<p>I'm building a tabletop RPG game that generates images on the fly: character portraits,
enemy illustrations, scene art. Cloud APIs work but get expensive when every combat
encounter wants several images. More importantly, I need character consistency. If I
generate a portrait of "Aria the rogue" in session one, future images of her should
look like the same character, not a random new person.</p>
<p>ComfyUI solves both. It runs locally on Apple Silicon, exposes a REST API, and
supports IP-Adapter for using an existing portrait as a visual reference for new
generations.</p>
<h1 id="hardware-reality">Hardware Reality</h1>
<p>MacBook Air M2, 16GB unified memory. The Air throttles under sustained load since
it has no active cooling. Fine for a game that generates at natural breaks (between
scenes, at combat start) rather than continuously.</p>
<p>16GB comfortably fits:</p>
<ul>
<li><strong>SDXL</strong> (fp16, ~6.5GB): mature IP-Adapter ecosystem, good portrait quality</li>
<li><strong>FLUX.2-klein-4B</strong> (fp8, ~8-9GB): Apache 2.0, released November 2025, 4-step
distilled generation, supersedes FLUX.1-schnell. IP-Adapter support less
mature than SDXL.</li>
<li><strong>FLUX.1-dev</strong> (fp8, ~8GB): previous generation but still the best quality
option that fits 16GB. FLUX.2-dev exists but won't fit.</li>
</ul>
<p>For this post I'm using SDXL. Its IP-Adapter tools are battle-tested for exactly
this use case, and 6.5GB leaves plenty of headroom for additional models.</p>
<h1 id="install-comfyui">Install ComfyUI</h1>
<p>ComfyUI has a <a href="https://github.com/Comfy-Org/desktop">Desktop app</a> that handles
everything automatically. Use that if you just want the UI. For API access and
full control, manual install is cleaner.</p>
<pre><code class="language-bash">git clone https://github.com/comfyanonymous/ComfyUI
cd ComfyUI
python3.13 -m venv venv
source venv/bin/activate
python --version  # must say 3.13 — if it says 3.9 you have an old venv, rm -rf venv and redo
pip install -r requirements.txt
</code></pre>
<p>On Apple Silicon, the requirements file pulls a PyTorch build with MPS support
automatically. No CUDA, no special flags.</p>
<p>Start it:</p>
<pre><code class="language-bash">source venv/bin/activate
python main.py --listen 0.0.0.0 --port 8188
</code></pre>
<p><code>--listen 0.0.0.0</code> binds to all interfaces instead of localhost only. Needed for
the API to be reachable from other processes or devices on your network.</p>
<p>I keep a <code>start-comfyui.sh</code> in <code>~/models/</code> next to the model README so there's
one place for everything model-related:</p>
<pre><code class="language-bash">#!/bin/bash
source /path/to/ComfyUI/venv/bin/activate
cd /path/to/ComfyUI
python main.py --listen 0.0.0.0 --port 8188
</code></pre>
<p>Open <code>http://localhost:8188</code>. You should see the node graph UI with a default
workflow loaded. No models needed for the server to start — the UI comes up
regardless.</p>
<h1 id="model-storage">Model Storage</h1>
<p>Models don't need to live inside the repo. ComfyUI will look in additional
directories if you tell it to via <code>extra_model_paths.yaml</code>:</p>
<pre><code class="language-bash">cp extra_model_paths.yaml.example extra_model_paths.yaml
</code></pre>
<p>Then add an entry pointing at wherever you keep models. For example, if you
store them under <code>~/models/</code>:</p>
<pre><code class="language-yaml">my_models:
  base_path: ~/models/
  checkpoints: sdxl/
  ipadapter: ipadapter/
  clip_vision: clip_vision/
</code></pre>
<p>Adjust the subdirectory names to match your layout. ComfyUI merges these with
its own paths and shows everything in the UI dropdowns.</p>
<h1 id="models">Models</h1>
<p>Download SDXL (~6.5GB):</p>
