# nano-banana and the batch economics Google's current Flash Image model is Gemini 3.1 Flash Image — Nano Banana 2, ID `gemini-3.1-flash-image`. Same family: Lite (`gemini-3.1-flash-lite-image`) and Nano Banana Pro (`gemini-3-pro-image`). The whole family lives behind the Gemini API. The call is `generate_content` with an IMAGE modality, one image per call. The reason it matters: it changed the math. ## The pricing As of September 2026, official Gemini API: - **Gemini 3.1 Flash Image** (Nano Banana 2, `gemini-3.1-flash-image`): **$0.045 at 0.5K**, **$0.067 at 1K**, **$0.101 at 2K**, **$0.151 at 4K**. - **Gemini 3.1 Flash Lite Image** (`gemini-3.1-flash-lite-image`): **$0.0336 at 1K only**. - **Gemini 3 Pro Image** (Nano Banana Pro, `gemini-3-pro-image`): **$0.134/image at 1K/2K**, **$0.24 at 4K**. - **Gemini 2.5 Flash Image** (original nano-banana): **$0.039/image**, retires October 2026. The preview ID is already gone. Compare against the rest: | Model | Per-image cost | |---|---| | gpt-image-2 quality=low (square) | $0.006 | | flux-2-klein | from $0.014 | | gemini-3.1-flash-lite-image (1K) | $0.0336 | | recraftv4_1 raster | $0.035 | | flux-2-pro | from $0.03/MP | | flux-pro-1.1 (previous-gen, still live) | $0.04 | | gemini-3.1-flash-image 0.5K / 1K | $0.045 / $0.067 | | gpt-image-2 quality=medium (square) | $0.053 | | Ideogram 4.0 Default | $0.06 | | recraftv4_1 vector | $0.08 | | Ideogram 4.0 Quality | $0.10 | | gemini-3.1-flash-image 2K / 4K | $0.101 / $0.151 | | gemini-3-pro-image 1K/2K / 4K | $0.134 / $0.24 | | gpt-image-2 quality=high (square) | $0.211 | ## The economic threshold Here's the math that actually matters. Suppose your product needs one shipping-ready image per user request. Three strategies: 1. **One-shot premium**: send one prompt to gpt-image-2 quality=high ($0.211) or Nano Banana Pro at 4K ($0.24). Hit rate (image is usable as-is): maybe 30-40%. So real cost per shipping-ready image: $0.50-0.80, and you spent a minute regenerating. 2. **Batch-and-filter**: send the same prompt to nano banana 2 at 1K 10 times. Cost: $0.067 × 10 = $0.67. Hit rate per individual image is lower (~20%), but you have 10 candidates. Probability that AT LEAST one is shipping-ready is 1 - (0.8 ^ 10) ≈ 89%. Real cost per shipping-ready image: $0.67 with 89% confidence, $1.34 with ~99% confidence on the second batch. Lite at $0.0336 (1K) is the same-family cheaper row when the quality ceiling can move. 3. **Self-hosted Flux**: rent a GPU, run FLUX.2. Marginal cost per image drops hard at volume. Worth it above ~50,000 images/month. klein (from $0.014) is the API version of that floor. The batch-and-filter strategy is the 2026 default for most consumer products. nano banana 2 made it economically obvious. At $0.067/image for 1K, you can generate 25 candidates for about $1.68, then use a filtering step (cheap LLM call with vision, or a CLIP-similarity score against a reference) to pick the best. ## Multi-turn editing The other reason nano-banana is interesting: it's built for multi-turn conversational editing in the Gemini API. You generate an image, then say "make the background blue and add a coffee cup on the left," and it edits in-place using world knowledge from Gemini's text base. Most other image models don't do this — they regenerate from scratch, losing identity. This makes nano banana 2 the right pick for any flow where the user is iterating: "show me an image of X" → "now change Y" → "now zoom in on Z." Stay on the same family for the follow-up turns (nano banana 2 or Lite) so identity holds. ## When to NOT use nano-banana - **Highest-end photorealism for hero shots.** `flux-2-pro` still beats it. nano banana 2 is good, Flux is best. - **Crisp text rendering at scale.** Use Ideogram 4.0. nano banana 2 got better, but Ideogram is still the specialist. - **Instruction-heavy composition.** gpt-image-2 or `gemini-3-pro-image`. - **Native vector.** Recraft V4.1 (`recraftv4_1`). - **Subjective magazine-shot taste.** Midjourney is still ahead here for now, and it's UI-only. For everything else — batch generation, multi-turn editing, "I need 50 variants under $5" — nano banana 2 is the default. ## What this changes about your harness Before nano-banana, the default image-gen pattern was "spend a lot, generate one, hope it's good." After nano banana 2, the default is "spend a little, generate many, filter." That changes the shape of the pipeline. Lesson 03 of this chapter walks the full pattern (brief → prompt → batch → filter → upscale → format). For now, just internalize: the cheap banana is a row in a table. Read the vendor row, then pick the count.2b:T1299,# Text in image, character consistency, and the specialty picks The three hardest things for an image model are, in order: text inside the image, hands, and keeping a character identical across multiple renders. In 2026 each one has a specialist that solves it well. Vector output has a specialist now too. ## Text in image Until early 2024, every image model produced garbage text. Posters with "Cumemr Sole" instead of "Summer Sale." Books with "Hapry Pottter." The problem is diffusion models don't reason about letterforms — they treat text as texture. The 2026 state: - **Ideogram** — purpose-built for text; 4.0 shipped June 2026. Claims 90-95% accuracy. Three tiers (as of September 2026): Turbo $0.03, Default $0.06, Quality $0.10. POST `/v1/ideogram-v4/generate`. If your brief has