September 1, 2026AIExplore

How to Edit and Refine Videos Iteratively in Luma

Use Dream Machine Modify with Adhere, Flex, or Reimagine to refine clips conversationally instead of regenerating from scratch.

Luma

Modify in Dream Machine adjusts an existing clip with conversational instructions. Official Modify Video docs describe strength from Adhere for subtle change through Flex to Reimagine for looser world change. Modify is available with paid Dream Machine subscriptions on web and iOS. Ray3 Modify adds keyframe and character reference controls. Confirm live credit rates on lumalabs.ai/pricing. Start at /explore/luma.

This guide covers the workflow in detail. Related Luma articles: /blog/how-to-use-luma-dream-machine-for-text-to-video, /blog/how-to-control-style-and-aesthetics-in-luma, /blog/how-to-use-keyframes-and-loops-in-luma.

When this workflow is the right job

Use Modify when a clip is close. Prefer a new generation when subject and camera are both wrong. Prefer Extend when you need more time rather than a visual change.

Step by step workflow

1. Decide what must stay

Name camera path, subject, and pacing that must remain before you ask for changes.

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Keep Camera path Subject pacing Foreground product Change Color warmth Background trees. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Keep Camera path Subject pacing Foreground product Change Color warmth Background trees" that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Inputs:
Use the actual source material required for this task, together with the intended audience, destination, format, and quality requirements. Specify relevant files, text, URLs, structured data, reference assets, dimensions, language, tone, timing, model or generation settings, credentials, field mappings, or integration values when they apply. Keep secrets out of the example and use the product's supported credential or configuration mechanism.

Workflow:
1. Start with one representative input that contains the important characteristics of the real workload.
2. Configure luma specifically for the requested task and make the important settings explicit.
3. Run the first pass and inspect the result against the objective.
4. Correct only the settings or source material responsible for a failed requirement instead of changing everything at once.
5. Validate the intermediate result before passing it to the next step.
6. When the result meets the requirements, save the successful configuration so the same process can be repeated consistently.
7. If this is a multi-step workflow, verify each handoff and keep a clear fallback or review path for failures.

Requirements:
The example must use supported functionality only. Do not invent product features, pricing, limits, integrations, model names, API behavior, or unavailable settings. Preserve important source data and formatting. Validate required fields before processing, handle empty or invalid input explicitly, prevent accidental duplicate processing where relevant, and stop or route the item for review when a required step fails. For credentials, use the supported secure configuration rather than placing secrets directly in the content.

Expected output:
The final result should directly satisfy the "Keep Camera path Subject pacing Foreground product Change Color warmth Background trees" requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

2. Pick a strength

Adhere for light restyle. Flex for meaningful style change with recognizable elements. Reimagine for larger world changes.

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Modify with Flex: Make the colors warmer and add more trees in the background. Keep the same camera path and subject pacing.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Modify with Flex: Make the colors warmer and add more trees in the background. Keep the same camera path and subject pacing." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Modify with Flex: Make the colors warmer and add more trees in the background. Keep the same camera path and subject pacing." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

3. Apply one change cluster

Group related visual tweaks. Do not rewrite camera, subject, and world in one pass.

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Modify with Adhere: Slightly warmer color temperature only. No new objects.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Modify with Adhere: Slightly warmer color temperature only. No new objects." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Modify with Adhere: Slightly warmer color temperature only. No new objects." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

4. Chain small passes

Review, then run another Modify if needed. Ask for ideas when stuck on the next beat.

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Next pass Keep warmer grade. Include a small cottage in the distance. Do not change camera.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Next pass Keep warmer grade. Include a small cottage in the distance. Do not change camera." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Next pass Keep warmer grade. Include a small cottage in the distance. Do not change camera." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Practical Modify prompts

Warmer trees Flex

Adhere temperature

Reimagine weather

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Modify with Reimagine: Turn the clear sky into soft overcast with light mist. Keep the road and car.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Modify with Reimagine: Turn the clear sky into soft overcast with light mist. Keep the road and car." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Modify with Reimagine: Turn the clear sky into soft overcast with light mist. Keep the road and car." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Remove clutter

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Modify with Flex: Remove the stray chair on the left. Keep lighting and camera.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Modify with Flex: Remove the stray chair on the left. Keep lighting and camera." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Modify with Flex: Remove the stray chair on the left. Keep lighting and camera." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Add cottage