<pre><code class="language-bash">hf download stabilityai/stable-diffusion-xl-base-1.0 \
  sd_xl_base_1.0.safetensors \
  --local-dir ~/models/sdxl/
</code></pre>
<p>For IP-Adapter, clone the custom node into ComfyUI's <code>custom_nodes/</code> directory:</p>
<pre><code class="language-bash">cd /path/to/ComfyUI/custom_nodes
git clone https://github.com/cubiq/ComfyUI_IPAdapter_plus
</code></pre>
<p>No pip install needed — it has no extra dependencies beyond what ComfyUI
already installs. ComfyUI picks it up automatically on next startup.</p>
<p>Then download the IP-Adapter weights:</p>
<pre><code class="language-bash"># IP-Adapter for SDXL
hf download h94/IP-Adapter \
  sdxl_models/ip-adapter_sdxl.bin \
  --local-dir ~/models/ipadapter/

# CLIP vision encoder (required by IP-Adapter — must be ViT-bigG, not ViT-L)
hf download laion/CLIP-ViT-bigG-14-laion2B-39B-b160k \
  --local-dir ~/models/clip_vision/CLIP-ViT-bigG-14-laion2B-39B-b160k/
</code></pre>
<p>Restart ComfyUI after installing custom nodes. It rescans the nodes directory on
startup.</p>
<h1 id="quick-test">Quick Test</h1>
<p>Before touching the API, confirm generation works at all.</p>
<p>The built-in Templates target newer models and won't work with SDXL. ComfyUI
ships a test workflow in the repo — open it with <strong>File > Open</strong>:</p>
<pre><code>/path/to/ComfyUI/tests/inference/graphs/default_graph_sdxl1_0.json
</code></pre>
<p><img src="/images/comfyui-web-interface.png" alt="ComfyUI web interface"></p>
<p>It's a base+refiner workflow with two Load Checkpoint nodes. The top one
should already show <code>sd_xl_base_1.0.safetensors</code>. The bottom one defaults to
<code>sd_xl_refiner_1.0.safetensors</code> — change it to <code>sd_xl_base_1.0.safetensors</code>
too. We're not doing base+refiner, just running base twice.</p>
<p>Drop a prompt into one of the CLIP Text Encode nodes and hit <strong>Run</strong>:</p>
<pre><code>a knight in plate armor holding a mace and shield, fantasy RPG portrait,
detailed face, dramatic lighting, painterly style, 4k, highly detailed
</code></pre>
<p>First run is slow while the model loads. On M2 Air, SDXL at 20 steps takes
90-120 seconds. Subsequent runs with the model cached are faster but still
in that range — the Air throttles under sustained load with no active cooling.</p>
<p><img src="/images/oss-img-gen-knight.png" alt="Knight portrait generated with SDXL"></p>
<h1 id="img2img-post-battle">Img2Img: Post-Battle</h1>
<p>Txt2img generates from noise. Img2img starts from an existing image and
nudges it toward a new prompt. Good way to stay close to a character while
changing context or condition.</p>
<p>Add two nodes to the current workflow. Right-click empty canvas space (not
on a node) to get the picker:</p>
<ul>
<li><strong>image → Load Image</strong> — then select your knight from <code>ComfyUI/output/</code>
using the dropdown in the node, or upload it from there</li>
<li><strong>latent → VAE Encode</strong> — converts a pixel image into the latent space
the sampler works in, replacing the EmptyLatentImage</li>
</ul>
<p>Then make three connections. To draw a wire: click and drag from an output
dot (right side of a node) to an input dot (left side of another node).
The dots are color-coded by type and will only connect to compatible ports.</p>
<ol>
<li>Drag from the <strong>IMAGE</strong> dot (blue, right side of Load Image) to the
<strong>pixels</strong> dot on VAE Encode</li>
<li>Drag from either <strong>Load Checkpoint VAE</strong> dot → <strong>VAE Encode vae</strong> dot</li>
<li>Drag from <strong>VAE Encode LATENT</strong> dot → <strong>KSampler latent_image</strong> dot
(disconnects EmptyLatentImage automatically)</li>
</ol>
<p>The test workflow uses <strong>KSampler Advanced</strong>, which has no <code>denoise</code> slider.
