“Hyper-realistic cinematic close-up of a professional speedcuber solving a 3x3 Rubik's Cube at world-record pace. His hands move with insane precision and blistering speed — fingers flying across the glossy colored faces in a complex sequence of advanced algorithms, rapid twists, and smooth layer turns. The cube rotates with perfect realistic physics, slight motion blur on fast turns, and flawless color consistency as it progresses toward a solved state. Subtle sweat glistening on skin, visible veins, hyper-detailed fingerprints and nail textures. Intense focused facial expression with micro-expressions of concentration in shallow depth of field. Dramatic cinematic side lighting with strong specular highlights and reflections dancing across the cube surfaces and skin. Smooth slow orbiting camera that circles the hands and cube, capturing every intricate finger movement from dynamic angles. Photorealistic, 8K, subtle film grain, anamorphic lens flare, moody intense atmosphere, 24fps.”
Seedance 2.0
AI Video Generation Model
ByteDance's flagship video model with text-to-video, image-to-video, and reference-to-video (multi-image/video/audio) generation, cinematic output, native synchronized audio, multi-shot editing, and director-level camera control.
Details
seedance-2.0
Starting from
Popular formats
Prices shown are in USD
Full pricing detailsProvider Performance
Fastest generation through fal at 308,906ms median latency with 100.0% success rate.
Aggregated from real API requests over the last 30 days.
Generation Time
Success Rate
Provider Rankings
| # | Provider | p50 Gen Time | p95 Gen Time | Success Rate | TTFB (p50) |
|---|---|---|---|---|---|
| 1 | fal | 308,906ms | 719,667ms | 100.0% | — |
Providers & Pricing (3)
Seedance 2.0 is available from 3 providers, with per-video pricing starting at $0.13608 through fal.ai.
fal/seedance-2.0-r2v
fal/seedance-2.0-i2v
Output
fal/seedance-2.0
Output
seedance-2-0 API Async video generation
https://api.lumenfall.ai/v1
seedance-2.0
Code Examples
Text to Video
/v1/videos/generations# Step 1: Submit video generation request
VIDEO_ID=$(curl -s -X POST \
https://api.lumenfall.ai/v1/videos \
-H "Authorization: Bearer $LUMENFALL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "seedance-2.0",
"prompt": "",
"size": "1024x1024"
}' | jq -r '.id')
echo "Video ID: $VIDEO_ID"
# Step 2: Poll for completion
while true; do
RESULT=$(curl -s \
https://api.lumenfall.ai/v1/videos/$VIDEO_ID \
-H "Authorization: Bearer $LUMENFALL_API_KEY")
STATUS=$(echo $RESULT | jq -r '.status')
echo "Status: $STATUS"
if [ "$STATUS" = "completed" ]; then
echo $RESULT | jq -r '.output.url'
break
elif [ "$STATUS" = "failed" ]; then
echo $RESULT | jq -r '.error.message'
break
fi
sleep 5
done
const BASE_URL = 'https://api.lumenfall.ai/v1';
const API_KEY = 'YOUR_API_KEY';
// Step 1: Submit video generation request
const submitRes = await fetch(`${BASE_URL}/videos`, {
method: 'POST',
headers: {
'Authorization': `Bearer ${API_KEY}`,
'Content-Type': 'application/json'
},
body: JSON.stringify({
model: 'seedance-2.0',
prompt: '',
size: '1024x1024'
})
});
const { id: videoId } = await submitRes.json();
console.log('Video ID:', videoId);
// Step 2: Poll for completion
while (true) {
const pollRes = await fetch(`${BASE_URL}/videos/${videoId}`, {
headers: { 'Authorization': `Bearer ${API_KEY}` }
});
const result = await pollRes.json();
if (result.status === 'completed') {
console.log('Video URL:', result.output.url);
break;
} else if (result.status === 'failed') {
console.error('Error:', result.error.message);
break;
}
await new Promise(r => setTimeout(r, 5000));
}
import requests
import time
BASE_URL = "https://api.lumenfall.ai/v1"
API_KEY = "YOUR_API_KEY"
HEADERS = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
# Step 1: Submit video generation request
response = requests.post(
f"{BASE_URL}/videos",
headers=HEADERS,
json={
"model": "seedance-2.0",
