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STAGE 01
🧊
3D Model
Varun creates furniture assets in 3D Max. James reviews architecture files for accuracy.
3D MaxCinema 4DBlender
STAGE 02
🗂
Assets Library
Upload a real interior photo. fal AI removes all furniture and people — preserving architecture and lighting — to produce a clean empty-room asset.
fal AInano-banana-pro/edit
STAGE 03
🖼
AI Background
Client selects from assets library. Team confirms art direction, generates multiple options, swaps floors. Final 4 options to client.
ComfyUINano Banana node
STAGE 04
📐
Space Layout
Client selects one space. Refine details: window view, add/remove elements, adjust architecture. Goal: client-approved space.
ComfyUI
STAGE 05
🔀
AI Compositing
3 inputs merged: product white-bg + white model/line drawing + background. LLM generates prompts. Output: realistic interior photo.
ComfyUIOpenrouter / LLM
STAGE 06
✂️
Post-Production
Quality control. Multiple AI runs → mask and layer the best parts. Fix micro-details. Colour grade.
Photoshop
STAGE 07
🎬
AI Video
Animate the approved still. Kling for high-res, SeedDance for complex showcase.
KlingSeedDanceFreepik AI
STAGE 08
🎞
Video Finishing
Upscale with Topaz, add motion graphics in After Effects, final cut in Premiere Pro.
After EffectsTopaz VideoPremiere Pro
STAGE 01
3D Model
Varun builds furniture and product assets in 3D Max. James reviews and verifies 3D architecture files provided by clients or suppliers.
3 Tools
🧊
3D Max
👤 Varun — Create furniture & assets
Model, texture, light, and render the product image
Steps
1
Open and set up the scene
File → New or File → Open an existing .max file. Set units first: Customize → Units Setup → Millimeters to match the project scale.
2
Import or build the furniture geometry
Existing file (.fbx / .obj / .dwg): File → Import. Building from scratch: Create Panel → Geometry → choose a primitive (Box, Cylinder), then refine with the Modify panel.
3
Apply materials and textures
Press M to open Material Editor. Use VRayMtl. Load the texture in the Diffuse slot (minimum 2048×2048px for fabrics and patterned surfaces). Drag material onto the object.
4
Set up lighting and camera
Use VRay Light or Arnold Area Light. Key Light at 45° front, Fill Light opposite side, Rim Light behind. Set up Target Camera — product should fill 60–70% of the frame.
5
Render and export
Press F10. Resolution: 3000×3000px minimum. Format: PNG 32-bit with alpha channel (transparent background). Save to the project folder.
⚠️
Delivery standardPNG with transparent background · minimum 2000px wide · product centred · no cropping. These are required for clean AI compositing in Stage 05.
🎬
Cinema 4D
👤 James — Review architecture files
Verify proportions, layout, and dimensions of client-provided 3D files
Steps
1
Open the file
File → Open. Supports .c4d, .fbx, .obj, .3ds, .dae. If the file is .max, ask Varun to export it as .fbx first.
2
Navigate and inspect
Middle Mouse + drag = pan. Alt + Left Mouse = rotate. Scroll = zoom. H = fit all in view. Click any object → Attributes panel → check Size (W/H/D). Compare with the client's real-world dimensions.
3
Screenshot and flag issues
Ctrl+Shift+E to capture viewport. Annotate with arrows, send to Varun with notes on what needs fixing.
🟠
Blender
👤 James — Backup file viewer
Use when Cinema 4D cannot open a file format
Steps
1
Import and inspect
File → Import, choose the format (FBX / OBJ / GLTF / DAE). Middle Mouse + drag = rotate. Shift + Middle Mouse = pan. Numpad 1/3/7 for orthographic views. Select an object → press NItem → Dimensions. Change units at Scene Properties → Units → Millimeters.
STAGE 02
Assets Library
Take a real interior photo and use AI image editing to remove all furniture and people — generating a clean, empty-room version of the same space. Used to build a browsable library that clients select from in Stage 03.
fal AI · nano-banana-pro
🗂
fal AI — nano-banana-pro/edit
👤 James — Upload, configure, and generate
Send a real interior photo to fal AI with the clearance prompt — receive a clean, furniture-free version of the same space
ℹ️
What this stage producesA clean empty-room photo derived from a real interior — all furniture, objects, and people removed, while preserving the original architecture, lighting, perspective, and spatial depth. The output becomes a library entry that clients browse in Stage 03 to choose their style direction.
Workflow — 5 Steps
1
Upload the original interior photo
Source a real interior photo of the target space type (office, restaurant, retail, residential). The photo should be high-resolution and show the room from a front-facing, eye-level angle. Drag or click to upload into the fal AI interface.
2
Select output parameters
Before running the model, set the output resolution and aspect ratio to match the project requirements.
ParameterOptionsRecommendation
Resolution1K / 2K / 4KUse 2K for library building, 4K for final client-facing assets
Aspect ratioMatch original photoKeep the original — do not crop or resize the source before upload
3
Call fal AI — model: nano-banana-pro/edit
In the fal AI interface, select the model nano-banana-pro/edit. Paste the clearance prompt below into the prompt field, then send the original image + prompt together to the model.
Analyze the uploaded image carefully. Identify whether the scene is an office or restaurant interior. Your goal is to remove all existing furniture and people while preserving the structure, lighting, and composition of the original scene. Specifically, remove: All office desks, office chairs, sofas, couches, and tables rug. Any objects on the tables (laptops, dishes, cups, decorations, etc.). Any people or human figures visible in the scene. Maintain: The walls, windows, floor, ceiling, and architectural layout. The original lighting, reflections, shadows, and camera angle. The overall composition and perspective unchanged. Output should depict an empty, photographed from the front VIEW WITH eye-level front shot OF THE clean interior space, as if it were newly built or just cleared for renovation -- no furniture, no people, no clutter, but retaining the same spatial depth and lighting realism. Perfectly symmetrical interior photograph, dead-centre one-point perspective, camera positioned exactly at room centre, equal distance to left and right walls, architectural photography, straight-on front view, no angle, no tilt
4
AI processing — what the model does
The model analyses the uploaded photo and executes three operations in sequence:

