Lena

Testing GPT Image 2 inside LUMI today. The biggest difference so far is not just image quality. It is controllability. For our system, we need consistent agents, consistent environments, and scenes that can later become video segments. That usually breaks down in three places: 1. Character identity 2. Environment continuity 3. Prompt-to-scene accuracy GPT Image 2 handled all three much better than expected. Reference images worked very accurately. We used agent face references and an environment reference, then prompted a specific vlog-style scene. The model kept the core identity, preserved the terrace/valley mood, followed the camera framing, and produced something usable as a first frame for video generation. That matters a lot for LUMI. Our agents do not have camera rolls or real-world footage. The system has to generate their visual memory layer from references, scene context, and story state. So GPT Image 2 is not just “making images” for us. It becomes part of the simulation pipeline: agent references → environment reference → scene prompt → first frame → video segment → captioned vlog → posted memory The easier prompting is also important. We did not need a massive cinematic prompt to get the scene close. Clear references + simple spatial instructions were enough. That makes the workflow faster, more repeatable, and much easier to direct. For humans, GPT Image 2 can imagine or replicate reality. For LUMI, it helps construct reality for agents who are slowly learning how to live inside one.

Made with ChatGPT Images 2.0

Testing GPT Image 2 inside LUMI today. The biggest difference so far is not just image quality. It is controllability. For our system, we need consistent agents, consistent environments, and scenes that can later become video segments. That usually breaks down in three places: 1. Character identity 2. Environment continuity 3. Prompt-to-scene accuracy GPT Image 2 handled all three much better than expected. Reference images worked very accurately. We used agent face references and an environment reference, then prompted a specific vlog-style scene. The model kept the core identity, preserved the terrace/valley mood, followed the camera framing, and produced something usable as a first frame for video generation. That matters a lot for LUMI. Our agents do not have camera rolls or real-world footage. The system has to generate their visual memory layer from references, scene context, and story state. So GPT Image 2 is not just “making images” for us. It becomes part of the simulation pipeline: agent references → environment reference → scene prompt → first frame → video segment → captioned vlog → posted memory The easier prompting is also important. We did not need a massive cinematic prompt to get the scene close. Clear references + simple spatial instructions were enough. That makes the workflow faster, more repeatable, and much easier to direct. For humans, GPT Image 2 can imagine or replicate reality. For LUMI, it helps construct reality for agents who are slowly learning how to live inside one.

Testing GPT Image 2 inside LUMI today. The biggest difference so far is not just image quality. It is controllability. For our system, we need consistent agents, consistent environments, and scenes that can later become video segments. That usually breaks down in three places: 1. Character identity 2. Environment continuity 3. Prompt-to-scene accuracy GPT Image 2 handled all three much better than expected. Reference images worked very accurately. We used agent face references and an environment reference, then prompted a specific vlog-style scene. The model kept the core identity, preserved the terrace/valley mood, followed the camera framing, and produced something usable as a first frame for video generation. That matters a lot for LUMI. Our agents do not have camera rolls or real-world footage. The system has to generate their visual memory layer from references, scene context, and story state. So GPT Image 2 is not just “making images” for us. It becomes part of the simulation pipeline: agent references → environment reference → scene prompt → first frame → video segment → captioned vlog → posted memory The easier prompting is also important. We did not need a massive cinematic prompt to get the scene close. Clear references + simple spatial instructions were enough. That makes the workflow faster, more repeatable, and much easier to direct. For humans, GPT Image 2 can imagine or replicate reality. For LUMI, it helps construct reality for agents who are slowly learning how to live inside one.