Kling-Omni Technical Report

🍞 Hook: You know how making a movie takes a whole team—writers for the script, artists for storyboards, camera crews for shots, and editors for the final cut? Imagine if one smart helper could understand all of them at once and make the movie.

🥬 The concept (Unified video AI): A unified video AI is a single system that can understand text, pictures, and videos together, then create or edit videos in one smooth process.

• How it works: (1) It reads your instructions (text, images, videos). (2) It blends them into one shared “language.” (3) It generates a draft video. (4) It polishes details to look cinematic. (5) It checks that the result follows your instructions.
• Why it matters: If parts are separated, each tool can misunderstand the others, causing lost details, broken motion, or off-target results.

🍞 Anchor: Say, “Use this selfie, put me in a snowy Tokyo street at sunset, wearing this jacket, filmed from below, then zoom out.” A unified model can do it all at once.

1. The World Before: Video AI used to be like a toolbox full of separate gadgets. One model did text-to-video. Another did editing. A different one handled understanding. They didn’t speak the same “language,” so connecting them felt like taping puzzle pieces together. Prompts in plain text often missed visual nuance—like exact colors, layouts, or camera motion—so outputs drifted from what users pictured. And many models were great at painting pixels but bad at “thinking” about physics, identity consistency, or story flow.

2. The Problem: People wanted an assistant that could: (a) take mixed inputs (text + reference images + video context), (b) keep identities, styles, and motion consistent over time, (c) follow complex, layered instructions, and (d) reason about scenes (e.g., what a camera move implies, or how objects should interact). Existing pipelines were fragmented, slow, and struggled with deep multimodal reasoning.

3. Failed Attempts:

• Single-task specialists: Excellent at one job (say, text-to-video) but brittle when asked to edit or combine multiple references.
• Static text encoders: Treated prompts as frozen text embeddings; they missed fine visual control.
• Adapter chains: Bolted separate tools together; errors stacked, latency grew, and consistency broke between steps.

1. The Gap: What was missing was a truly unified representation and workflow that treats words, images, and video snippets as one synchronized “multimodal visual language.” Without that, models can’t fully understand user intent or keep everything coherent across time.

2. Real Stakes:

• Creators want precise control: film shots, ad scenes, and game cutscenes that match their boards and references.
• Everyday users want easy edits: “Make this rainy,” “Add a friendly robot from this sketch,” “Keep my face the same.”
• Businesses want speed: fast iterations without hiring a whole pipeline of tools.
• Education and communication need clarity: turning comics/storyboards into videos or visualizing science demos.

🍞 Anchor: Think of a cooking show where you hand the chef a recipe (text), a taste sample (image), and a short clip of plating style (video). A unified model is the chef who understands all of it at once and serves a dish that matches your exact intent.

🍞 Hook: Imagine texting a friend “film me doing this dance” while showing a short clip of the dance and a selfie. It’s obvious to a human, but not to most AIs.

🥬 The concept (Multimodal Visual Language, MVL): MVL is a shared language the model uses to combine words, pictures, and videos into one meaning space.

• How it works: (1) Turn text, image, and video into tokens. (2) Put them in the same embedding space. (3) Let the model attend across them to align details (who, where, how they move). (4) Generate frames that satisfy all signals.
• Why it matters: Without a shared space, the text says one thing, the images say another, and the video context says a third—resulting in mixed-up outputs.

🍞 Anchor: You say “add the cat from this photo into the beach clip, holding this juice box,” and the MVL system figures out which pixels belong to which instruction and blends them naturally.

🍞 Hook: You know how a good editor fixes confusing instructions by asking clarifying questions?

🥬 The concept (Prompt Enhancer, PE): The PE rewrites messy, vague requests into clear, detailed guidance the generator was trained to follow.

• How it works: (1) Read all inputs. (2) Infer missing details using world knowledge. (3) Reformat the prompt to match the training distribution. (4) Output cleaner, more actionable instructions.
• Why it matters: If the generator receives ambiguous prompts, it guesses, leading to identity drift, wrong colors, or odd physics.

🍞 Anchor: “Make it epic” becomes “Use a wide-angle, low-angle shot, golden-hour lighting, slow dolly-in, subject wears the red jacket from @Image_2.”

🍞 Hook: Think of sculpting: first you shape the form, then you polish the details.

🥬 The concept (Base generation + Super-resolution): The Omni-Generator creates a draft video; a Multimodal Super-Resolution (VSR) module sharpens textures and fine details.