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Modify with Flex: Include a small cottage in the distance. Keep foreground subject and camera path.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Modify with Flex: Include a small cottage in the distance. Keep foreground subject and camera path." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Modify with Flex: Include a small cottage in the distance. Keep foreground subject and camera path." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Cooler night

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Modify with Adhere: Cool the highlights slightly toward evening blue. Keep practical lamps.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Modify with Adhere: Cool the highlights slightly toward evening blue. Keep practical lamps." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Modify with Adhere: Cool the highlights slightly toward evening blue. Keep practical lamps." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Softer grade

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Modify with Adhere: Soften contrast and reduce saturation a little. Keep composition.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Modify with Adhere: Soften contrast and reduce saturation a little. Keep composition." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Modify with Adhere: Soften contrast and reduce saturation a little. Keep composition." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

More foliage

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Modify with Flex: Add denser foliage on the right edge. Do not move the camera.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Modify with Flex: Add denser foliage on the right edge. Do not move the camera." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Modify with Flex: Add denser foliage on the right edge. Do not move the camera." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Cleaner backdrop

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Modify with Flex: Simplify the busy background to a soft gradient. Keep subject sharp.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Modify with Flex: Simplify the busy background to a soft gradient. Keep subject sharp." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Modify with Flex: Simplify the busy background to a soft gradient. Keep subject sharp." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Golden hour shift

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Modify with Flex: Shift lighting toward golden hour from camera left. Keep subject pose.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Modify with Flex: Shift lighting toward golden hour from camera left. Keep subject pose." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Modify with Flex: Shift lighting toward golden hour from camera left. Keep subject pose." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Less motion blur

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Modify with Adhere: Reduce motion blur on the subject. Keep camera move.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Modify with Adhere: Reduce motion blur on the subject. Keep camera move." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Modify with Adhere: Reduce motion blur on the subject. Keep camera move." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Product color lock

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Modify with Adhere: Keep product body color exact. Only warm the environment.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Modify with Adhere: Keep product body color exact. Only warm the environment." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Modify with Adhere: Keep product body color exact. Only warm the environment." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Rain add

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Modify with Flex: Add light rain and wet ground reflections. Keep architecture.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Modify with Flex: Add light rain and wet ground reflections. Keep architecture." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Modify with Flex: Add light rain and wet ground reflections. Keep architecture." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Crowd thin

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Modify with Flex: Thin the background crowd. Keep hero subject and camera.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Modify with Flex: Thin the background crowd. Keep hero subject and camera." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Modify with Flex: Thin the background crowd. Keep hero subject and camera." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Sky replace soft

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Modify with Flex: Replace blown sky with soft clouds. Keep horizon line.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Modify with Flex: Replace blown sky with soft clouds. Keep horizon line." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Modify with Flex: Replace blown sky with soft clouds. Keep horizon line." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Interior warmer lamps

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Modify with Adhere: Warm the practical lamps. Keep daylight from the window.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Modify with Adhere: Warm the practical lamps. Keep daylight from the window." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Modify with Adhere: Warm the practical lamps. Keep daylight from the window." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

Idea ask

Scenario:
A realistic luma workflow is being prepared for Use Luma Dream Machine for Text-to-Video using luma. The starting requirement is: Suggest three subtle Modify passes that keep this camera path and improve evening mood.. The work should be tested on a representative input first so the result can be reviewed before the same process is applied to the full project or production workload.

Objective:
Create a production-ready result for "Suggest three subtle Modify passes that keep this camera path and improve evening mood." that directly supports the article use case. The output should be specific, reviewable, and reproducible rather than a generic demonstration. Keep the requested goal as the primary outcome and avoid adding unrelated features, assumptions, or unsupported capabilities.

Expected output:
The final result should directly satisfy the "Suggest three subtle Modify passes that keep this camera path and improve evening mood." requirement and be understandable without guessing what was supplied or configured. A successful run should produce the intended output in the requested format, with the important values and workflow decisions clear enough to reproduce the result. If the workflow can fail, the expected behavior should also make the failure visible and provide a clear next action instead of silently producing an incomplete result.

How to iterate without wasting credits

Chain small Modify passes rather than one massive change that breaks structure.

