Prediction

7vqctwv4hnrge0cerpktxeya0w

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

15m 14s

Created

about 1 year ago

Input

seed: 33
chunk: 24
prompt: Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.
enhance
overlap: 8
num_steps: 50
num_frames: 56
image_guidance: 9
negative_prompt

{
  "seed": 33,
  "chunk": 24,
  "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
  "enhance": true,
  "overlap": 8,
  "num_steps": 50,
  "num_frames": 56,
  "image_guidance": 9,
  "negative_prompt": ""
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
  {
    input: {
      seed: 33,
      chunk: 24,
      prompt: "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
      enhance: true,
      overlap: 8,
      num_steps: 50,
      num_frames: 56,
      image_guidance: 9,
      negative_prompt: ""
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
    input={
        "seed": 33,
        "chunk": 24,
        "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
        "enhance": True,
        "overlap": 8,
        "num_steps": 50,
        "num_frames": 56,
        "image_guidance": 9,
        "negative_prompt": ""
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
    "input": {
      "seed": 33,
      "chunk": 24,
      "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
      "enhance": true,
      "overlap": 8,
      "num_steps": 50,
      "num_frames": 56,
      "image_guidance": 9,
      "negative_prompt": ""
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-04-10T01:52:59.159613Z",
  "created_at": "2024-04-10T01:37:45.613000Z",
  "data_removed": false,
  "error": null,
  "id": "7vqctwv4hnrge0cerpktxeya0w",
  "input": {
    "seed": 33,
    "chunk": 24,
    "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
    "enhance": true,
    "overlap": 8,
    "num_steps": 50,
    "num_frames": 56,
    "image_guidance": 9,
    "negative_prompt": ""
  },
  "logs": "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\nLOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\nrank_zero_warn(\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py:234: UserWarning: predict returned None if it was on purpose, ignore this warning...\nself._warning_cache.warn(\"predict returned None if it was on purpose, ignore this warning...\")\nPredicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:02:24 • 0:00:00 0.00it/s \n2024-04-10 01:50:29,984 - modelscope - WARNING - task video-to-video input definition is missing\n/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.\nwarnings.warn(f\"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.\")\n2024-04-10 01:52:58,707 - modelscope - WARNING - task video-to-video output keys are missing",
  "metrics": {
    "predict_time": 320.638649,
    "total_time": 913.546613
  },
  "output": [
    "https://replicate.delivery/pbxt/WddFc1cTKramHdBNuf0syY6f6ddLmXEreRd8woKzRe9ovojKB/output.mp4",
    "https://replicate.delivery/pbxt/bIAEbq6tviolC9ODGGK9sefOj6D3ZXb2eIPVTkQe1hDpvojKB/output_enhanced.mp4"
  ],
  "started_at": "2024-04-10T01:47:38.520964Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/7vqctwv4hnrge0cerpktxeya0w",
    "cancel": "https://api.replicate.com/v1/predictions/7vqctwv4hnrge0cerpktxeya0w/cancel"
  },
  "version": "776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad"
}

Generated in

5 minutes 21 seconds

camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad

You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
rank_zero_warn(
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
/usr/local/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py:234: UserWarning: predict returned None if it was on purpose, ignore this warning...
self._warning_cache.warn("predict returned None if it was on purpose, ignore this warning...")
Predicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:02:24 • 0:00:00 0.00it/s 
2024-04-10 01:50:29,984 - modelscope - WARNING - task video-to-video input definition is missing
/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.
warnings.warn(f"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.")
2024-04-10 01:52:58,707 - modelscope - WARNING - task video-to-video output keys are missing

Prediction

Model

0dczra67jxrge0cerpv9nfvkrm

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

8m 36s

Created

about 1 year ago

Input

seed: 33
chunk: 24
prompt: Experience the dance of jellyfish: float through mesmerizing swarms of jellyfish, pulsating with otherworldly grace and beauty.
enhance
overlap: 8
num_steps: 50
num_frames: 56
image_guidance: 9
negative_prompt