"prompt": "",
"size": "1024x1024"
}
)
video_id = response.json()["id"]
print(f"Video ID: {video_id}")
# Step 2: Poll for completion
while True:
result = requests.get(
f"{BASE_URL}/videos/{video_id}",
headers=HEADERS
).json()
if result["status"] == "completed":
print(f"Video URL: {result['output']['url']}")
break
elif result["status"] == "failed":
print(f"Error: {result['error']['message']}")
break
time.sleep(5)
Image to Video
/v1/videos/generations# Step 1: Submit image-to-video request
VIDEO_ID=$(curl -s -X POST \
https://api.lumenfall.ai/v1/videos \
-H "Authorization: Bearer $LUMENFALL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "seedance-2.0",
"prompt": "",
"image_url": "https://example.com/start-frame.jpg",
"duration": "10",
"aspect_ratio": "16:9"
}' | jq -r '.id')
echo "Video ID: $VIDEO_ID"
# Step 2: Poll for completion
while true; do
RESULT=$(curl -s \
https://api.lumenfall.ai/v1/videos/$VIDEO_ID \
-H "Authorization: Bearer $LUMENFALL_API_KEY")
STATUS=$(echo $RESULT | jq -r '.status')
echo "Status: $STATUS"
if [ "$STATUS" = "completed" ]; then
echo $RESULT | jq -r '.output.url'
break
elif [ "$STATUS" = "failed" ]; then
echo $RESULT | jq -r '.error.message'
break
fi
sleep 5
done
const BASE_URL = 'https://api.lumenfall.ai/v1';
const API_KEY = 'YOUR_API_KEY';
// Step 1: Submit image-to-video request
const submitRes = await fetch(`${BASE_URL}/videos`, {
method: 'POST',
headers: {
'Authorization': `Bearer ${API_KEY}`,
'Content-Type': 'application/json'
},
body: JSON.stringify({
model: 'seedance-2.0',
prompt: '',
image_url: 'https://example.com/start-frame.jpg',
duration: '10',
aspect_ratio: '16:9'
})
});
const { id: videoId } = await submitRes.json();
console.log('Video ID:', videoId);
// Step 2: Poll for completion
while (true) {
const pollRes = await fetch(`${BASE_URL}/videos/${videoId}`, {
headers: { 'Authorization': `Bearer ${API_KEY}` }
});
const result = await pollRes.json();
if (result.status === 'completed') {
console.log('Video URL:', result.output.url);
break;
} else if (result.status === 'failed') {
console.error('Error:', result.error.message);
break;
}
await new Promise(r => setTimeout(r, 5000));
}
import requests
import time
BASE_URL = "https://api.lumenfall.ai/v1"
API_KEY = "YOUR_API_KEY"
HEADERS = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
# Step 1: Submit image-to-video request
response = requests.post(
f"{BASE_URL}/videos",
headers=HEADERS,
json={
"model": "seedance-2.0",
"prompt": "",
"image_url": "https://example.com/start-frame.jpg",
"duration": "10",
"aspect_ratio": "16:9"
}
)
video_id = response.json()["id"]
print(f"Video ID: {video_id}")
# Step 2: Poll for completion
while True:
result = requests.get(
f"{BASE_URL}/videos/{video_id}",
headers=HEADERS
).json()
if result["status"] == "completed":
print(f"Video URL: {result['output']['url']}")
break
elif result["status"] == "failed":
print(f"Error: {result['error']['message']}")
break
time.sleep(5)
Video to Video
/v1/videos/generations# Step 1: Submit video-to-video request
VIDEO_ID=$(curl -s -X POST \
https://api.lumenfall.ai/v1/videos \
-H "Authorization: Bearer $LUMENFALL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "seedance-2.0",
"prompt": "Apply cinematic color grading to @Video1",
"video_url": "https://example.com/source.mp4",
"keep_audio": true,
"aspect_ratio": "16:9"
}' | jq -r '.id')
echo "Video ID: $VIDEO_ID"
# Step 2: Poll for completion
while true; do
RESULT=$(curl -s \
https://api.lumenfall.ai/v1/videos/$VIDEO_ID \
-H "Authorization: Bearer $LUMENFALL_API_KEY")
STATUS=$(echo $RESULT | jq -r '.status')
echo "Status: $STATUS"
if [ "$STATUS" = "completed" ]; then
echo $RESULT | jq -r '.output.url'
break
elif [ "$STATUS" = "failed" ]; then
echo $RESULT | jq -r '.error.message'
break
fi
sleep 5
done
const BASE_URL = 'https://api.lumenfall.ai/v1';
const API_KEY = 'YOUR_API_KEY';
// Step 1: Submit video-to-video request
const submitRes = await fetch(`${BASE_URL}/videos`, {
