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How to Use Dzine for Image generation projects
Learn Dzine image generation projects with step by step workflows, realistic examples, and verified plan notes.
Dzine works well for image generation projects when you run it like production work: locked brief, SOURCE facts, then review before publish. Dzine creates images, videos, and talking photo avatars (dzine.ai). Obtain consent for likeness use and disclose synthetic media where required. Verify plan limits on dzine.ai. Start at /explore/dzine.
This guide focuses on image generation projects in detail. Related Dzine articles: /blog/how-to-use-dzine-for-video-generation-projects, /blog/how-to-use-dzine-for-talking-photo-avatar-clips, /blog/how-to-use-dzine-for-likeness-consent-checklists.
When this workflow is the right job
Use image generation projects when the deliverable is specifically this Dzine job. Switch to video generation projects when that workflow already owns the asset.
Step by step workflow
1. Brief Image generation projects
Write what must stay true for image generation projects in Dzine before settings or spend.
Brief: Image generation projects Keep: verified SOURCE facts only Avoid: invented pricing or features Success: one reviewable output
2. Open Dzine for Image generation projects
Use the Dzine surface that owns image generation projects. Do not mix a neighboring workflow in the same pass.
Surface: Image generation projects Start: pilot with one representative input Plans: www.dzine.ai
3. Pilot Image generation projects
Run a single image generation projects pilot. Score clarity, grounding, and whether the output is reviewable.
Pilot: Image generation projects [ ] SOURCE facts match [ ] Output reviewable [ ] Settings logged
4. Refine Image generation projects
Change one image generation projects dimension only. Save a template from the best run.
Refine: Image generation projects Change: one control only Keep: SOURCE and success criteria
Practical image generation projects examples
Document to video
Scenario: L&D needs a Dzine Image generation projects video covering "Document to video" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Document to video. Inputs: - Approved script sections for Document to video - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Document to video → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Document to video is clear and misreads are listed for fixes.
Presenter style
Scenario: L&D needs a Dzine Image generation projects video covering "Presenter style" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Presenter style. Inputs: - Approved script sections for Presenter style - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Presenter style → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Presenter style is clear and misreads are listed for fixes.
Caption track
Scenario: L&D needs a Dzine Image generation projects video covering "Caption track" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Caption track. Inputs: - Approved script sections for Caption track - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Caption track → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Caption track is clear and misreads are listed for fixes.
Quiz recap
Scenario: L&D needs a Dzine Image generation projects video covering "Quiz recap" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Quiz recap. Inputs: - Approved script sections for Quiz recap - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Quiz recap → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Quiz recap is clear and misreads are listed for fixes.
Short pilot
Scenario: L&D needs a Dzine Image generation projects video covering "Short pilot" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Short pilot. Inputs: - Approved script sections for Short pilot - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Short pilot → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Short pilot is clear and misreads are listed for fixes.
Brand wardrobe
Scenario: L&D needs a Dzine Image generation projects video covering "Brand wardrobe" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Brand wardrobe. Inputs: - Approved script sections for Brand wardrobe - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Brand wardrobe → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Brand wardrobe is clear and misreads are listed for fixes.
Voice lock
Scenario: L&D needs a Dzine Image generation projects video covering "Voice lock" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Voice lock. Inputs: - Approved script sections for Voice lock - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Voice lock → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Voice lock is clear and misreads are listed for fixes.
Scene under 60s
Scenario: L&D needs a Dzine Image generation projects video covering "Scene under 60s" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Scene under 60s. Inputs: - Approved script sections for Scene under 60s - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Scene under 60s → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Scene under 60s is clear and misreads are listed for fixes.
Regen one scene
Scenario: L&D needs a Dzine Image generation projects video covering "Regen one scene" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Regen one scene. Inputs: - Approved script sections for Regen one scene - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Regen one scene → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Regen one scene is clear and misreads are listed for fixes.
Course outline
Scenario: L&D needs a Dzine Image generation projects video covering "Course outline" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Course outline. Inputs: - Approved script sections for Course outline - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Course outline → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Course outline is clear and misreads are listed for fixes.
Compliance disclaimer
Scenario: L&D needs a Dzine Image generation projects video covering "Compliance disclaimer" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Compliance disclaimer. Inputs: - Approved script sections for Compliance disclaimer - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Compliance disclaimer → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Compliance disclaimer is clear and misreads are listed for fixes.
L&D template
Scenario: L&D needs a Dzine Image generation projects video covering "L&D template" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for L&D template. Inputs: - Approved script sections for L&D template - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review L&D template → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where L&D template is clear and misreads are listed for fixes.
