(2)
+    
+    print(f"❌ Frontend failed to start within {max_retries * 2} seconds")
+    return False
+
+
+def start_frontend_instance(args, instance_id: int, backend_url: str):
+    """Start a single frontend instance"""
+    frontend_script = Path(__file__).parent / "gradio_frontend.py"
+    frontend_port = args.frontend_base_port + instance_id
+    
+    cmd = [
+        sys.executable, str(frontend_script),
+        "--backend_url", backend_url,
+        "--t2v_model_path", args.t2v_model_path,
+        "--i2v_model_path", args.i2v_model_path,
+        "--host", args.frontend_host,
+        "--port", str(frontend_port)
+    ]
+    
+    print(f"🎨 Starting Frontend {instance_id + 1} on port {frontend_port}...")
+    print(f"Command: {' '.join(cmd)}")
+    
+    # Start the frontend process
+    frontend_process = subprocess.Popen(
+        cmd,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        universal_newlines=True,
+        bufsize=1
+    )
+    
+    # Monitor frontend output
+    def monitor_frontend():
+        for line in frontend_process.stdout:
+            print(f"[FRONTEND-{instance_id + 1}] {line.rstrip()}")
+    
+    monitor_thread = threading.Thread(target=monitor_frontend, daemon=True)
+    monitor_thread.start()
+    
+    return frontend_process, frontend_port
+
+
+def main():
+    parser = argparse.ArgumentParser(description="FastVideo Multi-Frontend Launcher")
+    
+    # Model and output settings
+    parser.add_argument("--t2v_model_path",
+                        type=str,
+                        default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
+                        help="Path to the T2V model")
+    parser.add_argument("--i2v_model_path",
+                        type=str,
+                        default="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",
+                        help="Path to the I2V model")
+    
+    # Frontend settings
+    parser.add_argument("--frontend_host",
+                        type=str,
+                        default="0.0.0.0",
+                        help="Frontend host to bind to")
+    parser.add_argument("--frontend_base_port",
+                        type=int,
+                        default=7860,
+                        help="Base port for frontend instances")
+    parser.add_argument("--num_frontends",
+                        type=int,
+                        default=2,
+                        help="Number of frontend instances to start")
+    
+    # Backend settings
+    parser.add_argument("--backend_url",
+                        type=str,
+                        default="http://localhost:8000",
+                        help="Backend URL for frontends to connect to")
+    
+    # Other settings
+    parser.add_argument("--skip_health_check",
+                        action="store_true",
+                        help="Skip frontend health check")
+    
+    args = parser.parse_args()
+    
+    print("🎬 FastVideo Multi-Frontend Launcher")
+    print("=" * 50)
+    print(f"T2V Model: {args.t2v_model_path}")
+    print(f"I2V Model: {args.i2v_model_path}")
+    print(f"Backend URL: {args.backend_url}")
+    print(f"Number of Frontends: {args.num_frontends}")
+    print(f"Frontend Base Port: {args.frontend_base_port}")
+    print("=" * 50)
+    
+    # Start multiple frontend instances
+    frontend_processes = []
+    frontend_urls = []
+    
+    for i in range(args.num_frontends):
+        process, port = start_frontend_instance(args, i, args.backend_url)
+        frontend_processes.append(process)
+        frontend_urls.append(f"http://{args.frontend_host}:{port}")
+    
+    # Wait for frontends to be ready
+    if not args.skip_health_check:
+        print("\n⏳ Waiting for frontends to start...")
+        for i, url in enumerate(frontend_urls):
+            if not check_frontend_health(url):
+                print(f"❌ Frontend {i + 1} failed to start. Terminating...")
+                for process in frontend_processes:
+                    process.terminate()
+                sys.exit(1)
+    
+    print("\n🎉 All frontend instances are starting up!")
+    for i, url in enumerate(frontend_urls):
+        print(f"📺 Frontend {i + 1}: {url}")
+    print("\nPress Ctrl+C to stop all frontend instances...")
+    
+    # Signal handler for graceful shutdown
+    def signal_handler(signum, frame):
+        print("\n🛑 Shutting down frontend instances...")
+        for process in frontend_processes:
+            process.terminate()
+        
+        # Wait for processes to terminate
+        try:
+            for process in frontend_processes:
+                process.wait(timeout=5)
+        except subprocess.TimeoutExpired:
+            print("⚠️  Force killing processes...")
+            for process in frontend_processes:
+                process.kill()
+        
+        print("✅ Frontend instances stopped")
+        sys.exit(0)
+    
+    signal.signal(signal.SIGINT, signal_handler)
+    signal.signal(signal.SIGTERM, signal_handler)
+    
+    # Monitor processes
+    try:
+        while True:
+            # Check if processes are still running
+            for i, process in enumerate(frontend_processes):
+                if process.poll() is not None:
+                    print(f"❌ Frontend {i + 1} process died unexpectedly")
+                    break
+            
+            time.sleep(1)
+    
+    except KeyboardInterrupt:
+        signal_handler(signal.SIGINT, None)
+
+
+if __name__ == "__main__":
+    main()

examples/inference/gradio/nginx.conf

@@ -0,0 +1,181 @@
+# Nginx configuration for FastVideo load balancing
+# This configuration implements the architecture:
+# ngrok -> nginx reverse proxy -> frontend1/frontend2 -> backend1×8/backend2×8
+
+events {
+    worker_connections 1024;
+}
+
+http {
+    # Basic settings
+    sendfile on;
+    tcp_nopush on;
+    tcp_nodelay on;
+    keepalive_timeout 65;
+    types_hash_max_size 2048;
+    client_max_body_size 100M;  # Allow large video uploads
+
+    # Logging
+    access_log /mnt/fast-disks/nfs/hao_lab/FastVideo/outputs/nginx_access.log;
+    error_log  /mnt/fast-disks/nfs/hao_lab/FastVideo/outputs/nginx_error.log;
+
+    # Gzip compression
+    gzip on;
+    gzip_vary on;
+    gzip_min_length 1024;
+    gzip_proxied any;
+    gzip_comp_level 6;
+    gzip_types
+        text/plain
+        text/css
+        text/xml
+        text/javascript
+        application/json
+        application/javascript
+        application/xml+rss
+        application/atom+xml
+        image/svg+xml;
+
+    # Upstream for frontend load balancing
+    upstream frontend_servers {
+        # Round-robin load balancing between frontends
+        server 127.0.0.1:7860 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:7861 weight=1 max_fails=3 fail_timeout=30s;
+    upstream frontend_servers {
+        # Round-robin load balancing between frontends
+        server 127.0.0.1:7860 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:7861 weight=1 max_fails=3 fail_timeout=30s;
+        
+        # Health check
+        keepalive 32;
+    }
+
+    # Upstream for backend1 load balancing
+    upstream backend1_servers {
+        server 127.0.0.1:8000 weight=1 max_fails=3 fail_timeout=30s; (8 replicas)
+    upstream backend1_servers {
+        # Round-robin load balancing for backend1 replicas
+        server 127.0.0.1:8000 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8001 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8002 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8003 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8004 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8005 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8006 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8007 weight=1 max_fails=3 fail_timeout=30s;
+        
+        keepalive 32;
+    }
+
+    # Upstream for backend2 load balancing
+    upstream backend2_servers {
+        server 127.0.0.1:8000 weight=1 max_fails=3 fail_timeout=30s; (8 replicas)
+    upstream backend2_servers {
+        # Round-robin load balancing for backend2 replicas
+        server 127.0.0.1:8010 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8011 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8012 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8013 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8014 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8015 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8016 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8017 weight=1 max_fails=3 fail_timeout=30s;
+        
+        keepalive 32;
+    }
+
+    # Main server block
+    server {
+        listen 80;
+        server_name localhost;
+
+        # Security headers
+        add_header X-Frame-Options "SAMEORIGIN" always;
+        add_header X-Content-Type-Options "nosniff" always;
+        add_header X-XSS-Protection "1; mode=block" always;
+        add_header Referrer-Policy "no-referrer-when-downgrade" always;
+
+        # Frontend routes (Gradio interfaces)
+        location / {
+            proxy_pass http://frontend_servers;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+            proxy_set_header X-Forwarded-Proto $scheme;
+            
+            # WebSocket support for Gradio
+            proxy_http_version 1.1;
+            proxy_set_header Upgrade $http_upgrade;
+            proxy_set_header Connection "upgrade";
+            
+            # Timeouts
+            proxy_connect_timeout 60s;
+            proxy_send_timeout 60s;
+            proxy_read_timeout 60s;
+            
+            # Buffer settings
+            proxy_buffering on;
+            proxy_buffer_size 128k;
+            proxy_buffers 4 256k;
+            proxy_busy_buffers_size 256k;
+        }
+
+        # Backend API routes for frontend1
+        location /api/frontend1/ {
+            # Strip the /api/frontend1/ prefix
+            rewrite ^/api/frontend1/(.*) /$1 break;
+            proxy_pass http://backend1_servers;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+            proxy_set_header X-Forwarded-Proto $scheme;
+            
+            # Timeouts for video generation
+            proxy_connect_timeout 300s;
+            proxy_send_timeout 300s;
+            proxy_read_timeout 300s;
+            
+            # Buffer settings for large responses
+            proxy_buffering on;
+            proxy_buffer_size 128k;
+            proxy_buffers 4 256k;
+            proxy_busy_buffers_size 256k;
+        }
+
+        # Backend API routes for frontend2
+        location /api/frontend2/ {
+            # Strip the /api/frontend2/ prefix
+            rewrite ^/api/frontend2/(.*) /$1 break;
+            proxy_pass http://backend2_servers;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+            proxy_set_header X-Forwarded-Proto $scheme;
+            
+            # Timeouts for video generation
+            proxy_connect_timeout 300s;
+            proxy_send_timeout 300s;
+            proxy_read_timeout 300s;
+            
+            # Buffer settings for large responses
+            proxy_buffering on;
+            proxy_buffer_size 128k;
+            proxy_buffers 4 256k;
+            proxy_busy_buffers_size 256k;
+        }
+
+        # Health check endpoint
+        location /health {
+            access_log off;
+            return 200 "healthy\n";
+            add_header Content-Type text/plain;
+        }
+
+        # Static files (if needed)
+        location /static/ {
+            alias /var/www/static/;
+            expires 1y;
+            add_header Cache-Control "public, immutable";
+        }
+    }
+}

examples/inference/gradio/nginx.conf.backup

@@ -0,0 +1,173 @@
+# Nginx configuration for FastVideo load balancing
+# This configuration implements the architecture:
+# ngrok -> nginx reverse proxy -> frontend1/frontend2 -> backend1×8/backend2×8
+
+events {
+    worker_connections 1024;
+}
+
+http {
+    # Basic settings
+    sendfile on;
+    tcp_nopush on;
+    tcp_nodelay on;
+    keepalive_timeout 65;
+    types_hash_max_size 2048;
+    client_max_body_size 100M;  # Allow large video uploads
+
+    # Logging
+    access_log /var/log/nginx/access.log;
+    error_log /var/log/nginx/error.log;
+
+    # Gzip compression
+    gzip on;
+    gzip_vary on;
+    gzip_min_length 1024;
+    gzip_proxied any;
+    gzip_comp_level 6;
+    gzip_types
+        text/plain
+        text/css
+        text/xml
+        text/javascript
+        application/json
+        application/javascript
+        application/xml+rss
+        application/atom+xml
+        image/svg+xml;
+
+    # Upstream for frontend load balancing
+    upstream frontend_servers {
+        # Round-robin load balancing between frontends
+        server 127.0.0.1:7860 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:7861 weight=1 max_fails=3 fail_timeout=30s;
+        
+        # Health check
+        keepalive 32;
+    }
+
+    # Upstream for backend1 load balancing (8 replicas)
+    upstream backend1_servers {
+        # Round-robin load balancing for backend1 replicas
+        server 127.0.0.1:8000 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8001 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8002 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8003 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8004 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8005 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8006 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8007 weight=1 max_fails=3 fail_timeout=30s;
+        
+        keepalive 32;
+    }
+
+    # Upstream for backend2 load balancing (8 replicas)
+    upstream backend2_servers {
+        # Round-robin load balancing for backend2 replicas
+        server 127.0.0.1:8010 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8011 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8012 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8013 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8014 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8015 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8016 weight=1 max_fails=3 fail_timeout=30s;
+        server 127.0.0.1:8017 weight=1 max_fails=3 fail_timeout=30s;
+        
+        keepalive 32;
+    }
+
+    # Main server block
+    server {
+        listen 80;
+        server_name localhost;
+
+        # Security headers
+        add_header X-Frame-Options "SAMEORIGIN" always;
+        add_header X-Content-Type-Options "nosniff" always;
+        add_header X-XSS-Protection "1; mode=block" always;
+        add_header Referrer-Policy "no-referrer-when-downgrade" always;
+
+        # Frontend routes (Gradio interfaces)
+        location / {
+            proxy_pass http://frontend_servers;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+            proxy_set_header X-Forwarded-Proto $scheme;
+            
+            # WebSocket support for Gradio
+            proxy_http_version 1.1;
+            proxy_set_header Upgrade $http_upgrade;
+            proxy_set_header Connection "upgrade";
+            
+            # Timeouts
+            proxy_connect_timeout 60s;
+            proxy_send_timeout 60s;
+            proxy_read_timeout 60s;
+            
+            # Buffer settings
+            proxy_buffering on;
+            proxy_buffer_size 128k;
+            proxy_buffers 4 256k;
+            proxy_busy_buffers_size 256k;
+        }
+
+        # Backend API routes for frontend1
+        location /api/frontend1/ {
+            # Strip the /api/frontend1/ prefix
+            rewrite ^/api/frontend1/(.*) /$1 break;
+            proxy_pass http://backend1_servers;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+            proxy_set_header X-Forwarded-Proto $scheme;
+            
+            # Timeouts for video generation
+            proxy_connect_timeout 300s;
+            proxy_send_timeout 300s;
+            proxy_read_timeout 300s;
+            
+            # Buffer settings for large responses
+            proxy_buffering on;
+            proxy_buffer_size 128k;
+            proxy_buffers 4 256k;
+            proxy_busy_buffers_size 256k;
+        }
+
+        # Backend API routes for frontend2
+        location /api/frontend2/ {
+            # Strip the /api/frontend2/ prefix
+            rewrite ^/api/frontend2/(.*) /$1 break;
+            proxy_pass http://backend2_servers;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+            proxy_set_header X-Forwarded-Proto $scheme;
+            
+            # Timeouts for video generation
+            proxy_connect_timeout 300s;
+            proxy_send_timeout 300s;
+            proxy_read_timeout 300s;
+            
+            # Buffer settings for large responses
+            proxy_buffering on;
+            proxy_buffer_size 128k;
+            proxy_buffers 4 256k;
+            proxy_busy_buffers_size 256k;
+        }
+
+        # Health check endpoint
+        location /health {
+            access_log off;
+            return 200 "healthy\n";
+            add_header Content-Type text/plain;
+        }
+
+        # Static files (if needed)
+        location /static/ {
+            alias /var/www/static/;
+            expires 1y;
+            add_header Cache-Control "public, immutable";
+        }
+    }
+}