method: 'POST',
headers: {
'Authorization': `Bearer ${API_KEY}`,
'Content-Type': 'application/json'
},
body: JSON.stringify({
model: 'seedance-2.0',
prompt: 'Apply cinematic color grading to @Video1',
video_url: 'https://example.com/source.mp4',
keep_audio: true,
aspect_ratio: '16:9'
})
});
const { id: videoId } = await submitRes.json();
console.log('Video ID:', videoId);
// Step 2: Poll for completion
while (true) {
const pollRes = await fetch(`${BASE_URL}/videos/${videoId}`, {
headers: { 'Authorization': `Bearer ${API_KEY}` }
});
const result = await pollRes.json();
if (result.status === 'completed') {
console.log('Video URL:', result.output.url);
break;
} else if (result.status === 'failed') {
console.error('Error:', result.error.message);
break;
}
await new Promise(r => setTimeout(r, 5000));
}
import requests
import time
BASE_URL = "https://api.lumenfall.ai/v1"
API_KEY = "YOUR_API_KEY"
HEADERS = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
# Step 1: Submit video-to-video request
response = requests.post(
f"{BASE_URL}/videos",
headers=HEADERS,
json={
"model": "seedance-2.0",
"prompt": "Apply cinematic color grading to @Video1",
"video_url": "https://example.com/source.mp4",
"keep_audio": True,
"aspect_ratio": "16:9"
}
)
video_id = response.json()["id"]
print(f"Video ID: {video_id}")
# Step 2: Poll for completion
while True:
result = requests.get(
f"{BASE_URL}/videos/{video_id}",
headers=HEADERS
).json()
if result["status"] == "completed":
print(f"Video URL: {result['output']['url']}")
break
elif result["status"] == "failed":
print(f"Error: {result['error']['message']}")
break
time.sleep(5)
Parameter Reference
Core Parameters
| Parameter | Type | Description | Modes |
|---|---|---|---|
prompt
|
string | Required. Text prompt for video generation |
T2V
I2V
V2V
|
seed
|
integer | Random seed for reproducibility |
T2V
I2V
V2V
|
duration
|
number | Video duration in seconds |
T2V
I2V
V2V
|
Size & Layout
| Parameter | Type | Description | Modes |
|---|---|---|---|
size
|
string |
Video dimensions as WxH pixels (e.g. "1920x1080") or aspect ratio (e.g. "16:9")
auto
1563x670
1365x768
768x1365
887x1182
1024x1024
1183x887
WxH determines both shape and scale (aspect_ratio and resolution are ignored when size is provided). W:H format is equivalent to aspect_ratio.
|
T2V
I2V
V2V
|
aspect_ratio
|
string |
Aspect ratio of the output video (e.g. "16:9", "1:1")
auto
9:16
3:4
1:1
4:3
16:9
21:9
Controls shape independently of scale. Use with resolution to control both. If size is also provided, size takes precedence. Any ratio is accepted and mapped to the nearest supported value.
|
T2V
I2V
V2V
|
resolution
|
string |
Output resolution tier (e.g. "1K", "4K")
auto
1K
Controls scale independently of shape. Higher tiers produce larger videos and cost more. If size is also provided, size takes precedence for scale. Any tier is accepted and mapped to the nearest supported value.
|
T2V
I2V
V2V
|
| Output |
size
|
aspect_ratio
+
resolution
|
|
|---|---|---|---|
| Flexible | |||
| Auto | "auto" |
— | Model chooses optimal dimensions |
1K 6 sizes
| Output |
size
|
aspect_ratio
+
resolution
|
|
|---|---|---|---|
| 1183 × 887 | "1183x887" |
or |
"4:3"
+
"1K"
|
| 1024 × 1024 | "1024x1024" |
or |
"1:1"
+
"1K"
|
| 887 × 1182 | "887x1182" |
or |
"3:4"
+
"1K"
|
| 768 × 1365 | "768x1365" |
or |
"9:16"
+
"1K"
|
| 1365 × 768 | "1365x768" |
or |
"16:9"
+
"1K"
|
| 1563 × 670 | "1563x670" |
or |
"21:9"
+
"1K"
|
How these parameters work
size
Exact pixel dimensions
"1920x1080"
aspect_ratio
Shape only, default scale
"16:9"
resolution
Scale tier, preserves shape
"1K"
Priority when combined
size is most specific and always wins. aspect_ratio and resolution control shape and scale independently.
How matching works
7:1 on a model with
4:1 and 8:1,
you get 8:1.
0.5K 1K 2K 4K)
or megapixel tiers (0.25 1).
If the exact tier isn't available, you get the nearest one.
