A video game that never existed. Cinematic spy-thriller energy. A mysterious heroine in black, vintage cars, burning streets, grainy film texture, and retro broadcast graphics like a lost 1970s espionage game trailer brought to life. Every frame feels like a forgotten VHS transmission from another timeline. 🎻 Unlimited #MiniMaxH3# on Runway

Created a Bleach explainer using MiniMax Design’s Paper Cut Explainer skill. It automatically breaks the project down into tasks and handles the production process, while each stage moves forward based on your approval and direction. Visuals, video, voiceover, music and subtitles are all created and managed within a single project, leading to the final output. MiniMax H3 has huge potential. It's especially good at creating this kind of video. There are so many possibilities. #MiniMaxDesign# #MiniMaxH3#

An otome game that doesn't exist chapter header, nameplate, dialogue box, the branching choices in gold type. all of it rendered in frame with the character, not layered on after unlimited #MiniMaxH3# on Runway 🎻

Inside MiniMax H3: One DiT Stream for Text, Video, and Stereo Audio Earlier this month, @MiniMax_AI open-sourced H3, a multimodal model that accepts text, images, video, and audio, then jointly generates video with native stereo sound. The interesting part is not just the capability list. It is how H3 represents several modalities inside one diffusion transformer. Zhihu contributor 微卷的大白 analyzed the released code and checkpoints. Since MiniMax had not published the full technical report, some low-level details should be treated as code-based interpretation rather than official specification. 1️⃣ What exactly was open-sourced? H3 supports 4–15 seconds of video at 24 fps, together with 32 kHz stereo audio. The released model has two main checkpoints: 🔹 FL2VA handles text-to-audio-video and generation conditioned on optional first or last frames. 🔹 Ref2VA accepts mixed references, including images, videos, and audio. The open H3-Base path generates at 768p. The full 2K product pipeline relies on In-Context Regeneration, which was not open-sourced when the analysis was written. The complete Contextual Omni Representation processing chain and Native Sparse Attention were also not fully available. This distinction matters when evaluating local results or inference cost. 2️⃣ Every modality enters one packed sequence H3 uses a 50-layer Omni Transformer with a hidden size of 5,376. Text conditions, reference media, noisy video latents, and noisy audio latents are packed into the same attention sequence. They share one set of attention projections and the same SwiGLU feed-forward network. Only the target video and audio rows are updated during Euler denoising. Reference and conditioning rows remain context. At the output, two separate heads predict video and audio velocities from the shared hidden states. H3 is not generating video first and attaching sound afterward. Both modalities evolve inside the same denoising process. 3️⃣ The Token Refiner bridges understanding and generation Before entering the DiT, representations from Qwen3-VL pass through a two-layer Token Refiner. A simple linear projector can only transform each token independently. Self-attention allows every conditioning token to reread and reorganize the complete prompt context. The refiner does not see noisy video or audio latents, and it does not perform denoising. Its job is to convert the understanding model’s output into conditioning that the generative backbone can use. The author also found that short prompts often produced weak results. Rewriting them in the richer style of MiniMax’s official examples substantially improved generation quality. 4️⃣ RoPE creates a shared physical timeline The hardest positional problem is that one attention stream must represent several different structures: 🔹 Text has sequential order. 🔹 Video has time, height, and width. 🔹 Audio has time and stereo-channel identity. H3 solves this with three-axis positional coordinates. Video frames and audio samples are mapped onto a shared physical timeline, while the other axes encode spatial position or audio channel. Audio latents run at 40 Hz, while video runs at 24 fps. H3 therefore advances video time by 5/3 units per source frame so audio and video can periodically align on the same coordinates. Left and right audio channels share the same time coordinate but use different positions on another axis. This preserves synchronization while retaining channel identity. Importantly, packed row order does not define physical time. RoPE coordinates do. 5️⃣ Modulation is huge in parameters, tiny in FLOPs Each DiT block generates shift, scale, and gate parameters for both attention and MLP paths. The parameters are selected according to two signals: 🔹 The diffusion timestep 🔹 Whether the row represents text, video, or audio Across 50 layers, these AdaLN-related projections contain roughly 13 billion parameters, around 39% of the DiT. That sounds computationally expensive, but the projections operate on a small table of unique timesteps and modalities. The resulting parameters are then gathered for each row. For a five-second generation, this part contributes less than 0.002% of forward-pass FLOPs. It is a striking design choice: parameter-heavy conditioning without token-proportional projection cost. 6️⃣ Long video turns attention into the bottleneck After VAE compression and DiT patching, a five-second 768p sample still contains roughly 37,700 effective rows. At ten seconds, that grows to about 73,400. At fifteen seconds, it exceeds 109,000. With full attention: ✅ The sequence grows by about 2.9× from five to fifteen seconds. ✅ Compute per DiT forward grows by roughly 6.1×. ✅ Attention’s share of compute rises from 58.4% to 80.2%. H3 uses a distilled CFG path and executes 49 DiT forwards across its sigma schedule. For a fifteen-second sample, the author estimates aggregate DiT computation at roughly 1.04 exaFLOPs. Since Native Sparse Attention was not included in the initial open release, the public inference path analyzed here still pays the quadratic cost of full attention. That makes sparse attention and fused kernels the clearest opportunities for infrastructure optimization. 🔍 The architectural takeaway H3’s core idea is a shared generative space. Text provides instructions. Reference media supplies context. Video and stereo audio are denoised together. RoPE aligns them in space and physical time, while indexed modulation tells each row how to behave. The model’s biggest strength is therefore not simply “audio-video generation.” It is the attempt to make multiple modalities behave like one coordinated sequence. Its biggest constraint is equally clear: as duration grows, full attention rapidly becomes the dominant cost. 🔗 Full analysis: #MiniMaxH3# #VideoGeneration# #DiffusionTransformer# #MultimodalAI# #GenerativeAI# #AIInfra#