{
  "seed": 33,
  "chunk": 24,
  "prompt": "Experience the dance of jellyfish: float through mesmerizing swarms of jellyfish, pulsating with otherworldly grace and beauty.",
  "enhance": true,
  "overlap": 8,
  "num_steps": 50,
  "num_frames": 56,
  "image_guidance": 9,
  "negative_prompt": ""
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
  {
    input: {
      seed: 33,
      chunk: 24,
      prompt: "Experience the dance of jellyfish: float through mesmerizing swarms of jellyfish, pulsating with otherworldly grace and beauty.",
      enhance: true,
      overlap: 8,
      num_steps: 50,
      num_frames: 56,
      image_guidance: 9,
      negative_prompt: ""
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
    input={
        "seed": 33,
        "chunk": 24,
        "prompt": "Experience the dance of jellyfish: float through mesmerizing swarms of jellyfish, pulsating with otherworldly grace and beauty.",
        "enhance": True,
        "overlap": 8,
        "num_steps": 50,
        "num_frames": 56,
        "image_guidance": 9,
        "negative_prompt": ""
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
    "input": {
      "seed": 33,
      "chunk": 24,
      "prompt": "Experience the dance of jellyfish: float through mesmerizing swarms of jellyfish, pulsating with otherworldly grace and beauty.",
      "enhance": true,
      "overlap": 8,
      "num_steps": 50,
      "num_frames": 56,
      "image_guidance": 9,
      "negative_prompt": ""
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-04-10T02:03:10.356844Z",
  "created_at": "2024-04-10T01:54:34.007000Z",
  "data_removed": false,
  "error": null,
  "id": "0dczra67jxrge0cerpv9nfvkrm",
  "input": {
    "seed": 33,
    "chunk": 24,
    "prompt": "Experience the dance of jellyfish: float through mesmerizing swarms of jellyfish, pulsating with otherworldly grace and beauty.",
    "enhance": true,
    "overlap": 8,
    "num_steps": 50,
    "num_frames": 56,
    "image_guidance": 9,
    "negative_prompt": ""
  },
  "logs": "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\nLOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\nrank_zero_warn(\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py:234: UserWarning: predict returned None if it was on purpose, ignore this warning...\nself._warning_cache.warn(\"predict returned None if it was on purpose, ignore this warning...\")\nPredicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:02:25 • 0:00:00 0.00it/s\n2024-04-10 02:00:41,838 - modelscope - WARNING - task video-to-video input definition is missing\n/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.\nwarnings.warn(f\"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.\")\n2024-04-10 02:03:09,910 - modelscope - WARNING - task video-to-video output keys are missing",
  "metrics": {
    "predict_time": 317.994453,
    "total_time": 516.349844
  },
  "output": [
    "https://replicate.delivery/pbxt/Z2WXg4M8YZJfOSgfZWfgnDRnBh2U1EQxPNyzAnxms4h6q0RlA/output.mp4",
    "https://replicate.delivery/pbxt/WwgevDPt8oWmR6VRPfHfOlgTfvvkt6R2gqDSk7futXcwrSHVC/output_enhanced.mp4"
  ],
  "started_at": "2024-04-10T01:57:52.362391Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/0dczra67jxrge0cerpv9nfvkrm",
    "cancel": "https://api.replicate.com/v1/predictions/0dczra67jxrge0cerpv9nfvkrm/cancel"
  },
  "version": "776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad"
}

Generated in

5 minutes 18 seconds

camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad

You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
rank_zero_warn(
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
/usr/local/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py:234: UserWarning: predict returned None if it was on purpose, ignore this warning...
self._warning_cache.warn("predict returned None if it was on purpose, ignore this warning...")
Predicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:02:25 • 0:00:00 0.00it/s
2024-04-10 02:00:41,838 - modelscope - WARNING - task video-to-video input definition is missing
/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.
warnings.warn(f"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.")
2024-04-10 02:03:09,910 - modelscope - WARNING - task video-to-video output keys are missing

Prediction

Model

0d05rkf575rge0cerpz9mefe04

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

5m 17s

Created

about 1 year ago

Input

seed: 33
chunk: 24
prompt: People dancing in room filled with fog and colorful lights.
enhance
overlap: 8
num_steps: 50
num_frames: 56
image_guidance: 9
negative_prompt

{
  "seed": 33,
  "chunk": 24,
  "prompt": "People dancing in room filled with fog and colorful lights.",
  "enhance": true,
  "overlap": 8,
  "num_steps": 50,
  "num_frames": 56,
  "image_guidance": 9,
  "negative_prompt": ""
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
  {
    input: {
      seed: 33,
      chunk: 24,
      prompt: "People dancing in room filled with fog and colorful lights.",
      enhance: true,
      overlap: 8,
      num_steps: 50,
      num_frames: 56,
      image_guidance: 9,
      negative_prompt: ""
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
    input={
        "seed": 33,
        "chunk": 24,
        "prompt": "People dancing in room filled with fog and colorful lights.",
        "enhance": True,
        "overlap": 8,
        "num_steps": 50,
        "num_frames": 56,
        "image_guidance": 9,
        "negative_prompt": ""
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
    "input": {
      "seed": 33,
      "chunk": 24,
      "prompt": "People dancing in room filled with fog and colorful lights.",
      "enhance": true,
      "overlap": 8,
      "num_steps": 50,
      "num_frames": 56,
      "image_guidance": 9,
      "negative_prompt": ""
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-04-10T02:08:42.670720Z",
  "created_at": "2024-04-10T02:03:25.881000Z",
  "data_removed": false,
  "error": null,
  "id": "0d05rkf575rge0cerpz9mefe04",
  "input": {
    "seed": 33,
    "chunk": 24,
    "prompt": "People dancing in room filled with fog and colorful lights.",
    "enhance": true,
    "overlap": 8,
    "num_steps": 50,
    "num_frames": 56,
    "image_guidance": 9,
    "negative_prompt": ""
  },
  "logs": "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\nrank_zero_warn(\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nPredicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:02:24 • 0:00:00 0.00it/s\n/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.\nwarnings.warn(f\"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.\")",
  "metrics": {
    "predict_time": 316.774495,
    "total_time": 316.78972
  },
  "output": [
    "https://replicate.delivery/pbxt/S0NaFjYwqUZhChi2qp6gibrjAtNMqeLA0oGTyCejdXxqa6oSA/output.mp4",
    "https://replicate.delivery/pbxt/Q52lLhCFIqZMLpdq1E0A0mcOO0oeAy7jySs9NKbXZhRVNdUJA/output_enhanced.mp4"
  ],
  "started_at": "2024-04-10T02:03:25.896225Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/0d05rkf575rge0cerpz9mefe04",
    "cancel": "https://api.replicate.com/v1/predictions/0d05rkf575rge0cerpz9mefe04/cancel"
  },
  "version": "776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad"
}