Scene recognition — identifies whether the space is an office, restaurant, or other interior type, which informs which object categories to target.
Object removal — removes all furniture, objects, and people while reconstructing the floor, walls, and background behind them using inpainting.
Composition lock — preserves the exact camera angle, lighting direction, reflections, and shadows from the original photo.
5
Review the output, compare with original, save
Compare the output side-by-side with the original photo. Check:
① All furniture and people are removed (no residual artefacts)
② The floor behind where furniture stood is cleanly reconstructed
③ The perspective and spatial depth match the original
④ Lighting and shadows are consistent with the original photo

If the result passes, save it to the Assets Library: assets/office_clearfield_v01.png assets/restaurant_clearfield_v01.png Keep the original photo alongside it for reference and for client comparison.
ℹ️
Prompt logic — 3 layers
Layer 1 — Scene recognition: The model analyses the image and identifies whether it is an office or restaurant. This allows it to target the correct object categories accurately.

Layer 2 — Remove / Preserve boundaries:
RemovePreserve
Desks, chairs, sofas, tables, rugsWalls, windows, floor, ceiling
Items on tables (laptops, dishes, cups, decorations)Architectural structure and spatial layout
All people and human figuresOriginal lighting, reflections, camera angle

Layer 3 — Output style constraint: Forces a front-facing, eye-level, perfectly symmetrical one-point perspective composition — camera centred in the room, equal distance to both walls, no tilt, no angle. This simulates an architectural photography standard that is consistent across the entire library.
STAGE 03
AI Background
Client selects references from the Assets Library. Team confirms art direction in detail, runs multiple generation rounds, and presents 4 final options — each available with different floor finishes.
ComfyUI
🖼
ComfyUI — Background Generation
👤 James — Art direction + generation
Generate multiple space options with art direction, then swap floor finishes
ℹ️
About the image nodeThe generation uses the Nano Banana image node built into this ComfyUI workflow — it's not a separate platform. When the workflow runs, generation happens through this node automatically. You don't need to visit any external website.
Part A — Art Direction with the Client
1
Client browses the Assets Library and picks references
Share the Assets Library with the client (or walk through it together). Ask them to select 2–3 images that feel closest to their vision. These become the starting reference point — not the final output, just the direction.
2
Confirm art direction in detail
Go through each design decision with the client. Cover all of the following:
ElementQuestions to askExample options
CeilingStyle, height, finish, light fixturesExposed concrete / Timber slats / Coffered / Plain white
WallsMaterial, colour, texture, feature wallsPlaster / Brick / Glass / Timber panel
LayoutOpen plan vs zoned / partitions / room-within-roomFull open / Soft zoning / Meeting pod / Partition wall
LightNatural light direction / time of day / moodMorning soft / Afternoon warm / Evening dim / Overcast flat
FloorLeave flexible — will be swapped at step 6Timber / Carpet / Concrete / Marble / Tile
Part B — Generation Rounds
3
Open ComfyUI desktop and load the background workflow
Launch the ComfyUI desktop app. Load background_gen_v1.json via Workflow → Load.
4
Build the generation prompt from art direction notes
Translate the confirmed art direction into a generation prompt. Either write it manually, or paste the art direction notes into the LLM and ask it to convert them: Convert these interior design requirements into a concise Stable Diffusion generation prompt (max 80 words). Requirements: [paste art direction notes here]. End with: photorealistic, interior design photography, empty room, no furniture, 8k Paste the resulting prompt into the Positive Prompt node in ComfyUI.
5
Run multiple generation rounds and narrow to 4 options
Run Ctrl+Enter to generate a batch. Repeat 3–4 times, adjusting the prompt slightly each run to create variety (e.g. vary the light direction or ceiling details). After collecting 12–20 outputs, select the best 4 across different compositional and lighting variations.
6
Swap floors across the 4 selected spaces
For each of the 4 selected backgrounds, use the floor swap node in the workflow to generate the same space with different floor finishes (e.g. timber, carpet, concrete, tile). This gives the client meaningful options without needing a full re-generation. Each background may end up with 2–3 floor variants.
7
Present 4 final options to the client
Package the 4 options with their floor variants and share with the client. Present them clearly — label each option (Option A / B / C / D) with a brief description of its character (e.g. "warm, residential feel with timber floor"). Client selects one to carry forward to Stage 04.
💡
Keeping variety across the 4 optionsAim for clear differentiation: one option warmer/cosier, one cooler/more corporate, one with more natural light, one with a distinct layout feature. If all 4 look too similar, the client can't make a meaningful choice.
STAGE 04
Space Layout
The client has chosen one space. This stage refines that space to exactly what they want — adjusting architectural details, adding or removing elements, defining the window view. Goal: one client-approved space image.
ComfyUI
📐
ComfyUI — Space Refinement
👤 James — Space detailing with client input
Iterate on the selected background until the client approves every detail
ℹ️
What happens hereThe client reviews the selected space carefully and raises specific change requests — not broad style changes, but precise adjustments. The team implements these changes using AI generation with the selected space as the base image.
Part A — Client Briefing Session
1
Walk the client through the selected space
Share the selected space image and go through it element by element. Prompt them with specific questions for each area of the image.
2
Collect layout and detail change requests
Common requests to probe for: Window view — What should be visible outside? (city skyline, garden, abstract blur, nothing) Meeting room — Should one be added or removed? Feature walls — Any specific artwork, branding, or texture to add? Ceiling fixtures — Are the lights correct? Change style or remove? Distracting elements — Anything too dominant? (e.g. black frame, signage, bold column) Partitions — Add or remove dividers between zones? Spatial depth — Does the room feel deep enough / too enclosed? Write all requests down clearly — each one becomes an inpainting or generation task.
Part B — Implementing Changes in ComfyUI
3
Load the space refinement workflow
Launch ComfyUI desktop. Load space_refine_v1.json. Upload the selected background image into the Base Image node.
4
Handle each change request individually
Tackle one change at a time — don't try to address multiple requests in a single generation. For each change:
① Draw a mask over the area to be changed (inpaint mask node)
② Write a targeted prompt describing what should appear in that area
③ Generate, review, accept or retry

