We’re a group of digital pioneers passionate about building software. If you are too, you’re in the right place. Hear from real-life DEPT® engineers (and some of our non-DEPT® pals too) about what it’s like developing & designing software for clients ranging from exciting startups to Fortune 500 companies.
How to maintain character consistency, style consistency, etc in an AI video. Prosumers can use Google Veo 3’s "High-Quality Chaining" for fast social media content. Indie filmmakers can achieve narrative consistency by combining Midjourney V7 for style, Kling for lip-synced dialogue, and Runway Gen-4 for camera control, while professional studios gain full control with a layered ComfyUI pipeline to output multi-layer EXR files for standard VFX compositing.
Goal: Rapidly produce branded, short-form video for social media. This method bypasses Veo 3's weaker native "Extend" feature.
Clip 1
: Generate an 8s clip from a character sheet image.Extract Final Frame
: Save the last frame of Clip 1.Clip 2
: Use the extracted frame as the image input for the next clip, using a "this then that" prompt to continue the action. Repeat as needed.[Genre: ...], [Mood: ...]
) to generate and extend a music track.Goal: Create cinematic short films with consistent characters and storytelling focus, using a hybrid of specialized tools.
--cref
and --sref
parameters.--cref --cw 100
to create consistent character poses and with --sref
to replicate the visual style in other shots. Assemble a reference set.Goal: Achieve absolute pixel-level control, actor likeness, and integration into standard VFX pipelines using an open-source, modular approach.
Loaders
: Load base model, custom character LoRA, and text prompts (with LoRA trigger word).ControlNet Stack
: Chain multiple ControlNets to define structure (e.g., OpenPose for skeleton, Depth map for 3D layout).IPAdapter-FaceID
: Use the Plus v2 model as a final reinforcement layer to lock facial identity before animation.AnimateDiff
: Apply deterministic camera motion using Motion LoRAs (e.g., v2_lora_PanLeft.ckpt
).KSampler -> VAE Decode
: Generate the image sequence.mrv2SaveEXRImage
to save the output as an EXR sequence (.exr
). Configure for a professional pipeline: 32-bit float, linear color space, and PIZ/ZIP lossless compression. This preserves render passes (diffuse, specular, mattes) in a single file.Google Veo leads the generative video market with superior 4K photorealism and integrated audio, an advantage derived from its YouTube training data. OpenAI Sora is the top tool for narrative storytelling, while Kuaishou Kling excels at animating static images with realistic, high-speed motion.
The market leader due to superior visual quality, physics simulation, 4K resolution, and integrated audio generation, which removes post-production steps. It accurately interprets cinematic prompts ("timelapse," "aerial shots"). Its primary advantage is its integration with Google products, using YouTube's vast video library for rapid model improvement. The professional focus is clear with its filmmaking tool, "Flow."
User Profile | Primary Goal | Recommendation | Justification |
---|---|---|---|
The Indie Filmmaker | Pre-visualization, short films. | OpenAI Sora (Primary), Google Veo (Secondary) | Sora's storyboard feature is best for narrative construction. Veo is best for high-quality final shots. |
The VFX Artist | Creating animated elements for live-action. | Stable Diffusion (AnimateDiff/ComfyUI) | Offers the layer-based control and pipeline integration needed for professional VFX. |
The Creative Agency | Rapid prototyping, social content. | Runway (Primary Suite), Google Veo (For Hero Shots) | Runway's editing/variation tools are built for agency speed. Veo provides the highest quality for the main asset. |
The AI Artist / Animator | Art-directed animated pieces. | Midjourney + Kling | Pairs the best image generator with a top-tier motion engine for maximum aesthetic control. |
The Corporate Trainer | Training and personalized marketing videos. | HeyGen / Synthesia | Specialized tools for avatar-based video production at scale (voice cloning, translation). |
The AI image market has split: Midjourney creates the highest quality artistic images but fails at text and precision. For business use, OpenAI's GPT-4o offers the best conversational control, while Adobe Firefly provides the strongest commercial safety from its exclusively licensed training data.
The 2025 generative AI image market is defined by a split between two types of tools. "Artists" like Midjourney excel at creating beautiful, high-quality images but lack precise control. "Collaborators" like OpenAI's GPT-4o and Google's Imagen 4 are integrated into language models, excelling at following complex instructions and accurately rendering text. Standing apart are the open-source "Sovereign Toolkit" Stable Diffusion, which offers users total control, and Adobe Firefly, a "Professional's Walled Garden" focused on commercial safety.
The market is dominated by five platforms with distinct strengths and weaknesses.
Tool | Parent Company | Core Strength | Best For |
---|---|---|---|
Midjourney v7 | Midjourney, Inc. | Artistic Aesthetics & Photorealism | Fine Art, Concept Design, Stylized Visuals |
GPT-4o | OpenAI | Conversational Control & Instruction Following | Marketing Materials, UI/UX Mockups, Logos |
Google Imagen 4 | Ecosystem Integration & Speed | Business Presentations, Educational Content | |
Stable Diffusion 3 | Stability AI | Ultimate Customization & Control | Developers, Power Users, Bespoke Workflows |
Adobe Firefly | Adobe | Commercial Safety & Workflow Integration | Professional Designers, Agencies, Enterprise Use |
The choice of tool often depends on a single required feature.
Model | Text-in-Image Accuracy | Photorealism Quality | Complex Prompt Adherence |
---|---|---|---|
Midjourney v7 | Poor. A major weakness. | Best-in-Class | Fair |
GPT-4o | Excellent. A key strength. | Very Good | Best-in-Class |
Google Imagen 4 | Excellent | Excellent | Very Good |
Stable Diffusion 3 | Good to Excellent | Good to Excellent | Good to Excellent |
This leads to several hard rules for choosing a tool:
Auto encoders are neural networks that compress data into a smaller "code," enabling dimensionality reduction, data cleaning, and lossy compression by reconstructing original inputs from this code. Advanced auto encoder types, such as denoising, sparse, and variational auto encoders, extend these concepts for applications in generative modeling, interpretability, and synthetic data generation.
At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as chain-of-thought reasoning, structured few-shot prompts, positive instruction framing, and iterative self-correction.
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