Media Inputs
| Parameter | Type | Description | Modes |
|---|---|---|---|
input_reference
|
array | Required for I2V. Input image(s) to animate into video |
T2V
I2V
V2V
|
input_video
|
string | Required. Input video URL to transform |
T2V
I2V
V2V
|
Audio
| Parameter | Type | Description | Modes |
|---|---|---|---|
generate_audio
|
boolean | Whether to generate audio alongside video |
T2V
I2V
V2V
|
Output & Format
| Parameter | Type | Description | Modes |
|---|---|---|---|
n
|
integer |
Number of videos to generate
Default:
1Gateway generates multiple videos in parallel even if provider only supports 1.
|
T2V
I2V
V2V
|
Additional Parameters
Provider-specific passthrough fields, available only when the request is routed to the listed provider.
| Parameter | Type | Description | Modes |
|---|---|---|---|
|
Universal
|
|||
end_image
|
string | End frame image URL for video interpolation |
T2V
I2V
V2V
|
|
fal
|
|||
audio_urls
|
array | Reference audio clips (up to 3) for lip-sync or scoring. |
T2V
I2V
V2V
|
end_user_id
|
string | Optional end user identifier. |
T2V
I2V
V2V
|
Parameter Normalization
How we handle parameters across different providers
Not every provider speaks the same language. When you send a parameter, we handle it in one of four ways depending on what the model supports:
| Behavior | What happens | Example |
|---|---|---|
passthrough |
Sent as-is to the provider | style, quality |
renamed |
Same value, mapped to the field name the provider expects | prompt |
converted |
Transformed to the provider's native format | size |
emulated |
Works even if the provider has no concept of it | n, response_format |
Parameters we don't recognize pass straight through to the upstream API, so provider-specific options still work.
Seedance 2.0 Benchmarks
Seedance 2.0 is ranked #1 in Text-to-Video with an Elo of 1236 and #1 in Image-to-Video with an Elo of 1226 on the Lumenfall Arena, where real users pick the better image in blind comparisons. These rankings are based on 3 blind-vote competitions.
Text-to-Video Landscape
Elo vs Cost
Elo vs Speed
2 models waiting for enough speed data.
Image-to-Video Landscape
Elo vs Cost
Elo vs Speed
Competition Results
“Hyper-realistic cinematic video of an elegant young woman in a flowing white silk dress dancing gracefully in heavy pouring rain at night on a neon-lit Tokyo street. Her long wet hair whips dramatically in the wind, the dress clings and flows with realistic fabric and water physics, raindrops splash and create perfect reflections of pink and blue neon signs on the wet pavement. Subtle emotional expression of freedom mixed with melancholy on her face, water droplets on skin and eyelashes catching the light. Smooth dynamic orbiting camera with slight cinematic handheld feel, dramatic volumetric lighting with god rays piercing through the rain, photorealistic, 8K, film grain, shallow depth of field, anamorphic lens flare.”
“Hyper-realistic cinematic close-up of a professional speedcuber solving a 3x3 Rubik's Cube at world-record pace. His hands move with insane precision and blistering speed — fingers flying across the glossy colored faces in a complex sequence of advanced algorithms, rapid twists, and smooth layer turns. The cube rotates with perfect realistic physics, slight motion blur on fast turns, and flawless color consistency as it progresses toward a solved state. Subtle sweat glistening on skin, visible veins, hyper-detailed fingerprints and nail textures. Intense focused facial expression with micro-expressions of concentration in shallow depth of field. Dramatic cinematic side lighting with strong specular highlights and reflections dancing across the cube surfaces and skin. Smooth slow orbiting camera that circles the hands and cube, capturing every intricate finger movement from dynamic angles. Photorealistic, 8K, subtle film grain, anamorphic lens flare, moody intense atmosphere, 24fps.”
“Ultra-realistic celebrity arrival scene in New York City, filmed as one continuous handheld shot from inside a dense crowd behind barricades. Documentary realism, natural micro-shake, no cuts, no artificial camera moves. Use the subject from the reference image as the main character. Keep face identity highly consistent across all frames. The outfit must match the reference image exactly. The subject should appear calm and controlled, with a subtle confident smile. Nighttime outside a fancy hotel. Lighting comes from street lights, hotel entrance lights, media flashes, phone screens, and reflections on polished cars and glass. Soft realistic shadows and slight atmospheric haze. Natural environment audio only: loud crowd cheering, overlapping voices, shouting fans, camera shutter clicks, phones recording, distant city noise, footsteps, fabric movement, and security activity. Scene flow: The camera begins inside the crowd, partially blocked by heads, raised phones, waving hands, and phone screens. The crowd is chaotic and excited. The camera lifts slightly above shoulder level. Focus shifts naturally between the crowd and the hotel entrance. The subject exits the hotel in the distance as camera flashes go off. Security pushes the crowd back, causing natural camera shake. Through gaps in the crowd, the subject becomes visible, first soft and partially obscured, then clearer as he walks forward with a small escort team. The subject walks up to a fan and signs a printed photo of himself from the reference image. The camera pushes in naturally, not digitally. The subject is now clearly visible near center frame. He walks confidently, raises one hand, and gives a calm wave with a slight smile while the camera struggles to keep him framed. A convoy of three large premium SUVs appears by the curb. Security opens the back door of the middle black Suburban. The subject enters quickly, rolls the window down, and waves to the crowd as the vehicles begin moving away. The crowd jumps, cheers, and records the moment on their phones.”
