How to Use AI HUB by Picsart for Video generation workflows

AI HUB by Picsart works well for video generation workflows when you run it like production work: locked brief, SOURCE facts, then eval then prod focused on color drift. Confirm live plans on picsart.io/pricing. Start at /explore/ai-hub-by-picsart.

This guide focuses on video generation workflows in detail. Related AI HUB by Picsart articles: /blog/how-to-use-ai-hub-by-picsart-for-audio-generation-workflows, /blog/how-to-use-ai-hub-by-picsart-for-evaluation-credit-budgeting, /blog/how-to-use-ai-hub-by-picsart-for-prompt-template-libraries.

When this workflow is the right job

Use video generation workflows when the deliverable is specifically this AI HUB by Picsart job. Switch to model comparison with air urns when that workflow already owns the asset.

Step by step workflow

1. Brief Video generation workflows

Write what must stay true for video generation workflows in AI HUB by Picsart before settings or spend.

Brief: Video generation workflows
Keep: eval credits from SOURCE
Avoid: invented pricing or features
Success: one reviewable output

2. Open AI HUB by Picsart for Video generation workflows

Use the AI HUB by Picsart surface that owns video generation workflows. Do not mix a neighboring workflow in the same pass.

Surface: Video generation workflows
Start: document version
Plans: picsart.io/pricing

3. Pilot Video generation workflows

Run a single video generation workflows pilot. Score clarity, grounding, and whether commercial clean still matches.

Pilot: Video generation workflows
[ ] SOURCE facts match
[ ] sharpness score clear
[ ] Settings logged

4. Refine Video generation workflows

Change one video generation workflows dimension only. Save a template with variables for batch queue.

Refine: Video generation workflows
Change: credit budget
Keep: SOURCE and brand locked

Practical video generation workflows examples

eval credits

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "eval credits".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for eval credits
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for eval credits.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for eval credits with URN scores, latency, and a pin/rollback decision.

prompt template

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "prompt template".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for prompt template
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for prompt template.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for prompt template with URN scores, latency, and a pin/rollback decision.

API integration

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "API integration".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for API integration
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for API integration.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for API integration with URN scores, latency, and a pin/rollback decision.

rollback plan

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "rollback plan".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for rollback plan
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for rollback plan.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for rollback plan with URN scores, latency, and a pin/rollback decision.

latency log

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "latency log".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for latency log
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for latency log.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for latency log with URN scores, latency, and a pin/rollback decision.

negative list

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "negative list".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for negative list
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for negative list.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for negative list with URN scores, latency, and a pin/rollback decision.

color drift

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "color drift".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for color drift
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for color drift.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for color drift with URN scores, latency, and a pin/rollback decision.

sharpness score

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "sharpness score".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for sharpness score
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for sharpness score.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for sharpness score with URN scores, latency, and a pin/rollback decision.

export PNG

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "export PNG".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for export PNG
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for export PNG.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for export PNG with URN scores, latency, and a pin/rollback decision.

campaign set

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "campaign set".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for campaign set
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for campaign set.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for campaign set with URN scores, latency, and a pin/rollback decision.

model A vs B

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "model A vs B".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for model A vs B
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for model A vs B.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for model A vs B with URN scores, latency, and a pin/rollback decision.

batch queue

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "batch queue".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for batch queue
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for batch queue.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for batch queue with URN scores, latency, and a pin/rollback decision.

brand palette

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "brand palette".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for brand palette
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for brand palette.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for brand palette with URN scores, latency, and a pin/rollback decision.

shadow quality

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "shadow quality".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for shadow quality
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for shadow quality.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for shadow quality with URN scores, latency, and a pin/rollback decision.

handle detail

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "handle detail".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for handle detail
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for handle detail.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for handle detail with URN scores, latency, and a pin/rollback decision.

docs URN

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "docs URN".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for docs URN
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for docs URN.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for docs URN with URN scores, latency, and a pin/rollback decision.

hero mug shot

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "hero mug shot".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for hero mug shot
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for hero mug shot.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for hero mug shot with URN scores, latency, and a pin/rollback decision.

URN compare

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "URN compare".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for URN compare
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for URN compare.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for URN compare with URN scores, latency, and a pin/rollback decision.

production pin

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "production pin".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for production pin
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for production pin.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for production pin with URN scores, latency, and a pin/rollback decision.

video clip test

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "video clip test".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for video clip test
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for video clip test.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for video clip test with URN scores, latency, and a pin/rollback decision.

audio model test

Scenario:
A creative ops team is running AI HUB by Picsart video generation workflows comparing models for "audio model test".

