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hao-ai-lab-FastVideo/examples/inference/basic/basic_cosmos3_new_api.py
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Python

import os
import time
from fastvideo import VideoGenerator
from fastvideo.api import (
EngineConfig,
GenerationRequest,
GeneratorConfig,
OffloadConfig,
OutputConfig,
PipelineSelection,
SamplingConfig,
)
# NVIDIA Cosmos3-Nano omni world model — this example exercises the
# text-to-video (T2V) path through FastVideo's native Cosmos3 pipeline.
# Point COSMOS3_MODEL_PATH at a local diffusers checkpoint (e.g.
# ``official_weights/cosmos3``) to skip the Hugging Face download.
OUTPUT_PATH = "video_samples_cosmos3"
DEFAULT_REVISION = "411f42a8fdfb8c5b2583cb8786e0938f49796eaa"
def main():
model_name = os.environ.get("COSMOS3_MODEL_PATH", "nvidia/Cosmos3-Nano")
generator_config = GeneratorConfig(
model_path=model_name,
revision=os.environ.get("COSMOS3_REVISION", DEFAULT_REVISION),
pipeline=PipelineSelection(workload_type="t2v"),
engine=EngineConfig(
num_gpus=1,
use_fsdp_inference=False,
offload=OffloadConfig(
text_encoder=True,
pin_cpu_memory=True,
dit=False,
vae=False,
),
),
)
load_start_time = time.perf_counter()
generator = VideoGenerator.from_config(generator_config)
load_time = time.perf_counter() - load_start_time
prompt = (
"A golden retriever puppy runs across a sunlit meadow toward the camera, "
"ears flopping and wildflowers swaying in the breeze. Shallow depth of "
"field, warm afternoon light, smooth cinematic tracking shot."
)
request = GenerationRequest(
prompt=prompt,
sampling=SamplingConfig(
# cosmos3_nano native defaults are 720x1280, 189 frames, 35 steps;
# overridable via env for quick smoke runs.
num_frames=int(os.environ.get("COSMOS3_NUM_FRAMES", "189")),
height=int(os.environ.get("COSMOS3_HEIGHT", "720")),
width=int(os.environ.get("COSMOS3_WIDTH", "1280")),
num_inference_steps=int(os.environ.get("COSMOS3_STEPS", "35")),
guidance_scale=6.0,
fps=24,
seed=0,
),
output=OutputConfig(
output_path=OUTPUT_PATH,
save_video=True,
return_frames=False,
),
)
start_time = time.perf_counter()
result = generator.generate(request)
gen_time = time.perf_counter() - start_time
print(f"Time taken to load model: {load_time} seconds")
print(f"Time taken to generate video: {gen_time} seconds")
print(f"Output written to: {result.video_path}")
if __name__ == "__main__":
main()