examples/inference/gradio/ray_serve_backend.py

@@ -0,0 +1,621 @@
+import time
+import os
+import torch
+import base64
+import io
+from copy import deepcopy
+from typing import Dict, Any, Optional, List
+
+import ray
+from ray import serve
+from fastapi import FastAPI, Request
+from pydantic import BaseModel
+from PIL import Image
+import numpy as np
+from slowapi import Limiter, _rate_limit_exceeded_handler
+from slowapi.util import get_remote_address
+from slowapi.errors import RateLimitExceeded
+import imageio
+from ray.serve.handle import DeploymentHandle
+
+NUM_GPUS = 8
+SUPPORTED_MODELS = [
+    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers", 
+    "FastVideo/FastWan2.1-T2V-14B-Diffusers",
+    "FastVideo/FastWan2.2-TI2V-5B-Diffusers",
+    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+    "Wan-AI/Wan2.1-T2V-14B-Diffusers",
+    "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
+]
+
+
+class VideoGenerationRequest(BaseModel):
+    prompt: str
+    negative_prompt: Optional[str] = None
+    use_negative_prompt: bool = False
+    seed: int = 42
+    guidance_scale: float = 7.5
+    num_frames: int = 21
+    height: int = 448
+    width: int = 832
+    # num_inference_steps: int = 20
+    randomize_seed: bool = False
+    return_frames: bool = False  # Whether to return base64 encoded frames
+    image_path: Optional[str] = None  # Path to input image for I2V
+    model_type: str = "t2v"  # "t2v" or "i2v" to specify which model to use
+    model_path: Optional[str] = None  # Specific model path to use
+
+
+class VideoGenerationResponse(BaseModel):
+    video_data: Optional[str] = None  # Base64 encoded video data
+    seed: int
+    success: bool
+    error_message: Optional[str] = None
+    generation_time: Optional[float] = None
+    # Detailed timing information
+    model_load_time: Optional[float] = None
+    inference_time: Optional[float] = None
+    encoding_time: Optional[float] = None
+    total_time: Optional[float] = None
+    stage_names: Optional[List[str]] = None
+    stage_execution_times: Optional[List[float]] = None
+
+
+def encode_video_to_base64(frames: List[np.ndarray], fps: int = 24) -> str:
+    """Convert numpy frames to base64-encoded MP4 video"""
+    if not frames:
+        return ""
+    
+    try:
+        # Save frames to bytes buffer as MP4
+        buffer = io.BytesIO()
+        imageio.mimsave(buffer, frames, fps=fps, format="mp4")
+        buffer.seek(0)
+        
+        # Encode to base64
+        video_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
+        return f"data:video/mp4;base64,{video_base64}"
+        
+    except Exception as e:
+        print(f"Warning: Failed to encode video: {e}")
+        return ""
+
+
+
+
+
+# ---------------------------------------------------------------------------
+# Model-Specific Deployments (one per model type)
+# These deployments each load a single model and expose a `generate_video`   
+# method that can be invoked via a DeploymentHandle. Each deployment can be
+# scaled independently by configuring `num_replicas` when binding.
+# ---------------------------------------------------------------------------
+
+
+@serve.deployment(  # T2V 1.3B model deployment
+    ray_actor_options={"num_cpus": 10, "num_gpus": 1, "runtime_env": {"conda": "fv"}},
+)
+class T2VModelDeployment:
+    """Serve deployment wrapping the 1.3 B text-to-video model."""
+
+    def __init__(self, t2v_model_path: str, output_path: str = "outputs"):
+        self.model_path = t2v_model_path
+        self.output_path = output_path
+
+        # Ensure output directory exists
+        os.makedirs(self.output_path, exist_ok=True)
+
+        # Delay helps avoid GPU contention when many replicas start at once
+        time.sleep(5)
+
+        # Ensure correct attention backend for FastVideo
+        if "FastVideo" in self.model_path:
+            os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
+        else:
+            os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
+        os.environ["FASTVIDEO_STAGE_LOGGING"] = "1"
+
+        # Lazy import to keep the deployment import-safe on head node
+        from fastvideo.entrypoints.video_generator import VideoGenerator
+        from fastvideo.configs.sample.base import SamplingParam
+
+        print(f"Initializing T2V model: {self.model_path}")
+        self.generator = VideoGenerator.from_pretrained(
+            model_path=self.model_path,
+            num_gpus=1,
+            use_fsdp_inference=True,
+            text_encoder_cpu_offload=False,
+            dit_cpu_offload=False,
+            vae_cpu_offload=False,
+            VSA_sparsity=0.8,
+            enable_stage_verification=False,
+        )
+        self.default_params = SamplingParam.from_pretrained(self.model_path)
+        print("✅ T2V model initialized successfully")
+
+    def generate_video(self, video_request: "VideoGenerationRequest") -> "VideoGenerationResponse":
+        """Generate a video for the given request and return an encoded response."""
+        import time
+        
+        total_start_time = time.time()
+        
+        # Deep-copy default sampling params and override with request values
+        params = deepcopy(self.default_params)
+        params.prompt = video_request.prompt
+        if video_request.use_negative_prompt:
+            params.negative_prompt = video_request.negative_prompt
+
+        params.seed = video_request.seed if not video_request.randomize_seed else torch.randint(0, 1_000_000, (1,)).item()
+        # Ensure the generator respects the chosen seed strategy
+        params.randomize_seed = video_request.randomize_seed
+        params.guidance_scale = video_request.guidance_scale
+        params.num_frames = video_request.num_frames
+        params.height = video_request.height
+        params.width = video_request.width
+        # params.num_inference_steps = video_request.num_inference_steps
+
+        # Do not write to disk when called via API
+        params.save_video = False
+        params.return_frames = False
+
+        # Track inference time
+        inference_start_time = time.time()
+        
+        # Generate video frames
+        result = self.generator.generate_video(
+            prompt=video_request.prompt,
+            sampling_param=params,
+            save_video=False,
+            return_frames=False,
+        )
+        
+        inference_end_time = time.time()
+        inference_time = inference_end_time - inference_start_time
+
+        frames = result if isinstance(result, list) else result.get("frames", [])
+        generation_time = result.get("generation_time", 0.0) if isinstance(result, dict) else 0.0
+
+
+        logging_info = result.get("logging_info", None)
+        if logging_info:
+            stage_names = logging_info.get_execution_order()
+            stage_execution_times = [logging_info.get_stage_info(stage_name).get("execution_time", 0.0) for stage_name in stage_names]
+        else:
+            stage_names = []
+            stage_execution_times = []
+
+        # Track encoding time
+        encoding_start_time = time.time()
+        
+        # Encode outputs
+        video_data = encode_video_to_base64(frames, fps=16)
+        
+        encoding_end_time = time.time()
+        encoding_time = encoding_end_time - encoding_start_time
+        
+        total_end_time = time.time()
+        total_time = total_end_time - total_start_time
+
+        return VideoGenerationResponse(
+            video_data=video_data,
+            seed=params.seed,
+            success=True,
+            generation_time=generation_time,
+            inference_time=inference_time,
+            encoding_time=encoding_time,
+            total_time=total_time,
+            stage_names=stage_names,
+            stage_execution_times=stage_execution_times,
+        )
+
+
+# @serve.deployment(  # I2V 14B model deployment - I2V functionality commented out
+#     ray_actor_options={"num_cpus": 10, "num_gpus": 1, "runtime_env": {"conda": "fv"}},
+# )
+# class I2VModelDeployment:
+#     """Serve deployment wrapping the 14 B image-to-video model."""
+# 
+#     def __init__(self, i2v_model_path: str, output_path: str = "outputs"):
+#         self.model_path = i2v_model_path
+#         self.output_path = output_path
+# 
+#         os.makedirs(self.output_path, exist_ok=True)
+#         time.sleep(10)
+# 
+#         os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
+# 
+#         from fastvideo.entrypoints.video_generator import VideoGenerator
+#         from fastvideo.configs.sample.base import SamplingParam
+# 
+#         print(f"Initializing I2V model: {self.model_path}")
+#         self.generator = VideoGenerator.from_pretrained(
+#             model_path=self.model_path,
+#             num_gpus=1,
+#             use_fsdp_inference=True,
+#             text_encoder_cpu_offload=False,
+#             dit_cpu_offload=False,
+#             vae_cpu_offload=False,
+#             VSA_sparsity=0.8,
+#         )
+#         self.default_params = SamplingParam.from_pretrained(self.model_path)
+#         print("✅ I2V model initialized successfully")
+# 
+#     def generate_video(self, video_request: "VideoGenerationRequest") -> "VideoGenerationResponse":
+#         import time
+#         
+#         total_start_time = time.time()
+#         
+#         params = deepcopy(self.default_params)
+# 
+#         params.prompt = video_request.prompt
+#         if video_request.use_negative_prompt:
+#             params.negative_prompt = video_request.negative_prompt
+# 
+#         params.seed = video_request.seed if not video_request.randomize_seed else torch.randint(0, 1_000_000, (1,)).item()
+#         params.guidance_scale = video_request.guidance_scale
+#         params.num_frames = video_request.num_frames
+#         params.height = video_request.height
+#         params.width = video_request.width
+#         params.num_inference_steps = video_request.num_inference_steps
+# 
+#         if video_request.image_path:
+#             params.image_path = video_request.image_path
+# 
+#         params.save_video = False
+#         params.return_frames = True
+# 
+#         # Track inference time
+#         inference_start_time = time.time()
+#         
+#         result = self.generator.generate_video(
+#             prompt=video_request.prompt,
+#             sampling_param=params,
+#             save_video=False,
+#             return_frames=True,
+#         )
+#         
+#         inference_end_time = time.time()
+#         inference_time = inference_end_time - inference_start_time
+# 
+#         frames = result if isinstance(result, list) else result.get("frames", [])
+#         generation_time = result.get("generation_time", 0.0) if isinstance(result, dict) else 0.0
+# 
+#         # Track encoding time
+#         encoding_start_time = time.time()
+#         
+#         video_data = encode_video_to_base64(frames, fps=24)
+#         encoded_frames = encode_frames_to_base64(frames) if video_request.return_frames and frames else None
+#         
+#         encoding_end_time = time.time()
+#         encoding_time = encoding_end_time - encoding_start_time
+#         
+#         total_end_time = time.time()
+#         total_time = total_end_time - total_start_time
+# 
+#         return VideoGenerationResponse(
+#             video_data=video_data,
+#             frames=encoded_frames,
+#             seed=params.seed,
+#             success=True,
+#             generation_time=generation_time,
+#             inference_time=inference_time,
+#             encoding_time=encoding_time,
+#             total_time=total_time,
+#         )
+
+
+@serve.deployment(  # T2V 14B model deployment with optimized settings
+    ray_actor_options={"num_cpus": 16, "num_gpus": 1, "runtime_env": {"conda": "fv"}},
+)
+class T2V14BModelDeployment:
+    """Serve deployment wrapping the 14B text-to-video model with optimized settings."""
+
+    def __init__(self, t2v_14b_model_path: str, output_path: str = "outputs"):
+        self.model_path = t2v_14b_model_path
+        self.output_path = output_path
+
+        # Ensure output directory exists
+        os.makedirs(self.output_path, exist_ok=True)
+
+        # Delay helps avoid GPU contention when many replicas start at once
+        time.sleep(10)
+
+        # Ensure correct attention backend for FastVideo
+        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
+        os.environ["FASTVIDEO_STAGE_LOGGING"] = "1"
+
+        # Lazy import to keep the deployment import-safe on head node
+        from fastvideo.entrypoints.video_generator import VideoGenerator
+        from fastvideo.configs.sample.base import SamplingParam
+
+        print(f"Initializing T2V 14B model: {self.model_path}")
+        self.generator = VideoGenerator.from_pretrained(
+            model_path=self.model_path,
+            num_gpus=1,
+            use_fsdp_inference=True,
+            text_encoder_cpu_offload=True,  # Enable CPU offload for 14B model
+            dit_cpu_offload=True,           # Enable CPU offload for 14B model
+            vae_cpu_offload=False,
+            VSA_sparsity=0.9,               # Higher sparsity for 14B model
+            enable_stage_verification=False,
+        )
+        self.default_params = SamplingParam.from_pretrained(self.model_path)
+        print("✅ T2V 14B model initialized successfully")
+
+    def generate_video(self, video_request: "VideoGenerationRequest") -> "VideoGenerationResponse":
+        """Generate a video for the given request and return an encoded response."""
+        import time
+        
+        total_start_time = time.time()
+        
+        # Deep-copy default sampling params and override with request values
+        params = deepcopy(self.default_params)
+        params.prompt = video_request.prompt
+        if video_request.use_negative_prompt:
+            params.negative_prompt = video_request.negative_prompt
+
+        params.seed = video_request.seed if not video_request.randomize_seed else torch.randint(0, 1_000_000, (1,)).item()
+        # Ensure the generator respects the chosen seed strategy
+        params.randomize_seed = video_request.randomize_seed
+        params.guidance_scale = video_request.guidance_scale
+        params.num_frames = video_request.num_frames
+        params.height = video_request.height
+        params.width = video_request.width
+        # params.num_inference_steps = video_request.num_inference_steps
+
+        # Do not write to disk when called via API
+        params.save_video = False
+        params.return_frames = False
+
+        # Track inference time
+        inference_start_time = time.time()
+        
+        # Generate video frames
+        result = self.generator.generate_video(
+            prompt=video_request.prompt,
+            sampling_param=params,
+            save_video=False,
+            return_frames=False,
+        )
+        
+        inference_end_time = time.time()
+        inference_time = inference_end_time - inference_start_time
+
+        frames = result if isinstance(result, list) else result.get("frames", [])
+        generation_time = result.get("generation_time", 0.0) if isinstance(result, dict) else 0.0
+        logging_info = result.get("logging_info", None)
+        if logging_info:
+            stage_names = logging_info.get_execution_order()
+            stage_execution_times = [logging_info.get_stage_info(stage_name).get("execution_time", 0.0) for stage_name in stage_names]
+        else:
+            stage_names = []
+            stage_execution_times = []
+
+        # Track encoding time
+        encoding_start_time = time.time()
+        
+        # Encode outputs
+        video_data = encode_video_to_base64(frames, fps=16)
+        
+        encoding_end_time = time.time()
+        encoding_time = encoding_end_time - encoding_start_time
+        
+        total_end_time = time.time()
+        total_time = total_end_time - total_start_time
+
+        return VideoGenerationResponse(
+            video_data=video_data,
+            seed=params.seed,
+            success=True,
+            generation_time=generation_time,
+            inference_time=inference_time,
+            encoding_time=encoding_time,
+            total_time=total_time,
+            stage_names=stage_names,
+            stage_execution_times=stage_execution_times,
+        )
+
+
+# Create FastAPI app with rate limiting
+app = FastAPI()
+
+# Initialize rate limiter
+limiter = Limiter(key_func=get_remote_address)
+app.state.limiter = limiter
+app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)
+
+# Import Prometheus metrics (but don't instantiate at module level)
+from prometheus_client import Counter, Histogram, generate_latest, CONTENT_TYPE_LATEST
+import time
+
+
+@serve.deployment(ray_actor_options={"num_cpus": 2})
+@serve.ingress(app)
+class FastVideoAPI:
+    """Ingress deployment that routes requests to either the T2V or I2V model deployment."""
+
+    def __init__(self, t2v_deployments: Dict[str, DeploymentHandle]):  # Removed i2v_deployment
+        self.t2v_deployments = t2v_deployments
+        # self.t2v_14b_handle = t2v_14b_deployment
+        # self.i2v_handle = i2v_deployment  # I2V functionality commented out
+        
+        # Initialize Prometheus metrics inside the deployment
+        self.request_count = Counter('fastvideo_requests_total', 'Total FastVideo requests', ['model_type', 'status'])
+        self.request_duration = Histogram('fastvideo_request_duration_seconds', 'FastVideo request duration', ['model_type'])
+        self.video_generation_time = Histogram('fastvideo_video_generation_seconds', 'Video generation time', ['model_type'])
+    
+    @app.post("/generate_video", response_model=VideoGenerationResponse)
+    @limiter.limit("50/minute")  # Allow 50 requests per minute per IP
+    async def generate_video(self, request: Request, video_request: VideoGenerationRequest) -> VideoGenerationResponse:
+        """Route the request to the appropriate model deployment based on `model_type` and `model_path`."""
+        start_time = time.time()
+        model_type = video_request.model_path.split('/')[-1] if video_request.model_path else "unknown"
+        
+        try:
+            # assert video_request.model_type in self.t2v_deployments, f"Model {video_request.model_type} not found"
+            if video_request.model_type not in self.t2v_deployments:
+                raise ValueError(f"Model {video_request.model_type} not found")
+            response_ref = self.t2v_deployments[video_request.model_type].generate_video.remote(video_request)
+            # if video_request.model_type.lower() == "i2v":  # I2V functionality commented out
+            #     response_ref = self.i2v_handle.generate_video.remote(video_request)
+            # if video_request.model_type.lower() == "t2v":
+            #     # Route T2V requests based on model path
+            #     if video_request.model_path and "14b" in video_request.model_path.lower():
+            #         response_ref = self.t2v_14b_handle.generate_video.remote(video_request)
+            #     else:
+            #         # Default to 1.3B model
+            #         response_ref = self.t2v_handle.generate_video.remote(video_request)
+            # else:
+            #     # Default to 1.3B T2V model
+            #     response_ref = self.t2v_handle.generate_video.remote(video_request)
+
+            # Await the remote response
+            response = await response_ref
+            
+            # Record success metrics
+            self.request_count.labels(model_type=model_type, status="success").inc()
+            self.request_duration.labels(model_type=model_type).observe(time.time() - start_time)
+            if hasattr(response, 'generation_time_seconds') and response.generation_time_seconds:
+                self.video_generation_time.labels(model_type=model_type).observe(response.generation_time_seconds)
+            
+            return response
+
+        except Exception as e:
+            # Record error metrics
+            self.request_count.labels(model_type=model_type, status="error").inc()
+            self.request_duration.labels(model_type=model_type).observe(time.time() - start_time)
+            
+            return VideoGenerationResponse(
+                video_data=None,
+                seed=video_request.seed,
+                success=False,
+                error_message=str(e),
+            )
+    
+    @app.get("/health")
+    @limiter.limit("10/minute")  # Allow 10 health checks per minute per IP
+    async def health_check(self, request: Request):
+        return {"status": "healthy"}
+    
+    @app.get("/metrics")
+    async def metrics(self):
+        """Expose Prometheus metrics"""
+        from fastapi import Response
+        return Response(generate_latest(), media_type="text/plain")
+
+def start_ray_serve(
+    *,
+    t2v_model_paths: str,
+    t2v_model_replicas: str,
+    # t2v_14b_model_path: str = "FastVideo/FastWan2.1-T2V-14B-Diffusers",
+    # i2v_model_path: str = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",  # I2V functionality commented out
+    output_path: str = "outputs",
+    host: str = "0.0.0.0",
+    port: int = 8000,
+    # t2v_replicas: int = 4,
+    # t2v_14b_replicas: int = 4,  # Reduced replicas for 14B model due to higher resource requirements
+    # i2v_replicas: int = 4,  # I2V functionality commented out
+):
+    """Start Ray Serve with independently scalable deployments for each model."""
+
+    if not ray.is_initialized():
+        ray.init()
+
+    # Bind model deployments with configurable replica counts
+    t2v_deps = {}
+    for model_path, replicas in zip(t2v_model_paths.split(","), t2v_model_replicas.split(",")):
+        t2v_dep = T2VModelDeployment.options(num_replicas=int(replicas)).bind(model_path, output_path)
+        t2v_deps[model_path] = t2v_dep
+    # i2v_dep = I2VModelDeployment.options(num_replicas=i2v_replicas).bind(i2v_model_path, output_path)  # I2V functionality commented out
+
+    # Ingress
+    api = FastVideoAPI.bind(t2v_deps)  # Removed i2v_dep
+
+    serve.run(api, route_prefix="/", name="fast_video")
+
+    print(f"Ray Serve backend started at http://{host}:{port}")
+    for model_path, replicas in zip(t2v_model_paths.split(","), t2v_model_replicas.split(",")):
+        print(f"T2V Model: {model_path} | Replicas: {replicas}")
+    # print(f"T2V 14B Model: {t2v_14b_model_path} | Replicas: {t2v_14b_replicas}")
+    # print(f"I2V Model: {i2v_model_path} | Replicas: {i2v_replicas}")  # I2V functionality commented out
+    print(f"Health check: http://{host}:{port}/health")
+    print(f"Video generation endpoint: http://{host}:{port}/generate_video")
+
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="FastVideo Ray Serve Backend")
+    parser.add_argument("--t2v_model_paths",
+                        type=str,
+                        default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers,FastVideo/FastWan2.1-T2V-14B-Diffusers",
+                        help="Comma separated list of paths to the T2V model(s)")
+    parser.add_argument("--t2v_model_replicas",
+                        type=str,
+                        default="4,4",
+                        help="Comma separated list of number of replicas for the T2V model(s)")
+    # parser.add_argument("--t2v_14b_model_path",
+    #                     type=str,
+    #                     default="FastVideo/FastWan2.1-T2V-14B-Diffusers",
+    #                     help="Path to the T2V 14B model")
+    # parser.add_argument("--i2v_model_path",  # I2V functionality commented out
+    #                     type=str,
+    #                     default="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",
+    #                     help="Path to the I2V model")
+    parser.add_argument("--output_path",
+                        type=str,
+                        default="outputs",
+                        help="Path to save generated videos")
+    parser.add_argument("--host",
+                        type=str,
+                        default="0.0.0.0",
+                        help="Host to bind to")
+    parser.add_argument("--port",
+                        type=int,
+                        default=8000,
+                        help="Port to bind to")
+    # parser.add_argument("--t2v_replicas",
+    #                     type=int,
+    #                     default=4,
+    #                     help="Number of replicas for the T2V model deployment")
+    # parser.add_argument("--t2v_14b_replicas",
+    #                     type=int,
+    #                     default=4,  # Reduced default for 14B model due to higher resource requirements
+    #                     help="Number of replicas for the T2V 14B model deployment")
+    # parser.add_argument("--i2v_replicas",  # I2V functionality commented out
+    #                     type=int,
+    #                     default=4,  # Reduced default for 14B model due to higher resource requirements
+    #                     help="Number of replicas for the I2V model deployment")
+    
+    args = parser.parse_args()
+
+    split_models = args.t2v_model_paths.split(",")
+    split_replicas = [int(replica) for replica in args.t2v_model_replicas.split(",")]
+    assert len(split_models) == len(split_replicas), "Number of models and replicas must match"
+    assert sum(split_replicas) <= NUM_GPUS, "Total number of replicas must be less than or equal to 16"
+    for model, replicas in zip(split_models, split_replicas):
+        assert model in SUPPORTED_MODELS, f"Model {model} not supported"
+        assert replicas > 0, f"Replicas must be greater than 0"
+
+    
+    start_ray_serve(
+        t2v_model_paths=args.t2v_model_paths,
+        t2v_model_replicas=args.t2v_model_replicas,
+        # t2v_14b_model_path=args.t2v_14b_model_path,
+        # i2v_model_path=args.i2v_model_path,  # I2V functionality commented out
+        output_path=args.output_path,
+        host=args.host,
+        port=args.port,
+        # t2v_replicas=args.t2v_replicas,
+        # t2v_14b_replicas=args.t2v_14b_replicas,
+        # i2v_replicas=args.i2v_replicas,  # I2V functionality commented out
+    )
+
+    # ---- keep the process alive ---------------------------------
+    import signal, sys, time
+    signal.signal(signal.SIGINT,  lambda *_: sys.exit(0))   # Ctrl-C
+    signal.signal(signal.SIGTERM, lambda *_: sys.exit(0))   # docker stop etc.
+
+    print("✅ FastVideo backend is running. Press Ctrl-C to stop.")
+    while True:
+        time.sleep(3600) 