copy that has to be readable, this is the default. - **gpt-image-2** — instruction-tuned, decent text. Good when you want both text AND complex composition control ("logo top-left, headline center, subtle drop shadow"). Square output only. - **nano banana 2 / Pro** — better lettering than last year's banana, still behind Ideogram. Use `gemini-3-pro-image` when you want Google premium fidelity; use Ideogram when the letters must be exact. - **Flux** — improved with FLUX.2, still not Ideogram-tier. - **Midjourney** — V8.x finally made quoted text legible, a big step past earlier versions, but reviewers still put it below Ideogram for letter-perfect copy. Don't route a brief here when the letters must be exact. The rule: if the brief mentions specific text strings, route to Ideogram 4.0 unless you have another reason not to. If the brief mentions a logo or brand mark, composite a real PNG or SVG of the logo onto a generated background — don't ask the model to draw the logo from scratch. ## Vector If the brief needs an SVG or an editable mark, that's Recraft V4.1 (`recraftv4_1`): $0.035 raster / $0.08 vector. Asking nano banana 2 for "a vector logo" still gives you pixels. ## Character consistency The classic failure: you generate a portrait of a character you like, then ask for "the same character but in a forest" and get a totally different person. Three approaches in 2026: - **FLUX.2 editing** — the current best. You pass an image + a text edit instruction, it preserves identity. Used for: "same person, different scene/outfit/expression." - **Midjourney Edit Model (V8.x)** — the current UI lock: reference image plus instruction. The V7 Omni Reference flags (`--oref` / `--ow`) are version-routed; don't send them as today's brief. Works for casual use, lossy for production. Identity drifts across renders. - **nano-banana multi-turn editing** — preserves identity within a session because Gemini holds the prior context. Cheap, but only works conversationally — stay on the same family for the follow-up turns. - **Custom LoRAs / DreamBooth** — train a small model on 20 photos of your character. Then any Flux generation with that LoRA loaded gets perfect identity. The right answer for "same brand mascot across hundreds of assets." ## Hands Hands are still the universal tell. Every model still produces six-fingered or warped hands at a non-zero rate. The 2026 mitigation: - Generate, inspect, regenerate any image with bad hands. - Use a second pass with a hand-aware checkpoint or inpainting only the hand region. - Or just don't show the hands. Crop them out. Half the working photographers do this on real shoots too. ## When you need a document (slide, infographic, layout) None of these models are good at producing actual documents — slide decks, charts, infographics with structured data. The 2026 pattern is: generate the background and visual elements with an image model, then use a layout tool (HTML/CSS, Figma API, or a deck-gen library) to overlay the structured text and data. Don't expect any image model to produce a usable pitch deck slide end-to-end. ## What you're building toward By the end of this lesson you'll have a `pick_image_model(task)` function that looks at a task description and returns the right model name. The decision logic is the rule set you've now seen: - text-in-image required → Ideogram 4.0 (`ideogram-v4`) - vector output → Recraft (`recraftv4_1`) - photoreal + art-directed → `flux-2-pro` - batch volume + tight budget → `gemini-3.1-flash-lite-image` (or nano banana 2 by resolution, or gpt-image-2 quality=low, or klein) - instruction-heavy composition → `gpt-image-2` (or `gemini-3-pro-image`) - subjective taste / mood-driven, no API constraint → Midjourney (V8.x, UI only) - default → `gemini-3.1-flash-image` (Nano Banana 2) The rules. One function. Next step: drill on text-in-image, then read the decision tree, then write it.2c:Te03,# The decision tree, ordered You've now seen the six families and the specialty cases. Here's the routing logic in one place — the order matters. ## The rules, in priority order The reason for priority order: **specialty constraints beat general preferences**. If the brief needs text-in-image, it doesn't matter that you prefer Midjourney — Ideogram is the answer. So the routing function checks the hard constraints first, then the cheap floor, then style preferences, then defaults. The order: 1. **Vector required?** → `recraftv4_1`. Recraft V4.1. Raster $0.035, vector $0.08. 2. **Text in image required?** → `ideogram-v4`. Ideogram 4.0. POST `/v1/ideogram-v4/generate`. 3. **Cheap floor** (`budget_per_image < 0.04`) → `gemini-3.1-flash-lite-image`. $0.0336 at 1K. gpt-image-2 quality=low ($0.006) and `flux-2-klein` (from $0.014) sit on the same floor. 4. **Instruction-heavy composition (specific placements)?** → `gpt-image-2`. Google premium is `gemini-3-pro-image` ($0.134 at 1K/2K, $0.24 at 4K). 5. **Photoreal + art-directed?** → `flux-2-pro`. Strongest photoreal output with prompt fidelity. 