“Hyper-realistic cinematic video of an elegant young woman in a flowing white silk dress dancing gracefully in heavy pouring rain at night on a neon-lit Tokyo street. Her long wet hair whips dramatically in the wind, the dress clings and flows with realistic fabric and water physics, raindrops splash and create perfect reflections of pink and blue neon signs on the wet pavement. Subtle emotional expression of freedom mixed with melancholy on her face, water droplets on skin and eyelashes catching the light. Smooth dynamic orbiting camera with slight cinematic handheld feel, dramatic volumetric lighting with god rays piercing through the rain, photorealistic, 8K, film grain, shallow depth of field, anamorphic lens flare.”
“Hyper-realistic cinematic video of an elegant young woman in a flowing white silk dress dancing gracefully in heavy pouring rain at night on a neon-lit Tokyo street. Her long wet hair whips dramatically in the wind, the dress clings and flows with realistic fabric and water physics, raindrops splash and create perfect reflections of pink and blue neon signs on the wet pavement. Subtle emotional expression of freedom mixed with melancholy on her face, water droplets on skin and eyelashes catching the light. Smooth dynamic orbiting camera with slight cinematic handheld feel, dramatic volumetric lighting with god rays piercing through the rain, photorealistic, 8K, film grain, shallow depth of field, anamorphic lens flare.”
“Hyper-realistic cinematic close-up of a professional speedcuber solving a 3x3 Rubik's Cube at world-record pace. His hands move with insane precision and blistering speed — fingers flying across the glossy colored faces in a complex sequence of advanced algorithms, rapid twists, and smooth layer turns. The cube rotates with perfect realistic physics, slight motion blur on fast turns, and flawless color consistency as it progresses toward a solved state. Subtle sweat glistening on skin, visible veins, hyper-detailed fingerprints and nail textures. Intense focused facial expression with micro-expressions of concentration in shallow depth of field. Dramatic cinematic side lighting with strong specular highlights and reflections dancing across the cube surfaces and skin. Smooth slow orbiting camera that circles the hands and cube, capturing every intricate finger movement from dynamic angles. Photorealistic, 8K, subtle film grain, anamorphic lens flare, moody intense atmosphere, 24fps.”
“Ultra-realistic celebrity arrival scene in New York City, filmed as one continuous handheld shot from inside a dense crowd behind barricades. Documentary realism, natural micro-shake, no cuts, no artificial camera moves. Use the subject from the reference image as the main character. Keep face identity highly consistent across all frames. The outfit must match the reference image exactly. The subject should appear calm and controlled, with a subtle confident smile. Nighttime outside a fancy hotel. Lighting comes from street lights, hotel entrance lights, media flashes, phone screens, and reflections on polished cars and glass. Soft realistic shadows and slight atmospheric haze. Natural environment audio only: loud crowd cheering, overlapping voices, shouting fans, camera shutter clicks, phones recording, distant city noise, footsteps, fabric movement, and security activity. Scene flow: The camera begins inside the crowd, partially blocked by heads, raised phones, waving hands, and phone screens. The crowd is chaotic and excited. The camera lifts slightly above shoulder level. Focus shifts naturally between the crowd and the hotel entrance. The subject exits the hotel in the distance as camera flashes go off. Security pushes the crowd back, causing natural camera shake. Through gaps in the crowd, the subject becomes visible, first soft and partially obscured, then clearer as he walks forward with a small escort team. The subject walks up to a fan and signs a printed photo of himself from the reference image. The camera pushes in naturally, not digitally. The subject is now clearly visible near center frame. He walks confidently, raises one hand, and gives a calm wave with a slight smile while the camera struggles to keep him framed. A convoy of three large premium SUVs appears by the curb. Security opens the back door of the middle black Suburban. The subject enters quickly, rolls the window down, and waves to the crowd as the vehicles begin moving away. The crowd jumps, cheers, and records the moment on their phones.”
