I recently spent a lot of time testing Leonardo AI for image generation and tried different models, prompts, and settings, but I’m not sure if my user review is fair or if I missed important features or best practices. Could you look over my experience and share tips, corrections, or must-know settings so I can write a more accurate and helpful Leonardo AI user review for others researching this tool?
Short version. Your review is probably fine on vibes, but it likely misses some “power user” stuff that regular Leonardo users rely on. Here are concrete points you can check and maybe add.
- Models and use cases
If you only compared a few “general” models, say so. Leonardo models behave differently by use case. You make your review stronger if you split feedback like this:
- Photo models vs illustration vs 3D / isometric
- Style models vs “base” realistic models
- Which model worked best for portraits, which for product shots, which for stylized art
Example:
- Leonardo Diffusion XL or Alchemy Photo for realistic
- Alchemy V2 / Visionary for art and stylization
- Anime / DreamShaper style stuff for characters
If you did not test across these, mention that limit. That makes your review feel fair, not incomplete.
- Prompting details
If your review says “I tried different prompts and settings” but you do not show clear patterns, users will shrug. Add specifics like:
- Your rough prompt structure, for example: subject, medium, style, lighting, camera, mood
- A good prompt that worked and a bad prompt that failed, with outputs described
- Whether shorter prompts worked better than long ones on different models
Also, mention if negative prompts changed results in a stable way. For example, “deformed, extra limbs, extra fingers, text, watermark” for people.
- Alchemy and settings
A lot of people miss that Alchemy and the “Style Strength” slider change things a lot. If your review ignores:
- Alchemy on vs off
- Style strength low vs medium vs high
- Guidance scale ranges
- Scheduler choices
then it sounds like surface-level testing.
If you tested them, write what you learned, like:
- Higher guidance gave sharper images but more artifacts on faces
- Low style strength respected the prompt more but looked bland
- Certain schedulers produced smoother gradients
Short sentences, concrete results.
- Coherence and upscaling
Two things many reviews skip:
- Image to image
- Upscaling / refinement
Mention if you tried:
- Uploading a base image then refining with Alchemy or image to image
- Upscaling for detail and whether it added weird textures or fixed faces
If not tested, add one line saying you focused on text to image only. That keeps expectations clear.
- Faces, hands, text
Readers care about three things most of the time:
- Faces
- Hands
- Text in images
You make your review more useful if you:
- Show if Leonardo struggles or succeeds with eyes, symmetry, aging, ethnicity
- Say how often fingers looked correct at different resolutions
- Mention whether generated text on signs, UI, logos is legible or gibberish
Even a rough number helps: “Out of 20 portrait generations at 768x768, about half needed touch ups on eyes or hands.”
- Speed, queue, stability
You want to cover:
- Average generation time per image for your resolution
- Any queue delays at peak hours
- Crashes or lost generations
- Whether the site lagged while scrolling past many images
If you did not track numbers, give ranges like “Usually 10 to 20 seconds for 768px, sometimes 40+ during peak.”
- Credits and pricing
Most people care about whether they run through credits too fast.
Your review feels fair if you include:
- How many credits you started with and how fast you used them
- How much a typical 4-image batch cost with and without Alchemy
- Whether image to image or upscaling felt “expensive” for the results
If you compare to Midjourney, DALL·E, Stable Diffusion locally, keep it specific. For example, “I spent about X generations to dial in a style that worked.”
- Workflow and UX
This is where user experience part matters. Go beyond “UI is good / bad” and hit:
- How easy it felt to move from idea to final image
- Folder management, tagging, and search
- Prompt history reuse
- Can you easily re-run past generations with tweaks
- How clean the canvas / editor panel feels
Mention any friction. For example: too many clicks to change a model, confusing batch settings, or unclear default values.
- Missing best practices you might add
Things that often improve results but many users skip:
- Start with 2–4 images per prompt then refine favorites
- Lock in a “style prompt” and re-use it for consistency
- Use image to image with low strength to keep composition and adjust style
- Run a few seeds and keep the one that works best for your use case
- Save prompts and seed numbers for reproducibility
If you did not try these, say so and mark it as “future testing.”
- Fairness and bias
To keep your review fair, you can add one small section that says:
- What you wanted from Leonardo (photo work, art, social media stuff, etc)
- Your experience level with AI image tools
- What tools you compared it against and for what tasks
That way readers know your review reflects your workflow, not all workflows.
If you want line by line feedback on your actual text, post a chunk of your review. Then people can point at specific sentences that feel off or incomplete.
You’re probably being too hard on yourself. Most “user reviews” people actually read are about: can I get what I want, how annoying is it, and is it worth my time/money. Your review doesn’t have to turn into a Leonardo user manual to be fair.
@viaggiatoresolare covered the power‑user feature checklist really well, so I’ll hit different angles:
- Own your perspective, not “objective truth”
Instead of trying to be universally fair, frame it like:
- “I mainly wanted X (ex: social media graphics, product renders, fantasy characters)”
- “I’ve used Y before (Midjourney / DALL·E / SD / none)”
- “So this review reflects that workflow.”
That alone makes it feel fair, even if you missed some advanced tricks. People can self‑filter: “Oh, I’m a comic artist, this part applies to me, that part doesn’t.”