6. **Default fallthrough** → `gemini-3.1-flash-image`. Nano Banana 2. High batch count stays on this family (or Lite if you already hit the cheap floor). The call is `generate_content` with IMAGE, one image per call. Midjourney stays a UI pick (V8.x, no public API). ## The escape hatches A few real-world overrides that the priority list above doesn't capture: - **`latency_ms_max < 2000`** — drop to a Flash-tier model. nano banana 2 generates faster than Pro. Lite is the other "speed first" pick. - **`self_hosted = True` AND volume > 50K/month** — run FLUX.2 on a rented GPU. The economics flip when you amortize the hardware. ## How this becomes code The pseudocode for the routing function: ```python def pick_image_model(task): if task.get("needs_vector"): return "recraftv4_1" if task.get("needs_text_in_image"): return "ideogram-v4" if task.get("budget_per_image", 1.0) < 0.04: return "gemini-3.1-flash-lite-image" if task.get("instruction_heavy"): return "gpt-image-2" if task.get("google_premium"): return "gemini-3-pro-image" if task.get("photoreal_art_directed"): return "flux-2-pro" return "gemini-3.1-flash-image" ``` That's the spec for the write step. Notice the order: specialty constraints (vector, text), then the budget floor, then style preferences (instruction, Google premium, photoreal), then default. High `batch_count` is not its own branch — it falls through to nano banana 2. ## A note on the "default" pick `gemini-3.1-flash-image` as the fallthrough is a 2026-specific call. A year ago it would have been Imagen 4. Two years ago it would have been DALL-E 3. The default rotates as the landscape shifts. The shape of the function — specialty → preference → default — doesn't. If you're writing this for a long-lived production codebase, leave the rule list as a module-level constant (like the `RULES` tuple in the editor above) so a future maintainer can re-rank without rewriting the function body. That's the same pattern from @@pd-path:path.ref:agent-harnesses@@ — keep the registry out of the loop. ## What you'll write Step 8 asks you to implement `pick_image_model(task)` with the rules above. Step 9 runs it across five tasks and audits a pipeline. Both are pure stdlib, no API calls — just decision logic, exactly like the kind of routing function that sits at the front of a real image-gen feature in production.2d:T538,tasks = [ {"task_id": "T1", "needs_text_in_image": True, "budget_per_image": 0.10}, {"task_id": "T2", "instruction_heavy": True, "budget_per_image": 0.15}, {"task_id": "T3", "batch_count": 50, "budget_per_image": 0.07}, {"task_id": "T4", "needs_vector": True, "budget_per_image": 0.10}, {"task_id": "T5", "google_premium": True, "budget_per_image": 0.20}, ] SPECIALISTS = {"ideogram-v4", "recraftv4_1", "flux-2-pro"} def pick_image_model(task): if task.get("needs_vector"): return "recraftv4_1" if task.get("needs_text_in_image"): return "ideogram-v4" if task.get("budget_per_image", 1.0) < 0.04: return "gemini-3.1-flash-lite-image" if task.get("instruction_heavy"): return "gpt-image-2" if task.get("google_premium"): return "gemini-3-pro-image" if task.get("photoreal_art_directed"): return "flux-2-pro" return "gemini-3.1-flash-image" def audit_image_pipeline(tasks): rows = [] for t in tasks: model = pick_image_model(t) rows.append({ "task_id": t["task_id"], "model": model, "specialist_used": model in SPECIALISTS, }) return rows for row in audit_image_pipeline(tasks): print(f"{row['task_id']}: {row['model']:<26} specialist={row['specialist_used']}") 2e:Te7f,# Why "girl with red hair" gets you slop Every person who tries image generation for the first time types something like: > a girl with red hair …and gets back a generic stock-photo redhead with an awkward smile, plastic skin, and the standard "AI face." Then they conclude image gen isn't ready. The model is fine. The prompt is wrong. ## What the model does with a vague prompt A diffusion model is, roughly, learning a probability distribution over images conditioned on text. When you say "a girl with red hair," the model samples from the **average of every image in its training set that matched that description**. That average is: front-facing, neutral background, awkward smile, soft over-lit studio look, slightly uncanny — because the training set is full of stock photography and Instagram selfies, and "the mean of all those" is exactly that uncanny stock-photo look. The fix isn't a better model. The fix is a more specific prompt — one that narrows the distribution the model is sampling from. ## What good looks like Compare: **Bad:** > a girl with red hair **Good:** > Portrait of a young woman with curly auburn hair, looking off-camera at a 30-degree angle. Shot on 85mm f/1.4, shallow depth of field, golden-hour side-lighting from a window on her left, soft shadows. Muted earth-tone wardrobe (terracotta sweater). Composition: rule-of-thirds, subject in left third, blurred bookshelves in the background. Editorial portrait style, film grain, Kodak Portra 400 color palette. No text, no watermark, no harsh studio lighting. The good version did eight things. The bad version did one. ## Dense beats long A short, precise prompt usually beats a padded one. The goal is **dense** prompts, not long ones — every word should narrow the distribution. Filler ("beautiful," "amazing," "highly detailed") adds length without narrowing anything. Very long prompts can also lose coherence: the model starts losing track of which words apply to what, and you get composition errors — the lighting you described attaches to the wrong subject, the lens you specified gets ignored. And there's a hard mechanical limit to watch for — some text encoders truncate. The CLIP encoder used by many models reads only the first 77 tokens (a few dozen words); anything past that is silently dropped. Newer