examples/inference/gradio/ray_serve_backend_scalable.py

@@ -0,0 +1,334 @@
+import time
+import os
+import torch
+import base64
+import io
+from copy import deepcopy
+from typing import Dict, Any, Optional, List
+
+import ray
+from ray import serve
+from fastapi import FastAPI, Request
+from pydantic import BaseModel
+from PIL import Image
+import numpy as np
+from slowapi import Limiter, _rate_limit_exceeded_handler
+from slowapi.util import get_remote_address
+from slowapi.errors import RateLimitExceeded
+
+
+class VideoGenerationRequest(BaseModel):
+    prompt: str
+    negative_prompt: Optional[str] = None
+    use_negative_prompt: bool = False
+    seed: int = 42
+    guidance_scale: float = 7.5
+    num_frames: int = 21
+    height: int = 448
+    width: int = 832
+    num_inference_steps: int = 20
+    randomize_seed: bool = False
+    return_frames: bool = False  # Whether to return base64 encoded frames
+    image_path: Optional[str] = None  # Path to input image for I2V
+    model_type: str = "t2v"  # "t2v" or "i2v" to specify which model to use
+
+
+class VideoGenerationResponse(BaseModel):
+    output_path: str
+    seed: int
+    success: bool
+    error_message: Optional[str] = None
+    frames: Optional[List[str]] = None  # Base64 encoded frames
+
+
+def encode_frames_to_base64(frames: List[np.ndarray]) -> List[str]:
+    """Convert numpy frames (0-255) to base64-encoded PNG images"""
+    if not frames:
+        return []
+    
+    encoded_frames = []
+    
+    for i, frame in enumerate(frames):
+        try:
+            # Ensure frame is numpy array
+            if not isinstance(frame, np.ndarray):
+                print(f"Warning: Frame {i} is not a numpy array, skipping")
+                continue
+                
+            # Ensure frame is uint8
+            if frame.dtype != np.uint8:
+                # Clip values to 0-255 range and convert to uint8
+                frame = np.clip(frame, 0, 255).astype(np.uint8)
+            
+            # Convert numpy array to PIL Image
+            if len(frame.shape) == 3 and frame.shape[2] == 3:
+                # RGB image
+                pil_image = Image.fromarray(frame, mode='RGB')
+            elif len(frame.shape) == 3 and frame.shape[2] == 4:
+                # RGBA image
+                pil_image = Image.fromarray(frame, mode='RGBA')
+            elif len(frame.shape) == 2:
+                # Grayscale image
+                pil_image = Image.fromarray(frame, mode='L')
+            else:
+                print(f"Warning: Frame {i} has unsupported shape {frame.shape}, skipping")
+                continue
+            
+            # Save to bytes buffer as PNG
+            buffer = io.BytesIO()
+            pil_image.save(buffer, format='PNG')
+            buffer.seek(0)
+            
+            # Encode to base64
+            img_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
+            encoded_frames.append(f"data:image/png;base64,{img_base64}")
+            
+        except Exception as e:
+            print(f"Warning: Failed to encode frame {i}: {e}")
+            continue
+    
+    return encoded_frames
+
+
+# Create FastAPI app with rate limiting
+app = FastAPI()
+
+# Initialize rate limiter
+limiter = Limiter(key_func=get_remote_address)
+app.state.limiter = limiter
+app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)
+
+
+@serve.deployment(
+    num_replicas=3,  # Set to 3 for cluster 0
+    ray_actor_options={
+        "num_cpus": 10, 
+        "num_gpus": 1, 
+        "runtime_env": {"conda": "fv"},
+    },
+)
+@serve.ingress(app)
+class FastVideoMultiGPUAPI:
+    def __init__(self, t2v_model_path: str, i2v_model_path: str, output_path: str, gpu_id: int = 0):
+        self.t2v_model_path = t2v_model_path
+        self.i2v_model_path = i2v_model_path
+        self.output_path = output_path
+        self.gpu_id = gpu_id
+        
+        # Initialize the video generators
+        self.t2v_generator = None
+        self.i2v_generator = None
+        self.t2v_default_params = None
+        self.i2v_default_params = None
+        
+        # Ensure output directory exists
+        os.makedirs(output_path, exist_ok=True)
+        time.sleep(10)
+        self._initialize_models()
+    
+    def _initialize_models(self):
+        # Set VSA environment variable
+        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
+        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
+        
+        # Import only when needed
+        from fastvideo.entrypoints.video_generator import VideoGenerator
+        from fastvideo.configs.sample.base import SamplingParam
+        
+        # Initialize T2V model
+        if False:  # Disabled for now
+            print(f"Initializing T2V model on GPU {self.gpu_id}: {self.t2v_model_path}")
+            self.t2v_generator = VideoGenerator.from_pretrained(
+                model_path=self.t2v_model_path, 
+                num_gpus=1,
+                use_fsdp_inference=True,
+                text_encoder_cpu_offload=False,
+                dit_cpu_offload=False,
+                vae_cpu_offload=False,
+                VSA_sparsity=0.8,
+            )
+            self.t2v_default_params = SamplingParam.from_pretrained(self.t2v_model_path)
+            print(f"✅ T2V model initialized successfully on GPU {self.gpu_id}")
+        
+        # Initialize I2V model
+        if self.i2v_generator is None:
+            print(f"Initializing I2V model on GPU {self.gpu_id}: {self.i2v_model_path}")
+            self.i2v_generator = VideoGenerator.from_pretrained(
+                model_path=self.i2v_model_path, 
+                num_gpus=1,
+                use_fsdp_inference=True,
+                text_encoder_cpu_offload=False,
+                dit_cpu_offload=False,
+                vae_cpu_offload=False,
+                VSA_sparsity=0.8,
+            )
+            self.i2v_default_params = SamplingParam.from_pretrained(self.i2v_model_path)
+            print(f"✅ I2V model initialized successfully on GPU {self.gpu_id}")
+    
+    @app.post("/generate_video", response_model=VideoGenerationResponse)
+    @limiter.limit("2/minute")  # Allow 2 requests per minute per IP
+    async def generate_video(self, request: Request, video_request: VideoGenerationRequest) -> VideoGenerationResponse:
+        try:
+            # Select the appropriate model and parameters based on model_type
+            if video_request.model_type.lower() == "i2v":
+                generator = self.i2v_generator
+                params = deepcopy(self.i2v_default_params)
+                print(f"Using I2V model for generation on GPU {self.gpu_id}")
+            else:
+                generator = self.t2v_generator
+                params = deepcopy(self.t2v_default_params)
+                print(f"Using T2V model for generation on GPU {self.gpu_id}")
+            
+            # Update parameters with request values
+            params.prompt = video_request.prompt
+            
+            # Handle seed randomization
+            if video_request.randomize_seed:
+                params.seed = torch.randint(0, 1000000, (1,)).item()
+            
+            # Ensure negative_prompt is a non-None string
+            if params.negative_prompt is None:
+                params.negative_prompt = ""
+            
+            # Set up output path and video saving
+            params.save_video = True
+            params.output_path = self.output_path
+            
+            # Create a clean filename from the prompt
+            safe_prompt = video_request.prompt[:100].replace(' ', '_').replace('/', '_').replace('\\', '_')
+            setattr(params, "output_video_name", safe_prompt)
+            
+            # Handle image_path for I2V
+            if video_request.image_path:
+                params.image_path = video_request.image_path
+            
+            # Generate the video
+            result = generator.generate_video(
+                prompt=video_request.prompt,
+                sampling_param=params,
+                save_video=True,
+            )
+            
+            frames = result.get("frames", [])
+            
+            # Encode frames to base64 for web transmission only if requested
+            encoded_frames = None
+            if video_request.return_frames and frames:
+                try:
+                    encoded_frames = encode_frames_to_base64(frames)
+                except Exception as e:
+                    print(f"Warning: Failed to encode frames: {e}")
+                    encoded_frames = None
+            
+            response = VideoGenerationResponse(
+                output_path="",
+                frames=encoded_frames,
+                seed=params.seed,
+                success=True
+            )
+            
+            # Memory cleanup to avoid OOM in repeated generations
+            import gc
+            gc.collect()
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+            return response
+        except Exception as e:
+            return VideoGenerationResponse(
+                output_path="",
+                seed=video_request.seed,
+                success=False,
+                error_message=str(e)
+            )
+    
+    @app.get("/health")
+    @limiter.limit("10/minute")  # Allow 10 health checks per minute per IP
+    async def health_check(self, request: Request):
+        return {"status": "healthy", "gpu_id": self.gpu_id}
+
+
+def start_ray_serve_multi_gpu(
+    t2v_model_path: str = "FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
+    i2v_model_path: str = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",
+    output_path: str = "outputs",
+    host: str = "0.0.0.0",
+    port: int = 8000,
+    num_gpus: int = 8,
+    cluster_id: int = 0
+):
+    """Start the Ray Serve backend with multiple GPU replicas"""
+    # Initialize Ray
+    if not ray.is_initialized():
+        ray.init()
+    
+    # Use unique application name based on cluster_id
+    app_name = f"fast_video_cluster_{cluster_id}"
+    
+    # Deploy the API
+    api = FastVideoMultiGPUAPI.bind(t2v_model_path, i2v_model_path, output_path)
+    serve.run(api, route_prefix=f"/cluster_{cluster_id}", name=app_name)
+    
+    print(f"Ray Serve multi-GPU backend started at http://{host}:{port}")
+    print(f"T2V Model: {t2v_model_path}")
+    print(f"I2V Model: {i2v_model_path}")
+    print(f"Number of GPU replicas: {num_gpus}")
+    print(f"Cluster ID: {cluster_id}")
+    print(f"Application name: {app_name}")
+    print(f"Health check: http://{host}:{port}/cluster_{cluster_id}/health")
+    print(f"Video generation endpoint: http://{host}:{port}/cluster_{cluster_id}/generate_video")
+
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="FastVideo Ray Serve Multi-GPU Backend")
+    parser.add_argument("--t2v_model_path",
+                        type=str,
+                        default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
+                        help="Path to the T2V model")
+    parser.add_argument("--i2v_model_path",
+                        type=str,
+                        default="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",
+                        help="Path to the I2V model")
+    parser.add_argument("--output_path",
+                        type=str,
+                        default="outputs",
+                        help="Path to save generated videos")
+    parser.add_argument("--host",
+                        type=str,
+                        default="0.0.0.0",
+                        help="Host to bind to")
+    parser.add_argument("--port",
+                        type=int,
+                        default=8000,
+                        help="Port to bind to")
+    parser.add_argument("--num_gpus",
+                        type=int,
+                        default=8,
+                        help="Number of GPU replicas")
+    parser.add_argument("--cluster_id",
+                        type=int,
+                        default=0,
+                        help="Cluster ID for unique naming")
+    
+    args = parser.parse_args()
+    
+    start_ray_serve_multi_gpu(
+        t2v_model_path=args.t2v_model_path,
+        i2v_model_path=args.i2v_model_path,
+        output_path=args.output_path,
+        host=args.host,
+        port=args.port,
+        num_gpus=args.num_gpus,
+        cluster_id=args.cluster_id,
+    )
+
+    # Keep the process alive
+    import signal, sys, time
+    signal.signal(signal.SIGINT,  lambda *_: sys.exit(0))
+    signal.signal(signal.SIGTERM, lambda *_: sys.exit(0))
+
+    print("✅ FastVideo multi-GPU backend is running. Press Ctrl-C to stop.")
+    while True:
+        time.sleep(3600)