Instead set <strong>start_at_step</strong> to <code>6</code>. With 20 total steps that runs the last
14, equivalent to about 70% denoise. Lower start_at_step = more faithful to
original, higher = more change.</p>
<p>Update the positive prompt — that's the CLIP Text Encode node wired to the
KSampler's <strong>positive</strong> input, as opposed to the negative one (which holds
your "ugly, blurry..." text):</p>
<pre><code>a knight in plate armor, damaged armor, battle-worn, dented helmet,
scratches and dents, mud-stained, exhausted, fantasy RPG portrait,
detailed face, dramatic lighting, painterly style, 4k, highly detailed
</code></pre>
<p>Hit <strong>Run</strong>. If it's changing too much, drop start_at_step to 4. If the
damage isn't reading, push to 8.</p>
<p><img src="/images/img2image-knight.png" alt="Knight after img2img battle damage pass"></p>
<p>Img2img works, but it's coarse. Fine details like face structure get lost
as you increase denoise, and at low denoise the prompt has limited
influence. Good for "same scene, different state." Not what you want for
generating the same character across completely different scenes.</p>
<p>That's what IP-Adapter is for. Instead of perturbing the reference image,
it encodes it through CLIP vision into an embedding that gets injected
alongside the text conditioning during generation. The sampler still starts
from pure noise, but the attention layers are nudged toward the character's
appearance at every step. Face and body survive across very different
scenes because the conditioning runs throughout, not just at the start.</p>
<h1 id="the-api">The API</h1>
<p>ComfyUI's API accepts a workflow as JSON and queues the generation. The workflow
you build in the UI can be exported in API format: go to Settings, enable Dev
Mode Options, then use the "Save (API Format)" button on the workflow.</p>
<p>The API format represents each node as a dict entry keyed by node ID. Here's a
minimal SDXL workflow:</p>
<pre><code class="language-json">{
  "4": {
    "class_type": "CheckpointLoaderSimple",
    "inputs": { "ckpt_name": "sd_xl_base_1.0.safetensors" }
  },
  "5": {
    "class_type": "EmptyLatentImage",
    "inputs": { "batch_size": 1, "height": 1024, "width": 768 }
  },
  "6": {
    "class_type": "CLIPTextEncode",
    "inputs": {
      "clip": ["4", 1],
      "text": "a fantasy rogue in a dark alley, RPG portrait, detailed"
    }
  },
  "7": {
    "class_type": "CLIPTextEncode",
    "inputs": {
      "clip": ["4", 1],
      "text": "ugly, blurry, watermark, text, bad anatomy"
    }
  },
  "3": {
    "class_type": "KSampler",
    "inputs": {
      "model": ["4", 0],
      "positive": ["6", 0],
      "negative": ["7", 0],
      "latent_image": ["5", 0],
      "seed": 42,
      "steps": 20,
      "cfg": 7,
      "sampler_name": "dpmpp_2m",
      "scheduler": "karras",
      "denoise": 1
    }
  },
  "8": {
    "class_type": "VAEDecode",
    "inputs": { "samples": ["3", 0], "vae": ["4", 2] }
  },
  "9": {
    "class_type": "SaveImage",
    "inputs": { "filename_prefix": "rpg", "images": ["8", 0] }
  }
}
</code></pre>
<p>Submit it:</p>
<pre><code class="language-bash">curl -X POST http://localhost:8188/prompt \
  -H "Content-Type: application/json" \
  -d '{"prompt": &#x3C;workflow_json>}'
# Returns: {"prompt_id": "abc123...", "number": 1, "node_errors": {}}
</code></pre>
<p>Poll for completion:</p>
<pre><code class="language-bash">curl http://localhost:8188/history/abc123
# When done: history[prompt_id]["status"]["completed"] == true
</code></pre>
<p>Fetch the image:</p>
<pre><code class="language-bash">curl "http://localhost:8188/view?filename=rpg_00001_.png" \