Generated in

5 minutes 17 seconds

camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad

LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
rank_zero_warn(
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
Predicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:02:24 • 0:00:00 0.00it/s
/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.
warnings.warn(f"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.")

Prediction

Model

kd5476jj11rg80cerpzr1217gm

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

6m 19s

Created

about 1 year ago

Input

seed: 33
chunk: 24
prompt: sunset, orange sky, warm lighting, fishing boats, ocean waves seagulls, rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, coastal landscape, seaside scenery.
enhance
overlap: 8
num_steps: 50
num_frames: 56
image_guidance: 9
negative_prompt

{
  "seed": 33,
  "chunk": 24,
  "prompt": "sunset, orange sky, warm lighting, fishing boats, ocean waves seagulls, rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, coastal landscape, seaside scenery.",
  "enhance": true,
  "overlap": 8,
  "num_steps": 50,
  "num_frames": 56,
  "image_guidance": 9,
  "negative_prompt": ""
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
  {
    input: {
      seed: 33,
      chunk: 24,
      prompt: "sunset, orange sky, warm lighting, fishing boats, ocean waves seagulls, rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, coastal landscape, seaside scenery.",
      enhance: true,
      overlap: 8,
      num_steps: 50,
      num_frames: 56,
      image_guidance: 9,
      negative_prompt: ""
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
    input={
        "seed": 33,
        "chunk": 24,
        "prompt": "sunset, orange sky, warm lighting, fishing boats, ocean waves seagulls, rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, coastal landscape, seaside scenery.",
        "enhance": True,
        "overlap": 8,
        "num_steps": 50,
        "num_frames": 56,
        "image_guidance": 9,
        "negative_prompt": ""
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
    "input": {
      "seed": 33,
      "chunk": 24,
      "prompt": "sunset, orange sky, warm lighting, fishing boats, ocean waves seagulls, rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, coastal landscape, seaside scenery.",
      "enhance": true,
      "overlap": 8,
      "num_steps": 50,
      "num_frames": 56,
      "image_guidance": 9,
      "negative_prompt": ""
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-04-10T02:10:12.985631Z",
  "created_at": "2024-04-10T02:03:53.736000Z",
  "data_removed": false,
  "error": null,
  "id": "kd5476jj11rg80cerpzr1217gm",
  "input": {
    "seed": 33,
    "chunk": 24,
    "prompt": "sunset, orange sky, warm lighting, fishing boats, ocean waves seagulls, rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, coastal landscape, seaside scenery.",
    "enhance": true,
    "overlap": 8,
    "num_steps": 50,
    "num_frames": 56,
    "image_guidance": 9,
    "negative_prompt": ""
  },
  "logs": "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\nLOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\nrank_zero_warn(\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py:234: UserWarning: predict returned None if it was on purpose, ignore this warning...\nself._warning_cache.warn(\"predict returned None if it was on purpose, ignore this warning...\")\nPredicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:02:24 • 0:00:00 0.00it/s\n2024-04-10 02:07:45,470 - modelscope - WARNING - task video-to-video input definition is missing\n/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.\nwarnings.warn(f\"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.\")\n2024-04-10 02:10:12,535 - modelscope - WARNING - task video-to-video output keys are missing",
  "metrics": {
    "predict_time": 319.054031,
    "total_time": 379.249631
  },
  "output": [
    "https://replicate.delivery/pbxt/DM1lUEChGT4fZSbJXB1cOKUsWdDnVlSOoOXbNeJQPmmEc6oSA/output.mp4",
    "https://replicate.delivery/pbxt/lMbCTgZhwHYOOZWns8EsNhL7imZ88FMeVk0AsYFaeWSEc6oSA/output_enhanced.mp4"
  ],
  "started_at": "2024-04-10T02:04:53.931600Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/kd5476jj11rg80cerpzr1217gm",
    "cancel": "https://api.replicate.com/v1/predictions/kd5476jj11rg80cerpzr1217gm/cancel"
  },
  "version": "776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad"
}