• How it works: (1) Generate low-res but coherent motion and layout. (2) Upscale using MVL cues to restore crisp edges, materials, and tiny features. (3) Use efficient attention windows and caches to stay fast.
• Why it matters: Directly generating perfect 4K frames with long motion is too slow and error-prone.

🍞 Anchor: First make a clean cartoon of the scene, then ink and color it carefully guided by the references.

🍞 Hook: You know how orchestra conductors make strings, brass, and percussion play as one? A great video model should do that with text, images, and video.

🥬 The concept (Key insight): Treat text, images, and video as one unified language (MVL), then train a single generator that can understand, reason, and create videos end-to-end from that shared space.

• How it works: (1) Convert inputs to a common token space. (2) Use a Prompt Enhancer to clarify intentions. (3) Let a diffusion transformer (Omni-Generator) compose motion and layout. (4) Use a multimodal super-resolution head to refine details, while conditioning on the same MVL signals. (5) Align the whole system to human taste via preference learning and distillation for speed.
• Why it matters: Without a single shared language and unified path, complex edits and references fall apart across steps.

🍞 Anchor: “Put the capybara and guinea pig from these images into this red bumper car scene, then do a close-up.” Kling-Omni fuses the cues and outputs a coherent clip.

Multiple Analogies:

1. Film Studio Analogy:

• MVL is the script/storyboard everyone agrees on.
• The PE is the script doctor who clarifies vague scenes.
• The Omni-Generator is the director and cinematographer crafting shots.
• Super-Resolution is post-production polishing.
• DPO is test screening feedback aligning to audience tastes.
• Distillation is learning to shoot faster takes with the same quality.

1. Cooking Analogy:

• MVL is the recipe that links ingredients (images), cooking notes (text), and plating references (video).
• The PE standardizes the recipe steps.
• The generator cooks the dish.
• Super-resolution plates it beautifully.
• DPO tunes flavors to diners’ preferences.
• Distillation learns to cook in fewer steps without losing taste.

1. Map and GPS Analogy:

• MVL is a unified map that shows roads (text), landscapes (images), and traffic (video context).
• The PE plans the route precisely.
• The generator drives the car.
• Super-resolution cleans the windshield for clarity.
• DPO favors routes passengers prefer.
• Distillation learns shortcuts to arrive faster.

Before vs After:

• Before: Fragmented tools; text-only understanding; pixel painting without deep reasoning; identity/style drift; slow, chained pipelines.
• After: One end-to-end system; MVL for precise control; reasoning-enhanced editing; consistent identities and motion; faster, cheaper inference.

Why It Works (intuition):

• Shared MVL tokens let the model align “who, what, where, how it moves” across modalities.
• The PE reduces prompt uncertainty, pushing inputs into the distribution the model has mastered.
• Diffusion transformers excel at composing temporally consistent motion when guided by rich conditions.
• Super-resolution adds detail while staying faithful to MVL constraints, preventing drift.
• Human preference learning (DPO) teaches nuanced motion and aesthetics that metrics don’t capture.
• Distillation compresses long sampling into a few intelligent steps, preserving the teacher’s behavior.

Building Blocks (with sandwich mini-explanations):

• 🍞 Hook: Ever try to explain a picture using only words and feel it’s not enough? 🥬 MVL (Multimodal Visual Language): A single "language" that mixes text, images, and videos in one token space, so the model can connect them directly. How: encode each input, place in shared embeddings, allow cross-attention to align meanings. Why: words alone can’t capture layout or motion; MVL fills the gap. 🍞 Anchor: “Use this sketch as layout, color like this photo, animate like that clip.”
• 🍞 Hook: A coach turns a messy plan into clear plays. 🥬 Prompt Enhancer (PE): An MLLM that rewrites vague prompts into clear, model-friendly instructions using world knowledge. How: read inputs, reason, reformat to training style. Why: reduces ambiguity → better adherence. 🍞 Anchor: “Make it cool” → “low-angle, evening, neon signs, steady dolly, subject from @Image_1.”
• 🍞 Hook: First draw the scene, then add details. 🥬 Omni-Generator: A diffusion transformer that turns MVL tokens into a coherent base video. How: iterative denoising guided by cross-modal attention. Why: drafting motion/layout first is easier than perfecting all details at once. 🍞 Anchor: It blocks out the scene, moves the camera, keeps identities stable.
• 🍞 Hook: Glasses make the world sharper. 🥬 Multimodal Super-Resolution: Upscales and enhances detail using the same MVL signals. How: local/shifted attention windows, asymmetric attention with KV cache. Why: crisp detail at speed. 🍞 Anchor: Skin texture, fabric weave, reflections become clear.
• 🍞 Hook: Movie test screenings nudge the final cut. 🥬 DPO Preference Learning: Trains the model to prefer outputs humans like. How: generate variants, collect human picks, optimize to prefer winners. Why: pixel-perfect can still look wrong; human taste matters. 🍞 Anchor: Smoother motion, more natural lighting.
• 🍞 Hook: Learning a shortcut to do homework faster. 🥬 Distillation: Teaches a smaller/faster sampler to imitate a slow, high-quality teacher in ~10 steps (vs ~150). How: trajectory + distribution matching with ODE sampling. Why: huge speedup with fidelity. 🍞 Anchor: Same scene, far fewer steps.