Objective:
Compare URNs fairly with identical inputs, score quality + latency, and define rollback before pinning.

Inputs:
- Identical prompt/seed inputs for audio model test
- URN candidates
- Scoring rubric (sharpness, color drift, latency)
- Eval credit budget (confirm on official pages)

Workflow:
Lock inputs → Run A/B video generation workflows → Score → Decide pin/rollback → Log

Requirements:
- Never change the prompt mid A/B.
- Log latency beside visual quality for audio model test.
- Set rollback thresholds before pinning.
- Stop when eval budget is exhausted.

Expected output:
An eval sheet for audio model test with URN scores, latency, and a pin/rollback decision.

How to improve video generation workflows

Improve video generation workflows handoffs by recording which AI HUB by Picsart control produced the batch queue result.

Strengthen video generation workflows by adding a second reader who only checks brand palette spelling and facts in AI HUB by Picsart.

Lift video generation workflows consistency by reusing the same identical inputs vocabulary across related AI HUB by Picsart jobs.

Harden video generation workflows by testing an empty or incomplete handle detail input before trusting AI HUB by Picsart defaults.

Cut noise from video generation workflows by removing extra adjectives while preserving docs URN in AI HUB by Picsart.

Raise video generation workflows quality by insisting on log latency before any style debate in AI HUB by Picsart.

Make video generation workflows easier to review by labeling URN compare fields that must never change in AI HUB by Picsart.

Speed video generation workflows iteration by cloning the last good AI HUB by Picsart run and altering only template library.

Prompting and usage guidance

Name the video generation workflows job, the audience, and one measurable success check before opening AI HUB by Picsart.

Paste only verified facts under SOURCE so AI HUB by Picsart cannot invent details during video generation workflows.

Specify the video generation workflows deliverable shape up front, such as scenes, bullets, rows, or a signed note.

Call out fixed negative list details versus flexible compare export choices for video generation workflows.

Close with a review line that asks AI HUB by Picsart to flag unsupported claims for video generation workflows.

Limitations to respect

Check AI HUB by Picsart plan gates for video generation workflows on picsart.io/pricing before you promise timelines.

Keep video generation workflows drafts unpublished until a human confirms SOURCE facts.

Plan and region differences can change video generation workflows availability. Prefer official AI HUB by Picsart docs.

Beta or preview labels on AI HUB by Picsart mean you should pilot video generation workflows before wide rollout.

Practical tips for this workflow

Review video generation workflows while context is fresh; delayed checks miss 16:9 video mismatches on brand palette.

If video generation workflows touches compliance language about video clip test, lock verbatim strings outside AI HUB by Picsart first.

Retire video generation workflows templates when AI HUB by Picsart docs change names or gates for negative list workflows.

For video generation workflows, capture a before and after artifact of brand palette every time AI HUB by Picsart settings change.

Teach video generation workflows operators where AI HUB by Picsart controls for credit budget live so fixes are not person dependent.

Prefer idempotent video generation workflows steps when AI HUB by Picsart reruns are likely after a failed negative list pass.

Rank video generation workflows examples by reuse frequency, putting brand palette patterns that win reviews at the top.

Close each video generation workflows session by noting the next compare export tweak to try in AI HUB by Picsart.

When stakeholders want premium video generation workflows polish, change 16:9 video before you rewrite negative list facts.

Budget a second video generation workflows pass focused on edge cases around brand palette, not only the happy path in AI HUB by Picsart.

AI HUB by Picsart video generation workflows note: after compare export, recheck hero mug shot against SOURCE and confirm soft window light still matches the brief.

Common mistakes

• Skipping a written brief before starting video generation workflows in AI HUB by Picsart
• Inventing pricing, credits, or features not confirmed on official AI HUB by Picsart pages
• Scaling video generation workflows volume before one successful pilot
• Mixing a different AI HUB by Picsart workflow into the same video generation workflows session
• Ignoring plan gates while scheduling video generation workflows deadlines
• Publishing video generation workflows output without stakeholder review

For more on video generation workflows, see /blog/how-to-use-ai-hub-by-picsart-for-audio-generation-workflows, /blog/how-to-use-ai-hub-by-picsart-for-evaluation-credit-budgeting, /blog/how-to-use-ai-hub-by-picsart-for-prompt-template-libraries. Hub: /explore/ai-hub-by-picsart.