Raksha Wave is a plug-and-play solution for professional AI content generation workflows. Primitive prompting is a human trying to talk to a machine in human language. That’s inefficient. Models don’t care about adjectives, they care about vectors, constraints, and coordinates.
Everyone else writes: “cinematic shot, moving camera” and the model guesses: how fast, where, with what focus?
Raksha Wave (TSP) works differently. You move a slider. The system generates strict, mathematically defined syntax a human would take hours to write and still get wrong. The model reads it as an instruction, not a hint. Raksha Wave is a plug-and-play solution for professional AI content generation workflows.
Programming analogy:
Think Unreal Engine Blueprints for neural networks.
Game developers don’t write binary. They move nodes. The engine compiles them into complex C++.
Raksha Wave does the same: a human interface on top, neural “assembly” underneath.
Raksha Wave is not a generator and not just an AI tool. It is a DI tool. It is an operating system for cinema built on top of any neural network. You no longer guess what the model might “decide” to do, you control it through the TSP standard as strictly as a director controls the set, the camera, and the light. Instead of lucky accidents, you get repeatability. Instead of drift, you get locked details. Instead of “it just came out nice,” you get a result that can be reproduced, proven, and defended. That’s why Raksha Wave is not for everyone, it is for those who treat cinema, ads, game design, and visual storytelling as an industry, not a chancecraft. You don’t need to retrain to use the system, adapting an existing pipeline to work with this operating system can be done within 1–2 days.
Obey Intention
I → P → N → C → TSP → Output
AI can generate remarkable images, sound and motion, but it cannot stay consistent, stable, or predictable across a sequence.
Raksha Wave technology solves this problem by introducing a dedicated layer of creative control that works with any model or engine,
allowing studios, directors, animators, VFX supervisors, artists and musicians to steer AI output with clarity and intention.
Raksha Wave plugs into existing production pipelines without forcing changes to tools or workflows.
It is fully model agnostic and compatible with current generative image and video systems used in AI assisted filmmaking.
The underlying control logic acts as a universal AI cinematic control system that can direct the behavior of many different models.
TSP sets the standard for professional hybrid content workflows.
Raksha Wave gives AI systems what they normally lack:
reliability, precision, and directorial control.
ⓘDeterministic Control Pipeline
Raksha Wave is a proprietary creative framework and DI/AI pipeline developed by filmmaker Vladimir Raksha and Raksha World Ltd.
It is used for concept development, visual direction, story design, experimental animation, and advanced AI driven cinematic workflows.
Raksha Wave is not an AI that guesses. It is DI, Deterministic Intelligence. You decide. It executes.
Traditional AI relies on probability and interpretation. Raksha Wave translates intent into a strict structured control language. The same intent always produces the same result. No drift. No improvisation. This is why Raksha Wave can command AI. It removes agency from the control layer and turns generative AI into a reliable execution engine. Human intent remains the source of will. Deterministic intelligence is not artificial thinking, It is intelligent control.
This model agnostic approach allows the same Raksha Wave setup to guide different engines without rewriting prompts or templates.
At the core of the system is TSP, the Temporal Syntax Prompting language, which describes motion, timing, scene structure, and emotional rhythm in a clear, time based format.
TSP was designed as a cinematic language for directing AI and can be adapted to multiple backends and engines.
Even on models that do not support native audio, the TSP core can drive convincing lip-sync behaviour by controlling facial articulation, mouth shapes and timing as if sound were present. This is achieved through its own internal timing and behaviour engine, rather than relying on built-in audio features of a given model.
FAQ – Raksha Wave
What is Raksha Wave?
Raksha Wave is a proprietary creative technology and AI pipeline that helps directors, studios, animators, VFX supervisors, artists and musicians control AI generated imagery and motion for film, television, commercial work and beyond.
Is Raksha Wave a neural network?
No. Raksha Wave is not a neural network and not a model.
It is a control framework and production workflow that works alongside existing AI systems.
Where is Raksha Wave used?
The system is used in DI/AI assisted filmmaking, concept art, previs, visual style development, motion design, advertising, animation, music videos, and research projects across the UK and EU, the United States, and China.
Is Raksha Wave publicly available?
No. Access is limited to selected collaborators and studios. Partnership opportunities are available upon request.