High-level Recipe: Input (text + images + videos) → Prompt Enhancer (clarify and reason) → Omni-Generator (draft video via diffusion transformer) → Multimodal Super-Resolution (sharpen details) → Output

Step-by-step with sandwich explanations and examples:

1. Inputs as MVL 🍞 Hook: Imagine handing a director a script, character photos, and a rough clip of camera motion. 🥬 What: MVL turns text, image, and video inputs into a single set of tokens in one space.

• How: encode each modality; interleave tokens with markers like @Image_1 or @Video_1; use cross-attention so tokens inform each other (who/what/where/how it moves).
• Why: Without a common space, the model can’t match “the girl from this photo” to “zoom in on her smile” in the video. 🍞 Anchor: Instruction: “According to @Video_1, add the cat from @Image_1 laying on the ground, holding the juice from @Image_2.” The MVL pack keeps these links explicit.

1. Prompt Enhancer (PE) 🍞 Hook: A teacher helps you turn “do something cool” into a clear plan. 🥬 What: An MLLM that rewrites ambiguous prompts to be precise and training-aligned.

• How: SFT trains the model to show its chain of thought; RL then rewards factual correctness, richness, and similarity to high-quality training prompts; outputs include camera style, lighting, shot type, and references resolved.
• Why: Messy prompts cause identity drift and odd physics; PE reduces variance and ambiguity. 🍞 Anchor: “Make it magical” → “Night scene, soft blue rim light, low-angle, add lightning on sword, preserve face from @Image_3.”

1. Omni-Generator (Diffusion Transformer) 🍞 Hook: Think of fog lifting to reveal a scene. 🥬 What: A diffusion transformer generates a base video by gradually denoising latent frames guided by MVL tokens.

• How: pre-train on text–video (and image-to-video) to learn motion/layout; supervised fine-tuning adds reference-to-video, editing, and semantic tasks with interleaved modalities; cross-modal attention ensures identities and instructions remain aligned.
• Why: Generating coherent motion and composition first is more stable than trying to nail all fine details at once. 🍞 Anchor: Input: selfie + city-street video + text “change to worm’s-eye view and zoom.” Output: a low-res clip that respects identity, angle, and camera move.

1. Reinforcement Learning via Direct Preference Optimization (DPO) 🍞 Hook: A coach watches two takes and says which looks better. 🥬 What: DPO aligns outputs with human aesthetic preferences.

• How: sample MVL conditions; generate multiple videos with different noise; humans pick preferred vs. dispreferred; train with DPO loss using the noise and timesteps to bias the model toward winners.
• Why: Pixel metrics can’t measure “feels right”; DPO captures motion realism and integrity. 🍞 Anchor: Two variants: one jitters, one flows smoothly—DPO nudges toward the smooth one.

1. Distillation for Fast Sampling 🍞 Hook: Learn shortcuts without missing key steps. 🥬 What: Two-stage distillation reduces steps from ~150 to ~10 NFE with minimal loss.

• How: Stage 1 Trajectory Matching: partition timesteps into phases; train student to hit teacher’s denoise targets at phase ends—directly at 10 steps. Stage 2 Distribution Matching: adopt ODE-style few-step sampling (TDM-like) while keeping trajectory regularization to avoid drift.
• Why: Massive speedup enables interactive editing and longer videos. 🍞 Anchor: Same scene quality in a fraction of the time.

1. Multimodal Super-Resolution (VSR) 🍞 Hook: Put on sharper lenses after composing the shot. 🥬 What: Cascaded diffusion upscales and refines detail using MVL plus low-res latents.