Name what must stay in every Modify prompt.

Pick strength intentionally. Adhere is not Reimagine.

Ray3 Modify keyframe guidance: ensure the modification target is present in start or end frames when using those controls.

Video to video credit costs are listed per second by model and resolution on the pricing page.

Do not start over when Modify would fix one element.

Watch for aspect ratio crop warnings on non preset uploads per official Modify help.

Use boards so refined versions stay next to the source clip.

When identity matters, combine Modify with character reference where the UI offers it on paid plans.

Separate motion fixes from grade fixes across passes.

If Reimagine invents too much, step back to Flex with a tighter keep list.

Save successful Modify phrasing into your prompt library.

Prompting guidance that holds up in review

Open with Modify with Adhere, Flex, or Reimagine so strength is explicit.

List keep constraints before change requests when the clip is already valuable.

Ask for one cluster of related changes per pass.

Use idea prompts only after the base clip is stable enough to keep.

Map stakeholder comments to a single Modify sentence before you spend credits.

Limitations to respect

Modify requires a paid Dream Machine subscription on web and iOS per official docs.

High resolution video to video passes can consume credits quickly. Check the pricing table.

Reimagine can rewrite more of the world than you intend.

Extend is for length. Modify is for look. Do not confuse the tools.

Field tips from real boards

Draft explores at lower cost settings when your plan and model picker allow.

After Modify, recheck product accuracy before client send.

Ask the model for next beat ideas only after the current clip is stable.

Confirm Modify remains a paid subscription feature on official docs before planning a free tier workflow.

Open every Modify pass with an explicit keep list so valuable camera work survives the change.

Adhere is for temperature and polish. Flex is for meaningful set changes. Reimagine is a world rewrite.

Chain two small Modify passes when stakeholders pile on requests. One mega pass usually breaks structure.

Recheck product color after any warmth pass. Environment grade should not recolor the SKU.

If Reimagine invents props, step back to Flex with a tighter ban list.

Map each stakeholder comment to one Modify sentence before you spend credits.

Watch aspect crop warnings on odd uploads. Silent crops look like creative mistakes later.

Keep refined versions beside the source clip on the same board for honest A B review.

Separate motion fixes from grade fixes so you can roll back one axis without losing the other.

Confirm paid Modify access before promising a free tier workflow that cannot complete.

Video to video costs scale with seconds and resolution. Forecast before a long refine chain.

Ask for next beat ideas only after the current clip is stable enough to keep.

For Modify delivery, reject any take that fails a one sentence subject and motion intent test before you queue another credit spend.

Open every Modify pass with an explicit keep list.

Adhere is for polish. Flex is for set changes. Reimagine is a world rewrite.

Chain two small Modify passes when stakeholders pile on requests.

Recheck product color after any warmth pass.

If Reimagine invents props, step back to Flex with a ban list.

Map each stakeholder comment to one Modify sentence.

Watch aspect crop warnings on odd uploads.

Keep refined versions beside the source clip for A B review.

Confirm paid Modify access before promising a free tier workflow.

Forecast video to video costs before a long refine chain.

Ask for next beat ideas only after the clip is stable.

Never Modify a clip you would not keep. Bad bases waste video to video credits.

Write the keep list in the same order every time: camera, subject, product, then allowed changes.

Warmth passes can shift brand colors. Call out exact product color lock when needed.

If the UI shows Ray3 Modify keyframe tools, ensure the edit target exists in those frames.

Relaxed mode can finish Modify jobs when Fast credits are tight, but plan for longer waits per official notes.

Reject stacked stakeholder threads. Collapse them into one Modify sentence first.

After removing clutter, check whether the model filled the hole with invented objects.

Save Modify phrasing that worked as library snippets next to the campaign templates.

Common mistakes

Changing camera, subject, and style in one Modify pass
Starting over when Modify would fix one element
Using Reimagine when Adhere was enough
Ignoring keep constraints
Skipping credit checks on long video to video jobs
Modifying before the base clip is worth keeping

Related Luma articles: /blog/how-to-use-luma-dream-machine-for-text-to-video, /blog/how-to-control-style-and-aesthetics-in-luma, /blog/how-to-use-keyframes-and-loops-in-luma. Return to /explore/luma for the broader guide set.