TechPrufer
Get started
Replicate
Replicate logoReplicate · Feature trail

Video Generation Prompting and Control

5 posts · Dec 2024 – Aug 2026

What this trail covers

Replicate continues to expand its video generation capabilities with support for advanced models like Veo 3 and Wan 2.1. This post details how to craft effective prompts for Veo 3, focusing on specifying visual elements (subject, context, action, style, camera motion, composition, ambiance) and audio elements (dialogue, ambient noise, sound effects, music). It highlights Veo 3's unique consistency, where similar prompts yield similar results, which can be leveraged for character consistency. This post introduces FLUX 3, a multimodal foundation model trained on images, audio, and video, aiming to encapsulate a more comprehensive understanding of reality. It showcases FLUX 3's capabilities in text-to-video generation with detailed prompts, image-to-video transformation using start and end frames, video continuation from a base clip, generating multiple scenes and camera angles within a single prompt, and achieving diverse artistic styles beyond hyper-realism. Examples include VHS-style home videos and zero-gravity simulations.

All Replicate trails
  1. The Ultimate Guide to FLUX 3This post introduces FLUX 3, a multimodal foundation model for video generation. It details the model's architecture which learns from images, audio, and video to generate multimodal outputs, aiming for a more integrated understanding of reality. The post provides extensive examples and guidance on using FLUX 3 for text-to-video generation with detailed prompts, image-to-video transformations by specifying start and end frames, video continuation from an existing clip, generating multiple scenes and camera angles within a single prompt, and achieving various artistic styles (e.g., stop-motion claymation, comic-inked cityscape). It also highlights the model's ability to generate realistic VHS-style home videos and experimental zero-G footage. The post includes a Python code snippet demonstrating how to run FLUX 3 on Replicate, specifying parameters like prompt, duration, resolution, aspect ratio, and audio generation.
  2. How to prompt Grok Imagine Video 1.5This post provides a detailed guide on how to effectively prompt the Grok Imagine Video 1.5 model. It introduces specific prompting techniques, including writing a dedicated 'Sound:' section with granular descriptions, using intensity modifiers to convey scale and force, explicitly describing camera movements (e.g., 'slow push-in', 'tracking shot'), and keeping prompts focused on specific actions. The post also demonstrates how to leverage a pre-generated still image as a starting point, with the video prompt then defining only the changes and motion. Example prompts and their corresponding video outputs are provided, along with code snippets for running the model on Replicate.
  3. How to make remarkable videos with Seedance 2.0This post introduces Seedance 2.0, a new video generation model available on Replicate. It details Seedance 2.0's ability to reference multiple input assets (images, video, audio) for greater control over video generation, including character consistency, motion transfer, style, composition, and audio-driven rhythm. It highlights the model's unified architecture for synchronized audio and video generation, including dual-channel stereo and layered tracks. The post also emphasizes Seedance 2.0's advanced physics simulation capabilities and its support for multi-shot output with camera planning.
  4. How to prompt Veo 3 with imagesThis post introduces and demonstrates the image input capabilities of the Veo 3 video generation model on Replicate. It details how Veo 3 preserves the style of input images, handles typography within animations, and allows for more precise creative control by using pre-generated images as a starting point. Specific techniques for style preservation, typography animation, and selective image animation are showcased with examples and prompt guidance.
  5. Open source video is backThis post introduces optimized versions of the Wan 2.2 video generation model, significantly reducing generation cost to $0.05 per video and inference time to ~30 seconds. It details the availability of FAST image-to-video and text-to-video models at 480p and 720p resolutions, alongside unoptimized versions. API examples for both text-to-video and image-to-video generation using the optimized models are provided. The post also mentions ongoing collaboration with Pruna AI for further optimizations.
  7. How to prompt Veo 3 for the best resultsThis post provides detailed guidance on prompting Google's Veo 3 video generation model. It outlines key visual elements to include in prompts (subject, context, action, style, camera motion, composition, ambiance) and audio elements (dialogue, ambient noise, sound effects, music). A significant contribution is the observation and explanation of Veo 3's high prompt consistency, which can be exploited for character consistency across generations. It also offers specific techniques for prompting dialogue (explicit vs. implicit), managing pronunciation issues with phonetic spelling, differentiating speakers in conversations, avoiding unwanted subtitles, and controlling background audio. The post also briefly mentions how to influence the visual style beyond the default live-action output.
  8. Wan2.1 parameter sweepThis post details a parameter sweep experiment conducted on Alibaba's WAN2.1 text-to-video model to understand the impact of `guidance scale` and `shift` on generated video quality. It provides concrete observations on how different values of these parameters affect visual artifacts, realism, and motion dynamics. Recommendations are given for choosing parameter ranges based on desired output characteristics (e.g., creative vs. realistic). The post also shares the GitHub repository containing the code used for generating these parameter sweeps, enabling others to replicate or extend the experiment.
  9. Generate short videos with the Replicate playgroundThis post introduces a workflow for generating AI video using the Replicate playground. It details a step-by-step process: 1. Start with an image (generated by a Replicate image model or fine-tuned model like datacte/flux-aesthetic-anime). 2. Generate and refine video using models like minimax/video-01-live, utilizing the `first_frame_image` input and prompt optimization. 3. Add sound using models like zsxkib/mmaudio, matching video motion and prompt. 4. Share the video using code snippets from the playground for API integration. The post highlights the playground as a tool for quick iteration and experimentation with video models.
  10. AI video is having its Stable Diffusion momentThis post analyzes the current landscape of AI video generation models, drawing parallels to the impact of Stable Diffusion on image generation. It provides a comparative table of various video models (Sora, Minimax Video-01, Tencent Hunyuan Video, Genmo Mochi 1, Runway Gen3, Haiper 2.0, Luma Ray, Lightricks LTX-Video) based on technical metrics like ELO score, speed, duration, and resolution. It highlights specific models available on Replicate, detailing their strengths and weaknesses, and mentions models not yet integrated. The post frames the current state of AI video as a "Stable Diffusion moment" due to the rapid advancement and accessibility of capable models.