models pair in a T5 encoder that reads far more (Flux handles up to 512 tokens), so the ceiling depends entirely on which model you're targeting. When in doubt, front-load the words that matter most. ## What this lesson does Lesson 01 was the model-picker. This lesson is the prompt-builder. You'll learn: - The eight knobs that matter (subject, composition, lighting, style, camera, lens, color palette, negatives). - Why each one narrows the distribution. - How to score a prompt's completeness — turn the eight knobs into a checklist. - Common failure modes that good prompts dodge. At the end you'll write `score_image_prompt(prompt)` — a function that takes a prompt string and returns a quality score plus a list of missing knobs. You can run this in a CI step before sending any prompt to a model and catch the slop-bound prompts before they cost money. A note before we go further: this is general guidance. Each model family has its own prompt quirks. Flux likes complete sentences and real camera terms ("85mm f/1.4 lens"). Midjourney historically used `--ar 16:9 --stylize 100` style parameters. nano-banana uses conversational instructions ("make the background blue"). The eight knobs are the camera/composition shape; the syntax differs. The studio kit brief (subject, medium, style, palette, negatives) is a different job — a brand artifact, not a shot.2f:T16f4,# The eight knobs Every production-grade *photograph* prompt names eight things. This is the **camera brief** — subject, composition, lighting, style, camera, lens, palette, negatives. Skip any one and the model fills in the average of the training data on that axis. Name all eight and you've narrowed the distribution to roughly the image you want. It is not a brand-kit brief. @@pd-path:#path.has:graphic-design-studio@@ Graphic-design-studio compiles brand artifacts as five sections: subject, medium, style, palette, negatives. Medium is load-bearing there; camera and lens are not first-class, because the job is a designed object, not a shot. Don't grade a kit prompt as missing knobs 5 and 6. When a studio job *is* a photograph, stack: kit first (medium, palette, banned looks), then these eight.@@pd-path:/path.has@@ @@pd-path:#path.lacks:graphic-design-studio@@ A campaign image brief still names subject, style, palette, and negatives. Camera and lens are extra knobs for a photograph, not a designed object. Don't grade a campaign prompt as missing knobs 5 and 6 unless the job is a shot.@@pd-path:/path.lacks@@ The eight, in the order a working camera brief usually lists them: ## 1. Subject What is in the image. Be specific about **what kind of subject** (young woman, not "girl"), **identifying features** (curly auburn hair), and **action or pose** (looking off-camera at 30 degrees, holding a coffee cup, mid-stride). The trap: "subject" alone isn't enough. "A woman" produces a generic woman. "A 32-year-old woman with shoulder-length brown hair, mid-laugh, holding a vintage Leica" produces the same person across many renders. ## 2. Composition Where things sit in the frame. The vocabulary: **rule of thirds**, **centered**, **symmetric**, **leading lines**, **negative space**, **close-up / medium / wide**, **subject in left third**, **eye level / high angle / low angle / Dutch tilt**. One composition term changes the whole read: "a chef plating a dish" gets you a centered, catalog-flat shot; "a chef plating a dish, low angle, subject in the left third" gets you a frame that looks decided rather than defaulted. The best angle depends on mood: eye level for connection, high angle for vulnerability, low angle for strength. ## 3. Lighting How light hits the subject. **Golden hour, blue hour, harsh midday, overcast, studio three-point, rim light, chiaroscuro, volumetric god rays, bioluminescent glow.** Models understand specific physical descriptions ("golden-hour side-lighting from a window on her left") far better than vague terms ("beautiful lighting"). Lighting is the single biggest tell between AI slop and AI work that ships. Studio over-lighting is the default; specifying anything else immediately raises the output quality. ## 4. Style The visual language. **Editorial photography, fashion editorial, fine-art portrait, watercolor illustration, vector flat illustration, oil painting, cyberpunk concept art, brutalist architecture rendering, anime cel-shaded, 90s film stock.** Style words pull the model toward a specific cluster in its training distribution. "Editorial portrait" and "stock photo" are both photographs of people, but they sample completely different distributions of light, pose, color, and finish. ## 5. Camera The capture device. **Shot on 35mm film camera. Shot on Leica M11. Shot on iPhone 15 Pro. Drone shot. GoPro fisheye.** The model has learned the look of each because professional photographs are often captioned with their gear. Camera also encodes perspective: **eye-level, high-angle, low-angle, bird's-eye, worm's-eye, over-the-shoulder, POV**. Pick one. Don't make the model guess. ## 6. Lens The optics. **85mm f/1.4 (portrait, shallow depth of field), 50mm f/1.8 (natural, slight portrait), 24mm wide-angle (landscape, environmental, deeper depth), 100mm macro (close-up texture), 200mm telephoto (compression, distant subject).