Esempi di codice per la generazione di video con più riprese - Amazon Nova

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Esempi di codice per la generazione di video con più riprese

I seguenti esempi forniscono codice di esempio per diverse attività di generazione di video con più riprese (durata maggiore di 6 secondi).

Automated video generation

In questo esempio, tutte le riprese del video vengono generate a partire da un unico prompt e non viene fornita nessuna immagine di input.

import json import os import boto3 from dotenv import load_dotenv # Create the Bedrock Runtime client. bedrock_runtime = boto3.client(service_name="bedrock-runtime", region_name="us-east-1") # Configure Nova Reel model inputs. model_input = { "taskType": "MULTI_SHOT_AUTOMATED", "multiShotAutomatedParams": { "text": "Cinematic documentary showcasing the stunning beauty of the natural world. Drone footage flying over fantastical and varied natural wonders." }, "videoGenerationConfig": { "seed": 1234, "durationSeconds": 18, # Must be a multiple of 6 in range [12, 120] "fps": 24, # Must be 24 "dimension": "1280x720", # Must be "1280x720" }, } try: # Start the asynchronous video generation job. invocation = bedrock_runtime.start_async_invoke( modelId="amazon.nova-reel-v1:1", modelInput=model_input, outputDataConfig={"s3OutputDataConfig": {"s3Uri": "s3://your-s3-bucket"}}, ) # Print the response JSON. print(json.dumps(invocation, indent=2, default=str)) except Exception as err: print("Exception:") if hasattr(err, "response"): # Pretty print the response JSON. print(json.dumps(err.response, indent=2, default=str)) else: print(err)
Manual video generation - Amazon S3 input image

In questo esempio, viene generato un video con due riprese. Ogni ripresa viene generata con un prompt diverso e con l’immagine di input fornita in una posizione Amazon S3.

import json import os import boto3 from dotenv import load_dotenv # === Helper Function === def image_to_base64(image_path: str): """ Convert an image file to a base64 encoded string. """ import base64 with open(image_path, "rb") as image_file: encoded_string = base64.b64encode(image_file.read()) return encoded_string.decode("utf-8") # === Main Code === # Create the Bedrock Runtime client. bedrock_runtime = boto3.client(service_name="bedrock-runtime", region_name="us-east-1") # Configure Nova Reel model inputs. This example includes three shots, two of # which include images to use as starting frames. These images are stored in S3. model_input = { "taskType": "MULTI_SHOT_MANUAL", "multiShotManualParams": { "shots": [ {"text": "aerial view of a city with tall glass and metal skyscrapers"}, { "text": "closeup of a vehicle wheel in motion as the pavement speeds by with motion blur", "image": { "format": "png", # Must be "png" or "jpeg" "source": { "s3Location": { "uri": "s3://your-s3-bucket/images/SUV-wheel-closeup.png" } }, }, }, { "text": "tracking shot, the vehicle drives through the city, trees and buildings line the street", "image": { "format": "png", # Must be "png" or "jpeg" "source": { "s3Location": { "uri": "s3://your-s3-bucket/images/SUV-downtown-back.png" } }, }, }, ] }, "videoGenerationConfig": { "seed": 1234, "fps": 24, # Must be 24 "dimension": "1280x720", # Must be "1280x720" }, } try: # Start the asynchronous video generation job. invocation = bedrock_runtime.start_async_invoke( modelId="amazon.nova-reel-v1:1", modelInput=model_input, outputDataConfig={"s3OutputDataConfig": {"s3Uri": "s3://your-s3-bucket"}}, ) # Print the response JSON. print(json.dumps(invocation, indent=2, default=str)) except Exception as err: print("Exception:") if hasattr(err, "response"): # Pretty print the response JSON. print(json.dumps(err.response, indent=2, default=str)) else: print(err)
Manual video generation - base64 input image

In questo esempio, viene generato un video con tre riprese. La prima ripresa viene generata solo con un prompt, mentre le due riprese successive vengono generate con un nuovo prompt e un’immagine di input ciascuna.

import json import os import boto3 from dotenv import load_dotenv # === Helper Function === def image_to_base64(image_path: str): """ Convert an image file to a base64 encoded string. """ import base64 with open(image_path, "rb") as image_file: encoded_string = base64.b64encode(image_file.read()) return encoded_string.decode("utf-8") # === Main Code === # Create the Bedrock Runtime client. bedrock_runtime = boto3.client(service_name="bedrock-runtime", region_name="us-east-1") # Configure Nova Reel model inputs. This example includes three shots, two of # which include images to use as starting frames. model_input = { "taskType": "MULTI_SHOT_MANUAL", "multiShotManualParams": { "shots": [ { "text": "Drone footage of a Pacific Northwest forest with a meandering stream seen from a high altitude, top-down view" }, { "text": "camera arcs slowly around two SUV vehicles in a forest setting with a stream in the background", "image": { "format": "png", # Must be "png" or "jpeg" "source": {"bytes": image_to_base64("images/SUV-roadside.png")}, }, }, { "text": "tracking shot, a SUV vehicle drives toward the camera through a forest roadway, the SUV's ring-shaped headlights glow white", "image": { "format": "png", # Must be "png" or "jpeg" "source": {"bytes": image_to_base64("images/SUV-forest-front.png")}, }, }, ] }, "videoGenerationConfig": { "seed": 1234, "fps": 24, # Must be 24 "dimension": "1280x720", # Must be "1280x720" }, } try: # Start the asynchronous video generation job. invocation = bedrock_runtime.start_async_invoke( modelId="amazon.nova-reel-v1:1", modelInput=model_input, outputDataConfig={"s3OutputDataConfig": {"s3Uri": "s3://your-s3-bucket"}}, ) # Print the response JSON. print(json.dumps(invocation, indent=2, default=str)) except Exception as err: print("Exception:") if hasattr(err, "response"): # Pretty print the response JSON. print(json.dumps(err.response, indent=2, default=str)) else: print(err)