• How: replace full attention with local windows; use shifted windows every other layer to avoid isolated blocks; asymmetric attention lets condition tokens self-attend (cacheable) while noisy tokens attend broadly; KV caching reuses condition features across steps.
• Why: High-res + long video context is heavy; this makes it fast without hurting visuals. 🍞 Anchor: You keep the same face and motion, but now you can count the stitches on the jacket.

1. Training Optimization (throughput and stability) 🍞 Hook: Packing a moving truck smartly saves trips. 🥬 What: An end-to-end multimodal training pipeline for balance and efficiency.

• How: online VAE/text-encoder inference with a central scheduler balances DP/PP workloads; elastic Ulysses parallelism switches per microbatch to tame variable sequence lengths; two-tier all-to-all reduces network congestion; MM-FlashAttention packs variable-length, cross-modal masks in one fast kernel; selective recompute + pipeline-aware offloading save memory; virtual-stage reuse cuts duplicate compute; robust reliability stack speeds restarts and monitors stalls.
• Why: Without these, GPUs idle, memory overflows, or training stalls. 🍞 Anchor: Mixed batches of short texts and long videos still flow smoothly through the cluster.

1. Inference Optimization (speed at run time) 🍞 Hook: Carpooling reduces traffic and fuel. 🥬 What: Hybrid parallelism + quantization + caches for faster inference.

• How: Ulysses and tensor parallelism with compute–communication overlap; FP8 quantization for GEMMs and attention with fused quant/dequant and FP8 communication; specialized caches for long reference images/videos with offload to manage memory.
• Why: Long references slow generation; quantization and caches cut latency big-time (≈2× speedup from caching alone). 🍞 Anchor: A complex edit with multiple reference photos now renders fast enough for interactive tweaking.

Concrete data example across steps:

• Inputs: Text: “In anime style, the girl from @Girl wears the outfit in @Image_1 and hat in @Image_2, strolling through snowy Kyoto, hands in pockets, cinematic dolly.”
• PE expands: “Wide shot to medium, evening snow, warm shop lights, gentle dolly-in, soft depth of field; keep face identity from @Girl; outfit from @Image_1; hat from @Image_2.”
• Omni-Generator drafts: A low-res clip with correct identity, outfit, hat, snowy street, and dolly.
• VSR refines: Clean hair strands, fabric folds, snow sparkle, neon bokeh.

Secret Sauce:

• MVL unification + PE reasoning for precise, low-ambiguity control.
• Diffusion transformer for coherent motion and cross-modal alignment.
• VSR with windowed/shifted attention and asymmetric KV caching for crisp details at speed.
• DPO + two-stage distillation for human-preferred quality with fast sampling.

🍞 Hook: When you try out for a team, coaches don’t just time your sprint—they also watch your form and teamwork.

🥬 The concept (Evaluation that matches real use): Kling-Omni is tested on a wide, realistic benchmark (OmniVideo-1.0) with human raters judging what actually matters for creators.

• How it works: Build 500+ test cases that mix subjects (people, animals, props), scenarios (production, ads, social), and challenges (complex actions, wide angles, emotions, multi-element fusion). Evaluate side-by-side with baselines and ask experts to pick which result is better, same, or worse.
• Why it matters: Simple numbers (like only FID for images) miss motion realism, identity stability, or instruction faithfulness.

🍞 Anchor: It’s like judges scoring both the dance steps (accuracy) and the performance (style, flow, emotion).

1. The Test: Metrics and Motivation

• Dynamic Quality: Do motions flow, respect physics, and integrate subjects/backgrounds naturally? Are camera moves and multi-character interactions believable?
• Prompt Following: Does the output follow tricky, interleaved instructions (e.g., “girl from this set, hat from that image, zoom from below, anime style”)?
• Identity Consistency: Is the person/object the same across angles, lighting, and movements?
• Video Consistency (for edits): Are unedited regions preserved while changes are clean and smooth?

1. The Competition:

• Image-referencing tasks vs. Google Veo 3.1.
• Video editing tasks vs. Runway-Aleph.

1. The Scoreboard (GSB: Good–Same–Bad):

• Kling-Omni’s aggregated GSB shows strong superiority across dimensions, with an overall GSB of 247%. Think of this like getting more than double the “wins” over “losses” compared to strong peers—closer to an A+ when many others are around a solid B.
• Context: The human study is double-blind with professional annotators and domain experts judging outputs without knowing which model made them.