MiniMax H3 Fashion Lookbook | Anime Character Reveal + Editorial MV Prompt🔥 Made this high-saturation anime PV with 13 fast visual beats in 15 seconds. A few years ago, this would’ve been days of AE work. Now? Prompt → generate → refine. Prompt 👇 Create a **15s, 16:9, 24fps anime character reveal trailer** with **13 fast visual beats**. Style: **Japanese anime opening × premium AAA motion graphics × fashion campaign**. The video should feel **explosive, sexy, stylish, bold and high-impact**, with **80% motion graphics and 20% character action**. ## CHARACTER LOCK — HIGHEST PRIORITY AO is a **young adult East Asian anime woman** with a sexy, confident Japanese anime aesthetic. She has: * small refined face * sharp expressive crimson eyes * glossy lips * confident, teasing gaze * slim feminine curvy figure * long elegant legs * stylish, cool, alluring presence Keep her identical throughout: * chin-length vivid red bob with messy bangs * crimson-red eyes * black choker * fitted red cropped top * short white cropped jacket, worn open * black mini skirt or fitted shorts * red belt detail * black thigh strap * white-and-red platform sneakers * subtle silver accessories Preserve the same face, body proportions, hairstyle, outfit, materials and colors in every shot. **Never redesign AO. Never change her face or outfit. If a shot becomes too complex, simplify the action first.** ## VISUAL STYLE Palette: **vivid red, crimson, white, black, silver**. Use: giant kinetic typography, red circles, diagonal slashes, manga speed lines, split screens, halftone dots, barcode strips, UI ticks, freeze frames, RGB flashes, impact shakes, poster layouts and graphic wipes. Every beat should feel: **fast, sharp, sexy, explosive, graphic and iconic**. Keep typography bold and readable. Editing: hard cuts, aggressive snap zooms, whip pans, speed ramps, freeze frames, impact shakes and foreground wipes. ## 13 VISUAL BEATS **01 | 0.0–1.0s** White field. Massive red circle slams into frame. Black bars slash across. UI ticks flicker. Giant **A**, then **O**, hit with heavy impact shake. **02 | 1.0–2.0s** The O becomes a circular frame showing an extreme close-up of AO’s crimson eye and glossy lips. She gives a teasing side glance. RGB flash. Circle bursts into red-and-white fragments. **03 | 2.0–3.1s** Black background, huge white **AO**. AO enters fast, turns sharply and power-slides beneath the typography. Red speed streaks trail behind her. Whip-pan out. **04 | 3.1–4.0s** Three red/white/black vertical panels. AO appears in three poses: hip turn, hair touch, over-shoulder stare. Huge vertical **FULL SPEED** moves behind her. **05 | 4.0–5.1s** AO jumps through a rotating typography ring reading **NO BRAKES / ALL EYES ON ME**. One clean mid-air spin. Snap zoom into her confident face. **06 | 5.1–6.0s** White editorial frame. Huge black **HOT** with a red slash. AO crosses the frame with a runway-like step, one hand at her waist. Typography compresses and rebounds. **07 | 6.0–7.0s** Bright red field with black diagonal stripe. AO performs one smooth fast turn. Three ghosted freeze positions trace the movement. Giant outlined **TURN** rotates behind her. **08 | 7.0–8.0s** Words hit one per beat: **HOT / FAST / WILD / RED** AO changes pose with each word: direct stare, hair toss, hip shift, confident forward lean. **09 | 8.0–9.0s** Black frame with manga perspective lines and a graphic grid. AO steps forward and freezes in a strong hero pose. Red circular target graphics lock around her. **10 | 9.0–10.1s** AO moves toward camera through three red-and-white graphic panels. Each panel shatters as she passes. Large **A O** fragments appear