A good prompt gives you one image. A good graph gives you a hundred that belong to each other. Consistency is the hard part of generative work, and consistency is a pipeline problem, not a wording problem.
How I build a workflow
Every graph starts from a question about control: what has to stay fixed, and what is allowed to vary?
- SDXL and Flux as base models, chosen per job — Flux for coherence and text, SDXL when I want a specific LoRA ecosystem
- ControlNet for the parts that must not drift: pose, depth, composition, camera
- LoRA stacks for the parts that must stay recognizable across a release or campaign
- Export paths tuned for where the frames are actually going — Premiere, After Effects, Blender compositing, or straight to a Three.js texture
Treating a workflow like a product
Each graph has inputs, a naming convention, and post-processing baked in, because the version of me that opens this file in three months has forgotten everything. Batch output lands in predictable folders. Seeds and model versions are recorded. Collaborators can run it without me on the call.
That discipline is the whole difference between a tool and a party trick.
The honest part
Diffusion models lower the technical barrier to a striking image, which means a striking image is no longer evidence of anything. What still separates work is knowing why this frame and not the other ninety-nine — and having a pipeline fast enough that you can afford to look at all hundred before deciding.
Tools
ComfyUI, Stable Diffusion, Automatic1111 for quick tests, ControlNet, LoRA training outputs, SDXL, Flux, and local model serving on an RTX 3090.