  --output character.png
</code></pre>
<p>Images also land in <code>ComfyUI/output/</code> on disk if you'd rather read them
directly.</p>
<h1 id="image-consistency-with-ip-adapter">Image Consistency with IP-Adapter</h1>
<p>IP-Adapter encodes a reference image using CLIP vision and injects that embedding
alongside the text prompt during generation. The result is a new image that
respects the prompt but is visually pulled toward the reference: same hair, same
armor, similar face structure.</p>
<p>It's not a perfect clone. Think of it as a strong nudge. Consistent text prompts
help too. If every prompt for Aria includes "tall elf rogue, purple hair, leather
armor," you're conditioning from both directions.</p>
<p>Add these nodes to the workflow above:</p>
<pre><code class="language-json">"10": {
  "class_type": "IPAdapterModelLoader",
  "inputs": { "ipadapter_file": "ip-adapter_sdxl.bin" }
},
"11": {
  "class_type": "CLIPVisionLoader",
  "inputs": { "clip_name": "CLIP-ViT-bigG-14-laion2B-39B-b160k/open_clip_model.safetensors" }
},
"12": {
  "class_type": "LoadImage",
  "inputs": { "image": "aria_portrait.png" }
},
"13": {
  "class_type": "IPAdapterAdvanced",
  "inputs": {
    "model": ["4", 0],
    "ipadapter": ["10", 0],
    "image": ["12", 0],
    "clip_vision": ["11", 0],
    "weight": 0.6,
    "weight_type": "linear",
    "combine_embeds": "concat",
    "start_at": 0.0,
    "end_at": 1.0,
    "embeds_scaling": "V only"
  }
}
</code></pre>
<p>Then change node <code>"3"</code>'s <code>model</code> input from <code>["4", 0]</code> to <code>["13", 0]</code>.</p>
<p>The prompt here asks for the same knight in a tavern — completely different
scene, same character used as reference.</p>
<p><img src="/images/oss-img-gen-ipadapter.png" alt="Knight in a tavern, generated with IP-Adapter using the portrait as reference"></p>
<p><code>weight</code> is the main knob. At 0.5 you get style and general appearance influence.
At 0.8 it starts fighting the text prompt. For character identity, 0.6-0.7 is
the range to start with.</p>
<p>The <code>image</code> input in node <code>"12"</code> is a filename relative to <code>ComfyUI/input/</code>.
Copy your reference images there, or upload them via the API:</p>
<pre><code class="language-bash">curl -X POST http://localhost:8188/upload/image \
  -F "image=@aria_portrait.png"
# Returns: {"name": "aria_portrait.png", "subfolder": "", "type": "input"}
</code></pre>
<h2 id="face-consistency">Face Consistency</h2>
<p>Standard IP-Adapter works well for overall style and body type. Faces drift more
than you'd like, especially across very different scenes. IP-Adapter FaceID handles
this by extracting face embeddings specifically rather than encoding the whole image.</p>
<p>Download:</p>
<pre><code class="language-bash">hf download h94/IP-Adapter-FaceID \
  ip-adapter-faceid-plusv2_sdxl.bin \
  --local-dir ~/models/ipadapter/
</code></pre>
<p>FaceID also requires InsightFace, which isn't on HuggingFace. Download the
<code>antelopev2</code> model package from the
<a href="https://github.com/deepinsight/insightface/tree/master/model_zoo">InsightFace model zoo</a>
and place it under <code>~/models/insightface/models/antelopev2/</code> (or wherever your
<code>extra_model_paths.yaml</code> maps <code>insightface</code>).</p>
<p>Install the Python package into the ComfyUI venv:</p>
<pre><code class="language-bash">source /path/to/ComfyUI/venv/bin/activate
pip install insightface onnxruntime
</code></pre>
<p>With FaceID installed, use <code>IPAdapterFaceIDAdvanced</code> instead of <code>IPAdapterAdvanced</code>.
It takes the same inputs but runs the face embedding extraction automatically.