For changes that affect the whole composition (e.g. adding a window), regenerate the full image with an updated prompt rather than inpainting.
5
Review with the client at each major change
Don't implement all changes in one round without checking. After 2–3 changes, share with the client. Confirm before continuing. This avoids going too far in a direction they didn't intend.
6
Get final client approval on the space
When the client is satisfied with the space, save the approved image as the master: projectName_space_APPROVED_v01.png This image is the foundation for Stage 05 compositing. Do not modify it further without documenting the change.
⚠️
Common issue — distracting elementsStrong architectural elements like black-framed windows, bold columns, or overpowering ceiling fixtures often need to be toned down. When a client says "remove that," they usually mean "make it less prominent" — use inpainting to replace it with a softer version rather than removing it entirely.
STAGE 05
AI Compositing
Discuss product placement, pairing items, and layout with the client. Three inputs are merged by the image model: product white-bg render + white model/line drawing + approved background. Output: a realistic interior photo.
ComfyUI · LLM
🔀
ComfyUI — Product Composite Workflow
👤 James — Layout, pairing, and 3-input merge
Merge 3 inputs into a realistic interior product photo
Part A — Layout and Pairing Discussion with Client
1
Discuss product placement in the approved space
Using the approved space image from Stage 04, walk through the following with the client:
DecisionWhat to confirm
PositionWhere does the primary product sit? (centre, corner, left wall, etc.)
QuantityHow many units of the product should appear?
Pairing itemsWhat furniture accompanies the product? (desk style, chair type, rug, side table)
Pairing styleGather reference images for each pairing item — style, material, colour
Scale relationshipHow should the product relate in size to the surrounding furniture?
2
Collect reference images for pairing items
For each pairing item (desk, chair, rug, accessories), gather 1–2 reference images that show the intended style. These are fed into the workflow alongside the product image to guide the AI on what the surrounding furniture should look like.
Part B — Preparing the 3 Inputs
ℹ️
The 3-input systemThe composite workflow takes three separate image inputs and fuses them together. Each input serves a specific role in the final output.
🏷
Input 1 — Product Image
Product render on white or transparent background (from Stage 01). Defines the product's exact appearance, materials, and colour.
📐
Input 2 — White Model / Line Drawing
Rendered white model or line drawing of the full scene layout. Defines structure, scale, placement of the product and all pairing items.
🖼
Input 3 — Background
The approved space image from Stage 04. Provides the environment, lighting, and spatial context.
3
Prepare the white model or line drawing
Create a rough layout render showing where every element will be placed. This can be a 3D white model render (from 3D Max or Blender with a white material override and flat lighting), or a simple line drawing. It doesn't need to be detailed — it tells the AI the composition: where the sofa sits, where the desk is, how they relate in scale.
Part C — Generating the Composite
4
Load the composite workflow in ComfyUI
Launch ComfyUI desktop. Load composite_v1.json. Three Load Image nodes are visible — one for each input. Upload the corresponding image to each node.
5
Generate the prompt using the LLM
For complex scenes, use the LLM to generate the prompt from your layout brief. Send this to the LLM via Openrouter: Generate a Stable Diffusion prompt for a photorealistic interior composite. The scene contains: [describe product, pairing items, their materials and colours, placement]. Style: [architectural style from Stage 03]. Lighting: [from the background]. Output a single prompt under 100 words, ending with: photorealistic, professional interior photography, 8k, seamless composite. For simpler scenes, write the prompt directly — describe the product, the furniture around it, and the overall feel.