Consistent motion and behavior instead of random fluctuations
Reproducible results for previs, look development, and full film production
Faster R&D cycles with fewer iterations and lower costs
A unified control interface that integrates into existing pipelines
Full control of speech, voice, and pronunciation
ⓘHow Raksha Wave works
The structured TSP layer eliminates many global and local artefacts and reduces generation errors by providing the model with clear behavioural logic instead of internal guesswork.
No retraining.
No custom infrastructure.
No change to your current tools.
Works with all major and minor systems, HailuoAI, PromeAI, Veo, Stable Diffusion, Runway, Pika, Kling, Lumalabs, Nano Banana and any other in-house studio ML models. No extra tuning is required on top.
Raksha Wave technology and the TSP language are protected intellectual property of Raksha World Ltd.
All implementations, specifications, internal code, and UX logic are proprietary trade secrets and are provided only under strict confidentiality.
Any access to private tools, including the Raksha Wave Framer TSP and Raksha Wave Director TSP interfaces, is granted solely for evaluation and research.
Commercial use, replication, or reverse engineering of the system is not permitted without a separate written agreement.
The naming, structure, and behavior of the Temporal Syntax Prompting language, as well as all related documentation and examples, are protected under international copyright and intellectual property law.
Unauthorized copying, adaptation, or imitation may lead to legal action.
FAQ – TSP (Temporal Syntax Prompting)
What is TSP?
TSP, or Temporal Syntax Prompting, is a cinematic language created by Vladimir Raksha for describing scene timing, camera behavior, motion, and emotional beats in a precise, time based format that AI systems can follow.
How is TSP used in film and games?
In film and game production, TSP is used as a temporal control layer that allows AI systems to understand motion, timing, and action continuity across frames.
Inter-frame TSP codes help models maintain coherent movement and focus on key moments rather than guessing between frames.
The TSP method enables studio-grade models to be adapted for video and interactive content without full retraining, significantly reducing computational cost.
By embedding temporal data directly into the prompt structure, TSP improves the accuracy of complex actions and behaviors on screen.
All parameters are controlled through a unified Raksha Wave toolset, eliminating the need to switch between multiple applications.
Can I see the internal code or full specification?
No. The internal code, specification, and engine implementations of TSP are confidential trade secrets of Raksha World Ltd and are not publicly disclosed.
Can TSP be integrated into my own AI tools?
Integrations are possible only through direct collaboration with Raksha World Ltd and are evaluated on a case by case basis. Any integration requires a separate written agreement.
Who owns Raksha Wave and TSP?
Raksha Wave and the Temporal Syntax Prompting language were created by Vladimir Raksha. All rights are owned by Raksha World Ltd. Any attempt to recreate, copy, or rebrand the system without permission is considered infringement.
Raksha Wave Methodology
Raksha World Ltd · Digital Sanctum · London UK
Raksha Wave
रक्षा
TSP Methodology and System Guide: a model-agnostic control framework for professional hybrid AI generation workflows.
Generative artificial intelligence is capable of producing visually impressive images and video, but it does not always produce them with the level of predictability required by professional production. A new request to a generative model can alter facial features, shift lighting, change spatial relationships, weaken movement logic, or reinterpret the visual style of a shot. For a single experiment, this may be acceptable. For film, advertising, animation, serial content, or any professional workflow, this uncertainty becomes a production problem.
Raksha Wave was created to address this problem. It is not presented as a replacement for existing generative systems or as a new neural network. It functions as a control layer between the creative intention of a director and the generative model that executes the image or video. The purpose of this layer is to translate creative intent into a structured, explicit, and reproducible language of control.
The system is built around three core principles:
Reliability: the workflow is designed to support more stable results across repeated generations.
Precision: core parameters are stated explicitly rather than left to model interpretation.
Directorial control: the system operates through the language of cinema, including camera, light, movement, emotion, rhythm, and visual continuity.
The central technological component of Raksha Wave is TSP, or Temporal Scene Protocol. TSP is not a conventional prompt. It is a structured control format that defines the parameters of a scene in a form that can be read, reused, and adapted across generative workflows. It is designed as a professional methodology for hybrid AI production, in the same broad spirit in which timecode, EDLs, LUTs, and production bibles became practical coordination tools for film and post-production.
2. System Architecture: How Raksha Wave Works
2.1 Two Tools, One Integrated System
Raksha Wave consists of two specialised tools that operate together as a single production system.
Temporal control: movement, behaviour, event logic, sound, dialogue, music, camera dynamics, and emotional rhythm.