Faces stay recognizable across very different scenes much more reliably than with
the standard adapter.</p>
<p>You can combine both: use <code>IPAdapterAdvanced</code> for overall character appearance
and <code>IPAdapterFaceIDAdvanced</code> for face locking, chaining them in sequence.</p>
<h1 id="game-integration">Game Integration</h1>
<p>The pattern for a TTRPG character system:</p>
<pre class="mermaid">
graph TD
    A[Player describes character] --> B[Generate initial portrait\nno reference image]
    B --> C[Store portrait in character record]
    C --> D[Game event triggers image]
    D --> E[Build scene + character prompt]
    E --> F[Upload portrait, generate\nwith IP-Adapter]
    F --> G[Display in game]
    G --> D
</pre>


<p>I wrote about this blog's <a href="/ai-generate-blog-images.html">automated image generation</a> previously. The short version: <code>generate-images.mjs</code> runs before each build, finds posts with missing images, builds a prompt from the <code>imgprompt</code> front matter field (or has <code>gpt-4o-mini</code> summarize the post), then calls DALL-E 3 to produce a 1024x1024 image.</p>
<p>It works, but DALL-E 3 is frustrating. It ignores prompt details, can't make a white background to save its life, and the results look more "AI art" than "clean icon." Time to see what else is out there.</p>
<p><em>Lesson Learned</em>: Don't let Cursor/Claude look online to find the most recent model versions. It got it wrong twice. I had to manually go back and update it after noticing that Gemini's 2.0-flash was not the newer Nano Banana (2.5, 3).</p>
<h1 id="top-models-today">Top Models Today</h1>
<p>This space moves fast. As of February 2026, these are the top 3 image generation models on the <a href="https://arena.ai/leaderboard/text-to-image">LM Arena text-to-image leaderboard</a> that have public APIs. These rankings are based on blind human preference voting and will almost certainly be different by the time you read this.</p>
<p><strong>ELO Scores:</strong> ELO is a rating system where users are shown two AI-generated images side by side (without knowing which model made which) and pick the one they prefer. Wins and losses shift each model's score, so a higher ELO means the model's images are consistently preferred by real people.</p>
<table>
<thead>
<tr>
<th>Model</th>
<th>Provider</th>
<th>Released</th>
<th>ELO</th>
<th>Notes</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>GPT Image 1.5</strong></td>
<td>OpenAI</td>
<td>December 2025</td>
<td>1248</td>
<td>4x faster than GPT Image 1. Token-based pricing, 20% cheaper than its predecessor.</td>
</tr>
<tr>
<td><strong>Nano Banana Pro</strong></td>
<td>Google (Gemini 3 Pro Image)</td>
<td>November 2025</td>
<td>1237</td>
<td>Native Gemini image gen. Strong multimodal integration.</td>
</tr>
<tr>
<td><strong>Flux 2 Max</strong></td>
<td>Black Forest Labs</td>
<td>November 2025</td>
<td>1169</td>
<td>32B parameter model. Megapixel-based pricing. BFL's highest quality tier.</td>
</tr>
</tbody>
</table>
<p>GPT Image 1.5 and Nano Banana Pro are close in quality. Flux 2 Max sits ~80 ELO points behind them but is the strongest non-Google non-OpenAI option available.</p>
<p><strong>What about open source?</strong> Flux 2 Dev is open-weight and Stable Diffusion 3.5 is fully open source, but neither is practical for this use case. Running them on a MacBook Air M2 (8GB unified memory, passive cooling) means throttling after ~90 seconds, a max practical resolution of 512x512, and needing to close basically every other app first. The quality gap vs. the API models is real too. For a build-time script that needs to reliably produce clean images, cloud APIs win.</p>
<h1 id="setup-gpt-image-15">Setup: GPT Image 1.5</h1>
<p>This is the easiest since the blog already uses OpenAI.</p>
<ol>
<li><strong>API Key</strong>: Already set up. Same <code>OPENAI_API_KEY</code> used for DALL-E 3.</li>
<li><strong>Model change</strong>: Update the <code>model</code> parameter from <code>dall-e-3</code> to <code>gpt-image-1.5</code> in the images API call.</li>
<li><strong>API differences</strong>: GPT Image 1.5 supports both the Images API (<code>/v1/images/generations</code>) and the newer Responses API. The Images API is the closest drop-in replacement. Quality can be set to <code>low</code>, <code>medium</code>, <code>high</code>, or <code>auto</code>. It also supports streaming partial renders and up to 16 reference images for editing.</li>
</ol>
<pre><code class="language-bash">export OPENAI_API_KEY="sk-..."