“Hyper-realistic cinematic video of an elegant young woman in a flowing white silk dress dancing gracefully in heavy pouring rain at night on a neon-lit Tokyo street. Her long wet hair whips dramatically in the wind, the dress clings and flows with realistic fabric and water physics, raindrops splash and create perfect reflections of pink and blue neon signs on the wet pavement. Subtle emotional expression of freedom mixed with melancholy on her face, water droplets on skin and eyelashes catching the light. Smooth dynamic orbiting camera with slight cinematic handheld feel, dramatic volumetric lighting with god rays piercing through the rain, photorealistic, 8K, film grain, shallow depth of field, anamorphic lens flare.”
“Hyper-realistic cinematic close-up of a professional speedcuber solving a 3x3 Rubik's Cube at world-record pace. His hands move with insane precision and blistering speed — fingers flying across the glossy colored faces in a complex sequence of advanced algorithms, rapid twists, and smooth layer turns. The cube rotates with perfect realistic physics, slight motion blur on fast turns, and flawless color consistency as it progresses toward a solved state. Subtle sweat glistening on skin, visible veins, hyper-detailed fingerprints and nail textures. Intense focused facial expression with micro-expressions of concentration in shallow depth of field. Dramatic cinematic side lighting with strong specular highlights and reflections dancing across the cube surfaces and skin. Smooth slow orbiting camera that circles the hands and cube, capturing every intricate finger movement from dynamic angles. Photorealistic, 8K, subtle film grain, anamorphic lens flare, moody intense atmosphere, 24fps.”
“Ultra-realistic celebrity arrival scene in New York City, filmed as one continuous handheld shot from inside a dense crowd behind barricades. Documentary realism, natural micro-shake, no cuts, no artificial camera moves. Use the subject from the reference image as the main character. Keep face identity highly consistent across all frames. The outfit must match the reference image exactly. The subject should appear calm and controlled, with a subtle confident smile. Nighttime outside a fancy hotel. Lighting comes from street lights, hotel entrance lights, media flashes, phone screens, and reflections on polished cars and glass. Soft realistic shadows and slight atmospheric haze. Natural environment audio only: loud crowd cheering, overlapping voices, shouting fans, camera shutter clicks, phones recording, distant city noise, footsteps, fabric movement, and security activity. Scene flow: The camera begins inside the crowd, partially blocked by heads, raised phones, waving hands, and phone screens. The crowd is chaotic and excited. The camera lifts slightly above shoulder level. Focus shifts naturally between the crowd and the hotel entrance. The subject exits the hotel in the distance as camera flashes go off. Security pushes the crowd back, causing natural camera shake. Through gaps in the crowd, the subject becomes visible, first soft and partially obscured, then clearer as he walks forward with a small escort team. The subject walks up to a fan and signs a printed photo of himself from the reference image. The camera pushes in naturally, not digitally. The subject is now clearly visible near center frame. He walks confidently, raises one hand, and gives a calm wave with a slight smile while the camera struggles to keep him framed. A convoy of three large premium SUVs appears by the curb. Security opens the back door of the middle black Suburban. The subject enters quickly, rolls the window down, and waves to the crowd as the vehicles begin moving away. The crowd jumps, cheers, and records the moment on their phones.”
“Hyper-realistic cinematic close-up of a professional speedcuber solving a 3x3 Rubik's Cube at world-record pace. His hands move with insane precision and blistering speed — fingers flying across the glossy colored faces in a complex sequence of advanced algorithms, rapid twists, and smooth layer turns. The cube rotates with perfect realistic physics, slight motion blur on fast turns, and flawless color consistency as it progresses toward a solved state. Subtle sweat glistening on skin, visible veins, hyper-detailed fingerprints and nail textures. Intense focused facial expression with micro-expressions of concentration in shallow depth of field. Dramatic cinematic side lighting with strong specular highlights and reflections dancing across the cube surfaces and skin. Smooth slow orbiting camera that circles the hands and cube, capturing every intricate finger movement from dynamic angles. Photorealistic, 8K, subtle film grain, anamorphic lens flare, moody intense atmosphere, 24fps.”