Generated in

5 minutes 19 seconds

camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad

You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
rank_zero_warn(
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
/usr/local/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py:234: UserWarning: predict returned None if it was on purpose, ignore this warning...
self._warning_cache.warn("predict returned None if it was on purpose, ignore this warning...")
Predicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:02:24 • 0:00:00 0.00it/s
2024-04-10 02:07:45,470 - modelscope - WARNING - task video-to-video input definition is missing
/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.
warnings.warn(f"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.")
2024-04-10 02:10:12,535 - modelscope - WARNING - task video-to-video output keys are missing

Prediction

Model

hhk03y18z1rgc0cerqjasn8mww

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

8m 39s

Created

about 1 year ago

Input

seed: 33
chunk: 12
prompt: Discover the secret language of bees: delve into the complex communication system that allows bees to coordinate their actions and navigate the world.
enhance
overlap: 4
num_steps: 50
num_frames: 56
image_guidance: 9
negative_prompt

{
  "seed": 33,
  "chunk": 12,
  "prompt": "Discover the secret language of bees: delve into the complex communication system that allows bees to coordinate their actions and navigate the world.",
  "enhance": true,
  "overlap": 4,
  "num_steps": 50,
  "num_frames": 56,
  "image_guidance": 9,
  "negative_prompt": ""
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
  {
    input: {
      seed: 33,
      chunk: 12,
      prompt: "Discover the secret language of bees: delve into the complex communication system that allows bees to coordinate their actions and navigate the world.",
      enhance: true,
      overlap: 4,
      num_steps: 50,
      num_frames: 56,
      image_guidance: 9,
      negative_prompt: ""
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
    input={
        "seed": 33,
        "chunk": 12,
        "prompt": "Discover the secret language of bees: delve into the complex communication system that allows bees to coordinate their actions and navigate the world.",
        "enhance": True,
        "overlap": 4,
        "num_steps": 50,
        "num_frames": 56,
        "image_guidance": 9,
        "negative_prompt": ""
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
    "input": {
      "seed": 33,
      "chunk": 12,
      "prompt": "Discover the secret language of bees: delve into the complex communication system that allows bees to coordinate their actions and navigate the world.",
      "enhance": true,
      "overlap": 4,
      "num_steps": 50,
      "num_frames": 56,
      "image_guidance": 9,
      "negative_prompt": ""
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-04-10T02:52:46.717711Z",
  "created_at": "2024-04-10T02:44:08.056000Z",
  "data_removed": false,
  "error": null,
  "id": "hhk03y18z1rgc0cerqjasn8mww",
  "input": {
    "seed": 33,
    "chunk": 12,
    "prompt": "Discover the secret language of bees: delve into the complex communication system that allows bees to coordinate their actions and navigate the world.",
    "enhance": true,
    "overlap": 4,
    "num_steps": 50,
    "num_frames": 56,
    "image_guidance": 9,
    "negative_prompt": ""
  },
  "logs": "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\nLOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\nrank_zero_warn(\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py:234: UserWarning: predict returned None if it was on purpose, ignore this warning...\nself._warning_cache.warn(\"predict returned None if it was on purpose, ignore this warning...\")\nPredicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:02:25 • 0:00:00 0.00it/s \nVideo cannot be processed with chunk size {chunk_size} and overlap size {overlap_size}, trimming it to length {trim_length} to be able to process it\n2024-04-10 02:50:18,435 - modelscope - WARNING - task video-to-video input definition is missing\n/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.\nwarnings.warn(f\"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.\")\n2024-04-10 02:52:46,279 - modelscope - WARNING - task video-to-video output keys are missing",
  "metrics": {
    "predict_time": 315.864303,
    "total_time": 518.661711
  },
  "output": [
    "https://replicate.delivery/pbxt/uwxrIeW0uNW4YymMfbhE7wuvlbVXKbIPp5LaV4ySVeE9H2RlA/output.mp4",
    "https://replicate.delivery/pbxt/uArxMWer0foeGJJoqlLsMJjdmKhhPeOCTfcu3vJzVYekfhdUJA/output_enhanced.mp4"
  ],
  "started_at": "2024-04-10T02:47:30.853408Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/hhk03y18z1rgc0cerqjasn8mww",
    "cancel": "https://api.replicate.com/v1/predictions/hhk03y18z1rgc0cerqjasn8mww/cancel"
  },
  "version": "776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad"
}

Generated in

5 minutes 16 seconds

camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad

You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
rank_zero_warn(
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
/usr/local/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py:234: UserWarning: predict returned None if it was on purpose, ignore this warning...
self._warning_cache.warn("predict returned None if it was on purpose, ignore this warning...")
Predicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:02:25 • 0:00:00 0.00it/s 
Video cannot be processed with chunk size {chunk_size} and overlap size {overlap_size}, trimming it to length {trim_length} to be able to process it
2024-04-10 02:50:18,435 - modelscope - WARNING - task video-to-video input definition is missing
/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.
warnings.warn(f"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.")
2024-04-10 02:52:46,279 - modelscope - WARNING - task video-to-video output keys are missing

Prediction

Model

vy5w1wn121rge0cerqn9sn4jfc

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

2m 25s

Created

about 1 year ago

Input

seed: 1
chunk: 24
prompt: Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.
enhance
overlap: 8
num_steps: 50
num_frames: 24
image_guidance: 9
negative_prompt