Language pack
Scenario: L&D needs a Dzine Image generation projects video covering "Language pack" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Language pack. Inputs: - Approved script sections for Language pack - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Language pack → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Language pack is clear and misreads are listed for fixes.
Aspect 16:9
Scenario: L&D needs a Dzine Image generation projects video covering "Aspect 16:9" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Aspect 16:9. Inputs: - Approved script sections for Aspect 16:9 - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Aspect 16:9 → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Aspect 16:9 is clear and misreads are listed for fixes.
Source doc cite
Scenario: L&D needs a Dzine Image generation projects video covering "Source doc cite" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Source doc cite. Inputs: - Approved script sections for Source doc cite - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Source doc cite → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Source doc cite is clear and misreads are listed for fixes.
Review gate
Scenario: L&D needs a Dzine Image generation projects video covering "Review gate" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Review gate. Inputs: - Approved script sections for Review gate - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Review gate → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Review gate is clear and misreads are listed for fixes.
Publish checklist
Scenario: L&D needs a Dzine Image generation projects video covering "Publish checklist" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Publish checklist. Inputs: - Approved script sections for Publish checklist - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Publish checklist → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Publish checklist is clear and misreads are listed for fixes.
Training scene
Scenario: L&D needs a Dzine Image generation projects video covering "Training scene" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Training scene. Inputs: - Approved script sections for Training scene - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Training scene → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Training scene is clear and misreads are listed for fixes.
Onboarding avatar
Scenario: L&D needs a Dzine Image generation projects video covering "Onboarding avatar" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Onboarding avatar. Inputs: - Approved script sections for Onboarding avatar - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Onboarding avatar → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Onboarding avatar is clear and misreads are listed for fixes.
SCORM export
Scenario: L&D needs a Dzine Image generation projects video covering "SCORM export" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for SCORM export. Inputs: - Approved script sections for SCORM export - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review SCORM export → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where SCORM export is clear and misreads are listed for fixes.
Multilingual clip
Scenario: L&D needs a Dzine Image generation projects video covering "Multilingual clip" for learners. Objective: Produce a presenter/course video with scene-level scripts and regenerable scenes for Multilingual clip. Inputs: - Approved script sections for Multilingual clip - Avatar/voice selection - Language/locale if needed - Optional on-screen labels Workflow: Outline scenes → Script → Generate Image generation projects → Review Multilingual clip → Regen weak scenes → Export Requirements: - Stay within verified Dzine capabilities; do not invent features. - Confirm live plan notes on www.dzine.ai before promising volume. - Change one variable between iterations. - Human-review before external publish, send, billing, or clinical/legal use. Expected output: A reviewable Image generation projects output where Multilingual clip is clear and misreads are listed for fixes.
How to improve image generation projects
Cut noise from image generation projects by removing extra adjectives while preserving SOURCE facts in Dzine.
Raise quality by insisting on a single success check before debating style.
Make review easier by labeling fields that must never change.
Speed iteration by cloning the last good run and altering only one control.
Stabilize outputs by pinning settings after the pilot is approved.
Reduce rework by rejecting drafts that invent claims.
Improve handoffs by recording which control produced the best result.
Harden the workflow by testing an incomplete input before trusting defaults.
Prompting and usage guidance
Name the image generation projects job, audience, and success check before opening Dzine.
Paste only verified facts under SOURCE so Dzine cannot invent details.
Specify the deliverable shape up front.
Call out fixed details versus flexible style choices.
Ask Dzine to flag unsupported claims before you accept the draft.
Limitations to respect
Check Dzine plan gates for image generation projects on www.dzine.ai before you promise timelines.
Keep drafts unpublished until a human confirms SOURCE facts.
Dzine can be wrong. Treat image generation projects as provisional until review.
If documentation is silent on a claim, leave it out rather than guessing.
Practical tips for this workflow
Pilot once before batching image generation projects in Dzine.
Keep a reusable template with variables for image generation projects.
Separate creative instructions from SOURCE facts.
Log settings from the best run.
Common mistakes
- Skipping the pilot run before scaling volume
- Inventing pricing, quotas, or features not on official pages
- Mixing unrelated workflows in one session
- Publishing without a human review gate
Treat image generation projects in Dzine as a production workflow: brief, pilot, refine, then ship with review. Related reading: /blog/how-to-use-dzine-for-video-generation-projects, /blog/how-to-use-dzine-for-talking-photo-avatar-clips, /blog/how-to-use-dzine-for-likeness-consent-checklists.