Generate images in batch with a diffusion model on Deadline Cloud

The text_to_image_batch job bundle on GitHub runs high-throughput batch image generation on a JSONL of prompts using a diffusion model. The default model is FLUX.2 Klein 4B, which is Apache 2.0 licensed, ungated, and distilled to 4 steps. The scheduler distributes tasks across available GPU workers, with the diffusion pipeline loaded once per worker and reused for every task on that worker. An aggregate step produces a combined JSONL file and a static HTML gallery viewer.

When a JSONL line carries a caption field, or a generated_text field chained from the Run batch LLM inference with vLLM on Deadline Cloud bundle's output, the job composites the text over the generated image as crisp typography. Lines without a caption produce pure imagery.

To run this bundle, you need a service-managed fleet with NVIDIA GPUs and at least 32 GB RAM, and a queue with a conda queue environment that reads CondaPackages and CondaChannels job parameters. From the job_bundles directory of the samples repository, submit the job:

deadline bundle gui-submit text_to_image_batch

For a complete walkthrough that covers farm setup, input format, caption overlays, and the chaining workflow, see Generate images in batch with a diffusion model.