{
  "seed": 1,
  "chunk": 24,
  "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
  "enhance": false,
  "overlap": 8,
  "num_steps": 50,
  "num_frames": 24,
  "image_guidance": 9,
  "negative_prompt": ""
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
  {
    input: {
      seed: 1,
      chunk: 24,
      prompt: "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
      enhance: false,
      overlap: 8,
      num_steps: 50,
      num_frames: 24,
      image_guidance: 9,
      negative_prompt: ""
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "camenduru/streaming-t2v:776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
    input={
        "seed": 1,
        "chunk": 24,
        "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
        "enhance": False,
        "overlap": 8,
        "num_steps": 50,
        "num_frames": 24,
        "image_guidance": 9,
        "negative_prompt": ""
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad",
    "input": {
      "seed": 1,
      "chunk": 24,
      "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
      "enhance": false,
      "overlap": 8,
      "num_steps": 50,
      "num_frames": 24,
      "image_guidance": 9,
      "negative_prompt": ""
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-04-10T02:53:36.756470Z",
  "created_at": "2024-04-10T02:51:12.016000Z",
  "data_removed": false,
  "error": null,
  "id": "vy5w1wn121rge0cerqn9sn4jfc",
  "input": {
    "seed": 1,
    "chunk": 24,
    "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
    "enhance": false,
    "overlap": 8,
    "num_steps": 50,
    "num_frames": 24,
    "image_guidance": 9,
    "negative_prompt": ""
  },
  "logs": "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\nrank_zero_warn(\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nPredicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:00:29 • 0:00:00 0.00it/s",
  "metrics": {
    "predict_time": 49.968931,
    "total_time": 144.74047
  },
  "output": [
    "https://replicate.delivery/pbxt/d0Eyny2Dei2dRC3CU0a2ewOU3GFr8EB4GmtHkWNBkLtwE7oSA/output.mp4"
  ],
  "started_at": "2024-04-10T02:52:46.787539Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/vy5w1wn121rge0cerqn9sn4jfc",
    "cancel": "https://api.replicate.com/v1/predictions/vy5w1wn121rge0cerqn9sn4jfc/cancel"
  },
  "version": "776168ae57868838756d0fb6406b8aecbff6d746be9f8d917e322158d8c8faad"
}

Generated in

50.0 seconds

camenduru/streaming-t2v:ce9e09771a164b46913a7006bfa6c602735d82b748e6e433acc27103fc973b96

Prediction

Model

camenduru/streaming-t2v:ce9e0977

gkfgk6rwf1rgp0cers2sk3s09r

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

7m 32s

Created

about 1 year ago

Input

seed: 33
prompt: Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.
enhance
num_steps: 50
num_frames: 56
image_guidance: 9
negative_prompt

{
  "seed": 33,
  "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
  "enhance": true,
  "num_steps": 50,
  "num_frames": 56,
  "image_guidance": 9,
  "negative_prompt": ""
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "camenduru/streaming-t2v:ce9e09771a164b46913a7006bfa6c602735d82b748e6e433acc27103fc973b96",
  {
    input: {
      seed: 33,
      prompt: "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
      enhance: true,
      num_steps: 50,
      num_frames: 56,
      image_guidance: 9,
      negative_prompt: ""
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "camenduru/streaming-t2v:ce9e09771a164b46913a7006bfa6c602735d82b748e6e433acc27103fc973b96",
    input={
        "seed": 33,
        "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
        "enhance": True,
        "num_steps": 50,
        "num_frames": 56,
        "image_guidance": 9,
        "negative_prompt": ""
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "ce9e09771a164b46913a7006bfa6c602735d82b748e6e433acc27103fc973b96",
    "input": {
      "seed": 33,
      "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
      "enhance": true,
      "num_steps": 50,
      "num_frames": 56,
      "image_guidance": 9,
      "negative_prompt": ""
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-04-10T04:37:34.217029Z",
  "created_at": "2024-04-10T04:30:01.848000Z",
  "data_removed": false,
  "error": null,
  "id": "gkfgk6rwf1rgp0cers2sk3s09r",
  "input": {
    "seed": 33,
    "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
    "enhance": true,
    "num_steps": 50,
    "num_frames": 56,
    "image_guidance": 9,
    "negative_prompt": ""
  },
  "logs": "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\nLOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 128 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\nrank_zero_warn(\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py:234: UserWarning: predict returned None if it was on purpose, ignore this warning...\nself._warning_cache.warn(\"predict returned None if it was on purpose, ignore this warning...\")\nPredicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:02:23 • 0:00:00 0.00it/s\n2024-04-10 04:35:05,319 - modelscope - WARNING - task video-to-video input definition is missing\n/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.\nwarnings.warn(f\"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.\")\n2024-04-10 04:37:31,267 - modelscope - WARNING - task video-to-video output keys are missing",
  "metrics": {
    "predict_time": 309.690938,
    "total_time": 452.369029
  },
  "output": [
    "https://replicate.delivery/pbxt/Ine4fu7JgsrLwEYeec5Xb6UqcyjAZgBJ7IEzYRq3uz40YyjKB/output.mp4",
    "https://replicate.delivery/pbxt/wnuzP6LIHP4QBlw95wlrsqG7xsfzQBDPmLcnycUcN96GTeoSA/output_enhanced.mp4"
  ],
  "started_at": "2024-04-10T04:32:24.526091Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/gkfgk6rwf1rgp0cers2sk3s09r",
    "cancel": "https://api.replicate.com/v1/predictions/gkfgk6rwf1rgp0cers2sk3s09r/cancel"
  },
  "version": "ce9e09771a164b46913a7006bfa6c602735d82b748e6e433acc27103fc973b96"
}