2.2 Data Flow
The operating logic of the system can be summarised in a simple production chain.
Intent → Parameters → TSP Code → Generative Model → Controlled Result
The user defines parameters through a visual interface. The Wave Engine compiles those parameters into structured TSP code. That code can then be used as a control instruction inside a generative AI platform. This allows a director, designer, or studio team to work with creative controls while still producing a precise technical instruction set.
2.3 Model-Agnostic Design
Raksha Wave is designed as a model-agnostic control framework. Its output is text-based, which allows it to operate with a broad range of systems that accept textual instructions, including image generators, video generators, specialised studio tools, and custom ML pipelines. The system is not dependent on retraining the target model, modifying the model architecture, or replacing the existing production stack.
3. TSP: Temporal Scene Protocol
3.1 What TSP Is and Why It Is Not a Prompt
A conventional prompt is a request. TSP is closer to a structured production brief. A prompt may say: “make it cinematic.” TSP defines the camera type, lens profile, shot size, lighting pattern, colour grade, realism level, style constraints, movement behaviour, and continuity locks. The distinction is essential: the system does not merely describe a desired outcome; it defines how the scene should be interpreted and controlled.
TSP uses a YAML-like notation. It typically contains global locks, scene parameters, temporal control values, and a readable final prompt. Each block serves a defined role in the production logic.
3.2 Anatomy of TSP: The GLOBAL_LOCKS Block
The GLOBAL_LOCKS block is one of the most important parts of the methodology. It establishes the parameters that should remain stable across the generation process.
ANCHOR: spatial_state@phase_0 defines the initial spatial condition of the scene as a reference state.
world_relayout prevents the generative system from re-arranging the space of the shot.
object_rebase discourages objects from shifting to a different base position.
scale_drift helps preserve the proportions of characters and objects.
camera_as_effect prevents camera movement from being treated as a decorative effect rather than a physical movement.
aesthetic_over_physical_correction limits automatic “beautification” or stylistic corrections that may conflict with the specified scene logic.
Key Principle
GLOBAL_LOCKS is a declaration of continuity. It tells the model which elements should remain protected from casual reinterpretation, automatic improvement, or stylistic drift.
3.3 Scene Parameters: The image_scene Block
The second major block contains the technical specification of the frame. This includes camera, lens, aspect ratio, shot size, lighting, composition, skin parameters, realism level, detail level, and style logic.
Specifying the camera and lens helps define the character of the image in a way that resembles the work of a cinematographer. The model is not simply asked to produce a beautiful image; it is guided toward a particular optical logic.
The skin block helps preserve physical characteristics. In professional workflows, automatic smoothing, tone shifts, and beauty adjustments can damage continuity or conflict with a director’s intention. TSP allows those tendencies to be constrained.
Collectively, these locks create a visual lockset. The purpose is not to make the output mechanically rigid, but to create a stable visual profile that supports continuity across frames, scenes, and iterations.
4. Raksha Wave Framer: Creating a Frame Step by Step
4.1 Interface and Structure
Raksha Wave Framer is the tool used for static image generation: concept frames, keyframes, characters, locations, poses, visual look development, and controlled lighting tests. Its interface is organised like a cinematic shot sheet: scene description first, then camera and lens parameters, then character and realism parameters, and finally colour, time of day, weather, environment, and stylistic settings.
4.2 Step 1: Base Prompt
The base prompt is a short description of the content of the frame. It answers the question of what is in the scene. The technical layer answers how the scene should be captured.
Example
A slender woman, aged 26, standing by a waterfall wearing a blue hat. Golden hour, summer forest.
This separation is important. The creative description remains human and intuitive, while the cinematic execution is handled through structured parameters.
4.3 Step 2: Camera Parameters
Framer supports a wide range of camera profiles, from smartphone and CCTV-style systems to 35mm film cameras, digital cinema cameras, IMAX-style imaging, drone cameras, and experimental rigs. The choice of camera defines the visual behaviour of the image at the level of optics, framing, and production style.
Digital cinema camera: high resolution, clean dynamic range, modern production clarity.
VHS camcorder: analogue artifacts and low-fidelity image language.
Drone aerial camera: elevated perspective and spatial overview.
CCTV camera: observational distance and surveillance grammar.
4.4 Step 3: Lighting
Framer implements lighting as a controlled cinematic system rather than as a decorative style tag. Lighting schemes may include portrait patterns, cinematic low-key structures, high-key commercial lighting, backlight silhouettes, neon sources, volumetric beams, fashion gloss, or horror underlighting.
