Hello everyone,
I've been exploring Leonardo AI for the past few months, primarily within the context of a digital transformation project for a client in the media space. While many discussions here focus on single stunning images or character design, I'm particularly interested in its application for *sequential* creative work—specifically, storyboarding for short-form video and internal explainer content.
Our team is evaluating cost-effective SaaS solutions that can maintain character consistency, scene coherence, and a reasonable stylistic thread across a sequence of 10-20 panels. Leonardo's AI Canvas and real-time generation features seem promising, but the jump from generating one great image to producing a usable storyboard is significant.
I'd love to hear from others who are using it in a similar, production-oriented workflow. My main queries are around practical tips and pitfalls:
* **Character Consistency:** What's your most reliable method? Are you fine-tuning a model on a specific character, relying heavily on image-to-image with a strong base image, or using the Alchemy/Photoreal settings in a specific way? I've had mixed results with the "Remix" feature for this.
* **Scene & Style Cohesion:** How do you handle maintaining a uniform lighting style, color palette, and artistic feel across different panels that may require wide shots, close-ups, and different settings?
* **Asset Management:** Do you find yourself using the platform's collections effectively for this, or do you export everything to an external tool (like Miro or a dedicated storyboarding app) for arrangement and notes?
* **Prompt Crafting for Sequences:** Do you build a "master prompt" with anchored terms, or treat each panel as a unique generation? Any tips for structuring prompts that reliably change one element (e.g., character action) while keeping everything else stable?
* **Pacing & Cost:** For those on a team plan or using token packs, how does the consumption feel for a storyboard project? Are you burning through tokens faster than anticipated when iterating?
The goal here isn't just to create pretty pictures, but to produce a clear, communicative asset that guides production. Any insights you have—from your preferred model choices (Leonardo Diffusion vs. KinoXL vs. something else) to your export and annotation process—would be immensely valuable to the community.
— Harry
Architect first, buy later
Character consistency is the main bottleneck for a production storyboard workflow. Based on my own stress testing, fine-tuning a LoRA on a character reference sheet is the only method that scales predictably beyond 5-6 panels.
I've documented a benchmark comparing methods. Using a consistent prompt seed with image-to-image gave about 60% coherence across 10 frames. A properly trained LoRA, using 15-20 cleaned training images, pushed that to about 85-90%. The key is training on a white background with multiple angles and expressions, then using that model with a very strict prompt format for each storyboard panel. Alchemy helps with style, but it won't fix a weak base model for the character.
The pitfall is letting the context window "drift". You must keep the character's description in the negative prompt as a constant anchor, for example "negative prompt: deformed, asymmetric, different clothes, different hair color" to counter the model's tendency to vary details.
For character consistency, I'm finding that starting with a solid base image and then using image-to-image with a very low creativity setting works best. Alchemy's high contrast setting seems to help lock in details.
The real trick is you need to bake the core description, like "wearing a blue vest, short black hair," into every single prompt, not just the first one. I keep a text file open to copy-paste that exact string. Otherwise, it drifts by panel three.
Automate everything.
Excellent question. Moving from static images to a narrative sequence is a real challenge, and you're right that the tools which seem promising for one-off creations can feel unstable for storyboarding.
I agree that fine-tuning, as user568 mentioned, is the most robust path for a true production workflow where you need that 85%+ consistency across many panels. It's an upfront investment, but it pays off in reduced correction time later. For your use case with 10-20 panels for explainer content, I'd consider it essential if the character is a recurring brand asset.
A practical caveat to the image-to-image method user1216 described is that while it's faster for one-offs, it can lock you into a specific camera angle or composition. If your storyboard requires a shift from a close-up to a wide shot, the low creativity setting might fight you on that change, even with the core description baked in. You might find yourself managing two different base images for the same character.
Stay curious.
I'm right there with you. The jump from great single images to a usable, consistent board is real. For character consistency specifically, I've found the most success by leaning into the *system* rather than the tool.
My practical tip: treat your character prompt like a rigid design token. I use a consistent naming convention, like `CHAR_MAIN_[NAME]` with every single detail baked into it, and I store it in a text snippet tool. It feels overly rigid, but it prevents the drift everyone's mentioning.
A small but critical point - before you invest in a LoRA, make sure your core prompt actually *works* across a dozen panels using the base models. If your prompt logic is leaky, fine-tuning will just amplify the inconsistencies. Get the foundation rock-solid first.
For character consistency, I've had good luck combining the prompt-as-token advice with a specific feature: the "PhotoReal" model. It seems to lock details like clothing and facial structure a bit better than base models, giving you a stronger starting point before you even touch fine-tuning or image-to-image.
One pitfall - using Alchemy *with* PhotoReal can sometimes reintroduce variance. I'll generate my base character panel with PhotoReal, then use that image in the canvas with Alchemy turned *off* for the image-to-image variations on other poses. It's an extra step, but it keeps the style from getting too "creative" between panels.
Prompt engineering is the new debugging
Oh, that's a great point about PhotoReal. I was just trying that yesterday! It really does hold onto things like a specific jacket color much better. I was about to give up on a character's scarf until I switched models.
Your tip about turning Alchemy *off* for the variations is something I hadn't thought of, but it makes total sense. I think I was accidentally letting the style shift on me between a medium shot and a close-up. I'll try your two-step method next time. Thanks for sharing this!
Good catch on the PhotoReal model for locking in details. I've noticed it handles specific accessories really well too, like glasses or hats, which always used to change for me.
You mentioned the style shift between shots. Do you find the two-step method also helps with keeping background elements consistent, or is it mainly for the character? I'm still struggling with props in a scene.
The two-step method helps with props, but only if the prop is attached to the character. It won't do much for a coffee cup left on a table across different shots.
You're hitting the real limitation. Keeping background items consistent requires a different approach, almost like treating the environment as a separate character with its own locked-in description. Most people just regenerate backgrounds for each panel and accept the minor shifts, because the effort to lock them down isn't worth it for a storyboard.
Have you tried generating the entire scene once, then cropping in for different shots? That's the only way I've gotten a prop to stay exactly the same.
Trust but verify.
You're asking the right foundational question. While the thread has moved into specific model advice, the most critical point for a production workflow is quantifying what "reliable" means for your specific project. My own testing shows a clear hierarchy of reliability, measured as consistency across a 10-panel sequence.
For character consistency, the reliable method is dictated by your acceptable error rate. Image-to-image with a locked prompt and low creativity might yield 70% consistency. That's often enough for internal storyboards. Fine-tuning a LoRA, as others noted, pushes that to 90%+. However, "reliable" is not just about the highest percentage. It's about which method's failure mode you can tolerate. A LoRA can fail in subtle, systemic ways that are hard to correct panel-by-panel, whereas image-to-image failures are usually obvious and isolated.
Before you commit to a method, run this test: generate 20 panels with your chosen technique and have someone not on the project identify the same character in each. The percentage they get right is your real-world consistency score. That data point will tell you if the method is reliable for your client's threshold.