1,5121 USD (151,21 inneign) eru fráteknar við upphaf; raunverulegur GPU-tími er innheimtaður.
Fyrir reikninga án kaupa: keyrslur yfir 2 inneign þurfa áfyllingu.
Nýr hér?
10 ókeypis inneign (0,10 USD) þegar þú skráir þig með Google
Hægt að nota 24 klst. eftir skráningu, allt að 5 keyrslur á dag og að hámarki 2 inneign á keyrslu. Aðrar innskráningaraðferðir byrja án inneignar.
02
Examples
Real outputs from the public examples of this model on Replicate, with the prompt and settings that produced them. They were not generated live on this page.
Prompt
Close-up of a chameleon's eye, with its scaly skin changing color. Ultra high resolution 4k.
Lengd: 0:05
Settings
num_frames
121
fps
24
Prompt
A pristine snowglobe featuring a winter scene sits peacefully. The globe violently explodes, sending glass, water, and glittering fake snow in all directions. The scene is captured with high-speed photography.
Lengd: 0:05
Settings
num_frames
121
fps
24
Prompt
The video opens with a close-up of a woman in a white and purple outfit, holding a glowing purple butterfly. She has dark hair and walks gracefully through a traditional Japanese-style village at night
Lengd: 0:05
Settings
num_frames
121
fps
24
03
Um Mochi 1
StuttlegaFrá og með 23. september 2026
Mochi 1 er módel frá Community í flokknum Myndbandasmíð. Á Railwail kostar Mochi 1 ≈ 0,5041 USD fyrir hverja keyrslu. Studd stærðarhlutföll: 16:9, 9:16 og 1:1.
Bakgrunnur
Um Genmo
Stofnað 2023 · San Francisco, USA
Genmo was founded in 2023 by Paras Jain (CEO) and Ajay Jain (CTO), both PhDs from UC Berkeley's BAIR lab, with a focus on open-source generative video. The company released the early Replay product and the smaller GEN-1 video model before launching Mochi 1 in October 2024 as a 10B-parameter open-weight text-to-video model under the Apache 2.0 licence -- at the time the largest open-source video model with a fully permissive licence. Genmo positions Mochi 1 as a research foundation for the community to fine-tune, extend and study, in deliberate contrast to closed competitors. The company has raised over $30M from investors including NEA and the Y Combinator network.
Asymmetric Diffusion Transformer (AsymmDiT) at 10B parameters with custom 3D VAE
Mochi 1 is a 10B-parameter Asymmetric Diffusion Transformer (AsymmDiT) operating on a high-compression 3D causal Variational Autoencoder. The 'asymmetric' design uses dramatically more parameters for the video stream than for the text stream while sharing self-attention, on the hypothesis that visual modeling is the bottleneck for video generation. Position information uses 3D RoPE; the model is trained with Rectified Flow Matching at full resolution and high motion intensity. Text conditioning uses a T5-XXL encoder. Mochi 1 generates 5-second clips at 480p / 30 fps natively (with a 720p HD variant in preview at launch). Training is done on a curated, filtered video corpus with dense captions produced by an in-house captioner. The team explicitly report ablations on resolution scheduling, motion intensity filtering and caption quality.
Breytur
10 billion
Geta
10B open-weight text-to-video model under Apache 2.0 (most permissive in class)
Asymmetric DiT design biased toward visual capacity
curl https://railwail.com/api/v1/videos/generations \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "mochi-1-genmo",
"prompt": "A slow drone shot over a misty pine forest at sunrise"
}'
# Response: {"job_id": "...", "status": "queued", ...}
# Poll until status is completed, failed or cancelled:
curl https://railwail.com/api/v1/jobs/JOB_ID \
-H "Authorization: Bearer $RAILWAIL_API_KEY"
import os
import time
import requests
API = "https://railwail.com/api/v1"
headers = {"Authorization": f"Bearer {os.environ['RAILWAIL_API_KEY']}"}
job = requests.post(
f"{API}/videos/generations",
headers=headers,
json={
"model": "mochi-1-genmo",
"prompt": "A slow drone shot over a misty pine forest at sunrise",
},
).json()
while True:
status = requests.get(f"{API}/jobs/{job['job_id']}", headers=headers).json()
if status["status"] in ("completed", "failed", "cancelled"):
break
time.sleep(5)
print(status["status"], status.get("output_url"))
const API = "https://railwail.com/api/v1";
const headers = {
Authorization: `Bearer ${process.env.RAILWAIL_API_KEY}`,
"Content-Type": "application/json",
};
const job = await fetch(`${API}/videos/generations`, {
method: "POST",
headers,
body: JSON.stringify({
model: "mochi-1-genmo",
prompt: "A slow drone shot over a misty pine forest at sunrise"
}),
}).then((r) => r.json());
let status;
do {
await new Promise((r) => setTimeout(r, 5000));
status = await fetch(`${API}/jobs/${job.job_id}`, { headers }).then((r) => r.json());
} while (!["completed", "failed", "cancelled"].includes(status.status));
console.log(status.status, status.output_url);
Mochi 1 er módel frá Community í flokknum Myndbandasmíð. Á Railwail geturðu kallað það með API-lykli í gegnum Railwail API.
Hvað kostar Mochi 1 á Railwail?
Á Railwail kostar Mochi 1 ≈ 0,5041 USD fyrir hverja keyrslu. Þér er rukkað fyrir það sem hver beiðni notar í raun. Notkun er greidd úr fyrirframgreiddum inneign; 1 inneign jafngildir 0,01 USD.
Hvaða stillingar styður Mochi 1?
Samkvæmt inntaksskema sínu þekkir Mochi 1 þessa færibreytu: prompt (allt að 2.000 stafir), fps (8 til 30), seed, aspect_ratio (16:9, 9:16 eða 1:1), duration_sec (1 til 6) og motion_strength (0 til 1).
Hversu hratt er Mochi 1?
Það eru ekki nóg mældar keyrslur af Mochi 1 á Railwail ennþá til að tilgreina keyrslutíma. Það fer eftir inntakinu, stillingum og álagi hjá veitandanum.
Er Mochi 1 betra en Google Veo 3.1?
Það fer eftir verkefninu. Mochi 1 (Community) og Google Veo 3.1 (Google DeepMind) eru bæði módel í flokknum Myndbandasmíð. Samanburðarsíðan sýnir verð og forskriftir þeirra hlið við hlið.
Búðu til Railwail API-lykil og sendu beiðni þína með módel-auðkenninu mochi-1-genmo. Kóðadæmi fyrir curl, Python og JavaScript eru í API-hlutanum á þessari síðu.
Tencent's HunyuanVideo, a 13B open-weights text-to-video diffusion transformer. Produces high-motion, photorealistic clips with smooth temporal consistency and was one of the first open models to rival closed systems on motion quality.