Once a lighting scheme is selected, it can be locked so that subsequent generations preserve the direction, ratio, intensity, and character of the lighting rather than reinterpreting it casually.
4.5 Step 4: Realism, Detail, and Genre Weights
Framer also includes genre weights. These parameters allow a director to influence the emotional and formal character of the image through a combination of comedy, action, drama, thriller, and horror intensities.
The realism parameter controls how close the image should remain to photographic reality. The detail parameter determines the density of visual information, from a cleaner schematic image to a richer textured frame.
4.6 Step 5: Generate and Copy TSP Code
After the parameters are set, the Generate + Copy operation compiles the full TSP Framer code. The result is a ready-to-use instruction set containing the base prompt, camera specifications, lighting, visual locks, skin parameters, style settings, and final readable prompt.
5. Raksha Wave Director: Directing Motion and Behaviour
5.1 What Director Controls
If Framer defines how the frame looks, Director defines how the frame lives in time. Director controls movement, behaviour, emotional transitions, camera dynamics, sound, voice, and temporal rhythm through sliders and curves that compile into TSP Director code.
The IDENTITY block protects the character’s appearance, facial structure, body morphology, and voice identity. STATE allows emotion and expression to change, but only within controlled degrees of freedom. KINEMATICS limits movement to transformation and pose behaviour rather than uncontrolled mesh deformation. SPACE_LOCK protects scale, proportions, depth logic, and horizon reference.
5.4 Temporal Layer: Parameters at Time t
Director allows parameters to be assigned at specific moments in the duration of a clip. A block such as t=0.40 represents the state that should be reached at 40% of the clip duration.
The curve syntax defines the temporal behaviour of the change. ease_out creates a soft deceleration into the target value, while ease_in, ease_in_out, linear, and cubic offer different forms of motion and transition.
6. Workflow Efficiency and Compact Control
6.1 The Iteration Problem
Without a structured control layer, a typical generative workflow often becomes iterative and unstable: write a prompt, receive a result, correct the prompt, receive another result, correct again, and repeat. Even when the first result is visually strong, preserving the same identity, lighting, camera logic, or emotional continuity across multiple frames can become difficult.
TSP is designed to reduce this uncertainty by making the controllable parameters explicit. When the scene structure, locks, lens, lighting, and temporal behaviour are declared in advance, the model receives a more precise instruction set. This can support a more efficient workflow and reduce the need for broad prompt rewrites.
6.2 Observed Efficiency Indicators
Within practical development and production-oriented testing, structured TSP workflows have shown useful efficiency patterns. These observations should be understood as workflow indicators rather than universal guarantees, since final results depend on the selected model, input material, target format, and production context.
Iteration efficiency
Improved
Structured instructions can reduce repetitive prompt correction by making parameters explicit.
Compact format
Denser
Compact TSP can carry the same control logic in a shorter, more organised form.
Character continuity
Supported
Identity anchors and locksets help preserve visual continuity across related generations.
Production clarity
Higher
The system separates creative content from technical execution, making revision easier.
6.3 Why TSP Can Improve Workflow
A generative model must interpret ambiguous language. The more ambiguous the prompt, the more room the model has to invent, reinterpret, or “improve” details that may not need changing. TSP reduces this ambiguity by separating content, camera, light, emotion, movement, and constraints into readable control blocks. The result is a clearer communication layer between the human director and the generative system.
7. Raksha Bridge: Long Sequences and Continuity
7.1 The Challenge of Long Scenes
Current generative video systems often have practical duration limits. A single generation may only cover a short clip. For short social content this may be acceptable, but longer scenes, advertising sequences, music videos, trailers, and narrative work often require continuity across multiple generated segments.
Raksha Bridge is an optional workflow designed to address this challenge through keyframes, anchors, and a multipass pipeline.
7.2 How Raksha Bridge Works
Create a starting keyframe using Raksha Wave Framer.
Create an ending keyframe using the same visual logic and compatible control structure.
Place TSP Director code between keyframe A and keyframe B as a temporal instruction layer.
Render each phase separately while preserving the same TSP logic.
Assemble the generated phases into a continuous sequence in post-production.
The essential principle is that the keyframes and temporal behaviour must share a compatible control structure. Without that structure, the system has fewer anchor points for continuity alignment.