examples/inference/gradio/start_ray_serve_app.py

@@ -0,0 +1,243 @@
+"""
+Startup script for FastVideo with Ray Serve backend and Gradio frontend.
+This script starts both the backend and frontend services.
+"""
+
+import argparse
+import os
+import subprocess
+import sys
+import time
+import threading
+import signal
+import requests
+from pathlib import Path
+
+# Add the project root to the Python path
+project_root = Path(__file__).parent.parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+
+def check_backend_health(backend_url: str, max_retries: int = 100) -> bool:
+    """Check if the backend is healthy"""
+    health_url = f"{backend_url}/health"
+    
+    for i in range(max_retries):
+        try:
+            response = requests.get(health_url, timeout=5)
+            if response.status_code == 200:
+                print(f"✅ Backend is healthy at {backend_url}")
+                return True
+        except requests.exceptions.RequestException:
+            pass
+        
+        if i < max_retries - 1:
+            print(f"⏳ Waiting for backend to start... ({i+1}/{max_retries})")
+            time.sleep(2)
+    
+    print(f"❌ Backend failed to start within {max_retries * 2} seconds")
+    return False
+
+
+def start_backend(args):
+    """Start the Ray Serve backend"""
+    backend_script = Path(__file__).parent / "ray_serve_backend.py"
+    
+    cmd = [
+        sys.executable, str(backend_script),
+        "--t2v_model_paths", args.t2v_model_paths,
+        "--t2v_model_replicas", args.t2v_model_replicas,
+        # "--i2v_model_path", args.i2v_model_path,  # I2V functionality commented out
+        "--output_path", args.output_path,
+        "--host", args.backend_host,
+        "--port", str(args.backend_port)
+    ]
+    
+    print(f"🚀 Starting Ray Serve backend...")
+    print(f"Command: {' '.join(cmd)}")
+    
+    # Start the backend process
+    backend_process = subprocess.Popen(
+        cmd,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        universal_newlines=True,
+        bufsize=1
+    )
+    
+    # Monitor backend output
+    def monitor_backend():
+        for line in backend_process.stdout:
+            print(f"[BACKEND] {line.rstrip()}")
+    
+    monitor_thread = threading.Thread(target=monitor_backend, daemon=True)
+    monitor_thread.start()
+    
+    return backend_process
+
+
+def start_frontend(args):
+    """Start the Gradio frontend"""
+    frontend_script = Path(__file__).parent / "gradio_frontend.py"
+    backend_url = f"http://{args.backend_host}:{args.backend_port}"
+    
+    cmd = [
+        sys.executable, str(frontend_script),
+        "--backend_url", backend_url,
+        "--t2v_model_paths", args.t2v_model_paths,
+        # "--t2v_model_replicas", args.t2v_model_replicas,
+        # "--i2v_model_path", args.i2v_model_path,  # I2V functionality commented out
+        "--host", args.frontend_host,
+        "--port", str(args.frontend_port)
+    ]
+    
+    print(f"🎨 Starting Gradio frontend...")
+    print(f"Command: {' '.join(cmd)}")
+    
+    # Start the frontend process
+    frontend_process = subprocess.Popen(
+        cmd,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        universal_newlines=True,
+        bufsize=1
+    )
+    
+    # Monitor frontend output
+    def monitor_frontend():
+        for line in frontend_process.stdout:
+            print(f"[FRONTEND] {line.rstrip()}")
+    
+    monitor_thread = threading.Thread(target=monitor_frontend, daemon=True)
+    monitor_thread.start()
+    
+    return frontend_process
+
+
+def main():
+    parser = argparse.ArgumentParser(description="FastVideo Ray Serve App")
+    
+    # Model and output settings
+    parser.add_argument("--t2v_model_paths",
+                        type=str,
+                        default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers,FastVideo/FastWan2.1-T2V-14B-Diffusers",
+                        help="Comma separated list of paths to the T2V model(s)")
+    parser.add_argument("--t2v_model_replicas",
+                        type=str,
+                        default="4,4",
+                        help="Comma separated list of number of replicas for the T2V model(s)")
+    # parser.add_argument("--t2v_14b_model_path",
+    #                     type=str,
+    #                     default="FastVideo/FastWan2.1-T2V-14B-Diffusers",
+    #                     help="Path to the T2V 14B model")
+    # parser.add_argument("--i2v_model_path",  # I2V functionality commented out
+    #                     type=str,
+    #                     default="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",
+    #                     help="Path to the I2V model")
+    parser.add_argument("--output_path",
+                        type=str,
+                        default="outputs",
+                        help="Path to save generated videos")
+    
+    # Backend settings
+    parser.add_argument("--backend_host",
+                        type=str,
+                        default="0.0.0.0",
+                        help="Backend host to bind to")
+    parser.add_argument("--backend_port",
+                        type=int,
+                        default=8000,
+                        help="Backend port to bind to")
+    
+    # Frontend settings
+    parser.add_argument("--frontend_host",
+                        type=str,
+                        default="0.0.0.0",
+                        help="Frontend host to bind to")
+    parser.add_argument("--frontend_port",
+                        type=int,
+                        default=7861,
+                        help="Frontend port to bind to")
+    
+    # Other settings
+    parser.add_argument("--skip_backend_check",
+                        action="store_true",
+                        help="Skip backend health check")
+    
+    args = parser.parse_args()
+    
+    # Ensure output directory exists
+    os.makedirs(args.output_path, exist_ok=True)
+    
+    print("🎬 FastVideo Ray Serve App")
+    print("=" * 50)
+    print(f"T2V Models: {args.t2v_model_paths}")
+    print(f"T2V Model Replicas: {args.t2v_model_replicas}")
+    # print(f"I2V Model: {args.i2v_model_path}")  # I2V functionality commented out
+    print(f"Output: {args.output_path}")
+    print(f"Backend: http://{args.backend_host}:{args.backend_port}")
+    print(f"Frontend: http://{args.frontend_host}:{args.frontend_port}")
+    print("=" * 50)
+    
+    # Start backend
+    backend_process = start_backend(args)
+    
+    # Wait for backend to be ready
+    backend_url = f"http://{args.backend_host}:{args.backend_port}"
+    
+    if not args.skip_backend_check:
+        if not check_backend_health(backend_url):
+            print("❌ Backend failed to start. Terminating...")
+            backend_process.terminate()
+            sys.exit(1)
+    
+    # Start frontend
+    frontend_process = start_frontend(args)
+    
+    print("\n🎉 Both services are starting up!")
+    print(f"📺 Frontend will be available at: http://{args.frontend_host}:{args.frontend_port}")
+    print(f"🔧 Backend API will be available at: {backend_url}")
+    print("\nPress Ctrl+C to stop both services...")
+    # return
+    
+    # Signal handler for graceful shutdown
+    def signal_handler(signum, frame):
+        print("\n🛑 Shutting down services...")
+        frontend_process.terminate()
+        backend_process.terminate()
+        
+        # Wait for processes to terminate
+        try:
+            frontend_process.wait(timeout=5)
+            backend_process.wait(timeout=5)
+        except subprocess.TimeoutExpired:
+            print("⚠️  Force killing processes...")
+            frontend_process.kill()
+            backend_process.kill()
+        
+        print("✅ Services stopped")
+        sys.exit(0)
+    
+    signal.signal(signal.SIGINT, signal_handler)
+    signal.signal(signal.SIGTERM, signal_handler)
+    
+    # Monitor processes
+    try:
+        while True:
+            # Check if processes are still running
+            if frontend_process.poll() is not None:
+                print("❌ Frontend process died unexpectedly")
+                break
+            
+            if backend_process.poll() is not None:
+                print("❌ Backend process died unexpectedly")
+                break
+            
+            time.sleep(1)
+    
+    except KeyboardInterrupt:
+        signal_handler(signal.SIGINT, None)
+
+
+if __name__ == "__main__":
+    main()