6
Paste the prompt and run generation
Paste the LLM-generated prompt into the Positive Prompt node. Press Ctrl+Enter. The workflow fuses the 3 inputs into a single realistic interior image. Generate 4–6 variants.
7
Evaluate the composites — layout, product, pairing
Review each output against these criteria:
① Product appearance — materials, colours, and texture match the original render
② Pairing items — match the reference images and feel appropriate in style
③ Placement — product and furniture are in the correct positions per the layout
④ Scale — everything feels proportionally correct in the space
⑤ Integration — the composite feels like a real photo, not a paste-up
8
Iterate until the layout is right
If the layout is off, adjust the white model/line drawing and regenerate. If the product appearance is wrong, review Input 1 quality. If the pairing style is off, update the reference images or the prompt. Each input controls a different aspect of the result — troubleshoot by changing one at a time.
💡
Best compositeSave the best 2–3 variants from this stage — even if one has a near-perfect product and another has better furniture, both will be useful in the Photoshop masking stage (Stage 06).
STAGE 06
Post-Production
Quality control. Multiple AI-generated versions are combined using masks and layers to keep the best parts of each. Micro-details are fixed manually. Colour grade applied. This stage polishes the image to client-delivery standard.
QC · Photoshop
✂️
Adobe Photoshop — QC and Finishing
👤 Team — Mask/layer combine, detail fix, grade
Combine multiple AI outputs using masks, fix micro-details, colour grade
ℹ️
The core technique: mask and layer combiningNo single AI-generated image is perfect. One variant may have a perfect product but awkward lighting. Another may have beautiful lighting but a slightly off product. Photoshop's masking system lets you take the best region from each and combine them into one complete image — without any visible seam.
Part A — Setting Up the Multi-Layer Stack
1
Open all usable AI-generated variants
From Stage 05, take your 2–3 best variants. File → Scripts → Load Files into Stack — select all variants. Photoshop stacks them as separate layers in one document. This is your working file.
2
Name and order the layers
Rename each layer to describe what's good about it (e.g. "v03 — best product", "v01 — best lighting", "v05 — best background"). Put the strongest overall image at the bottom as the base. Other layers sit on top and will be masked.
Part B — Masking to Combine the Best Parts
3
Add a black mask to each upper layer
Click an upper layer → Layer → Layer Mask → Hide All. This hides the layer completely. You will paint white on the mask to reveal only the regions where this layer is better than the layer below.
4
Paint white to reveal the better region
Select the mask thumbnail. Set foreground colour to white. Use a soft Brush tool (B) — start with a large soft brush and paint over the area in this layer that is better than what's below. The revealed pixels replace the equivalent area from the layer below. Use a hard brush for clean product edges, soft brush for environment areas like lighting and shadows.
5
Refine the mask edges
Zoom in to 200%. Check where the two layers meet — if there's a visible seam, paint over it with grey (50% opacity brush) to blend the transition. The goal is a completely seamless composite where you can't tell where one layer ends and another begins.
💡
Mask tipUse black to hide, white to reveal, grey to blend. Keep the original layer visible while painting by holding Alt and clicking the mask thumbnail to toggle between viewing the mask and viewing the result.
Part C — Fixing Micro-Details
6
Identify areas the AI got wrong
Zoom to 200–400% and slowly pan across the image. AI generation commonly fails on: ① very small objects (hardware, buttons, small text) ② reflective surfaces ③ areas where two materials meet ④ fine product stitching or grid patterns ⑤ any area at the edge of inpainting masks. Mark these for manual fixing.
7
Fix with Clone Stamp, Healing Brush, or manual painting