</code></pre>
<p>If you don't already have a key, create one at <a href="https://platform.openai.com/api-keys">platform.openai.com/api-keys</a>.</p>
<h1 id="setup-nano-banana">Setup: Nano Banana</h1>
<p>Nano Banana is Google's image generation feature built on Gemini. You can access it through the official Google Gemini API.</p>
<ol>
<li><strong>Get an API key</strong>: Go to <a href="https://aistudio.google.com/app/apikey">Google AI Studio</a> and create a new API key.</li>
<li><strong>Install the SDK</strong>:
<pre><code class="language-bash">npm install @google/genai
</code></pre>
</li>
<li><strong>Set the environment variable</strong>:
<pre><code class="language-bash">export GEMINI_API_KEY="your-key-here"
</code></pre>
</li>
<li><strong>API usage</strong>: Nano Banana image gen uses the Gemini API with <code>responseModalities: ["TEXT", "IMAGE"]</code>. The model name for the standard tier is <code>gemini-2.5-flash-preview-image</code> and for Pro it's <code>gemini-3-pro-image</code>.</li>
</ol>
<p>Note: the API flow is different from OpenAI. You send a chat completion-style request with image output enabled, and the response includes inline image data.</p>
<h1 id="setup-flux-2-max">Setup: Flux 2 Max</h1>
<p>Black Forest Labs runs their own API platform.</p>
<ol>
<li><strong>Create an account</strong>: Register at <a href="https://dashboard.bfl.ai">dashboard.bfl.ai</a> and confirm your email.</li>
<li><strong>Add credits</strong>: Navigate to API → Credits and add funds via Stripe. Pricing is simple: 1 credit = $0.01 USD.</li>
<li><strong>Create an API key</strong>: Go to API → Keys, click "Add Key", and copy it immediately (it's only shown once).</li>
<li><strong>Set the environment variable</strong>:
<pre><code class="language-bash">export BFL_API_KEY="your-key-here"
</code></pre>
</li>
<li><strong>API usage</strong>: POST to <code>https://api.bfl.ai/v1/flux-2-max</code> with your prompt, width, and height. The API is async — you submit a task and poll for the result using the returned task ID.</li>
</ol>
<h1 id="setup-verification">Setup Verification</h1>
<p>Once you've configured your API keys, you can verify everything is wired up correctly by running:</p>
<pre><code class="language-bash">node apps/blog/blog/scripts/generate-images.mjs --verify
</code></pre>
<p>This will check that each configured API key is valid and can reach its respective service without actually generating any images. We'll implement this flag in a follow-up change.</p>
<h1 id="cost">Cost</h1>
<p>All prices are USD per image at 1024x1024.</p>
<table>
<thead>
<tr>
<th>Model</th>
<th>Cost</th>
<th>Pricing Model</th>
<th>Notes</th>
</tr>
</thead>
<tbody>
<tr>
<td>DALL-E 3 (prior)</td>
<td>~$0.04</td>
<td>Per image</td>
<td>Fixed price, single quality tier</td>
</tr>
<tr>
<td>GPT Image 1.5</td>
<td>$0.009–$0.14</td>
<td>Per token (output)</td>
<td>Varies by quality: low ~$0.009, medium ~$0.03, high ~$0.14. ~20% cheaper than GPT Image 1</td>
</tr>
<tr>
<td>Nano Banana</td>
<td>~$0.02</td>
<td>Per image</td>
<td>Standard tier (Gemini 2.5 Flash). Pro tier is ~$0.10/image</td>
</tr>
<tr>
<td>Flux 2 Max</td>
<td>~$0.07</td>
<td>Per megapixel</td>
<td>Scales with resolution. 1024x1024 ≈ 1MP ≈ 7 credits</td>
</tr>
</tbody>
</table>
<p>The ambiguity here is mostly around GPT Image 1.5 — the token-based pricing means cost depends heavily on the quality setting and prompt length. For a fair comparison we'll use "medium" quality, which lands close to what we're paying for DALL-E 3 today. Flux 2 Max's megapixel pricing is predictable but gets more expensive at higher resolutions.</p>
<h1 id="results">Results</h1>
<p>Each model was given the same prompt, built from <code>imgprompt: "A cute robot wearing a short chefs hat and holding a paint brush"</code>:</p>
<pre><code class="language-text">Subject: "A cute robot wearing a short chefs hat and holding a paint brush".