examples/inference/gradio/start_scalable_app.py

@@ -0,0 +1,465 @@
+"""
+Startup script for FastVideo scalable architecture:
+ngrok -> nginx reverse proxy -> frontend1/frontend2 -> backend1×8/backend2×8
+
+This script starts:
+1. Multiple backend instances (8 GPU replicas each)
+2. Multiple frontend instances (2 instances)
+3. Nginx reverse proxy
+4. Optional ngrok tunnel
+"""
+
+import argparse
+import os
+import subprocess
+import sys
+import time
+import threading
+import signal
+import requests
+import json
+from pathlib import Path
+
+# Add the project root to the Python path
+project_root = Path(__file__).parent.parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+
+def check_service_health(url: str, max_retries: int = 50) -> bool:
+    """Check if a service is healthy"""
+    for i in range(max_retries):
+        try:
+            response = requests.get(url, timeout=5)
+            if response.status_code == 200:
+                print(f"✅ Service is healthy at {url}")
+                return True
+        except requests.exceptions.RequestException:
+            pass
+        
+        if i < max_retries - 1:
+            print(f"⏳ Waiting for service to start... ({i+1}/{max_retries})")
+            time.sleep(2)
+    
+    print(f"❌ Service failed to start within {max_retries * 2} seconds")
+    return False
+
+
+def start_backend_cluster(args, cluster_id: int):
+    """Start one backend cluster (Ray-Serve application)."""
+    backend_script = Path(__file__).parent / "ray_serve_backend_scalable.py"
+
+    # All Ray Serve apps share the same HTTP server (default 8000).
+    # We still forward the port flag for completeness, but keep it
+    # identical for every cluster.
+    base_port = args.backend_base_port
+    
+    cmd = [
+        sys.executable, str(backend_script),
+        "--t2v_model_path", args.t2v_model_path,
+        "--i2v_model_path", args.i2v_model_path,
+        "--output_path", args.output_path,
+        "--host", args.backend_host,
+        "--port", str(base_port),
+        "--num_gpus", str(args.num_gpus_per_cluster),
+        "--cluster_id", str(cluster_id),
+    ]
+    
+    print(f"🚀 Starting Backend Cluster {cluster_id + 1} (HTTP port {base_port})...")
+    print(f"Command: {' '.join(cmd)}")
+    
+    # Start the backend process
+    backend_process = subprocess.Popen(
+        cmd,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        universal_newlines=True,
+        bufsize=1
+    )
+    
+    # Monitor backend output
+    def monitor_backend():
+        for line in backend_process.stdout:
+            print(f"[BACKEND-{cluster_id + 1}] {line.rstrip()}")
+    
+    monitor_thread = threading.Thread(target=monitor_backend, daemon=True)
+    monitor_thread.start()
+    
+    return backend_process, base_port
+
+
+def start_frontend_instance(args, instance_id: int, backend_url: str):
+    """Start a single frontend instance"""
+    frontend_script = Path(__file__).parent / "gradio_frontend.py"
+    frontend_port = args.frontend_base_port + instance_id
+    
+    # Update backend URL to include cluster-specific path
+    cluster_id = instance_id % args.num_backend_clusters
+    backend_url_with_cluster = f"{backend_url}/cluster_{cluster_id}"
+    
+    cmd = [
+        sys.executable, str(frontend_script),
+        "--backend_url", backend_url_with_cluster,
+        "--t2v_model_path", args.t2v_model_path,
+        "--i2v_model_path", args.i2v_model_path,
+        "--host", args.frontend_host,
+        "--port", str(frontend_port)
+    ]
+    
+    print(f"🎨 Starting Frontend {instance_id + 1} on port {frontend_port}...")
+    print(f"Command: {' '.join(cmd)}")
+    
+    # Start the frontend process
+    frontend_process = subprocess.Popen(
+        cmd,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        universal_newlines=True,
+        bufsize=1
+    )
+    
+    # Monitor frontend output
+    def monitor_frontend():
+        for line in frontend_process.stdout:
+            print(f"[FRONTEND-{instance_id + 1}] {line.rstrip()}")
+    
+    monitor_thread = threading.Thread(target=monitor_frontend, daemon=True)
+    monitor_thread.start()
+    
+    return frontend_process, frontend_port
+
+
+def start_nginx(args):
+    """Start nginx reverse proxy"""
+    nginx_conf = Path(__file__).parent / "nginx.conf"
+    
+    # Update nginx configuration with actual ports
+    update_nginx_config(args)
+    
+    cmd = [
+        "nginx",
+        "-c", str(nginx_conf),
+        "-g", "daemon off;"
+    ]
+    
+    print(f"🌐 Starting Nginx reverse proxy...")
+    print(f"Command: {' '.join(cmd)}")
+    
+    # Start nginx process
+    nginx_process = subprocess.Popen(
+        cmd,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        universal_newlines=True,
+        bufsize=1
+    )
+    
+    # Monitor nginx output
+    def monitor_nginx():
+        for line in nginx_process.stdout:
+            print(f"[NGINX] {line.rstrip()}")
+    
+    monitor_thread = threading.Thread(target=monitor_nginx, daemon=True)
+    monitor_thread.start()
+    
+    return nginx_process
+
+
+def update_nginx_config(args):
+    """Rewrite nginx.conf with the correct ports – NO “/cluster_X” in upstreams."""
+    nginx_conf        = Path(__file__).parent / "nginx.conf"
+    nginx_conf_backup = Path(__file__).parent / "nginx.conf.backup"
+
+    if not nginx_conf_backup.exists():
+        nginx_conf_backup.write_text(nginx_conf.read_text())
+
+    config_content = nginx_conf_backup.read_text()
+
+    # ── 1. front-end pool ───────────────────────────────────────────────
+    frontend_servers = "\n        ".join(
+        f"server 127.0.0.1:{args.frontend_base_port + i} "
+        f"weight=1 max_fails=3 fail_timeout=30s;"
+        for i in range(args.num_frontends)
+    )
+    config_content = config_content.replace(
+        "# Upstream for frontend load balancing",
+        f"# Upstream for frontend load balancing\n    upstream frontend_servers {{\n"
+        f"        # Round-robin load balancing between frontends\n        {frontend_servers}"
+    )
+
+    # Shared Ray-Serve HTTP port
+    backend_port = args.backend_base_port           # default 8000
+    backend_line = (f"server 127.0.0.1:{backend_port} "
+                    f"weight=1 max_fails=3 fail_timeout=30s;")
+
+    # ── 2. backend-1 pool ───────────────────────────────────────────────
+    config_content = config_content.replace(
+        "# Upstream for backend1 load balancing",
+        f"# Upstream for backend1 load balancing\n    upstream backend1_servers {{\n"
+        f"        {backend_line}"
+    )
+
+    # ── 3. backend-2 pool ───────────────────────────────────────────────
+    config_content = config_content.replace(
+        "# Upstream for backend2 load balancing",
+        f"# Upstream for backend2 load balancing\n    upstream backend2_servers {{\n"
+        f"        {backend_line}"
+    )
+
+    # ── 4. strip any stray “/cluster_X” fragments ───────────────────────
+    config_content = config_content.replace("/cluster_0", "").replace("/cluster_1", "")
+
+    # ── 5. use user-writable log directory ---------------------------------
+    log_dir = Path(args.output_path).resolve()
+    config_content = config_content.replace(
+        "access_log /var/log/nginx/access.log;",
+        f"access_log {log_dir}/nginx_access.log;")
+    config_content = config_content.replace(
+        "error_log /var/log/nginx/error.log;",
+        f"error_log  {log_dir}/nginx_error.log;")
+
+    nginx_conf.write_text(config_content)
+    print("✅ nginx.conf updated (no path suffixes & custom log paths)")
+
+
+def start_ngrok(args):
+    """Start ngrok tunnel"""
+    if not args.use_ngrok:
+        return None
+    
+    cmd = [
+        "ngrok",
+        "http",
+        str(args.nginx_port),
+        "--log=stdout"
+    ]
+    
+    print(f"🌍 Starting ngrok tunnel to port {args.nginx_port}...")
+    print(f"Command: {' '.join(cmd)}")
+    
+    # Start ngrok process
+    ngrok_process = subprocess.Popen(
+        cmd,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        universal_newlines=True,
+        bufsize=1
+    )
+    
+    # Monitor ngrok output
+    def monitor_ngrok():
+        for line in ngrok_process.stdout:
+            print(f"[NGROK] {line.rstrip()}")
+    
+    monitor_thread = threading.Thread(target=monitor_ngrok, daemon=True)
+    monitor_thread.start()
+    
+    return ngrok_process
+
+
+def main():
+    parser = argparse.ArgumentParser(description="FastVideo Scalable Architecture Launcher")
+    
+    # Model and output settings
+    parser.add_argument("--t2v_model_path",
+                        type=str,
+                        default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
+                        help="Path to the T2V model")
+    parser.add_argument("--i2v_model_path",
+                        type=str,
+                        default="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",
+                        help="Path to the I2V model")
+    parser.add_argument("--output_path",
+                        type=str,
+                        default="outputs",
+                        help="Path to save generated videos")
+    
+    # Backend settings
+    parser.add_argument("--backend_host",
+                        type=str,
+                        default="0.0.0.0",
+                        help="Backend host to bind to")
+    parser.add_argument("--backend_base_port",
+                        type=int,
+                        default=8000,
+                        help="Base port for backend clusters")
+    parser.add_argument("--num_backend_clusters",
+                        type=int,
+                        default=2,
+                        help="Number of backend clusters")
+    parser.add_argument("--num_gpus_per_cluster",
+                        type=int,
+                        default=3,  # Changed from 8 to 3 (3+3=6 GPUs total, leaving 1 GPU buffer)
+                        help="Number of GPUs per backend cluster")
+    
+    # Frontend settings
+    parser.add_argument("--frontend_host",
+                        type=str,
+                        default="0.0.0.0",
+                        help="Frontend host to bind to")
+    parser.add_argument("--frontend_base_port",
+                        type=int,
+                        default=7860,
+                        help="Base port for frontend instances")
+    parser.add_argument("--num_frontends",
+                        type=int,
+                        default=2,
+                        help="Number of frontend instances")
+    
+    # Nginx settings
+    parser.add_argument("--nginx_port",
+                        type=int,
+                        default=80,
+                        help="Port for nginx reverse proxy")
+    
+    # Ngrok settings
+    parser.add_argument("--use_ngrok",
+                        action="store_true",
+                        help="Start ngrok tunnel")
+    
+    # Other settings
+    parser.add_argument("--skip_health_check",
+                        action="store_true",
+                        help="Skip health checks")
+    
+    args = parser.parse_args()
+    
+    # Ensure output directory exists
+    os.makedirs(args.output_path, exist_ok=True)
+    
+    print(" FastVideo Scalable Architecture")
+    print("=" * 60)
+    print(f"Architecture: ngrok -> nginx -> frontend1/frontend2 -> backend1×{args.num_gpus_per_cluster}/backend2×{args.num_gpus_per_cluster}")
+    print(f"T2V Model: {args.t2v_model_path}")
+    print(f"I2V Model: {args.i2v_model_path}")
+    print(f"Output: {args.output_path}")
+    print(f"Backend Clusters: {args.num_backend_clusters}")
+    print(f"GPUs per Cluster: {args.num_gpus_per_cluster}")
+    print(f"Total GPUs needed: {args.num_backend_clusters * args.num_gpus_per_cluster}")
+    print(f"Frontend Instances: {args.num_frontends}")
+    print(f"Nginx Port: {args.nginx_port}")
+    print(f"Use Ngrok: {args.use_ngrok}")
+    print("=" * 60)
+    
+    # Start backend clusters
+    backend_processes = []
+    backend_urls = []
+    
+    for i in range(args.num_backend_clusters):
+        process, _ = start_backend_cluster(args, i)
+        backend_processes.append(process)
+        backend_urls.append(f"http://{args.backend_host}:{args.backend_base_port}")
+    
+    # Wait for backends to be ready
+    if not args.skip_health_check:
+        print("\n⏳ Waiting for backend clusters to start...")
+        for i, url in enumerate(backend_urls):
+            if not check_service_health(f"{url}/cluster_{i}/health"):
+                print(f"❌ Backend cluster {i + 1} failed to start. Terminating...")
+                for process in backend_processes:
+                    process.terminate()
+                sys.exit(1)
+    
+    # Start frontend instances
+    frontend_processes = []
+    frontend_urls = []
+    
+    for i in range(args.num_frontends):
+        # Each frontend connects to a different backend cluster
+        backend_url = backend_urls[i % len(backend_urls)]
+        process, port = start_frontend_instance(args, i, backend_url)
+        frontend_processes.append(process)
+        frontend_urls.append(f"http://{args.frontend_host}:{port}")
+    
+    # Wait for frontends to be ready
+    if not args.skip_health_check:
+        print("\n⏳ Waiting for frontend instances to start...")
+        for i, url in enumerate(frontend_urls):
+            if not check_service_health(url):
+                print(f"❌ Frontend {i + 1} failed to start. Terminating...")
+                for process in backend_processes + frontend_processes:
+                    process.terminate()
+                sys.exit(1)
+    
+    # Start nginx reverse proxy
+    nginx_process = start_nginx(args)
+    
+    # Wait for nginx to be ready
+    if not args.skip_health_check:
+        print("\n⏳ Waiting for nginx to start...")
+        if not check_service_health(f"http://localhost:{args.nginx_port}/health"):
+            print("❌ Nginx failed to start. Terminating...")
+            for process in backend_processes + frontend_processes + [nginx_process]:
+                process.terminate()
+            sys.exit(1)
+    
+    # Start ngrok tunnel (optional)
+    ngrok_process = start_ngrok(args)
+    
+    print("\n🎉 All services are starting up!")
+    print(f"🌐 Nginx reverse proxy: http://localhost:{args.nginx_port}")
+    for i, url in enumerate(frontend_urls):
+        print(f"📺 Frontend {i + 1}: {url}")
+    for i, url in enumerate(backend_urls):
+        print(f" Backend Cluster {i + 1}: {url}")
+    if args.use_ngrok:
+        print("🌍 Ngrok tunnel is starting...")
+    print("\nPress Ctrl+C to stop all services...")
+    
+    # Signal handler for graceful shutdown
+    def signal_handler(signum, frame):
+        print("\n🛑 Shutting down all services...")
+        all_processes = backend_processes + frontend_processes + [nginx_process]
+        if ngrok_process:
+            all_processes.append(ngrok_process)
+        
+        for process in all_processes:
+            if process:
+                process.terminate()
+        
+        # Wait for processes to terminate
+        try:
+            for process in all_processes:
+                if process:
+                    process.wait(timeout=5)
+        except subprocess.TimeoutExpired:
+            print("⚠️  Force killing processes...")
+            for process in all_processes:
+                if process:
+                    process.kill()
+        
+        print("✅ All services stopped")
+        sys.exit(0)
+    
+    signal.signal(signal.SIGINT, signal_handler)
+    signal.signal(signal.SIGTERM, signal_handler)
+    
+    # Monitor processes
+    try:
+        while True:
+            # Check if processes are still running
+            for i, process in enumerate(backend_processes):
+                if process.poll() is not None:
+                    print(f"❌ Backend cluster {i + 1} process died unexpectedly")
+                    break
+            
+            for i, process in enumerate(frontend_processes):
+                if process.poll() is not None:
+                    print(f"❌ Frontend {i + 1} process died unexpectedly")
+                    break
+            
+            if nginx_process and nginx_process.poll() is not None:
+                print("❌ Nginx process died unexpectedly")
+                break
+            
+            if ngrok_process and ngrok_process.poll() is not None:
+                print("❌ Ngrok process died unexpectedly")
+                break
+            
+            time.sleep(1)
+    
+    except KeyboardInterrupt:
+        signal_handler(signal.SIGINT, None)
+
+
+if __name__ == "__main__":
+    main()

examples/inference/gradio/test_i2v.py

@@ -0,0 +1,147 @@
+#!/usr/bin/env python3
+"""
+Test script for T2V and I2V functionality in FastVideo Gradio app.
+This script tests both the backend and frontend modifications.
+"""
+
+import requests
+import json
+import os
+from PIL import Image
+import numpy as np
+
+def test_backend_t2v():
+    """Test the backend T2V functionality directly"""
+    backend_url = "http://localhost:8000"
+    
+    try:
+        # Test T2V request data
+        request_data = {
+            "prompt": "A beautiful sunset over the ocean with gentle waves",
+            "negative_prompt": "",
+            "use_negative_prompt": False,
+            "seed": 42,
+            "guidance_scale": 7.5,
+            "num_frames": 21,
+            "height": 448,
+            "width": 832,
+            "num_inference_steps": 20,
+            "randomize_seed": False,
+            "return_frames": True,
+            "image_path": None,
+            "model_type": "t2v"
+        }
+        
+        # Send request to backend
+        response = requests.post(
+            f"{backend_url}/generate_video",
+            json=request_data,
+            timeout=300  # 5 minutes timeout
+        )
+        
+        if response.status_code == 200:
+            result = response.json()
+            print("✅ Backend T2V test successful!")
+            print(f"Success: {result.get('success')}")
+            print(f"Seed used: {result.get('seed')}")
+            if result.get('frames'):
+                print(f"Frames returned: {len(result.get('frames'))}")
+            else:
+                print("No frames returned")
+        else:
+            print(f"❌ Backend T2V test failed with status {response.status_code}")
+            print(f"Response: {response.text}")
+            
+    except Exception as e:
+        print(f"❌ Backend T2V test failed with exception: {e}")
+
+def test_backend_i2v():
+    """Test the backend I2V functionality directly"""
+    backend_url = "http://localhost:8000"
+    
+    # Create a simple test image
+    test_image = Image.new('RGB', (256, 256), color='red')
+    temp_image_path = "test_image.png"
+    test_image.save(temp_image_path)
+    
+    try:
+        # Test I2V request data
+        request_data = {
+            "prompt": "The red square gently animates with subtle movement",
+            "negative_prompt": "",
+            "use_negative_prompt": False,
+            "seed": 42,
+            "guidance_scale": 7.5,
+            "num_frames": 21,
+            "height": 448,
+            "width": 832,
+            "num_inference_steps": 20,
+            "randomize_seed": False,
+            "return_frames": True,
+            "image_path": temp_image_path,
+            "model_type": "i2v"
+        }
+        
+        # Send request to backend
+        response = requests.post(
+            f"{backend_url}/generate_video",
+            json=request_data,
+            timeout=300  # 5 minutes timeout
+        )
+        
+        if response.status_code == 200:
+            result = response.json()
+            print("✅ Backend I2V test successful!")
+            print(f"Success: {result.get('success')}")
+            print(f"Seed used: {result.get('seed')}")
+            if result.get('frames'):
+                print(f"Frames returned: {len(result.get('frames'))}")
+            else:
+                print("No frames returned")
+        else:
+            print(f"❌ Backend I2V test failed with status {response.status_code}")
+            print(f"Response: {response.text}")
+            
+    except Exception as e:
+        print(f"❌ Backend I2V test failed with exception: {e}")
+    
+    finally:
+        # Clean up test image
+        if os.path.exists(temp_image_path):
+            os.remove(temp_image_path)
+
+def test_backend_health():
+    """Test if the backend is running"""
+    backend_url = "http://localhost:8000"
+    
+    try:
+        response = requests.get(f"{backend_url}/health", timeout=5)
+        if response.status_code == 200:
+            print("✅ Backend is healthy")
+            return True
+        else:
+            print(f"❌ Backend health check failed: {response.status_code}")
+            return False
+    except Exception as e:
+        print(f"❌ Backend health check failed: {e}")
+        return False
+
+if __name__ == "__main__":
+    print("🧪 Testing FastVideo T2V and I2V functionality...")
+    print("=" * 50)
+    
+    # Test backend health first
+    if test_backend_health():
+        # Test T2V functionality
+        print("\n📝 Testing T2V functionality...")
+        test_backend_t2v()
+        
+        # Test I2V functionality
+        print("\n🖼️  Testing I2V functionality...")
+        test_backend_i2v()
+    else:
+        print("⚠️  Backend is not running. Please start the backend first.")
+        print("You can start it with: python start_ray_serve_app.py")
+    
+    print("=" * 50)
+    print("Test completed!") 