For blurry small details: Clone Stamp (S) — sample from a clean nearby area and paint over the blurred section. For garbled texture (AI hallucinated a pattern): paint over it with the Mixer Brush using surrounding colour. For edge artefacts: Healing Brush (J) blends seamlessly. For intentional manual drawing (e.g. redrawing a product handle): use a small hard brush and sample the exact colour from the product.
Part D — Colour Grading
8
Apply a global colour grade
Add a Curves adjustment layer at the very top of the stack (no clipping mask — affects everything). Adjust:
• RGB channel: overall exposure and contrast
• Red/Blue channels: colour temperature (warm or cool)
• Green channel: optional tonal push for the environment mood
Keep all adjustments subtle. A well-composited image needs very little grading.
9
Check product colour accuracy
Compare the product in the composite against the original client-supplied product reference. If the colour has shifted (common after grading), add a Hue/Saturation adjustment layer with a clipping mask to the product layer only, and nudge it back to the correct colour.
Part E — Final Export
10
Quality check before export
View the image at 100% zoom. Pan across the entire image once more. Check: ① no visible layer seams, ② all product details are accurate, ③ colours match the client reference, ④ overall image feels like a professional interior photograph.
11
Export the final image
File → Export → Export AsJPG, quality 95. Save as: projectName_final_v01.jpg Also save the .psd source file — if the client requests changes later, you can return to the layer stack and adjust without starting over.
STAGE 07
AI Video Generation
Animate the approved final still into a product video. Choose the right tool based on resolution and motion complexity requirements.
3 Tools
ℹ️
Which tool to useKling → high-res output, main client deliverable. SeedDance → complex multi-element motion, product showcase (lower res, must go through Topaz in Stage 08). Freepik AI → temporary fallback only.
🎬
Kling 2.5 / 2.6 / 3.0
High-resolution video output
Animate product stills into high-quality motion content
Steps
1
Log in and upload image
Open klingai.com, sign in. Go to Image to Video. Upload the final product image from Stage 06 (JPG/PNG, min 1024px).
2
Write the motion prompt
Describe camera movement + lighting + subtle environment dynamics. Do NOT describe the product moving dramatically — this distorts the composite. slow cinematic camera push in, natural light rays shifting through window, soft fabric texture visible, product showcase, luxury feel
3
Set parameters and generate
Model: Kling 3.0 (or 2.5 for faster turnaround). Duration: 5 seconds. Mode: Pro / High Quality. Camera Control: Push In (or Pan Left/Right). Click Generate. Download as projectName_video_kling_v01.mp4.
🌿
SeedDance 2.0
Product showcase / complex motion
Nuanced multi-element motion — must be upscaled via Topaz
⚠️
Resolution noteSeedDance outputs at lower resolution. It must go through Topaz Video AI in Stage 08 before client delivery. Never send SeedDance output directly.
Steps
1
Log in, upload, and prompt
Sign in to SeedDance. Select Image to Video. Upload the product image. Write a prompt focused on organic, material-level motion: gentle fabric draping motion, cushions softly settling, warm ambient light shifting, organic natural movement, product reveal, cinematic Download as projectName_video_seeddance_v01.mp4. Flag for Topaz upscaling.
Freepik AI Video (Temporary)
⚠️ Fallback only
Access additional video models when main tools are unavailable
⚠️
Temporary solutionFreepik AI Video is not a permanent production tool. Use it only when Kling and SeedDance are unavailable. Always mark output with _TEMP in the filename to signal it needs replacing.
Steps
1
Generate and mark as temporary
Log in to freepik.com → AI Tools → AI Video Generator. Upload image, enter a short motion prompt in English. Try different underlying models. Download and save as projectName_video_freepik_TEMP.mp4.
ℹ️
ComfyUI Automated Pipeline — This workflow automates the production of 9-shot product advertisement videos using Gemini + Kling V3 in ComfyUI.