** Diffusion models have learned the optical characteristics of specific lenses. Naming a lens does roughly what naming it on a real shoot does. ## 7. Color palette The chromatic direction. **Muted earth tones, terracotta and forest green, pastel pinks and creams, high-contrast monochrome, Kodak Portra 400 (warm shadows, soft highlights), Fuji Velvia (saturated, punchy), teal-and-orange film grade.** Palette is the second-biggest aesthetic lever after lighting. A studio-lit portrait in muted earth tones reads as editorial. The same portrait in saturated cyan-and-magenta reads as cyberpunk. Same composition. Same lighting. Different output, because you moved this knob. ## 8. Negatives What you do NOT want in the image. **No text, no watermark, no harsh studio lighting, no extra fingers, no plastic skin, no smile, no logo.** Negative prompts work differently across models — Flux and Stable Diffusion forks honor them as a separate input field, Midjourney has `--no`, gpt-image-2 accepts them in the prompt. But every model handles "negatives" of some kind. The most useful are usually the slop-defaults: text, watermarks, the AI smile, harsh studio light. ## Why eight, in this order The order matches what the model resolves first. Subject is non-negotiable — without it, no image. Composition tells the model where to place the subject. Lighting and style set the visual register. Camera and lens narrow the optical look. Palette pulls the color cluster. Negatives prune the slop defaults out. You can shuffle the order in your final prompt — the model doesn't care about syntax as long as each concept is named — but the **completeness** matters. Completeness here means the camera brief, not the studio kit brief. The next step gives you a broken prompt; you'll fix it. Then a multiple-choice on identifying which knob is missing from a real-world brief. Then the score function you can run as a pre-flight check.30:T114b,# Reference images and ControlNet — when prompts aren't enough The eight knobs get you to a specific-looking image. But sometimes "specific-looking" still isn't enough. You need the **exact** pose. You need **this** building's silhouette. You need a character whose face matches **this photo**. For these cases, you stop describing the image in text and start showing the model a reference. ## Three flavors of image conditioning The 2026 toolbox has three distinct ways to condition generation on an existing image. Each does a different thing. ### 1. Image-to-image (the strength dial) You give the model an image plus a prompt, plus a `strength` parameter between 0.0 and 1.0. At strength 0.1 the output is nearly your input image with light prompt changes. At strength 0.9 the input is just a vague composition starter and the prompt dominates. - **Flux** — supports image-to-image at all tiers; pass `image` field with the source URL or base64, and a `strength` between 0 and 1. - **Fal.ai** — many models on Fal expose image-to-image as `image_url` + `strength` in the input schema. - **nano banana 2** — supports image input as part of the multi-turn conversation. Edit-by-instruction is the same surface; you pass the image then describe what to change. Use it for: rough sketch → polished render, color reference → matching scene, hand-drawn composition → photorealistic version. ### 2. Edit-by-instruction (FLUX.2, nano banana 2) Newer than i2i. Instead of "give me this image but adjusted by strength X," you say "change the background to a beach" and the model edits in-place while preserving identity. - **FLUX.2** (`flux-2-pro`, klein) — built specifically for this. Maintains character consistency across edit chains. Supports multi-step edits (edit, then edit again, identity holds). - **nano banana 2 family** — the same shape inside a Gemini conversation; cheaper per turn but less precise than FLUX.2 for serious editing work. Use it for: same product on different backgrounds, same character in different scenes, swapping text on a sign, recoloring an outfit while keeping the model identical. ### 3. ControlNet (structural conditioning) The most powerful and most fiddly. You give the model: - An image (or a pre-extracted control signal — an edge map, a depth map, a pose skeleton, a segmentation mask). - A prompt. - A control type (`canny`, `depth`, `pose`, `seg`, `scribble`, `lineart`, `mlsd`, etc.). The output follows the **structure** of the control signal while filling in the **content** from the prompt. ControlNet pose conditioning is how you get "this exact pose, but as a knight in armor in a fantasy setting." ControlNet canny is how you get "this exact building silhouette, but rendered in oil painting style." ControlNet is most commonly used through the open-weights Flux and Stable Diffusion ecosystem. You'll see it referenced in ComfyUI workflows, in self-hosted Flux setups, and via specific Replicate/Fal models like `controlnet-pose` or `controlnet-depth`. API surfaces vary widely; if you need this in a hosted API, search the model index on Replicate or Fal for "controlnet" plus your control type. The call shape varies by provider — don't memorize one signature. ## When you'd actually reach for these The decision tree: - **Pure text prompt is enough** for most marketing/social/illustration work. The eight knobs cover the case. - **Image-to-image** when you have a rough that you want polished — a sketch, a color reference, a low-quality phone shot. - **Edit-by-instruction (FLUX.2)** when you have a finished image and want variants that preserve identity — same product on twelve