fastvideo-logos/light-mode/svg/icon-bg.svg

@@ -0,0 +1,14 @@
+<svg width="200" height="201" viewBox="0 0 200 201" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect width="200" height="200" transform="translate(0 0.691925)" fill="white"/>
+<path d="M86.4467 27.9646L64.2778 97.1314H84.6732L91.7672 71.4156L124.445 71.4156L134.898 57.2275L96.201 57.2275L100.635 43.9262H146.844L159.278 27.9646H86.4467Z" fill="#0C0D6F"/>
+<path d="M158.423 107.852H131.821L91.6798 160.17L84.5858 107.852H64.4273L73.2948 174.359H95.4637L158.423 107.852Z" fill="#0C0D6F"/>
+<path d="M86.4467 27.9646L64.2778 97.1314H84.6732L91.7672 71.4156L124.445 71.4156L134.898 57.2275L96.201 57.2275L100.635 43.9262H146.844L159.278 27.9646H86.4467Z" stroke="#0C0D6F" stroke-width="1.77351"/>
+<path d="M158.423 107.852H131.821L91.6798 160.17L84.5858 107.852H64.4273L73.2948 174.359H95.4637L158.423 107.852Z" stroke="#0C0D6F" stroke-width="1.77351"/>
+<path d="M158.297 107.923H131.694L166.713 34.6841L97.1109 126.545H118.393L95.3374 174.429L158.297 107.923Z" fill="#FDC717" stroke="#FDC717" stroke-width="1.77351" stroke-miterlimit="16"/>
+<path d="M53.6055 107.773L62.473 174.279L67.7935 174.279L58.926 107.772L53.6055 107.773Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="1.77351"/>
+<path d="M42.9648 107.773L51.8324 174.279L53.6059 174.279L44.7384 107.772L42.9648 107.773Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="1.77351"/>
+<path d="M32.3232 107.772L41.1908 174.279L42.0775 174.279L33.21 107.772L32.3232 107.772Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="0.886754"/>
+<path d="M53.6055 97.1314L75.7743 27.9646H81.0949L58.926 97.1314H53.6055Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="1.77351"/>
+<path d="M42.9648 97.1314L65.1337 27.9646H66.9072L44.7384 97.1314H42.9648Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="1.77351"/>
+<path d="M32.3232 97.1315L54.4921 27.9646H55.3789L33.21 97.1315H32.3232Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="0.886754"/>
+</svg>

fastvideo-logos/light-mode/svg/icon-simple-bg.svg

@@ -0,0 +1,7 @@
+<svg width="200" height="201" viewBox="0 0 200 201" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect width="200" height="200" transform="translate(0 0.308105)" fill="white"/>
+<path d="M49.8511 145.66L78.6319 55.8637H85.5394L56.7585 145.66H49.8511Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="2.30244"/>
+<path d="M36.0376 145.66L64.8185 55.8637H67.1209L38.3401 145.66H36.0376Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="2.30244"/>
+<path d="M22.2222 145.66L51.003 55.8637H52.1543L23.3734 145.66H22.2222Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="1.15122"/>
+<path d="M92.4465 55.8648L63.666 145.66H90.144L99.3538 112.275H144.251L150.007 93.855H105.11L110.866 76.5868H173.032L178.788 55.8648H92.4465Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="2.30244"/>
+</svg>

fastvideo-logos/light-mode/svg/icon-simple-bolt-bg.svg

@@ -0,0 +1,8 @@
+<svg width="200" height="201" viewBox="0 0 200 201" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect width="200" height="200" transform="translate(0 0.308105)" fill="white"/>
+<path d="M49.8511 145.66L78.6319 55.8637H85.5394L56.7585 145.66H49.8511Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="2.30244"/>
+<path d="M36.0376 145.66L64.8185 55.8637H67.1209L38.3401 145.66H36.0376Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="2.30244"/>
+<path d="M22.2222 145.66L51.003 55.8637H52.1543L23.3734 145.66H22.2222Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="1.15122"/>
+<path d="M92.4465 55.8648L63.666 145.66H90.144L99.3538 112.275H144.251L150.007 93.855H105.11L110.866 76.5868H173.032L178.788 55.8648H92.4465Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="2.30244"/>
+<path d="M131.799 93.8999H104.705L134.343 30.1061L69.483 112.866H91.1583L67.6768 161.635L131.799 93.8999Z" fill="#FDC717" stroke="#FDC717" stroke-width="1.80627" stroke-miterlimit="16"/>
+</svg>

fastvideo-logos/light-mode/svg/icon-sm-bg.svg

@@ -0,0 +1,14 @@
+<svg width="200" height="201" viewBox="0 0 200 201" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect width="200" height="200" transform="translate(0 0.691925)" fill="white"/>
+<path d="M89.2368 40.0858L70.8901 97.3273H87.769L93.64 76.0452L120.684 76.0453L129.334 64.3034L97.3093 64.3034L100.979 53.2954H139.22L149.511 40.0858H89.2368Z" fill="#0C0D6F"/>
+<path d="M148.804 106.199H126.788L93.5676 149.498L87.6967 106.199H71.0138L78.3525 161.239H96.6991L148.804 106.199Z" fill="#0C0D6F"/>
+<path d="M89.2368 40.0858L70.8901 97.3273H87.769L93.64 76.0452L120.684 76.0453L129.334 64.3034L97.3093 64.3034L100.979 53.2954H139.22L149.511 40.0858H89.2368Z" stroke="#0C0D6F" stroke-width="1.46773"/>
+<path d="M148.804 106.199H126.788L93.5676 149.498L87.6967 106.199H71.0138L78.3525 161.239H96.6991L148.804 106.199Z" stroke="#0C0D6F" stroke-width="1.46773"/>
+<path d="M148.699 106.258H126.683L155.664 45.6468L98.062 121.669H115.675L96.5942 161.298L148.699 106.258Z" fill="#FDC717" stroke="#FDC717" stroke-width="1.46773" stroke-miterlimit="16"/>
+<path d="M62.0576 106.134L69.3963 161.174L73.7995 161.174L66.4608 106.134L62.0576 106.134Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="1.46773"/>
+<path d="M53.2515 106.134L60.5901 161.174L62.0579 161.174L54.7192 106.134L53.2515 106.134Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="1.46773"/>
+<path d="M44.4443 106.134L51.783 161.174L52.5169 161.174L45.1782 106.134L44.4443 106.134Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="0.733866"/>
+<path d="M62.0576 97.3273L80.4043 40.0858H84.8075L66.4608 97.3273H62.0576Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="1.46773"/>
+<path d="M53.2515 97.3273L71.5981 40.0858H73.0658L54.7192 97.3273H53.2515Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="1.46773"/>
+<path d="M44.4443 97.3274L62.791 40.0859H63.5248L45.1782 97.3274H44.4443Z" fill="#0C0D6F" stroke="#0C0D6F" stroke-width="0.733866"/>
+</svg>

fastvideo-logos/main/svg/full.svg

@@ -0,0 +1,18 @@
+<svg width="252" height="105" viewBox="0 0 252 105" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M89.4843 55.5457H101.361L87.7028 101H74.638L89.4843 55.5457Z" fill="#356CFF"/>
+<path fill-rule="evenodd" clip-rule="evenodd" d="M96.0167 1.00057H112.645L118.583 48.273H104.924L103.737 39.7882H79.9827L67.5117 55.5457H85.3273L43.1638 101H28.3174L22.3789 55.5457H33.6621L38.4129 91.3031L58.604 68.2729H44.3515L96.0167 1.00057ZM100.768 13.1217L87.7028 29.4852H103.143L100.768 13.1217Z" fill="#356CFF"/>
+<path d="M37.2252 1.00057L22.3789 48.273H36.0375L40.7884 30.6974L62.6727 30.6974L69.6727 21.0005L43.7576 21.0004L46.7269 11.9096L77.6727 11.9096L86 1.00057L37.2252 1.00057Z" fill="#356CFF"/>
+<path fill-rule="evenodd" clip-rule="evenodd" d="M108.488 55.5457L94.2351 101C94.2351 101 105.518 101 120.959 101C136.399 101 144.078 93.0133 148.276 79.788C152.432 68.0157 153.027 55.5457 136.399 55.5457C119.771 55.5457 108.488 55.5457 108.488 55.5457ZM109.081 90.697L116.802 65.8487C116.802 65.8487 120.959 65.8487 132.242 65.8487C143.525 65.8487 137.586 78.5759 135.211 84.0304C133.307 88.4021 127.491 90.697 122.74 90.697C117.989 90.697 109.081 90.697 109.081 90.697Z" fill="#356CFF"/>
+<path d="M173.188 1.00056L168.625 11.9096C168.625 11.9096 149.386 11.9092 142.525 11.9095C135.664 11.9098 136.586 20.3944 141.337 20.3944H159.747C168.654 20.3944 166.961 33.6899 163.904 38.5761C160.467 44.0675 157.371 48.273 148.463 48.273L125.188 48.273L124 37.97L147.87 37.97C153.808 37.97 156.184 29.4852 151.433 29.4852H131.836C120.142 29.4852 125.897 1.00043 141.337 1.00043L173.188 1.00056Z" fill="#356CFF"/>
+<path d="M179.938 1.00056L175.688 11.9096L191.221 11.9096L179.938 48.273H192.409L203.692 11.9096L219.132 11.9095L223.289 1.00043L179.938 1.00056Z" fill="#356CFF"/>
+<path d="M161.341 55.5457H202.845L198.5 65.8487H169.654L167.279 73.7268H188.5L184.749 82.8177H164.31L161.934 90.697H190.251L186.624 101H146.494L161.341 55.5457Z" fill="#356CFF"/>
+<path fill-rule="evenodd" clip-rule="evenodd" d="M230.821 54.9391C255.169 54.9391 251.776 67.0602 249.231 77.9692C246.686 88.8783 240.917 101 217.757 101C194.596 101 195.606 88.8783 199.347 77.9692C203.089 67.0602 206.473 54.9391 230.821 54.9391ZM237.948 77.9692C239.984 70.6965 240.917 65.242 228.446 65.242C215.975 65.242 211.818 71.9087 210.037 77.9692C208.255 84.0298 208.255 91.3025 219.538 91.3025C230.821 91.3025 235.911 85.2419 237.948 77.9692Z" fill="#356CFF"/>
+<path d="M173.188 1.00056L168.625 11.9096C168.625 11.9096 149.386 11.9092 142.525 11.9095C135.664 11.9098 136.586 20.3944 141.337 20.3944M173.188 1.00056C173.188 1.00056 156.777 1.00043 141.337 1.00043M173.188 1.00056L141.337 1.00043M141.337 20.3944C146.088 20.3944 150.839 20.3944 159.747 20.3944M141.337 20.3944H159.747M159.747 20.3944C168.654 20.3944 166.961 33.6899 163.904 38.5761C160.467 44.0675 157.371 48.273 148.463 48.273M148.463 48.273C139.556 48.273 125.188 48.273 125.188 48.273M148.463 48.273L125.188 48.273M125.188 48.273L124 37.97M124 37.97C124 37.97 141.931 37.97 147.87 37.97M124 37.97L147.87 37.97M147.87 37.97C153.808 37.97 156.184 29.4852 151.433 29.4852M151.433 29.4852C146.682 29.4852 138.962 29.4852 131.836 29.4852M151.433 29.4852H131.836M131.836 29.4852C120.142 29.4852 125.897 1.00043 141.337 1.00043M37.2252 1.00057L22.3789 48.273H36.0375L40.7884 30.6974L62.6727 30.6974L69.6727 21.0005L43.7576 21.0004L46.7269 11.9096L77.6727 11.9096L86 1.00057L37.2252 1.00057ZM96.0167 1.00057H112.645L118.583 48.273H104.924L103.737 39.7882H79.9827L67.5117 55.5457H85.3273L43.1638 101H28.3174L22.3789 55.5457H33.6621L38.4129 91.3031L58.604 68.2729H44.3515L96.0167 1.00057ZM87.7028 29.4852L100.768 13.1217L103.143 29.4852H87.7028ZM89.4843 55.5457H101.361L87.7028 101H74.638L89.4843 55.5457ZM108.488 55.5457L94.2351 101C94.2351 101 105.518 101 120.959 101C136.399 101 144.078 93.0133 148.276 79.788C152.432 68.0157 153.027 55.5457 136.399 55.5457C119.771 55.5457 108.488 55.5457 108.488 55.5457ZM116.802 65.8487L109.081 90.697C109.081 90.697 117.989 90.697 122.74 90.697C127.491 90.697 133.307 88.4021 135.211 84.0304C137.586 78.5759 143.525 65.8487 132.242 65.8487C120.959 65.8487 116.802 65.8487 116.802 65.8487ZM179.938 1.00056L175.688 11.9096L191.221 11.9096L179.938 48.273H192.409L203.692 11.9096L219.132 11.9095L223.289 1.00043L179.938 1.00056ZM161.341 55.5457H202.845L198.5 65.8487H169.654L167.279 73.7268H188.5L184.749 82.8177H164.31L161.934 90.697H190.251L186.624 101H146.494L161.341 55.5457ZM230.821 54.9391C255.169 54.9391 251.776 67.0602 249.231 77.9692C246.686 88.8783 240.917 101 217.757 101C194.596 101 195.606 88.8783 199.347 77.9692C203.089 67.0602 206.473 54.9391 230.821 54.9391ZM228.446 65.242C240.917 65.242 239.984 70.6965 237.948 77.9692C235.911 85.2419 230.821 91.3025 219.538 91.3025C208.255 91.3025 208.255 84.0298 210.037 77.9692C211.818 71.9087 215.975 65.242 228.446 65.242Z" stroke="#356CFF" stroke-width="1.18771"/>
+<path d="M15.2524 55.5451L21.191 100.999L24.7541 100.999L18.8156 55.5451L15.2524 55.5451Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.18771"/>
+<path d="M8.12646 55.5451L14.065 100.999L15.2527 100.999L9.31417 55.5451L8.12646 55.5451Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.18771"/>
+<path d="M1 55.5451L6.93853 100.999L7.53239 100.999L1.59385 55.5451L1 55.5451Z" fill="#356CFF" stroke="#356CFF" stroke-width="0.593853"/>
+<path d="M15.2524 48.2724L30.0988 1H33.6619L18.8156 48.2724H15.2524Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.18771"/>
+<path d="M8.12646 48.2724L22.9728 1H24.1605L9.31417 48.2724H8.12646Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.18771"/>
+<path d="M1 48.2724L15.8463 1H16.4402L1.59385 48.2724H1Z" fill="#356CFF" stroke="#356CFF" stroke-width="0.593853"/>
+<path d="M85.3271 55.5457H67.5116L87 12.7363L44.3513 68.2729H58.6038L43.1636 101L85.3271 55.5457Z" fill="#FDC717" stroke="#FDC717" stroke-width="1.18771" stroke-miterlimit="16"/>
+</svg>

fastvideo-logos/main/svg/icon-bg.svg

@@ -0,0 +1,14 @@
+<svg width="200" height="201" viewBox="0 0 200 201" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect width="200" height="200" transform="translate(0 0.691925)" fill="black"/>
+<path d="M86.4467 27.9646L64.2778 97.1314H84.6732L91.7672 71.4156L124.445 71.4156L134.898 57.2275L96.201 57.2275L100.635 43.9262H146.844L159.278 27.9646H86.4467Z" fill="#356CFF"/>
+<path d="M158.423 107.852H131.821L91.6798 160.17L84.5858 107.852H64.4273L73.2948 174.359H95.4637L158.423 107.852Z" fill="#356CFF"/>
+<path d="M86.4467 27.9646L64.2778 97.1314H84.6732L91.7672 71.4156L124.445 71.4156L134.898 57.2275L96.201 57.2275L100.635 43.9262H146.844L159.278 27.9646H86.4467Z" stroke="#356CFF" stroke-width="1.77351"/>
+<path d="M158.423 107.852H131.821L91.6798 160.17L84.5858 107.852H64.4273L73.2948 174.359H95.4637L158.423 107.852Z" stroke="#356CFF" stroke-width="1.77351"/>
+<path d="M158.297 107.923H131.694L166.713 34.6841L97.1109 126.545H118.393L95.3374 174.429L158.297 107.923Z" fill="#FDC717" stroke="#FDC717" stroke-width="1.77351" stroke-miterlimit="16"/>
+<path d="M53.6055 107.773L62.473 174.279L67.7935 174.279L58.926 107.772L53.6055 107.773Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.77351"/>
+<path d="M42.9648 107.773L51.8324 174.279L53.6059 174.279L44.7384 107.772L42.9648 107.773Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.77351"/>
+<path d="M32.3232 107.772L41.1908 174.279L42.0775 174.279L33.21 107.772L32.3232 107.772Z" fill="#356CFF" stroke="#356CFF" stroke-width="0.886754"/>
+<path d="M53.6055 97.1314L75.7743 27.9646H81.0949L58.926 97.1314H53.6055Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.77351"/>
+<path d="M42.9648 97.1314L65.1337 27.9646H66.9072L44.7384 97.1314H42.9648Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.77351"/>
+<path d="M32.3232 97.1315L54.4921 27.9646H55.3789L33.21 97.1315H32.3232Z" fill="#356CFF" stroke="#356CFF" stroke-width="0.886754"/>
+</svg>

fastvideo-logos/main/svg/icon-simple-bg.svg

@@ -0,0 +1,7 @@
+<svg width="200" height="201" viewBox="0 0 200 201" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect width="200" height="200" transform="translate(0 0.308105)" fill="black"/>
+<path d="M49.8511 145.66L78.6319 55.8637H85.5394L56.7585 145.66H49.8511Z" fill="#356CFF" stroke="#356CFF" stroke-width="2.30244"/>
+<path d="M36.0376 145.66L64.8185 55.8637H67.1209L38.3401 145.66H36.0376Z" fill="#356CFF" stroke="#356CFF" stroke-width="2.30244"/>
+<path d="M22.2222 145.66L51.003 55.8637H52.1543L23.3734 145.66H22.2222Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.15122"/>
+<path d="M92.4465 55.8648L63.666 145.66H90.144L99.3538 112.275H144.251L150.007 93.855H105.11L110.866 76.5868H173.032L178.788 55.8648H92.4465Z" fill="#356CFF" stroke="#356CFF" stroke-width="2.30244"/>
+</svg>