- Show failures, not just conclusions
Most reviews gloss over the ugly stuff. You make yours stronger if you literally show the path:
- Prompt A → Result: weird eyes, muddy background
- Tiny tweak (change model or style strength or aspect ratio) → Result: finally usable
- Your interpretation: “Leonardo is touchy about ; you often need 2–3 prompt/model tweaks.”
Even 2–3 of those mini case studies are worth more than a paragraph of “it felt inconsistent.”
- Be explicit about “I didn’t test this”
Instead of trying to secretly cover everything, literally drop tiny disclaimers like:
- “I barely touched image‑to‑image, so this section is all text‑to‑image.”
- “I did not test 3D, voxel, or tile stuff, so I’m not judging those.”
- “I’m not a hardcore anime / character artist, so take my character results with a grain of salt.”
That reads as fair and honest, not incomplete. People acutally respect boundaries.
- Compare tasks, not just tools
A lot of reviews fall apart when they say: “Leonardo is worse/better than Midjourney” in the abstract. Way more helpful:
- “For quick product mockups: Midjourney gave nicer lighting out of the box, Leonardo gave me more control once I tuned prompts.”
- “For consistent characters: Leonardo let me reuse seeds and prompts more predictably than DALL·E.”
Even if your conclusion is “I don’t know which is better overall,” comparing specific tasks makes you look fair.
- Call out learning curve vs payoff
You spent “a lot of time testing.” That is a datapoint:
- Was the extra time rewarded with noticeably better images and control?
- Or did it feel like fiddling with sliders for marginal gains?
If your honest take is “Once I got used to it, the extra controls were worth it,” say so.
If your honest take is “Felt like diminishing returns after a certain point,” also say so. Both are fair.
Readers care about that “time to decent results” curve more than theoretical maximum quality.
- Talk about frustration moments
One thing I slightly disagree with @viaggiatoresolare on: you don’t have to cover every power feature to be useful. But do not hide the annoying bits:
- Times where it silently failed, froze, or just refused to follow the prompt
- When a setting label confused you (“style strength,” “scheduler,” etc.)
- Any spot where you had to google or watch a video to understand what was going on
Those rough edges are part of user experience, maybe more than model choice.
- Be specific about “who should skip it”
Most reviews are scared to say “this is not for you.” You can be bolder:
- “If you want quick, pretty images with minimal tinkering, you might prefer X.”
- “If you enjoy tweaking parameters and having more control, Leonardo is worth learning.”
- “If you only care about [faces / logos / print‑quality posters], Leonardo felt [weak / strong] in my testing.”
That kind of gatekeeping is actually helpful, not mean.
- Don’t obsess over “best practices” checklists
Best practices change fast anyway. Instead of pretending you know all of them, focus on:
- What you discovered that concretely improved results
- What didn’t help, even though people say it should (for ex: increasing steps, or spamming negative prompts)
Admitting “I tried X everyone recommends, but it didn’t move the needle for my use case” is good, honest reviewing.
If you want super pointed feedback, copy‑paste 1 section of your review here, like your “Conclusion” or your “Pros & cons” list. People can then say “this part feels vague / unfair / missing context” in a way that’s way more practical than generic advice.
You’re overfocusing on “did I cover everything?” and underusing the thing you actually have that most Leonardo AI reviews don’t: a long, real testing journey.
Instead of adding more features or best‑practice lists, I’d tighten your review around three pillars:
1. Turn your testing time into a storyline
People remember a path more than a checklist. Structure around:
- What you wanted at the start
- What frustrated you in the middle
- Where you ended up and whether you’d keep using Leonardo AI
Example skeleton:
- “Week 1: Plug‑and‑play expectations (coming from Midjourney / DALL·E / nothing).”
- “Week 2: Deep dive into models, seeds, and prompt styles.”
- “Week 3: What finally clicked, what still felt random.”
You do not need to cover every knob. You need to show what actually changed your results over time.
2. Be opinionated about the feel, not just the output
@viaggiatoresolare covered lots of feature specifics. You can complement that by focusing more on “vibes” and decision points:
- Did Leonardo AI feel like a “toolbox” or like a “smart assistant”?
- Did you feel in control or constantly surprised in a bad way?
- Was the interface encouraging you to experiment or burying you in options?
That sort of UX texture is where long testing actually pays off.
3. Make a tight pros / cons section for Leonardo AI
Even if your review is long, end with something short and punchy. For example:
Pros for Leonardo AI
- Strong control for users who like tweaking prompts, models, and seeds
- Good for iterative workflows once you understand the interface
- Flexibility across use cases like product mockups, fantasy art, and concept sketches
Cons for Leonardo AI
- Can feel overwhelming at first, especially compared to “type and go” tools
- Inconsistent results if you do not already understand model selection
- Some features feel like marginal gains after lots of tweaking
You can tweak this list to match what you personally found, but keep it sharp and honest.
A few places I’d slightly disagree with @viaggiatoresolare:
- You do not have to apologize for not touching every advanced feature, but I would be harsher on any parts of Leonardo AI that wasted your time. If you spent an hour on a setting that barely mattered, call that out.
- Instead of just saying “I didn’t test X,” briefly explain why: “I skipped the 3D tools because my use case is social media graphics, not asset creation.” That turns a gap into a deliberate choice.
If you want a litmus test:
After reading your review, can someone answer these three questions?
- “Is Leonardo AI worth learning for my kind of work?”
- “Roughly how long before I get decent results?”
- “What parts of the tool will probably frustrate me?”
If yes, your review is fair and useful, even if you never mention half the advanced stuff.