Result
Raksha Bridge is designed to extend the practical usefulness of short generative clips by treating them as controlled phases inside a larger production sequence.
8. Local Edits: Changing Details Without Breaking the Scene
8.1 The Principle of Modularity
One of the most practical advantages of TSP is the ability to make local edits without rewriting the entire prompt. Since each parameter belongs to a defined block, a user can adjust one variable while keeping the rest of the scene stable.
emotion.sadness = 0.80 adjusts the emotional value.
camera.dolly += 0.300 adds or increases camera movement.
camera.type: "handheld nervous" changes the camera behaviour profile.
In a conventional prompt, a small textual change can cause the model to reinterpret the whole scene. In TSP, the intended behaviour is that a local parameter influences a local part of the control structure while locked elements remain protected.
9. Sound, Voice, and Dialogue in Raksha Wave
9.1 Voice and Speech Control
Raksha Wave Director includes parameters for voice and sound behaviour. On platforms with audio support, these parameters may influence voice style, music, atmospheric balance, and sound design. On platforms without audio output, the same parameters can still inform articulation, facial rhythm, body timing, and the emotional logic of the character.
These parameters support emotional and sonic continuity across platforms with different levels of audio capability.
10. Application in Film, Animation, and Advertising
10.1 Film Production: Previsualisation and Look Development
Previsualisation is one of the most direct use cases for Raksha Wave. A director or storyboard artist may need to create a sequence of frames in which character identity, camera language, lighting, and style remain coherent. TSP can function as a reusable look-development passport for a project.
10.2 Advertising: Brand and Product Continuity
Advertising workflows depend heavily on consistency. Product texture, surface behaviour, wardrobe material, skin tone, lighting, and brand palette must remain stable. A TSP-based workflow allows these parameters to be specified, protected, and reused across multiple frames or iterations.
10.3 Animation: Behavioural Continuity
For animation, Director can define the behavioural signature of a character: body sway, eye rhythm, head movement, emotional timing, and camera relationship. Framer preserves the character’s visual profile, while Director preserves the character’s temporal behaviour.
10.4 Documentary and Journalistic Contexts
In documentary or journalistic contexts, the value of a control layer is not only aesthetic. It can support traceability, restraint, and auditability by making the transformation logic of an image or clip more explicit.
11. Full Production Workflow: From Intent to Final Output
11.1 Standard Pipeline
Open Raksha Wave Framer and enter a concise base prompt.
Set camera, lens, lighting, skin, realism, detail, genre weights, colour grade, time of day, weather, and environment.
Generate and copy the TSP Framer code.
Use the TSP code in a compatible generative model.
Review the output and make local parameter adjustments if needed.
Open Raksha Wave Director and define movement, emotion, camera behaviour, sound, and temporal curves.
Generate and copy the TSP Director code.
Use the approved frame and Director code in a video generation workflow.
For longer sequences, apply the Raksha Bridge method using controlled keyframes and phased rendering.
11.2 Continuity Across Workflows
In generative production, continuity is strongest when the workflow maintains a consistent model context, visual anchor, and control structure. If the generation context changes, the internal interpretation of details may also change. TSP helps define the structure that can be reused when a new stable context is established.
12. Compatible Platforms and System Boundaries
12.1 Broad Compatibility
TSP code is text-based. This makes it usable across a broad range of tools and platforms that accept textual input, including major image and video models, specialised production tools, and studio-developed systems.
The framework is intended to operate without requiring model retraining, proprietary model access, or infrastructure replacement.
12.2 Responsibility Boundary
Raksha Wave controls how instructions are structured and how creative intent is translated into model-readable parameters. It does not change the internal architecture, training data, rendering capacity, artifact profile, or duration limits of the underlying model.
Final image quality, sharpness, colour fidelity, duration, and artifacts remain dependent on the selected model and production environment. Raksha Wave provides a control methodology, not a universal override of model limitations.
13. Practical Use Scenarios
Scenario A: Advertising Film
A 30-second commercial requires one performer across three locations while preserving a consistent brand look. Framer defines the character, skin, wardrobe, lighting, and colour grade. Director defines camera movement, emotional tone, music intensity, and transition behaviour. Raksha Bridge can then support phased continuity across the final sequence.
Scenario B: Short Film Dialogue Scene
A two-minute dialogue scene requires two characters, close-ups, emotional reaction, and consistent interior lighting. Each character receives a controlled visual profile. Director defines subtle head movement, emotional curves, camera drift, and acoustic mood.