fastvideo-logos/main/svg/icon-simple-bolt-bg.svg

@@ -0,0 +1,8 @@
+<svg width="200" height="201" viewBox="0 0 200 201" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect width="200" height="200" transform="translate(0 0.308105)" fill="black"/>
+<path d="M49.8511 145.66L78.6319 55.8637H85.5394L56.7585 145.66H49.8511Z" fill="#356CFF" stroke="#356CFF" stroke-width="2.30244"/>
+<path d="M36.0376 145.66L64.8185 55.8637H67.1209L38.3401 145.66H36.0376Z" fill="#356CFF" stroke="#356CFF" stroke-width="2.30244"/>
+<path d="M22.2222 145.66L51.003 55.8637H52.1543L23.3734 145.66H22.2222Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.15122"/>
+<path d="M92.4465 55.8648L63.666 145.66H90.144L99.3538 112.275H144.251L150.007 93.855H105.11L110.866 76.5868H173.032L178.788 55.8648H92.4465Z" fill="#356CFF" stroke="#356CFF" stroke-width="2.30244"/>
+<path d="M131.799 93.8999H104.705L134.343 30.1061L69.483 112.866H91.1583L67.6768 161.635L131.799 93.8999Z" fill="#FDC717" stroke="#FDC717" stroke-width="1.80627" stroke-miterlimit="16"/>
+</svg>

fastvideo-logos/main/svg/icon-simple-bolt.svg

@@ -0,0 +1,7 @@
+<svg width="160" height="144" viewBox="0 0 160 144" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M28.8511 122.66L57.6319 32.8637H64.5394L35.7585 122.66H28.8511Z" fill="#356CFF" stroke="#356CFF" stroke-width="2.30244"/>
+<path d="M15.0376 122.66L43.8185 32.8637H46.1209L17.3401 122.66H15.0376Z" fill="#356CFF" stroke="#356CFF" stroke-width="2.30244"/>
+<path d="M1.22217 122.66L30.003 32.8637H31.1543L2.3734 122.66H1.22217Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.15122"/>
+<path d="M71.4465 32.8648L42.666 122.66H69.144L78.3538 89.2745H123.251L129.007 70.855H84.1099L89.866 53.5868H152.032L157.788 32.8648H71.4465Z" fill="#356CFF" stroke="#356CFF" stroke-width="2.30244"/>
+<path d="M110.799 70.8999H83.7053L113.343 7.10608L48.483 89.8657H70.1583L46.6768 138.635L110.799 70.8999Z" fill="#FDC717" stroke="#FDC717" stroke-width="1.80627" stroke-miterlimit="16"/>
+</svg>

fastvideo-logos/main/svg/icon-simple.svg

@@ -0,0 +1,6 @@
+<svg width="160" height="93" viewBox="0 0 160 93" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M28.8511 91.66L57.6319 1.86368H64.5394L35.7585 91.66H28.8511Z" fill="#356CFF" stroke="#356CFF" stroke-width="2.30244"/>
+<path d="M15.0376 91.66L43.8185 1.86368H46.1209L17.3401 91.66H15.0376Z" fill="#356CFF" stroke="#356CFF" stroke-width="2.30244"/>
+<path d="M1.22217 91.66L30.003 1.86366H31.1543L2.3734 91.66H1.22217Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.15122"/>
+<path d="M71.4465 1.86483L42.666 91.6599H69.144L78.3538 58.2746H123.251L129.007 39.855H84.1099L89.866 22.5868H152.032L157.788 1.86483H71.4465Z" fill="#356CFF" stroke="#356CFF" stroke-width="2.30244"/>
+</svg>

fastvideo-logos/main/svg/icon-sm-bg.svg

@@ -0,0 +1,14 @@
+<svg width="200" height="201" viewBox="0 0 200 201" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect width="200" height="200" transform="translate(0 0.691925)" fill="black"/>
+<path d="M89.2368 40.0858L70.8901 97.3273H87.769L93.64 76.0452L120.684 76.0453L129.334 64.3034L97.3093 64.3034L100.979 53.2954H139.22L149.511 40.0858H89.2368Z" fill="#356CFF"/>
+<path d="M148.804 106.199H126.788L93.5676 149.498L87.6967 106.199H71.0138L78.3525 161.239H96.6991L148.804 106.199Z" fill="#356CFF"/>
+<path d="M89.2368 40.0858L70.8901 97.3273H87.769L93.64 76.0452L120.684 76.0453L129.334 64.3034L97.3093 64.3034L100.979 53.2954H139.22L149.511 40.0858H89.2368Z" stroke="#356CFF" stroke-width="1.46773"/>
+<path d="M148.804 106.199H126.788L93.5676 149.498L87.6967 106.199H71.0138L78.3525 161.239H96.6991L148.804 106.199Z" stroke="#356CFF" stroke-width="1.46773"/>
+<path d="M148.699 106.258H126.683L155.664 45.6468L98.062 121.669H115.675L96.5942 161.298L148.699 106.258Z" fill="#FDC717" stroke="#FDC717" stroke-width="1.46773" stroke-miterlimit="16"/>
+<path d="M62.0576 106.134L69.3963 161.174L73.7995 161.174L66.4608 106.134L62.0576 106.134Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.46773"/>
+<path d="M53.2515 106.134L60.5901 161.174L62.0579 161.174L54.7192 106.134L53.2515 106.134Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.46773"/>
+<path d="M44.4443 106.134L51.783 161.174L52.5169 161.174L45.1782 106.134L44.4443 106.134Z" fill="#356CFF" stroke="#356CFF" stroke-width="0.733866"/>
+<path d="M62.0576 97.3273L80.4043 40.0858H84.8075L66.4608 97.3273H62.0576Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.46773"/>
+<path d="M53.2515 97.3273L71.5981 40.0858H73.0658L54.7192 97.3273H53.2515Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.46773"/>
+<path d="M44.4443 97.3274L62.791 40.0859H63.5248L45.1782 97.3274H44.4443Z" fill="#356CFF" stroke="#356CFF" stroke-width="0.733866"/>
+</svg>

fastvideo-logos/main/svg/icon.svg

@@ -0,0 +1,13 @@
+<svg width="141" height="153" viewBox="0 0 141 153" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M55.4467 0.964615L33.2778 70.1314H53.6732L60.7672 44.4156L93.4453 44.4156L103.898 30.2275L65.201 30.2275L69.6347 16.9262H115.844L128.278 0.964615H55.4467Z" fill="#356CFF"/>
+<path d="M127.423 80.852H100.821L60.6798 133.17L53.5858 80.852H33.4273L42.2948 147.359H64.4637L127.423 80.852Z" fill="#356CFF"/>
+<path d="M55.4467 0.964615L33.2778 70.1314H53.6732L60.7672 44.4156L93.4453 44.4156L103.898 30.2275L65.201 30.2275L69.6347 16.9262H115.844L128.278 0.964615H55.4467Z" stroke="#356CFF" stroke-width="1.77351"/>
+<path d="M127.423 80.852H100.821L60.6798 133.17L53.5858 80.852H33.4273L42.2948 147.359H64.4637L127.423 80.852Z" stroke="#356CFF" stroke-width="1.77351"/>
+<path d="M127.297 80.9227H100.694L135.713 7.68411L66.1109 99.5445H87.393L64.3374 147.429L127.297 80.9227Z" fill="#FDC717" stroke="#FDC717" stroke-width="1.77351" stroke-miterlimit="16"/>
+<path d="M22.6055 80.7725L31.473 147.279L36.7935 147.279L27.926 80.7725L22.6055 80.7725Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.77351"/>
+<path d="M11.9648 80.7725L20.8324 147.279L22.6059 147.279L13.7384 80.7725L11.9648 80.7725Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.77351"/>
+<path d="M1.32324 80.7725L10.1908 147.279L11.0775 147.279L2.21 80.7725L1.32324 80.7725Z" fill="#356CFF" stroke="#356CFF" stroke-width="0.886754"/>
+<path d="M22.6055 70.1314L44.7743 0.964615H50.0949L27.926 70.1314H22.6055Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.77351"/>
+<path d="M11.9648 70.1314L34.1337 0.964615H35.9072L13.7384 70.1314H11.9648Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.77351"/>
+<path d="M1.32324 70.1315L23.4921 0.964645H24.3789L2.21 70.1315H1.32324Z" fill="#356CFF" stroke="#356CFF" stroke-width="0.886754"/>
+</svg>

fastvideo/configs/pipelines/base.py

@@ -85,6 +85,9 @@ class PipelineConfig:
     # DMD parameters
     dmd_denoising_steps: list[int] | None = field(default=None)
 
+    # Wan2.2 TI2V parameters
+    ti2v_task: bool = False
+
     # Compilation
     # enable_torch_compile: bool = False
 

fastvideo/configs/pipelines/registry.py

@@ -8,6 +8,7 @@ from fastvideo.configs.pipelines.base import PipelineConfig
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
 from fastvideo.configs.pipelines.wan import (FastWanT2V480PConfig,
+                                             Wan2_2_TI2V_5B_Config,
                                              WanI2V480PConfig, WanI2V720PConfig,
                                              WanT2V480PConfig, WanT2V720PConfig)
 from fastvideo.logger import init_logger
@@ -26,9 +27,12 @@ PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
     "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V720PConfig,
     "Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V720PConfig,
     "FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWanT2V480PConfig,
+    "FastVideo/FastWan2.1-T2V-14B-480P-Diffusers": FastWanT2V480PConfig,
     "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VConfig,
     "FastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers": WanT2V720PConfig,
-    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": WanT2V720PConfig
+    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_Config,
+    # "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_Config,
+    # "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_Config,
     # Add other specific weight variants
 }
 

fastvideo/configs/pipelines/wan.py

@@ -111,3 +111,26 @@ class FastWanT2V480PConfig(WanT2V480PConfig):
     def __post_init__(self) -> None:
         self.vae_config.load_encoder = True
         self.vae_config.load_decoder = True
+
+
+@dataclass
+class Wan2_2_TI2V_5B_Config(WanT2V480PConfig):
+    """Base configuration for FastWan T2V 1.3B 480P pipeline architecture with DMD"""
+
+    # Denoising stage
+    flow_shift: int = 5
+    ti2v_task: bool = True
+
+    def __post_init__(self) -> None:
+        self.vae_config.load_encoder = True
+        self.vae_config.load_decoder = True
+
+
+@dataclass
+class Wan2_2_T2V_A14B_Config(WanT2V480PConfig):
+    pass
+
+
+@dataclass
+class Wan2_2_I2V_A14B_Config(WanT2V480PConfig):
+    pass

fastvideo/configs/sample/registry.py

@@ -6,7 +6,7 @@ from typing import Any
 from fastvideo.configs.sample.hunyuan import (FastHunyuanSamplingParam,
                                               HunyuanSamplingParam)
 from fastvideo.configs.sample.stepvideo import StepVideoT2VSamplingParam
-from fastvideo.configs.sample.wan import (FastWanT2V480PConfig,
+from fastvideo.configs.sample.wan import (Wan2_1_Fun_1_3B_InP_SamplingParam,
                                           Wan2_2_TI2V_5B_SamplingParam,
                                           WanI2V_14B_480P_SamplingParam,
                                           WanI2V_14B_720P_SamplingParam,
@@ -25,9 +25,18 @@ SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
     "Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V_14B_SamplingParam,
     "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V_14B_480P_SamplingParam,
     "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V_14B_720P_SamplingParam,
+    "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers":
+    Wan2_1_Fun_1_3B_InP_SamplingParam,
     "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VSamplingParam,
-    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWanT2V480PConfig,
+    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers":
+    Wan2_1_Fun_1_3B_InP_SamplingParam,
+    "FastVideo/FastWan2.1-T2V-14B-Diffusers":
+    Wan2_1_Fun_1_3B_InP_SamplingParam,
     "Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
+    # "Wan-AI/Wan2.2-T2V-A14B-Diffusers":
+    # Wan2_2_T2V_A14B_SamplingParam,
+    # "Wan-AI/Wan2.2-I2V-A14B-Diffusers":
+    # Wan2_2_I2V_A14B_SamplingParam,
     # Add other specific weight variants
 }
 

fastvideo/configs/sample/wan.py

@@ -107,6 +107,21 @@ class FastWanT2V480PConfig(WanT2V_1_3B_SamplingParam):
     fps: int = 16
 
 
+# =============================================
+# ============= Wan2.1 Fun Models =============
+# =============================================
+@dataclass
+class Wan2_1_Fun_1_3B_InP_SamplingParam(SamplingParam):
+    """Sampling parameters for Wan2.1 Fun 1.3B InP model."""
+    height: int = 480
+    width: int = 832
+    num_frames: int = 81
+    fps: int = 16
+    negative_prompt: str | None = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+    guidance_scale: float = 6.0
+    num_inference_steps: int = 50
+
+
 # =============================================
 # ============= Wan2.2 TI2V Models =============
 # =============================================
@@ -134,4 +149,4 @@ class Wan2_2_T2V_A14B_SamplingParam(Wan2_2_Base_SamplingParam):
 
 @dataclass
 class Wan2_2_I2V_A14B_SamplingParam(Wan2_2_Base_SamplingParam):
-    pass
+    pass

fastvideo/entrypoints/video_generator.py

@@ -9,6 +9,7 @@ diffusion models.
 import math
 import os
 import time
+from copy import deepcopy
 from typing import Any
 
 import imageio
@@ -202,6 +203,8 @@ class VideoGenerator:
         if sampling_param is None:
             sampling_param = SamplingParam.from_pretrained(
                 fastvideo_args.model_path)
+        else:
+            sampling_param = deepcopy(sampling_param)
 
         kwargs["prompt"] = prompt
         sampling_param.update(kwargs)
@@ -275,6 +278,7 @@ class VideoGenerator:
                        width: {target_width}
                 video_length: {sampling_param.num_frames}
                       prompt: {prompt}
+                      image_path: {sampling_param.image_path}
                   neg_prompt: {sampling_param.negative_prompt}
                         seed: {sampling_param.seed}
                  infer_steps: {sampling_param.num_inference_steps}
@@ -303,8 +307,9 @@ class VideoGenerator:
 
         # Run inference
         start_time = time.perf_counter()
-        output_batch = self.executor.execute_forward(batch, fastvideo_args)
-        samples = output_batch
+        output_batch  = self.executor.execute_forward(batch, fastvideo_args)
+        samples = output_batch.output
+        logging_info = output_batch.logging_info
 
         gen_time = time.perf_counter() - start_time
         logger.info("Generated successfully in %.2f seconds", gen_time)
@@ -334,9 +339,11 @@ class VideoGenerator:
         else:
             return {
                 "samples": samples,
+                "frames": frames,
                 "prompts": prompt,
                 "size": (target_height, target_width, batch.num_frames),
-                "generation_time": gen_time
+                "generation_time": gen_time,
+                "logging_info": logging_info,
             }
 
     def set_lora_adapter(self,

fastvideo/models/dits/wanvideo.py

@@ -82,6 +82,7 @@ class WanTimeTextImageEmbedding(nn.Module):
         encoder_hidden_states: torch.Tensor,
         encoder_hidden_states_image: torch.Tensor | None = None,
     ):
+        logger.info(f"WTF timestep shape: {timestep.shape}")
         temb = self.time_embedder(timestep)
         timestep_proj = self.time_modulation(temb)
 

fastvideo/pipelines/basic/wan/wan_pipeline.py

@@ -62,6 +62,7 @@ class WanPipeline(LoRAPipeline, ComposedPipelineBase):
                        stage=DenoisingStage(
                            transformer=self.get_module("transformer"),
                            scheduler=self.get_module("scheduler"),
+                           vae=self.get_module("vae"),
                            pipeline=self))
 
         self.add_stage(stage_name="decoding_stage",

fastvideo/pipelines/pipeline_batch_info.py

@@ -16,6 +16,41 @@ import torch
 
 from fastvideo.attention import AttentionMetadata
 from fastvideo.configs.sample.teacache import TeaCacheParams, WanTeaCacheParams
+from collections import OrderedDict
+from typing import Any, Dict, List
+import time
+
+class PipelineLoggingInfo:
+    """Simple approach using OrderedDict to track stage metrics."""
+    
+    def __init__(self):
+        # OrderedDict preserves insertion order and allows easy access
+        self.stages: OrderedDict[str, Dict[str, Any]] = OrderedDict()
+    
+    def add_stage_execution_time(self, stage_name: str, execution_time: float):
+        """Add execution time for a stage."""
+        if stage_name not in self.stages:
+            self.stages[stage_name] = {}
+        self.stages[stage_name]['execution_time'] = execution_time
+        self.stages[stage_name]['timestamp'] = time.time()
+    
+    def add_stage_metric(self, stage_name: str, metric_name: str, value: Any):
+        """Add any metric for a stage."""
+        if stage_name not in self.stages:
+            self.stages[stage_name] = {}
+        self.stages[stage_name][metric_name] = value
+    
+    def get_stage_info(self, stage_name: str) -> Dict[str, Any]:
+        """Get all info for a specific stage."""
+        return self.stages.get(stage_name, {})
+    
+    def get_execution_order(self) -> List[str]:
+        """Get stages in execution order."""
+        return list(self.stages.keys())
+    
+    def get_total_execution_time(self) -> float:
+        """Get total pipeline execution time."""
+        return sum(stage.get('execution_time', 0) for stage in self.stages.values())
 