Style: minimalist flat vector icon, clean lines, crisp edges, simplified geometric shapes.
Colors: black, white, and one primary accent color only. No gradients.
Composition: centered subject, generous negative space, wide landscape 16:9 aspect ratio.
Background: pure solid white (#FFFFFF), filling the entire image.
No text, no shadows, no textures, no transparency, no background elements.
</code></pre>
<p>All images were center-cropped to 533x300 (16:9) after generation.</p>
<h2 id="gpt-image-15-openai">GPT Image 1.5 (OpenAI)</h2>
<p>Model: <code>gpt-image-1.5</code>, size: 1024x1024, quality: medium. Estimated cost: ~$0.03.</p>
<p><img src="/images/ai-image-gen-bakeoff-openai.png" alt="GPT Image 1.5 result"></p>
<h2 id="gemini-20-flash-google">Gemini 2.0 Flash (Google)</h2>
<p>Model: <code>gemini-2.0-flash-exp-image-generation</code>. Estimated cost: ~$0.02 (free tier may apply).</p>
<p><img src="/images/ai-image-gen-bakeoff-gemini-2.0.png" alt="Nano Banana 2.0 result"></p>
<h2 id="nano-banana-gemini-25-flash">Nano Banana (Gemini 2.5 Flash)</h2>
<p>Model: <code>gemini-2.5-flash-image</code>. Estimated cost: ~$0.02.</p>
<p><img src="/images/ai-image-gen-bakeoff-gemini-2.5.png" alt="Nano Banana 2.5 Flash result"></p>
<h2 id="nano-banana-pro-gemini-3-pro">Nano Banana Pro (Gemini 3 Pro)</h2>
<p>Model: <code>gemini-3-pro-image-preview</code>. Estimated cost: ~$0.10.</p>
<p><img src="/images/ai-image-gen-bakeoff-gemini-3-pro.png" alt="Nano Banana 3 Pro result"></p>
<h2 id="flux-2-max-bfl">Flux 2 Max (BFL)</h2>
<p>Model: <code>flux-2-max</code>, size: 1024x576. Estimated cost: ~$0.04 (~0.6MP at $0.07/MP).</p>
<p><img src="/images/ai-image-gen-bakeoff-flux.png" alt="Flux 2 Max result"></p>
<h1 id="conclusion">Conclusion</h1>
<p>It's pretty close between OpenAI and Nano Banana 2.5. I'll likely switch back and forth between the two, but to pick a flat winner as default I'm going with OpenAI for now.</p>
<p>OpenAI has that annoying same-y generic feel that lets you know it's AI art, and I don't love that, but it's also definitely more cute.</p>
<p>Nano Banana's ones are pretty original. I like 3-pro the most but not the price. 2.5-flash is fun, a bit cleaner than OpenAI's art, just kind of <em>busy</em>. I'll probably pick it when I'm aiming for cleaner images than cute ones.</p>
<p>Flux's was more artsy. It's just kind of... weird? It kept drawing unique stuff but just sort of unsettling.</p>


Kyle Pericak

"It works in my environment"

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