“Ultra-realistic celebrity arrival scene in New York City, filmed as one continuous handheld shot from inside a dense crowd behind barricades. Documentary realism, natural micro-shake, no cuts, no artificial camera moves. Use the subject from the reference image as the main character. Keep face identity highly consistent across all frames. The outfit must match the reference image exactly. The subject should appear calm and controlled, with a subtle confident smile. Nighttime outside a fancy hotel. Lighting comes from street lights, hotel entrance lights, media flashes, phone screens, and reflections on polished cars and glass. Soft realistic shadows and slight atmospheric haze. Natural environment audio only: loud crowd cheering, overlapping voices, shouting fans, camera shutter clicks, phones recording, distant city noise, footsteps, fabric movement, and security activity. Scene flow: The camera begins inside the crowd, partially blocked by heads, raised phones, waving hands, and phone screens. The crowd is chaotic and excited. The camera lifts slightly above shoulder level. Focus shifts naturally between the crowd and the hotel entrance. The subject exits the hotel in the distance as camera flashes go off. Security pushes the crowd back, causing natural camera shake. Through gaps in the crowd, the subject becomes visible, first soft and partially obscured, then clearer as he walks forward with a small escort team. The subject walks up to a fan and signs a printed photo of himself from the reference image. The camera pushes in naturally, not digitally. The subject is now clearly visible near center frame. He walks confidently, raises one hand, and gives a calm wave with a slight smile while the camera struggles to keep him framed. A convoy of three large premium SUVs appears by the curb. Security opens the back door of the middle black Suburban. The subject enters quickly, rolls the window down, and waves to the crowd as the vehicles begin moving away. The crowd jumps, cheers, and records the moment on their phones.”
“Ultra-realistic celebrity arrival scene in New York City, filmed as one continuous handheld shot from inside a dense crowd behind barricades. Documentary realism, natural micro-shake, no cuts, no artificial camera moves. Use the subject from the reference image as the main character. Keep face identity highly consistent across all frames. The outfit must match the reference image exactly. The subject should appear calm and controlled, with a subtle confident smile. Nighttime outside a fancy hotel. Lighting comes from street lights, hotel entrance lights, media flashes, phone screens, and reflections on polished cars and glass. Soft realistic shadows and slight atmospheric haze. Natural environment audio only: loud crowd cheering, overlapping voices, shouting fans, camera shutter clicks, phones recording, distant city noise, footsteps, fabric movement, and security activity. Scene flow: The camera begins inside the crowd, partially blocked by heads, raised phones, waving hands, and phone screens. The crowd is chaotic and excited. The camera lifts slightly above shoulder level. Focus shifts naturally between the crowd and the hotel entrance. The subject exits the hotel in the distance as camera flashes go off. Security pushes the crowd back, causing natural camera shake. Through gaps in the crowd, the subject becomes visible, first soft and partially obscured, then clearer as he walks forward with a small escort team. The subject walks up to a fan and signs a printed photo of himself from the reference image. The camera pushes in naturally, not digitally. The subject is now clearly visible near center frame. He walks confidently, raises one hand, and gives a calm wave with a slight smile while the camera struggles to keep him framed. A convoy of three large premium SUVs appears by the curb. Security opens the back door of the middle black Suburban. The subject enters quickly, rolls the window down, and waves to the crowd as the vehicles begin moving away. The crowd jumps, cheers, and records the moment on their phones.”
“Hyper-realistic cinematic close-up of a professional speedcuber solving a 3x3 Rubik's Cube at world-record pace. His hands move with insane precision and blistering speed — fingers flying across the glossy colored faces in a complex sequence of advanced algorithms, rapid twists, and smooth layer turns. The cube rotates with perfect realistic physics, slight motion blur on fast turns, and flawless color consistency as it progresses toward a solved state. Subtle sweat glistening on skin, visible veins, hyper-detailed fingerprints and nail textures. Intense focused facial expression with micro-expressions of concentration in shallow depth of field. Dramatic cinematic side lighting with strong specular highlights and reflections dancing across the cube surfaces and skin. Smooth slow orbiting camera that circles the hands and cube, capturing every intricate finger movement from dynamic angles. Photorealistic, 8K, subtle film grain, anamorphic lens flare, moody intense atmosphere, 24fps.”
“Ultra-realistic celebrity arrival scene in New York City, filmed as one continuous handheld shot from inside a dense crowd behind barricades. Documentary realism, natural micro-shake, no cuts, no artificial camera moves. Use the subject from the reference image as the main character. Keep face identity highly consistent across all frames. The outfit must match the reference image exactly. The subject should appear calm and controlled, with a subtle confident smile. Nighttime outside a fancy hotel. Lighting comes from street lights, hotel entrance lights, media flashes, phone screens, and reflections on polished cars and glass. Soft realistic shadows and slight atmospheric haze. Natural environment audio only: loud crowd cheering, overlapping voices, shouting fans, camera shutter clicks, phones recording, distant city noise, footsteps, fabric movement, and security activity. Scene flow: The camera begins inside the crowd, partially blocked by heads, raised phones, waving hands, and phone screens. The crowd is chaotic and excited. The camera lifts slightly above shoulder level. Focus shifts naturally between the crowd and the hotel entrance. The subject exits the hotel in the distance as camera flashes go off. Security pushes the crowd back, causing natural camera shake. Through gaps in the crowd, the subject becomes visible, first soft and partially obscured, then clearer as he walks forward with a small escort team. The subject walks up to a fan and signs a printed photo of himself from the reference image. The camera pushes in naturally, not digitally. The subject is now clearly visible near center frame. He walks confidently, raises one hand, and gives a calm wave with a slight smile while the camera struggles to keep him framed. A convoy of three large premium SUVs appears by the curb. Security opens the back door of the middle black Suburban. The subject enters quickly, rolls the window down, and waves to the crowd as the vehicles begin moving away. The crowd jumps, cheers, and records the moment on their phones.”