backgrounds, same character in five scenes. - **ControlNet** when the structure must be exact — pose-locked character animation frames, building-silhouette-preserved architectural rendering, matching the layout of a wireframe to a finished UI mockup. The mistake to avoid: starting with ControlNet because it sounds powerful. It's a power-user feature with significant setup overhead. Most production briefs are solved with a good text prompt and, if needed, FLUX.2 for the multi-variant case. ## What the next step asks You'll be given five real-world briefs, each with one knob obviously missing. The drill is identifying which one. After that you'll read the failure modes (hands, text, character drift), then write the scoring function.31:T12f6,# Failure modes — what still goes wrong in 2026 The eight knobs cover prompt quality. But three categories of failure are still inherent to the models themselves. Knowing them tells you when to inspect output before shipping and when to route a brief to a different tool entirely. ## 1. Text in image — the hardest universal failure Until late 2024, every image model produced garbage text. Posters with "Hapry Birthay" instead of "Happy Birthday." The 2026 state: - **Ideogram** (4.0 as of June 2026) claims 90-95% accuracy on rendered text. - **gpt-image-2** and **nano banana 2 / Pro** are decent for short copy (5-10 words). - **flux-2-pro** and **FLUX.2 klein** improved, but trail Ideogram. - **Midjourney** — V8's quoted-text rendering closed much of the old gap (earlier versions clocked roughly 30-40%), but it still trails the specialists for letter-perfect copy. The rule: if the brief contains a specific text string, use Ideogram. If you can't route to Ideogram, generate the text in a second compositing layer (HTML/Figma/Photoshop) on top of an image-model background — don't ask the model to render the text inside the image. ## 2. Hands — still tells Hands are the universal failure. Six fingers. Bent thumbs. Hands fused with objects. Every model still produces broken hands at a non-zero rate. Mitigations: - **Generate-and-inspect.** Always look at the hands before approving. This is one of the costs of using AI image gen — there's a manual check step. - **Inpaint the hand region.** If a generation is otherwise great, mask the hand region and regenerate just that part with a hand-aware checkpoint. Most production pipelines have this as a pass. - **Crop them out.** Half the working photographers do this on real shoots. If hands aren't critical to the brief, frame around them. - **Hide them.** Hands in pockets, behind back, holding a prop that obscures fingers. The composition naturally fixes the problem. If hands are critical to the image (a portrait of a watchmaker, a chef plating food), expect to regenerate 3-5x or to do an inpainting pass. ## 3. Character consistency — handled, but with effort You want the same character — same face, same proportions, same wardrobe — across 10 renders. Out of the box, no model does this with a text prompt alone. The same prompt run twice will produce two visibly different people. The 2026 solutions: - **FLUX.2 editing**. Pass a reference image plus an edit instruction; identity is preserved. Best in class for production. - **Midjourney Edit Model (V8.x)**. The current lock: a reference image plus an instruction. `--oref` / `--ow` are the V7 Omni Reference flags — version-routed, not the brief to send on current Midjourney (V6 `--cref` is already rejected). Works for casual use, lossy for production. Identity drifts. - **LoRA fine-tuning on FLUX.2**. Train a small adapter (~30 minutes on an H100) on 15-30 photos of your character. Then any Flux generation with that LoRA gets perfect identity. The right answer for "same brand mascot across hundreds of assets" or "this client's actual face." - **DreamBooth on open Stable Diffusion** — older, less clean than Flux LoRAs, but still around. The wrong approach: trying to describe the character precisely enough in text that the model produces the same face. The text encoder isn't accurate enough. Use an image reference. ## 4. The "AI face" A meta-failure across all models: people generated end up with a recognizable "AI look." Slightly too symmetric, slightly too smooth, slightly too lit. Even with great prompts. Mitigations: - **Specify imperfection.** "Slight asymmetry, faint freckles, slight skin texture, faint under-eye shadow." The training data is full of retouched studio photography; you have to actively pull the model away from it. - **Specify a real camera and film.** "Shot on 35mm Kodak Portra 400" produces less of the plastic look than the default of "studio portrait." - **Avoid the smile.** "Smiling" → AI face. "Mid-laugh, looking off-camera" or "neutral expression, half-turned away" → not AI face. ## What this means for your pipeline Three takeaways: 1. **Build inspection into the pipeline.** Generate, then look. Don't ship un-reviewed image-model output to customers. 2. **Route by failure mode.** Text-in-image → Ideogram. Character consistency → FLUX.2 or LoRA. Hands matter → expect inspection and inpainting. 3. **The eight knobs are necessary, not sufficient.