1. Representative Capabilities Observed:

• Multi-Modal Precise Referencing: Subject libraries (multiple photos per subject) improve identity stability. Complex compositions (image + element library + stylization) maintain coherence.
• Video Referencing: New camera angles; motion and camera motion transfer; next/previous shot generation—all while keeping identities intact.
• Interactive Editing: Addition/removal/replacement, stylization, attribute and material changes, special effects, weather swaps, and background replacement—including reference-guided variants.
• Temporal Narrative: Turn multi-grid comics or storyboards into flowing videos.

1. Surprising Findings:

• Visual signal control: Simple annotations (arrows, boxes) can be interpreted as constraints, hinting at sketch-to-motion workflows.
• Reasoning-enhanced generation: The system can leverage world knowledge (e.g., GPS → Eiffel Tower) and show geometric or semantic reasoning in generation guidance—early signs of “world simulation.” (Note: some of these are exploratory and not yet in the online version.)

1. Takeaways with Meaning:

• 247% GSB isn’t just a big number; it reflects fewer jitters, truer identities, tighter instruction following, and more faithful edits—what users actually feel when they watch the clip.
• Unified MVL inputs plus PE clarification matter: the better the input alignment, the better the output.
• Engineering choices (window attention, KV cache, FP8, distillation) enable this quality at practical speeds.

🍞 Hook: Even the best multi-tool can’t replace every specialized instrument in every situation.

🥬 The concept (Honest limits and trade-offs): Kling-Omni is powerful but not magic; knowing when and how to use it keeps results great and time well-spent.

• How it works: We examine current constraints in data, compute, control, and evaluation.
• Why it matters: Clear expectations guide better projects and future research.

🍞 Anchor: A great camera still needs good lighting, planning, and a steady hand.

Limitations:

• Extremely precise physical interactions (e.g., complex contact dynamics, fluid–object coupling) can still look uncanny, especially in crowded or chaotic scenes.
• Long-form narrative with strict continuity (many minutes, multiple scenes) may accumulate drift without careful conditioning or splitting into segments.
• Some advanced reasoning-and-control features are exploratory and not yet available in the online version.
• PE depends on strong MLLM behavior; if the enhancer hallucinates or over-specifies, it can bias generation away from the user’s exact intent.
• Human preference data (for DPO) reflects annotator tastes; different audiences may prefer different styles.

Required Resources:

• GPUs with sufficient memory for diffusion transformers, plus fast storage/network for large video batches.
• A curated multimodal dataset with alignment checks; data governance and safety filters.
• Inference stack with hybrid parallelism, quantization, and caching for responsiveness.

When NOT to Use:

• Tasks needing exact physical simulation or scientific accuracy (e.g., engineering-grade fluid dynamics).
• Cases where strict reproducibility frame-by-frame is required without stochastic variation.
• Sensitive domains where identity/style manipulation could cause harm without clear consent and safeguards.

Open Questions:

• How to push truly grounded physics and multi-agent interactions while keeping speed and fidelity?
• How to make controllability more explicit (sketches, control rigs) without sacrificing creativity?
• How to generalize preference alignment across diverse cultures and genres?
• How to evaluate reasoning in video beyond human studies—can we design robust, automated tests?
• How to scale to long, multi-minute stories with scene graphs and consistent lore?

Three-Sentence Summary: Kling-Omni unifies text, image, and video inputs into a Multimodal Visual Language so one model can understand, reason, generate, and edit videos end-to-end. A Prompt Enhancer clarifies intent; a diffusion transformer drafts motion; a multimodal super-resolution head polishes details; DPO and distillation align outputs to human taste and speed. Strong data curation and systems engineering make it both high-quality and efficient, outperforming strong baselines in human evaluations.

Main Achievement: Turning fragmented video tools into a single MVL-driven system that reliably follows complex, mixed-modality instructions while producing cinematic, identity-consistent, and temporally stable videos at practical speeds.

Future Directions: Expand explicit visual control (sketches, arrows, bounding boxes), deepen world knowledge and physics grounding, scale to longer narratives, broaden multilingual/multicultural preference alignment, and further compress sampling for real-time interactivity. Strengthen safety, consent, and provenance for responsible deployment.

Why Remember This: Kling-Omni marks a step from “pixel generators” toward early “world simulators” by making text, images, and videos speak the same language—and then composing them into moving stories that look and feel right to human viewers.