Workflow Overview

Give the system a storyboard grid and product photos, and it produces 9 ready-to-edit video clips — one per storyboard cell. Uses Gemini to interpret the storyboard and write motion prompts, then sends those prompts to Kling V3.

Stage 1 — Storyboard Analysis (Gemini)

A single 3x3 grid storyboard image is loaded and fed into a Gemini 3 Pro Preview node. The system prompt instructs Gemini to act as a motion director for high-end product ads.

What Gemini does:

  • Reads the 9-cell storyboard (left-to-right, top-to-bottom → Shot 01–09)
  • Writes a Kling-compatible image-to-video motion prompt for each shot
  • Outputs structured XML containing a <shotN_prompt> and <shotN_duration> for each shot

System prompt rules:

  • Describe ONLY camera movement and light changes (Kling already sees the image)
  • Very slow, subtle movements only — product must not deform
  • Close-ups → slow orbit + subtle light shift
  • Medium shots → slow arc (~15°) or slow pull out
  • Wide shots → slow pull out or slow dolly forward
  • Never use: fast, zoom, spin, rotate 360, handheld, shaky
  • Each prompt is 1–2 sentences max
  • Movements must be varied across shots
Storyboard Example
Example: 3×3 storyboard grid — each cell maps to one shot

Output format (XML):

<shot1_duration>5</shot1_duration>
<shot1_prompt>Very slow orbit around product. Subtle lighting shift...</shot1_prompt>
<shot2_duration>5</shot2_duration>
<shot2_prompt>...</shot2_prompt>
...through shot 9

Stage 2 — Prompt Parsing

The Gemini output is a single block of structured XML text. A custom parser node splits it into 9 individual prompt strings. Each output is routed to its corresponding Kling generation node.

Note: The Gemini output is previewed via a "Show Anything" node. The structured text is currently entered into a DF_Text node which feeds the parser. There may be a manual copy-paste step between Gemini output and DF_Text input.

Each parsed output is also routed to a PreviewAny display node (labelled "Shot N: # of Seconds") so you can verify per-shot prompt and duration before generation runs.

Stage 3 — Video Generation (Kling V3 x 9)

Nine parallel KlingVideoNode instances each receive a start frame image and a motion prompt.

SettingValue
Modelkling-v3
Resolution1080p
Aspect Ratio16:9
Duration3 seconds per clip
SeedRandomised per node

Shot → Start Frame Mapping:

ShotSource Image
Shot 1Image_0014 1.png
Shot 22 (1).png
Shot 33 (3).png
Shot 44 (2).png
Shot 55 (2).png
Shot 66 (1).png
Shot 77.png
Shot 88.png
Shot 99.png
Generated Video Frames
Example: 9 generated video frames from Kling V3

Stage 4 — Save

Each of the 9 Kling outputs is connected to a dedicated SaveVideo node, saving clips to video/ComfyUI/ with automatic naming.