** They get you to "specific image." They don't fix hands. They don't fix the AI face by themselves. You also need to know what the model can't do. The next step asks you to write `score_image_prompt(prompt)` — a checker that catches the missing-knob bugs before you spend money sending a bad prompt. Then the checkpoint asks you to rank five prompts by completeness.32:T60a,KNOB_KEYWORDS = { "subject": ["woman", "man", "person", "portrait", "subject"], "composition": ["rule of thirds", "centered", "third", "frame", "composition"], "lighting": ["golden hour", "rim light", "side-lighting", "window light", "natural light", "studio light"], "style": ["editorial", "cinematic", "documentary", "fashion", "fine art", "illustration"], "camera": ["35mm camera", "leica", "shot on", "eye level", "low angle", "high angle"], "lens": ["85mm", "50mm", "f/1.4", "f/1.8", "shallow depth", "telephoto", "wide angle"], "palette": ["earth tones", "muted", "kodak portra", "warm shadows", "monochrome", "saturated"], "negatives": ["no text", "no watermark", "no harsh", "no extra", "no smile", "no plastic"], } good = ( "Portrait of a young woman with curly auburn hair. " "Rule of thirds, subject in left third. " "Golden hour side-lighting from a window. " "Editorial documentary style. " "Shot on 35mm camera, eye level. " "85mm f/1.4 lens, shallow depth of field. " "Muted earth tones, Kodak Portra palette. " "No text, no watermark, no harsh studio light." ) mid = ( "Portrait of a young woman with auburn hair. " "Centered composition. " "Editorial style, muted earth tones. " "Shot on 35mm camera, eye level." ) bad = "a girl with red hair" # define score_image_prompt(prompt) below print(f"good: {score_image_prompt(good)}") print(f"mid: {score_image_prompt(mid)}") print(f"bad: {score_image_prompt(bad)}") 33:T70b,KNOB_KEYWORDS = { "subject": ["woman", "man", "person", "portrait", "subject"], "composition": ["rule of thirds", "centered", "third", "frame", "composition"], "lighting": ["golden hour", "rim light", "side-lighting", "window light", "natural light", "studio light"], "style": ["editorial", "cinematic", "documentary", "fashion", "fine art", "illustration"], "camera": ["35mm camera", "leica", "shot on", "eye level", "low angle", "high angle"], "lens": ["85mm", "50mm", "f/1.4", "f/1.8", "shallow depth", "telephoto", "wide angle"], "palette": ["earth tones", "muted", "kodak portra", "warm shadows", "monochrome", "saturated"], "negatives": ["no text", "no watermark", "no harsh", "no extra", "no smile", "no plastic"], } good = ( "Portrait of a young woman with curly auburn hair. " "Rule of thirds, subject in left third. " "Golden hour side-lighting from a window. " "Editorial documentary style. " "Shot on 35mm camera, eye level. " "85mm f/1.4 lens, shallow depth of field. " "Muted earth tones, Kodak Portra palette. " "No text, no watermark, no harsh studio light." ) mid = ( "Portrait of a young woman with auburn hair. " "Centered composition. " "Editorial style, muted earth tones. " "Shot on 35mm camera, eye level." ) bad = "a girl with red hair" def score_image_prompt(prompt): low = prompt.lower() missing = [] score = 0 for knob, keywords in KNOB_KEYWORDS.items(): if any(k in low for k in keywords): score += 1 else: missing.append(knob) return {"score": score, "missing": missing} print(f"good: {score_image_prompt(good)}") print(f"mid: {score_image_prompt(mid)}") print(f"bad: {score_image_prompt(bad)}") 34:T6e3,KNOB_KEYWORDS = { "subject": ["woman", "man", "person", "portrait", "subject"], "composition": ["rule of thirds", "centered", "third", "frame", "composition"], "lighting": ["golden hour", "rim light", "side-lighting", "window light", "natural light", "studio light"], "style": ["editorial", "cinematic", "documentary", "fashion", "fine art", "illustration"], "camera": ["35mm camera", "leica", "shot on", "eye level", "low angle", "high angle"], "lens": ["85mm", "50mm", "f/1.4", "f/1.8", "shallow depth", "telephoto", "wide angle"], "palette": ["earth tones", "muted", "kodak portra", "warm shadows", "monochrome", "saturated"], "negatives": ["no text", "no watermark", "no harsh", "no extra", "no smile", "no plastic"], } prompts = { "P1": "a girl with red hair", "P2": "Portrait of a man. Editorial style, muted tones.", "P3": "Portrait of a young woman. Centered composition. Window light. Editorial. 85mm f/1.4.", "P4": ( "Portrait of a young woman with auburn hair. " "Rule of thirds. Golden hour side-lighting. " "Editorial style. Shot on 35mm camera. 85mm f/1.4. " "Muted earth tones." ), "P5": ( "Portrait of a young woman with curly auburn hair. " "Rule of thirds, subject in left third. " "Golden hour side-lighting from a window. " "Editorial documentary style. " "Shot on 35mm camera, eye level. " "85mm f/1.4 lens, shallow depth of field. " "Muted earth tones, Kodak Portra palette. " "No text, no watermark." ), } # define score_image_prompt(prompt) and rank_prompts(prompts) below for pid, score in rank_prompts(prompts): print(f"{pid}: score={score}") 35:T824,KNOB_KEYWORDS = { "subject": ["woman", "man", "person", "portrait", "subject"], "composition": ["rule of thirds", "centered", "third", "frame", "composition"], "lighting": ["golden hour", "rim light", "side-lighting", "window light", "natural light", "studio light"], "style": ["editorial", "cinematic", "documentary", "fashion", "fine art", "illustration"], "camera": ["35mm camera", "leica", "shot on", "eye level", "low angle", "high angle"], "lens": ["85mm", "50mm", "f/1.4", "f/1.8", "shallow depth", "telephoto", "wide angle"], "palette": ["earth tones", "muted", "kodak portra", "warm shadows", "monochrome", "saturated"], "negatives": ["no text", "no watermark", "no harsh", "no extra", "no smile", "no plastic"], } prompts = { "P1": "a girl with red hair", "P2": "Portrait of a man. Editorial style, muted tones.", "P3": "Portrait of a young woman. Centered composition. Window light. Editorial. 