Generated in

5 minutes 10 seconds

camenduru/streaming-t2v:eb3fcb5cd02651d0b54ac51547c4e862f6977c587098e8049ed41f5b3bf45294

You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 128 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
rank_zero_warn(
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
/usr/local/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py:234: UserWarning: predict returned None if it was on purpose, ignore this warning...
self._warning_cache.warn("predict returned None if it was on purpose, ignore this warning...")
Predicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:02:23 • 0:00:00 0.00it/s
2024-04-10 04:35:05,319 - modelscope - WARNING - task video-to-video input definition is missing
/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.
warnings.warn(f"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.")
2024-04-10 04:37:31,267 - modelscope - WARNING - task video-to-video output keys are missing

Prediction

Model

camenduru/streaming-t2v:eb3fcb5c

33n6tqw0r1rgp0cers8tsgqcfw

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

7m 13s

Created

about 1 year ago

Input

seed: 33
chunk: 24
prompt: Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.
enhance
overlap: 8
num_steps: 50
num_frames: 56
image_guidance: 9
negative_prompt

{
  "seed": 33,
  "chunk": 24,
  "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
  "enhance": true,
  "overlap": 8,
  "num_steps": 50,
  "num_frames": 56,
  "image_guidance": 9,
  "negative_prompt": ""
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "camenduru/streaming-t2v:eb3fcb5cd02651d0b54ac51547c4e862f6977c587098e8049ed41f5b3bf45294",
  {
    input: {
      seed: 33,
      chunk: 24,
      prompt: "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
      enhance: true,
      overlap: 8,
      num_steps: 50,
      num_frames: 56,
      image_guidance: 9,
      negative_prompt: ""
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "camenduru/streaming-t2v:eb3fcb5cd02651d0b54ac51547c4e862f6977c587098e8049ed41f5b3bf45294",
    input={
        "seed": 33,
        "chunk": 24,
        "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
        "enhance": True,
        "overlap": 8,
        "num_steps": 50,
        "num_frames": 56,
        "image_guidance": 9,
        "negative_prompt": ""
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "eb3fcb5cd02651d0b54ac51547c4e862f6977c587098e8049ed41f5b3bf45294",
    "input": {
      "seed": 33,
      "chunk": 24,
      "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
      "enhance": true,
      "overlap": 8,
      "num_steps": 50,
      "num_frames": 56,
      "image_guidance": 9,
      "negative_prompt": ""
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-04-10T04:50:46.949742Z",
  "created_at": "2024-04-10T04:43:33.952000Z",
  "data_removed": false,
  "error": null,
  "id": "33n6tqw0r1rgp0cers8tsgqcfw",
  "input": {
    "seed": 33,
    "chunk": 24,
    "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
    "enhance": true,
    "overlap": 8,
    "num_steps": 50,
    "num_frames": 56,
    "image_guidance": 9,
    "negative_prompt": ""
  },
  "logs": "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\nLOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 128 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\nrank_zero_warn(\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py:234: UserWarning: predict returned None if it was on purpose, ignore this warning...\nself._warning_cache.warn(\"predict returned None if it was on purpose, ignore this warning...\")\nPredicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:02:24 • 0:00:00 0.00it/s\n2024-04-10 04:48:20,346 - modelscope - WARNING - task video-to-video input definition is missing\n/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.\nwarnings.warn(f\"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.\")\n2024-04-10 04:50:46,155 - modelscope - WARNING - task video-to-video output keys are missing",
  "metrics": {
    "predict_time": 307.229312,
    "total_time": 432.997742
  },
  "output": [
    "https://replicate.delivery/pbxt/wt2ucmHM3XI3I9gU2jkFf4YGp1Fwoh4Celn3o0ruXcSmy8oSA/output.mp4",
    "https://replicate.delivery/pbxt/5YPFmQhuTYqfOKzpgp5OgdoteigVItpLgbZTAQFve5eaKzjKB/output_enhanced.mp4"
  ],
  "started_at": "2024-04-10T04:45:39.720430Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/33n6tqw0r1rgp0cers8tsgqcfw",
    "cancel": "https://api.replicate.com/v1/predictions/33n6tqw0r1rgp0cers8tsgqcfw/cancel"
  },
  "version": "eb3fcb5cd02651d0b54ac51547c4e862f6977c587098e8049ed41f5b3bf45294"
}