 
 @dataclass
@@ -128,6 +163,9 @@ class ForwardBatch:
     # VSA parameters
     VSA_sparsity: float = 0.0
 
+    # Logging info
+    logging_info: PipelineLoggingInfo = field(default_factory=PipelineLoggingInfo)
+
     def __post_init__(self):
         """Initialize dependent fields after dataclass initialization."""
 

fastvideo/pipelines/pipeline_registry.py

@@ -21,6 +21,7 @@ _PIPELINE_NAME_TO_ARCHITECTURE_NAME: dict[str, str] = {
     "WanPipeline": "wan",
     "WanDMDPipeline": "wan",
     "WanImageToVideoPipeline": "wan",
+    "WanDMDPipeline": "wan",
     "StepVideoPipeline": "stepvideo",
     "HunyuanVideoPipeline": "hunyuan",
 }

fastvideo/pipelines/stages/base.py

@@ -155,6 +155,7 @@ class PipelineStage(ABC):
                 execution_time = time.perf_counter() - start_time
                 logger.info("[%s] Execution completed in %s ms", stage_name,
                             execution_time * 1000)
+                batch.logging_info.add_stage_execution_time(stage_name, execution_time)
             except Exception as e:
                 execution_time = time.perf_counter() - start_time
                 logger.error("[%s] Error during execution after %s ms: %s",

fastvideo/pipelines/stages/denoising.py

@@ -30,7 +30,7 @@ from fastvideo.pipelines.stages.base import PipelineStage
 from fastvideo.pipelines.stages.validators import StageValidators as V
 from fastvideo.pipelines.stages.validators import VerificationResult
 from fastvideo.platforms import AttentionBackendEnum
-from fastvideo.utils import dict_to_3d_list
+from fastvideo.utils import dict_to_3d_list, masks_like
 
 try:
     from fastvideo.attention.backends.sliding_tile_attn import (
@@ -57,10 +57,11 @@ class DenoisingStage(PipelineStage):
     the initial noise into the final output.
     """
 
-    def __init__(self, transformer, scheduler, pipeline=None) -> None:
+    def __init__(self, transformer, scheduler, vae=None, pipeline=None) -> None:
         super().__init__()
         self.transformer = transformer
         self.scheduler = scheduler
+        self.vae = vae
         self.pipeline = weakref.ref(pipeline) if pipeline else None
         attn_head_size = self.transformer.hidden_size // self.transformer.num_attention_heads
         self.attn_backend = get_attn_backend(
@@ -184,8 +185,43 @@ class DenoisingStage(PipelineStage):
             assert neg_prompt_embeds is not None
             assert torch.isnan(neg_prompt_embeds[0]).sum() == 0
 
+        latent_model_input = latents.to(target_dtype)
+        assert latent_model_input.shape[0] == 1, "only support batch size 1"
+
+        if fastvideo_args.pipeline_config.ti2v_task and batch.pil_image is not None:
+            logger.info("===========Using TI2V task===========")
+            # TI2V directly replaces the first frame of the latent with
+            # the image latent instead of appending along the channel dim
+            assert batch.image_latent is None, "TI2V task should not have image latents"
+            assert self.vae is not None, "VAE is not provided for TI2V task"
+            z = self.vae.encode(batch.pil_image).mean.float()
+            logger.info(f"z shape: {z.shape}")
+            logger.info(f"latent_model_input shape: {latent_model_input.shape}")
+            latent_model_input = latent_model_input.squeeze(0)
+            mask1, mask2 = masks_like([latent_model_input], zero=True)
+            # logger.info(f"mask1 shape: {mask1.shape}")
+            # logger.info(f"mask2 shape: {mask2.shape}")
+            latent_model_input = (1. -
+                                  mask2[0]) * z + mask2[0] * latent_model_input
+            # latent_model_input = latent_model_input.unsqueeze(0)
+            latent_model_input = latent_model_input.to(get_local_torch_device())
+            latents = latent_model_input
+            F = batch.num_frames
+            temporal_scale = fastvideo_args.pipeline_config.vae_config.arch_config.scale_factor_temporal
+            spatial_scale = fastvideo_args.pipeline_config.vae_config.arch_config.scale_factor_spatial
+            patch_size = fastvideo_args.pipeline_config.dit_config.arch_config.patch_size
+            seq_len = ((F - 1) // temporal_scale +
+                       1) * (batch.height // spatial_scale) * (
+                           batch.width // spatial_scale) // (patch_size[1] *
+                                                             patch_size[2])
+            import math
+            seq_len = int(math.ceil(seq_len / sp_world_size)) * sp_world_size
+        logger.info("latents shape: %s", latents.shape)
+
         # Run denoising loop
         with self.progress_bar(total=num_inference_steps) as progress_bar:
+            # logger.info(f"seq_len: {seq_len}")
+            logger.info(f"init timesteps: {timesteps}")
             for i, t in enumerate(timesteps):
                 # Skip if interrupted
                 if hasattr(self, 'interrupt') and self.interrupt:
@@ -194,15 +230,42 @@ class DenoisingStage(PipelineStage):
                 # Expand latents for I2V
                 latent_model_input = latents.to(target_dtype)
                 if batch.image_latent is not None:
+                    assert not fastvideo_args.pipeline_config.ti2v_task, "image latents should not be provided for TI2V task"
                     latent_model_input = torch.cat(
                         [latent_model_input, batch.image_latent],
                         dim=1).to(target_dtype)
+                if fastvideo_args.pipeline_config.ti2v_task and batch.pil_image is not None:
+                    logger.info(f"before ti2v timestep: {t}")
+                    timestep = [t]
+                    timestep = torch.stack(timestep).to(
+                        get_local_torch_device())
+
+                    logger.info(f"mask2 shape: {mask2[0].shape}")
+                    logger.info(f"mask[0][0] shape: {mask2[0][0].shape}")
+                    logger.info(
+                        f"mask[0][0][:, ::2, ::2] shape: {mask2[0][0][:, ::2, ::2].shape}"
+                    )
+                    temp_ts = (mask2[0][0][:, ::2, ::2] * timestep).flatten()
+                    logger.info(f"temp_ts: {temp_ts}")
+                    logger.info(f"temp_ts shape: {temp_ts.shape}")
+                    temp_ts = torch.cat([
+                        temp_ts,
+                        temp_ts.new_ones(seq_len - temp_ts.size(0)) * timestep
+                    ])
+                    # timestep = temp_ts.unsqueeze(0)
+                    timestep = temp_ts
+                    logger.info(f"after ti2v timestep: {timestep}")
+                    t = timestep
+                # else:
+                t_expand = t.repeat(latent_model_input.shape[0])
+                # logger.info(f"t_expand shape: {t_expand.shape}")
+                # logger.info(f"t_expand: {t_expand}")
+
                 assert torch.isnan(latent_model_input).sum() == 0
                 latent_model_input = self.scheduler.scale_model_input(
                     latent_model_input, t)
 
                 # Prepare inputs for transformer
-                t_expand = t.repeat(latent_model_input.shape[0])
                 guidance_expand = (
                     torch.tensor(
                         [fastvideo_args.pipeline_config.embedded_cfg_scale] *
@@ -303,6 +366,10 @@ class DenoisingStage(PipelineStage):
                                                   latents,
                                                   **extra_step_kwargs,
                                                   return_dict=False)[0]
+                    if fastvideo_args.pipeline_config.ti2v_task and batch.pil_image is not None:
+                        latents = latents.squeeze(0)
+                        latents = (1. - mask2[0]) * z + mask2[0] * latents
+                        # latents = latents.unsqueeze(0)
 
                 # Update progress bar
                 if i == len(timesteps) - 1 or (

fastvideo/pipelines/stages/input_validation.py

@@ -12,6 +12,9 @@ from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
 from fastvideo.pipelines.stages.base import PipelineStage
 from fastvideo.pipelines.stages.validators import (StageValidators,
                                                    VerificationResult)
+from fastvideo.utils import best_output_size
+from PIL import Image
+import torchvision.transforms.functional as TF
 
 logger = init_logger(__name__)
 
@@ -100,6 +103,35 @@ class InputValidationStage(PipelineStage):
                 image = load_image(batch.image_path)
             batch.pil_image = image
 
+            img = batch.pil_image
+            ih, iw = img.height, img.width
+            logger.info(f"img height: {ih}, img width: {iw}")
+            patch_size = fastvideo_args.pipeline_config.dit_config.arch_config.patch_size
+            vae_stride = fastvideo_args.pipeline_config.vae_config.arch_config.scale_factor_spatial
+            logger.info(f"patch_size: {patch_size}, vae_stride: {vae_stride}")
+            dh, dw = patch_size[1] * vae_stride, patch_size[2] * vae_stride
+            max_area = 704 * 1280
+            ow, oh = best_output_size(iw, ih, dw, dh, max_area)
+
+            scale = max(ow / iw, oh / ih)
+            img = img.resize((round(iw * scale), round(ih * scale)), Image.LANCZOS)
+            logger.info(f"resized img height: {img.height}, img width: {img.width}")
+
+            # center-crop
+            x1 = (img.width - ow) // 2
+            y1 = (img.height - oh) // 2
+            img = img.crop((x1, y1, x1 + ow, y1 + oh))
+            assert img.width == ow and img.height == oh
+            # logger.info(f"img shape: {img.shape}")
+
+            # to tensor
+            img = TF.to_tensor(img).sub_(0.5).div_(0.5).to(self.device).unsqueeze(1)
+            logger.info(f"img shape: {img.shape}")
+            img = img.unsqueeze(0)
+            batch.height = oh
+            batch.width = ow
+            batch.pil_image = img
+
         return batch
 
     def verify_input(self, batch: ForwardBatch,

fastvideo/utils.py

@@ -812,3 +812,66 @@ def set_random_seed(seed: int) -> None:
 @lru_cache(maxsize=1)
 def is_vsa_available() -> bool:
     return importlib.util.find_spec("vsa") is not None
+
+
+# adapted from: https://github.com/Wan-Video/Wan2.2/blob/main/wan/utils/utils.py
+def masks_like(tensor,
+               zero=False,
+               generator=None,
+               p=0.2) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
+    assert isinstance(tensor, list)
+    out1 = [torch.ones(u.shape, dtype=u.dtype, device=u.device) for u in tensor]
+
+    out2 = [torch.ones(u.shape, dtype=u.dtype, device=u.device) for u in tensor]
+
+    if zero:
+        if generator is not None:
+            for u, v in zip(out1, out2, strict=False):
+                random_num = torch.rand(1,
+                                        generator=generator,
+                                        device=generator.device).item()
+                if random_num < p:
+                    u[:, 0] = torch.normal(mean=-3.5,
+                                           std=0.5,
+                                           size=(1, ),
+                                           device=u.device,
+                                           generator=generator).expand_as(
+                                               u[:, 0]).exp()
+                    v[:, 0] = torch.zeros_like(v[:, 0])
+                else:
+                    u[:, 0] = u[:, 0]
+                    v[:, 0] = v[:, 0]
+
+        else:
+            for u, v in zip(out1, out2, strict=False):
+                u[:, 0] = torch.zeros_like(u[:, 0])
+                v[:, 0] = torch.zeros_like(v[:, 0])
+
+    return out1, out2
+
+
+# adapted from: https://github.com/Wan-Video/Wan2.2/blob/main/wan/utils/utils.py
+def best_output_size(w, h, dw, dh, expected_area):
+    # float output size
+    ratio = w / h
+    ow = (expected_area * ratio)**0.5
+    oh = expected_area / ow
+
+    # process width first
+    ow1 = int(ow // dw * dw)
+    oh1 = int(expected_area / ow1 // dh * dh)
+    assert ow1 % dw == 0 and oh1 % dh == 0 and ow1 * oh1 <= expected_area
+    ratio1 = ow1 / oh1
+
+    # process height first
+    oh2 = int(oh // dh * dh)
+    ow2 = int(expected_area / oh2 // dw * dw)
+    assert oh2 % dh == 0 and ow2 % dw == 0 and ow2 * oh2 <= expected_area
+    ratio2 = ow2 / oh2
+
+    # compare ratios
+    if max(ratio / ratio1, ratio1 / ratio) < max(ratio / ratio2,
+                                                 ratio2 / ratio):
+        return ow1, oh1
+    else:
+        return ow2, oh2

fastvideo/worker/gpu_worker.py

@@ -21,6 +21,7 @@ from fastvideo.pipelines import ForwardBatch, build_pipeline
 from fastvideo.platforms import current_platform
 from fastvideo.utils import (get_exception_traceback,
                              kill_itself_when_parent_died)
+import fastvideo.envs as envs
 
 logger = init_logger(__name__)
 
@@ -140,7 +141,10 @@ class Worker:
                     fastvideo_args = recv_rpc['kwargs']['fastvideo_args']
                     output_batch = self.execute_forward(forward_batch,
                                                         fastvideo_args)
-                    self.pipe.send({"output_batch": output_batch.output.cpu()})
+                    logging_info = None
+                    if envs.FASTVIDEO_STAGE_LOGGING:
+                        logging_info = output_batch.logging_info
+                    self.pipe.send({"output_batch": output_batch.output.cpu(), "logging_info": logging_info})
                 elif method_name == 'set_lora_adapter':
                     lora_nickname = recv_rpc['kwargs']['lora_nickname']
                     lora_path = recv_rpc['kwargs']['lora_path']

fastvideo/worker/multiproc_executor.py

@@ -15,6 +15,7 @@ from fastvideo.logger import init_logger
 from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
 from fastvideo.worker.executor import Executor
 from fastvideo.worker.gpu_worker import run_worker_process
+import fastvideo.envs as envs
 
 logger = init_logger(__name__)
 
@@ -40,7 +41,8 @@ class MultiprocExecutor(Executor):
             logger.info("Using provided master port: %s", self.master_port)
         else:
             # Auto-find available port
-            for port in range(29503, 65535):
+            import random
+            for port in range(29503 + random.randint(0, 10000), 65535):
                 with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
                     if s.connect_ex(('localhost', port)) != 0:
                         self.master_port = port
@@ -80,7 +82,21 @@ class MultiprocExecutor(Executor):
                                             "forward_batch": forward_batch,
                                             "fastvideo_args": fastvideo_args
                                         })
-        return cast(ForwardBatch, responses[0]["output_batch"])
+        output = responses[0]["output_batch"]
+
+        logging_info = None
+        if envs.FASTVIDEO_STAGE_LOGGING:
+            logging_info = responses[0]["logging_info"]
+        else:
+            logging_info = None
+
+        result_batch = ForwardBatch(
+            data_type=forward_batch.data_type,
+            output=output,
+            logging_info=logging_info
+        )
+
+        return result_batch
 
     def set_lora_adapter(self,
                          lora_nickname: str,