Data Flow Diagram

┌─────────────────┐      ┌──────────────┐      ┌────────────────┐
│  Storyboard      │─────▶│  Gemini 3    │─────▶│  Show Anything │
│  (3×3 grid)      │      │  Pro Preview │      └────────────────┘
└─────────────────┘      └──────┬───────┘
                                │
                                ▼
                         ┌──────────────┐
                         │  DF_Text      │
                         └──────┬───────┘
                                │
                                ▼
                         ┌──────────────┐
                         │  XML Parser   │ (splits into 9 outputs)
                         └──┬──┬──┬──┬──┘
                            │  │  │  │  ... (×9)
                            ▼  ▼  ▼  ▼
┌──────────────┐     ┌─────────────────┐     ┌─────────────┐
│ Product Image │────▶│  Kling V3 Node  │────▶│  SaveVideo   │
│ (per shot)    │     │  (per shot)     │     │  (per shot)  │
└──────────────┘     └─────────────────┘     └─────────────┘

How to Use

  1. Prepare your storyboard — Create a 3×3 grid image where each cell represents one shot.
  2. Load product images — Place 9 product photographs into the corresponding LoadImage nodes.
  3. Run the Gemini stage — The storyboard is analysed and motion prompts are generated. Review the output.
  4. Transfer prompts to DF_Text — Copy the Gemini XML output into the DF_Text node (or verify it's wired directly).
  5. Run Kling generation — All 9 video clips generate in parallel. Output saves to video/ComfyUI/.
  6. Post-production — Import the 9 clips into your NLE (Premiere, DaVinci, etc.) and assemble the final sequence.

Key Customisation Points

  • System prompt (Node 5) — Edit to change motion style, add/remove movement types, adjust pacing rules
  • Product images — Swap out per-shot start frames for different products or angles
  • Kling settings — Adjust resolution, aspect ratio, or duration on the KlingVideoNodes
  • Storyboard — Change the input grid to get completely different motion direction from Gemini

Final Output

The 9 clips assembled into a finished product video:

Final assembled product advertisement video
STAGE 08
Video Finishing
Upscale AI video with Topaz, add branded motion graphics in After Effects, final assembly and client export in Premiere Pro.
3 Tools
🔬
Topaz Video AI
Upscaling · Frame interpolation · Stabilisation
Boost resolution and remove AI video artefacts
Steps
1
Import and choose model
Drag the MP4 into Topaz Video AI. Choose model: Proteus (Renior default — best detail preservation). Use Artemis for high-motion clips, Apollo for frame rate boost, Stabilization for jitter removal.
2
Set output resolution and export
SeedDance source (720p) → target 4K (2160p). Kling source (1080p) → target 4K or keep 1080p. Format: MP4 H.264 or ProRes for Premiere. Click Preview first, then Export. Save as projectName_video_topaz_4K.mp4.
Adobe After Effects
Motion graphics · Brand elements · Colour grade
Layer animated brand elements on top of the upscaled video
Steps
1
Create composition and import video
Composition → New Composition. Match resolution to the Topaz output (e.g. 3840×2160), 24fps, match duration. File → Import the Topaz MP4. Drag to Timeline.
2
Add text and logo
Layer → New → Text for product/brand name. Animate with Animation → Text → Animator → Opacity (0→100 over 0.5s). Import client logo PNG, position bottom-right, same fade-in. Add subtle colour grade via Layer → New → Adjustment Layer → Lumetri Color.
3
Export via Media Encoder
Composition → Add to Adobe Media Encoder Queue. Preset: H.264 — Match Source — High Bitrate. Save as projectName_AE_v01.mp4.
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Adobe Premiere Pro
Final edit · Audio · Delivery export
Assemble all clips, add audio, export the client deliverable
Steps
1
New project, import, and arrange
File → New → Project. File → New → Sequence (match video preset). File → Import all After Effects exports, Topaz video, audio. Drag to Timeline, use Razor tool (C) to trim, Effects → Dissolve for transitions.
2
Add audio and export
Drag audio to Timeline. Right-click → Audio Gain to adjust level. Drag clip handles for fade-in/out. Export: File → Export → Media (Ctrl+M) → H.264 — Match Source High Bitrate. Save as projectName_FINAL_v01.mp4.
⚠️
Pre-delivery checklist① Duration matches brief ② Resolution 1080p minimum (4K preferred) ③ Audio no clipping, ~-14 LUFS ④ File size reasonable (1080p 5-sec ≈ 30–80MB, 4K ≈ 100–300MB)