Generated in

5 minutes 7 seconds

camenduru/streaming-t2v:1fe245aad4bb7f209074a231142ac3eceb3b1f2adc9cf77b46e8ffa2662323cf

You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 128 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
rank_zero_warn(
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
/usr/local/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py:234: UserWarning: predict returned None if it was on purpose, ignore this warning...
self._warning_cache.warn("predict returned None if it was on purpose, ignore this warning...")
Predicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:02:24 • 0:00:00 0.00it/s
2024-04-10 04:48:20,346 - modelscope - WARNING - task video-to-video input definition is missing
/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.
warnings.warn(f"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.")
2024-04-10 04:50:46,155 - modelscope - WARNING - task video-to-video output keys are missing

Prediction

Model

camenduru/streaming-t2v:1fe245aa

es10zwpm81rg80cersd80w878c

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

15m 32s

Created

about 1 year ago

Input

seed: 33
chunk: 24
prompt: Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.
enhance
overlap: 8
num_steps: 50
num_frames: 56
image_guidance: 9
negative_prompt

{
  "seed": 33,
  "chunk": 24,
  "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
  "enhance": true,
  "overlap": 8,
  "num_steps": 50,
  "num_frames": 56,
  "image_guidance": 9,
  "negative_prompt": ""
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "camenduru/streaming-t2v:1fe245aad4bb7f209074a231142ac3eceb3b1f2adc9cf77b46e8ffa2662323cf",
  {
    input: {
      seed: 33,
      chunk: 24,
      prompt: "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
      enhance: true,
      overlap: 8,
      num_steps: 50,
      num_frames: 56,
      image_guidance: 9,
      negative_prompt: ""
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "camenduru/streaming-t2v:1fe245aad4bb7f209074a231142ac3eceb3b1f2adc9cf77b46e8ffa2662323cf",
    input={
        "seed": 33,
        "chunk": 24,
        "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
        "enhance": True,
        "overlap": 8,
        "num_steps": 50,
        "num_frames": 56,
        "image_guidance": 9,
        "negative_prompt": ""
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "1fe245aad4bb7f209074a231142ac3eceb3b1f2adc9cf77b46e8ffa2662323cf",
    "input": {
      "seed": 33,
      "chunk": 24,
      "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
      "enhance": true,
      "overlap": 8,
      "num_steps": 50,
      "num_frames": 56,
      "image_guidance": 9,
      "negative_prompt": ""
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-04-10T05:09:16.984223Z",
  "created_at": "2024-04-10T04:53:45.152000Z",
  "data_removed": false,
  "error": null,
  "id": "es10zwpm81rg80cersd80w878c",
  "input": {
    "seed": 33,
    "chunk": 24,
    "prompt": "Dive into the depths of the ocean: explore vibrant coral reefs, mysterious underwater caves, and the mesmerizing creatures that call the sea home.",
    "enhance": true,
    "overlap": 8,
    "num_steps": 50,
    "num_frames": 56,
    "image_guidance": 9,
    "negative_prompt": ""
  },
  "logs": "You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\nLOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\nrank_zero_warn(\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py:234: UserWarning: predict returned None if it was on purpose, ignore this warning...\nself._warning_cache.warn(\"predict returned None if it was on purpose, ignore this warning...\")\nPredicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:02:25 • 0:00:00 0.00it/s\n2024-04-10 05:06:46,981 - modelscope - WARNING - task video-to-video input definition is missing\n/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.\nwarnings.warn(f\"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.\")\n2024-04-10 05:09:16,498 - modelscope - WARNING - task video-to-video output keys are missing",
  "metrics": {
    "predict_time": 332.706564,
    "total_time": 931.832223
  },
  "output": [
    "https://replicate.delivery/pbxt/8GtrwGiZJqJWIxDFzzjimIb7vwBZF97ScaijkU9vz1NfheoSA/output.mp4",
    "https://replicate.delivery/pbxt/ur5ZA0bGNKbeGqI0fjIKz8HSPam6VIqisT13KbJWebT4H6RlA/output_enhanced.mp4"
  ],
  "started_at": "2024-04-10T05:03:44.277659Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/es10zwpm81rg80cersd80w878c",
    "cancel": "https://api.replicate.com/v1/predictions/es10zwpm81rg80cersd80w878c/cancel"
  },
  "version": "1fe245aad4bb7f209074a231142ac3eceb3b1f2adc9cf77b46e8ffa2662323cf"
}

Generated in

5 minutes 33 seconds

camenduru/streaming-t2v:1fe245aad4bb7f209074a231142ac3eceb3b1f2adc9cf77b46e8ffa2662323cf

You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
rank_zero_warn(
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
INFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}
/usr/local/lib/python3.10/site-packages/pytorch_lightning/loops/prediction_loop.py:234: UserWarning: predict returned None if it was on purpose, ignore this warning...
self._warning_cache.warn("predict returned None if it was on purpose, ignore this warning...")
Predicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:02:25 • 0:00:00 0.00it/s
2024-04-10 05:06:46,981 - modelscope - WARNING - task video-to-video input definition is missing
/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.
warnings.warn(f"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.")
2024-04-10 05:09:16,498 - modelscope - WARNING - task video-to-video output keys are missing