behind her. Finish with a hair or leg foreground wipe. **11 | 10.1–11.1s** Rapid poster montage: four frames of the same AO — close-up stare, walking, side pose, hands at waist. Add **01–04**, barcodes, halftone dots and sharp Japanese poster graphics. **12 | 11.1–13.0s** Hero moment on a clean white background. AO lands in a powerful fashion pose: one leg forward, one hand at her waist, chin lifted, direct eye contact. Huge red shockwave rings explode behind her. Typography fragments and speed lines burst outward. Hold an iconic confident freeze. **13 | 13.0–15.0s** Final identity card. Huge black **AO** on a bright white field. AO stands relaxed and alluring in front of the letters. Red circles, halftone, technical arcs and sharp speed accents surround her. Final red pulse flashes through the frame and ends on a hard stinger. ## ANIME STYLE Premium modern Japanese anime rendering: * clean cel shading * sharp linework * polished highlights * cinematic close-ups * dynamic perspective * fashion-editorial full-body framing * smooth hair and fabric motion AO must look like a **stylish adult anime heroine**, not chibi and not childish. ## AUDIO Hard-hitting **electro / future bass / anime-opening style music**. Use: heavy drums, bass hits, risers, glitch fills, synth stabs, typography slams, whooshes and shutter impacts. Build continuously. Peak at Beat 12. End with a sharp electronic stinger. ## PRIORITIES 1. AO identity consistency 2. sexy adult anime character design 3. red-black-white outfit consistency 4. maximum visual impact 5. readable typography 6. premium anime rendering 7. fast beat-synced editing Avoid: childlike proportions, chibi style, blue clothing, face changes, outfit changes, extra characters, unreadable typography, weak motion, dull compositions or generic schoolgirl styling. Final result: **explosive, sexy, red-hot, premium, graphic-driven and visually unforgettable.** Try MiniMax H3 on Ima Studio 👉 #MiniMaxH3# #MotionDesign# #Anime# #AIVideo# #ImaStudio#

MiniMax H3 - Prompt Share Give it a character reference and it turns it into a high-tech digital assembly sequence. You can use parts of the result in your music videos too. Prompt: Use the provided reference as the final character target. Create a short premium cinematic character creation video where the character is built step by step through a high-tech digital assembly process. The sequence should feel fast, intense and visually sophisticated. Show a clear staged progression. Begin with abstract digital fragments, prototype sketches, blueprint-style drawings, construction lines and technical concept sheets appearing in the background. Then gradually assemble the character from the inside out, with limbs, body parts, clothing and facial features forming through scanning passes, holographic layers, wireframes, segmented image fragments, luminous particles and sleek interface elements. Let the arms, legs and other body parts visibly generate in stages, as if engineered by an advanced system. Some moments should show unfinished digital anatomy, partial structures and evolving forms before they lock into the final design. Keep the background alive with passing prototype art, silhouette studies, detail callouts and design variations to reinforce the feeling of a real creation process. The pacing should be rapid and aggressive, with dense motion, layered depth, sharp transitions and constant visual activity, while keeping the character build clearly readable as the main focus. The overall style should feel futuristic, polished, luxurious and cinematic. End with the fully completed character, cleanly revealed and clearly matching the reference.