Who should use the Diffusion Models workflow?
Teams or solo builders working on creativity tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
7-step pipeline — follow each step in order
1Setup · Step 1 of 7
Open task pageDiffusion Modeling
Prepare inputs and settings through Diffusion Modeling before running diffusion models.
Why this matters
Diffusion Modeling sets up the foundation for diffusion models; clean inputs here reduce downstream rework.
What you get
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
2Input · Step 2 of 7
Open task pageStable Diffusion
Use Stable Diffusion to build supporting assets that improve diffusion models quality.
Why this matters
Stable Diffusion strengthens diffusion models by feeding better supporting material into the pipeline.
What you get
Supporting assets from stable diffusion are prepared and connected to the main workflow.
3Input · Step 3 of 7
Open task pageModel Fine-tuning
Use Model Fine-tuning to build supporting assets that improve diffusion models quality.
Why this matters
Model Fine-tuning strengthens diffusion models by feeding better supporting material into the pipeline.
What you get
Supporting assets from model fine-tuning are prepared and connected to the main workflow.
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4Core · Step 4 of 7
Open task pageDiffusion Models
Execute diffusion models with Diffusion Models to produce the primary final deliverable.
Why this matters
This is the core step where diffusion models actually happens, so it determines baseline quality for everything after it.
What you get
A first-pass final deliverable is generated and ready for refinement in the next steps.
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5Refine · Step 5 of 7
Open task page3D Modeling
Refine and validate diffusion models output using 3D Modeling before final delivery.
Why this matters
3D Modeling adds quality control so issues are caught before the workflow is finalized.
What you get
The final deliverable is improved, validated, and prepared for final delivery.
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6Refine · Step 6 of 7
Open task pageVirtual Model Generation
Refine and validate diffusion models output using Virtual Model Generation before final delivery.
Why this matters
Virtual Model Generation adds quality control so issues are caught before the workflow is finalized.
What you get
The final deliverable is improved, validated, and prepared for final delivery.
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7Deliver · Step 7 of 7
Open task pageLoRA Model Training
Package and ship the output through LoRA Model Training so diffusion models reaches end users.
Why this matters
LoRA Model Training is what turns intermediate output into a usable, publishable result for real users.
What you get
A finalized final deliverable is ready for publishing, handoff, or integration.
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