Prediction

Model

camenduru/streaming-t2v:1fe245aa

j81wsngpn1rgc0cerspazkgs6m

Status

Succeeded

Source

Web

Hardware

A100 (40GB)

Total duration

9m 22s

Created

about 1 year ago

Input

seed: 33
chunk: 24
prompt: Experience the dance of jellyfish: float through mesmerizing swarms of jellyfish, pulsating with otherworldly grace and beauty.
enhance
overlap: 8
num_steps: 50
num_frames: 120
image_guidance: 9
negative_prompt

{
  "seed": 33,
  "chunk": 24,
  "prompt": "Experience the dance of jellyfish: float through mesmerizing swarms of jellyfish, pulsating with otherworldly grace and beauty.",
  "enhance": true,
  "overlap": 8,
  "num_steps": 50,
  "num_frames": 120,
  "image_guidance": 9,
  "negative_prompt": ""
}

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "camenduru/streaming-t2v:1fe245aad4bb7f209074a231142ac3eceb3b1f2adc9cf77b46e8ffa2662323cf",
  {
    input: {
      seed: 33,
      chunk: 24,
      prompt: "Experience the dance of jellyfish: float through mesmerizing swarms of jellyfish, pulsating with otherworldly grace and beauty.",
      enhance: true,
      overlap: 8,
      num_steps: 50,
      num_frames: 120,
      image_guidance: 9,
      negative_prompt: ""
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

pip install replicate

Import the client:

import replicate

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "camenduru/streaming-t2v:1fe245aad4bb7f209074a231142ac3eceb3b1f2adc9cf77b46e8ffa2662323cf",
    input={
        "seed": 33,
        "chunk": 24,
        "prompt": "Experience the dance of jellyfish: float through mesmerizing swarms of jellyfish, pulsating with otherworldly grace and beauty.",
        "enhance": True,
        "overlap": 8,
        "num_steps": 50,
        "num_frames": 120,
        "image_guidance": 9,
        "negative_prompt": ""
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run camenduru/streaming-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "1fe245aad4bb7f209074a231142ac3eceb3b1f2adc9cf77b46e8ffa2662323cf",
    "input": {
      "seed": 33,
      "chunk": 24,
      "prompt": "Experience the dance of jellyfish: float through mesmerizing swarms of jellyfish, pulsating with otherworldly grace and beauty.",
      "enhance": true,
      "overlap": 8,
      "num_steps": 50,
      "num_frames": 120,
      "image_guidance": 9,
      "negative_prompt": ""
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-04-10T05:21:57.916845Z",
  "created_at": "2024-04-10T05:12:36.264000Z",
  "data_removed": false,
  "error": null,
  "id": "j81wsngpn1rgc0cerspazkgs6m",
  "input": {
    "seed": 33,
    "chunk": 24,
    "prompt": "Experience the dance of jellyfish: float through mesmerizing swarms of jellyfish, pulsating with otherworldly grace and beauty.",
    "enhance": true,
    "overlap": 8,
    "num_steps": 50,
    "num_frames": 120,
    "image_guidance": 9,
    "negative_prompt": ""
  },
  "logs": "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n/usr/local/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, predict_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\nrank_zero_warn(\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nINFERENCE PARAMS = {'concat_video': True, 'conditioning_from_all_past': False, 'conditioning_type': 'fixed', 'eta': 1.0, 'eval_loss_metrics': False, 'frame_rate': 8, 'guidance_scale': 7.5, 'height': 256, 'mode': 'long_video', 'n_autoregressive_generations': 4, 'negative_prompt': '', 'num_inference_steps': 50, 'result_formats': ['eval_mp4'], 'scheduler_cls': '', 'seed': 33, 'start_from_real_input': False, 'use_dec_scaling': True, 'validation_samples': 80, 'video_length': 16, 'width': 256}\nPredicting ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1/1 0:06:16 • 0:00:00 0.00it/s\n/usr/local/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=4.164773464202881 and t1=4.164773.\nwarnings.warn(f\"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.\")",
  "metrics": {
    "predict_time": 561.635751,
    "total_time": 561.652845
  },
  "output": [
    "https://replicate.delivery/pbxt/DmwNVZX9sd41EZZpO5cf8GgS4wtVs9WM0DEncQdvsSe1P9oSA/output.mp4",
    "https://replicate.delivery/pbxt/TGxCwYt5I16LLhxOpUUEpDio8RfOofpKwV281XDz9921P9oSA/output_enhanced.mp4"
  ],
  "started_at": "2024-04-10T05:12:36.281094Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/j81wsngpn1rgc0cerspazkgs6m",
    "cancel": "https://api.replicate.com/v1/predictions/j81wsngpn1rgc0cerspazkgs6m/cancel"
  },
  "version": "1fe245aad4bb7f209074a231142ac3eceb3b1f2adc9cf77b46e8ffa2662323cf"
}

Generated in

9 minutes 22 seconds