“Hyper-realistic cinematic video of an elegant young woman in a flowing white silk dress dancing gracefully in heavy pouring rain at night on a neon-lit Tokyo street. Her long wet hair whips dramatically in the wind, the dress clings and flows with realistic fabric and water physics, raindrops splash and create perfect reflections of pink and blue neon signs on the wet pavement. Subtle emotional expression of freedom mixed with melancholy on her face, water droplets on skin and eyelashes catching the light. Smooth dynamic orbiting camera with slight cinematic handheld feel, dramatic volumetric lighting with god rays piercing through the rain, photorealistic, 8K, film grain, shallow depth of field, anamorphic lens flare.”
“Hyper-realistic cinematic video of an elegant young woman in a flowing white silk dress dancing gracefully in heavy pouring rain at night on a neon-lit Tokyo street. Her long wet hair whips dramatically in the wind, the dress clings and flows with realistic fabric and water physics, raindrops splash and create perfect reflections of pink and blue neon signs on the wet pavement. Subtle emotional expression of freedom mixed with melancholy on her face, water droplets on skin and eyelashes catching the light. Smooth dynamic orbiting camera with slight cinematic handheld feel, dramatic volumetric lighting with god rays piercing through the rain, photorealistic, 8K, film grain, shallow depth of field, anamorphic lens flare.”
“Ultra-realistic celebrity arrival scene in New York City, filmed as one continuous handheld shot from inside a dense crowd behind barricades. Documentary realism, natural micro-shake, no cuts, no artificial camera moves. Use the subject from the reference image as the main character. Keep face identity highly consistent across all frames. The outfit must match the reference image exactly. The subject should appear calm and controlled, with a subtle confident smile. Nighttime outside a fancy hotel. Lighting comes from street lights, hotel entrance lights, media flashes, phone screens, and reflections on polished cars and glass. Soft realistic shadows and slight atmospheric haze. Natural environment audio only: loud crowd cheering, overlapping voices, shouting fans, camera shutter clicks, phones recording, distant city noise, footsteps, fabric movement, and security activity. Scene flow: The camera begins inside the crowd, partially blocked by heads, raised phones, waving hands, and phone screens. The crowd is chaotic and excited. The camera lifts slightly above shoulder level. Focus shifts naturally between the crowd and the hotel entrance. The subject exits the hotel in the distance as camera flashes go off. Security pushes the crowd back, causing natural camera shake. Through gaps in the crowd, the subject becomes visible, first soft and partially obscured, then clearer as he walks forward with a small escort team. The subject walks up to a fan and signs a printed photo of himself from the reference image. The camera pushes in naturally, not digitally. The subject is now clearly visible near center frame. He walks confidently, raises one hand, and gives a calm wave with a slight smile while the camera struggles to keep him framed. A convoy of three large premium SUVs appears by the curb. Security opens the back door of the middle black Suburban. The subject enters quickly, rolls the window down, and waves to the crowd as the vehicles begin moving away. The crowd jumps, cheers, and records the moment on their phones.”
Top Matchups
See how Seedance 2.0 performs head-to-head against other AI models, ranked by community votes in blind comparisons.
Seedance 2.0 is best for
See all Use CasesSeedance 2.0 FAQ
How much does Seedance 2.0 cost?
Seedance 2.0 starts at $0.13608 per video through Lumenfall. Pricing varies by provider. Lumenfall does not add any markup to provider pricing.
How do I use Seedance 2.0 via API?
You can use Seedance 2.0 through Lumenfall's OpenAI-compatible API. Send requests to the unified endpoint with model ID "seedance-2.0". Code examples are available in Python, JavaScript, and cURL.
Which providers offer Seedance 2.0?
Seedance 2.0 is available through fal.ai on Lumenfall. Lumenfall automatically routes requests to the best available provider.
Try Seedance 2.0 in Playground
Generate images with custom prompts — no API key needed.