Updated Changes

Author: naeemaa00

AIQuickLabs/Bedrock/CreateImage.py

@@ -0,0 +1,46 @@
+# Use the native inference API to create an image with SDXL 1.0
+
+import base64
+import boto3
+import json
+import os
+import random
+
+# Create a Bedrock Runtime client in the AWS Region of your choice.
+client = boto3.client("bedrock-runtime", region_name="us-west-2")
+
+# Set the model ID, e.g., Titan Image Generator G1.
+model_id = "stability.stable-diffusion-xl-v1"
+
+# Define the image generation prompt for the model.
+prompt = "Lion in the Jungle"
+
+# Format the request payload using the model's native structure.
+native_request = {"text_prompts":[{"text":prompt,"weight":1}],"cfg_scale":10,"steps":50,"seed":0,"width":1024,"height":1024,"samples":1}
+
+# Convert the native request to JSON.
+request = json.dumps(native_request)
+
+# Invoke the model with the request.
+response = client.invoke_model(modelId=model_id, body=request)
+
+# Decode the response body.
+model_response = json.loads(response["body"].read())
+
+# Extract the image data.
+base64_image_data = model_response["artifacts"][0]["base64"]
+
+# Save the generated image to a local folder.
+i, output_dir = 1, "output"
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+while os.path.exists(os.path.join(output_dir, f"image_{i}.png")):
+    i += 1
+
+image_data = base64.b64decode(base64_image_data)
+
+image_path = os.path.join(output_dir, f"image_{i}.png")
+with open(image_path, "wb") as file:
+    file.write(image_data)
+
+print(f"The generated image has been saved to {image_path}.")

AIQuickLabs/Bedrock/output/image_1.png

@@ -6,7 +6,7 @@
 
 # Specify the input data and model ID
 input_data = {
-  "modelId": "stability.stable-diffusion-xl-v0",
+  "modelId": "stability.stable-diffusion-xl-v1",
   "contentType": "application/json",
   "accept": "application/json",
    "body": {"text_prompts":[{"text":"Sri lanka tea plantation."}],"cfg_scale":10,"seed":0,"steps":50}