> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Accessing the Bedrock Console Part 2 > Guide to using Amazon Bedrock console to discover, test, and run image and video models via the Model Catalog and Playground, including region availability and permissions In this lesson we continue exploring the Amazon Bedrock console, focusing on discovering and testing image and video generation models from the Model Catalog and using the Playground to run quick experiments. We'll cover model details you should watch for (such as parameter counts and token limits), how to run image and video generation jobs in Playground, and what to expect for region-based availability. We’ll start by filtering the Model Catalog to only show image-generation models. A screenshot of the Amazon Bedrock "Model catalog" web page showing a list of image-generation models (Stable Image series, Titan Image Generator, etc.) with filters applied. The left sidebar has navigation and filter options while the main panel displays model names, versions, providers, popularity, and deployment info. ## Inspecting an image model (Stable Diffusion) I opened the Stable Diffusion entry because it’s a widely used family for text-to-image generation. The model overview highlights useful technical details you should note when designing prompts: * Model size: 8 billion parameters (for this entry). * Hosting: serverless (no provisioning required). * Input limits: about 77 tokens — typical of CLIP-style encoders (see CLIP for details). These details help with prompt design and understanding model capabilities and limits. A browser screenshot of the Amazon Web Services Amazon Bedrock model catalog page showing the "Stable Diffusion 3.5 Large" model with its overview and details. The left navigation sidebar and an "Open in playground" button are also visible. ## Generating images in Playground From the Model Catalog you can open Playground to run quick, interactive tests. Example workflow: 1. Choose the provider (e.g., Stability AI) and the model (e.g., Stable Diffusion). 2. Pick a profile (for example, `On-Demand`) and click Apply. 3. Enter a descriptive prompt — e.g., “Solar system showing Earth's position relative to the Sun.” 4. Generate the image, then inspect, create variations, or download the resulting asset. Playground makes it easy to iterate on prompts and quickly evaluate model outputs. A screenshot of the Amazon Bedrock “Playground” page showing a Stable Diffusion 3.5 Large output. The output image is a sun-centered solar system diagram with Earth and other planets, and a large mouse cursor is visible on the screen. ## Video models and region availability Video-capable models are less common and often depend on the selected AWS region. In the Oregon region (us-west-2 at the time of this lesson) there was only one video model listed: Ray by Luma AI. Note that Ray may be deprecated in that region — model lifecycle and availability can change. If a given video model is deprecated or unavailable, switching regions is a common workaround. For example, switching the console to US East (N. Virginia) surfaces Amazon’s Nova Reel, which supports text-to-video and image-to-video workflows. A screenshot of the Amazon Bedrock web console open to the Model catalog, showing filters on the left and two serverless models named "Nova Reel" listed in the main pane. The "Video" output modality filter is selected and the navigation sidebar is visible. Clicking the Nova Reel entry shows lifecycle information (this model was marked legacy when captured). “Legacy” means it may be retired later, but it can still be functional for testing while available. A screenshot of the Amazon Bedrock model catalog page for "Nova Reel," showing an overview and model details (text-to-video, image-to-video, v1.1). The AWS console navigation is visible on the left and an "Open in playground" button appears at the top. ## Generating video in Playground (Nova Reel example) Steps to generate video in Playground: 1. Open Playground and select the Nova Reel model (choose `Output: Video` and the desired version, e.g., `1.1`). 2. Apply the model. Playground will warn that video generation runs as a background job and that the output will be stored in an `S3` bucket. * If you don’t have an `S3` bucket set up, the console can prompt you to create one. * Note: `S3` storage incurs standard `S3` charges in addition to Bedrock request charges. 3. Enter a prompt (for example, “sports car driving through a city at night”) and submit the job. 4. Video jobs can take a few minutes. Playground shows the job history and the `S3` bucket location for the resulting asset. In my test the job completed in roughly two minutes and produced a short clip that matched the prompt. A screenshot of the Amazon Bedrock web console showing a "Select model" dialog with the Nova Reel model listed and the Output option set to Video. A large pointer/cursor is shown selecting the model in the center panel. A screenshot of the Amazon Bedrock Playground web interface displaying a video thumbnail of a red sports car driving through a city at night. The page also shows navigation menus on the left and a job history panel on the right. ## Model availability summary Model availability varies significantly by region and provider. Use the Model Catalog to verify which modalities are offered in your selected region and check the model lifecycle state (stable, legacy, deprecated) before relying on a model for production. | Output Modality | Example Region (captured) | Example Models | Notes | | - | -: | - | - | | Image | Oregon (us-west-2) | Stable Diffusion (Stability AI) | Serverless, CLIP-style encoder limits (\~77 tokens) | | Video | Oregon (us-west-2) | Ray (Luma AI) | May be deprecated — check lifecycle | | Video | US East (N. Virginia) | Nova Reel (Amazon) | Background job, output stored to `S3` | ## Model access and permissions The console used to include a “Model access” page where you requested access to third-party models. That page has been retired: most serverless models are now enabled by default, and access control is handled through `IAM` policies. Use `IAM` to control which users and roles can call specific models. A screenshot of the AWS Management Console showing Amazon Bedrock's "Model access" page with a notice that the model access page has been retired. A large mouse cursor is visible over a mostly blank content area and the left sidebar lists Bedrock navigation items. The Model Access page has been retired. Use `IAM` to control which users and roles can call specific models. If you see older documentation that instructs you to request model access, follow the current `IAM`-based controls instead. ## What this enables for your team * Faster developer onboarding to Amazon Bedrock without needing to write code first. * Rapid, interactive experimentation with different foundation models via Playground. * Faster prompt engineering and identification of candidate models for your use case. * Reduced development risk by validating model behavior in a sandbox before integrating into production. A presentation slide titled "Results" with four numbered panels listing benefits: faster developer onboarding to Amazon Bedrock, rapid experimentation with foundation models, quicker identification of the best model for a use case, and reduced development risk through safe testing environments. Key takeaway: the Bedrock console (Model Catalog + Playground) provides a safe, interactive way for teams to discover, test, and iterate on foundation models available in a region so they can identify the best models and prompts for their applications. A presentation slide with a dark left panel reading "Key Takeaway" and a light right area containing a numbered point. The text explains that the Bedrock console enables safe experimentation with foundation models, helping teams quickly discover the best models and prompts for their applications. This concludes the short lesson on using the Bedrock console for model discovery and quick experimentation with text, image, and video modalities. If you want to automate workflows or integrate Bedrock into CI/CD, the next step is to access Bedrock programmatically via the command line or SDKs. A presentation slide titled "What's Next? Accessing Bedrock with Command Line LAB" with a teal circuit/brain-style tech icon on the right. The slide has a dark curved design element and a small "© Copyright KodeKloud" note at the bottom left. Further reading and references: * Amazon Bedrock documentation: [https://docs.aws.amazon.com/bedrock/](https://docs.aws.amazon.com/bedrock/) * Amazon S3: [https://aws.amazon.com/s3/](https://aws.amazon.com/s3/) * AWS Identity and Access Management (`IAM`): [https://aws.amazon.com/iam/](https://aws.amazon.com/iam/) * Stability AI (Stable Diffusion): [https://stability.ai/](https://stability.ai/) * Luma AI (Ray): [https://www.luma.ai/](https://www.luma.ai/) This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.