Scenario C: Animated Episodic Character
An animated series requires one lead character to remain recognisable across episodes. Framer acts as the visual passport. Director defines the character’s movement signature, gaze rhythm, emotional pacing, and body behaviour.
14. TSP as a Production Framework
14.1 What Makes a Framework Useful
A production framework becomes valuable when teams can build repeatable workflows around it. TSP is designed to support that role in hybrid AI generation by offering a readable, structured, extensible way to define cinematic control parameters.
Universality: the format is text-based and designed for broad compatibility.
Readability: the structure can be understood by both humans and machines.
Completeness: the code can contain visual, temporal, behavioural, and sound-related parameters.
Extensibility: additional parameters can be added without breaking the overall logic.
Operational clarity: creative decisions become explicit and reusable.
14.2 Positioning Within Current AI Workflows
Prompt engineering is useful but often inconsistent across models and users. Model-specific systems such as adapters, fine-tunes, ControlNet-style workflows, LoRA methods, and custom pipelines may be powerful, but they are usually tied to particular architectures or technical environments. TSP addresses a different layer: the structured communication of intent and control across generative workflows.
For this reason, TSP should be understood as a production methodology rather than a claim to replace existing model-specific techniques. It can work alongside them, providing a clearer control language above the model layer.
15. Technical Safeguards and Legal Positioning
15.1 No Retraining or Infrastructure Replacement
Raksha Wave is an overlay system. It does not require retraining generative models, altering their architecture, installing a proprietary model layer, or changing a studio’s entire infrastructure. The core requirement is access to Raksha Wave and the ability to use generated TSP instructions inside a compatible creative system.
15.2 Legal and Trademark Note
All third-party product names, platform names, and trademarks are used descriptively only. Their mention does not imply endorsement, affiliation, licensing, or approval by their respective owners. Raksha Wave does not reproduce proprietary software, datasets, firmware, models, or protected materials, and does not rely on reverse engineering of third-party systems.
15.3 human_touch and Authorship Traceability
TSP may include a human_touch layer that records session-related technical context such as browser, platform, language, and timestamp. This is not designed as a surveillance mechanism. Its purpose is to support auditability and indicate that the code was generated through a human interaction with the interface rather than as a purely automated batch output.
15.4 Validation and Supporting Evidence
The document presents observations regarding workflow efficiency, consistency, and control. These conclusions are based on practical development experience, structured testing, and iterative production workflows. As the platform continues to expand, additional benchmarking, comparative studies, production case histories, and third-party evaluation may further illustrate its performance across different models, content types, and production scenarios.
The core value proposition of Raksha Wave does not depend on a single metric. Its primary contribution lies in the introduction of a structured method for translating creative intent into explicit, reusable control parameters that can be applied across multiple generative environments.
16. Conclusion
Raksha Wave responds to a real production problem: generative AI can create powerful images, but professional production requires more than isolated visual success. It requires repeatability, continuity, directorial control, and the ability to revise details without collapsing the entire scene.
TSP provides a structured layer for that work. It translates camera, light, movement, emotion, rhythm, sound, identity, and continuity into a readable control format. The result is a practical framework for directing generative systems with greater clarity.
The system is designed to work with existing models and future platforms because it is based on structured textual control rather than model-specific retraining. It connects human imagination and computational precision into a single production workflow.
Render Examples
Toolset Demo
Raksha Wave Framer 1.1
FRAME
StyleCinema
Your project’s look
CAMERA
Camera Type35mm film camera
Lens35 mm
Lens Profilespherical cinema prime
Vignette0%
Lens Flare (optional)none, 0%
Aspect Ratio2.39:1
Frame Compositionrule of thirds
Lighting Schemerembrandt lighting
Shot Size / Planmedium shot
SKIN
Skin Agenot_specified
Skin Conditionnot_specified
Skin Cleanlinessnot_specified
Skin Texturenot_specified
Skin Stylenot_specified
REALISM & DETAIL
Realism80%
Detail Level80%
GENRE MIX
Comedy50%
Action50%
Drama50%
Thriller50%
Horror50%
LOOK & MOOD
Color Gradecinematic neutral
Time of Day12:00 PM
Locationunspecified
Continent: –Coords: –
Environmenturban exterior
Seasonunspecified
Weatherclear
After copying, paste this TSP code into your AI prompt input field →