85mm f/1.4.", "P4": ( "Portrait of a young woman with auburn hair. " "Rule of thirds. Golden hour side-lighting. " "Editorial style. Shot on 35mm camera. 85mm f/1.4. " "Muted earth tones." ), "P5": ( "Portrait of a young woman with curly auburn hair. " "Rule of thirds, subject in left third. " "Golden hour side-lighting from a window. " "Editorial documentary style. " "Shot on 35mm camera, eye level. " "85mm f/1.4 lens, shallow depth of field. " "Muted earth tones, Kodak Portra palette. " "No text, no watermark." ), } def score_image_prompt(prompt): low = prompt.lower() score = 0 for knob, keywords in KNOB_KEYWORDS.items(): if any(k in low for k in keywords): score += 1 return {"score": score} def rank_prompts(prompts): scored = [(pid, score_image_prompt(p)["score"]) for pid, p in prompts.items()] scored.sort(key=lambda x: (-x[1], x[0])) return scored for pid, score in rank_prompts(prompts): print(f"{pid}: score={score}") 36:T17cb,# Brief → prompt → generate → filter → upscale → format The hobbyist workflow is: open the tool, type a prompt, save the result. The production workflow is six stages, each with its own quality gate. Skip a stage and either the cost balloons or the output looks like AI slop. ## Stage 1 — Brief Before any prompt, you write down what the image is FOR. - **Channel**: Instagram square, TikTok 9:16, web hero 16:9, email header 600x300, print 300dpi. - **Intent**: drive a click, illustrate a product, set a mood, identify a person. - **Audience**: who sees this and what they expect. - **Constraints**: brand colors, must-include elements (logo, product), must-avoid elements (competitor product, controversial imagery). The brief shapes the prompt and the platform-format step. Skipping it means you generate beautiful images that don't fit the format you actually need. ## Stage 2 — Prompt Eight knobs (lesson 02) — the camera brief. Subject, composition, lighting, style, camera, lens, palette, negatives. Score it with `score_image_prompt` before you spend money. Don't send a 4/8 prompt to a $0.10/image model — fix the prompt first. A studio kit brief is a different shape (subject, medium, style, palette, negatives); don't run the eight-knob scorer on a poster compile and call lens "missing." ## Stage 3 — Generate The API call. The 2026 pattern is **batch, not single-shot**. nano banana 2 at $0.067/image (1K) makes generating 25 candidates cost about $1.68. For most briefs that beats one Midjourney render in expected value (better odds at least one is shipping-ready, plus options). Where the actual API call lives depends on the model: ```python # pseudocode — real call shape varies by provider # Flux via Replicate # import replicate # out = replicate.run("black-forest-labs/flux-2-pro", input={"prompt": prompt}) # Flux via Fal.ai # import fal_client # out = fal_client.subscribe("fal-ai/flux-2-pro", arguments={"prompt": prompt}) # nano banana 2 via Google Gemini API — generate_content + IMAGE, one image per call # from google import genai # from google.genai.types import GenerateContentConfig, Modality # client = genai.Client(api_key=GEMINI_API_KEY) # resp = client.models.generate_content( # model="gemini-3.1-flash-image", # contents=[prompt], # config=GenerateContentConfig( # response_modalities=[Modality.IMAGE], # ), # ) ``` Don't memorize call signatures. They change. Memorize the shape: **prompt + model + size → bytes**. ## Stage 4 — Filter You generated 25. You need 3. How to pick: - **Manual review.** Slow but the most reliable. A human picks the top K. - **LLM-as-judge with vision.** Send the candidates to a vision model (Claude, GPT-4o vision, Gemini) with the original brief and the scoring criteria. The model returns a ranked list. Fast and cheap (~$0.001-0.005 per candidate evaluated). - **CLIP similarity score.** Embed the brief into CLIP space, embed each candidate, rank by cosine similarity. Deterministic and free but ranks "matches the brief" not "is beautiful." - **Aesthetic scorer.** Open-source models like `LAION-Aesthetics-V2` score "how aesthetic does this look" on a 0-10 scale. Pair with the CLIP score for a combined rank. The 2026 default for an app: LLM-as-judge with vision. It's the only one that handles "does this match the brief AND look good AND not have broken hands." ## Stage 5 — Upscale The image-gen models output at fixed sizes — usually 1K, or up to 4K on nano banana 2 / Pro. For hero use, print, or large-display banners, you need 4K+. The 2026 options: - **Generate at 4K on the same model.** nano banana 2 at 4K is $0.151/image. `gemini-3-pro-image` at 4K is $0.24. Pick the row instead of a second vendor. - **`flux-2-pro`** — billed per megapixel (from $0.03/MP). Higher resolution is a bigger bill on the same ID. - **Dedicated upscalers** — `clarity-upscaler`, `topaz-photo-ai` via API, `real-esrgan` open-source. For most pipelines, generating at the highest resolution the chosen model supports and skipping the dedicated upscale is the right tradeoff. Reserve upscaling for "I have a 1024 image and I need 4K" exceptions. ## Stage 6 — Format The final step. Often skipped, always matters: - **Convert to platform format.** WebP for web (smaller than JPG, supported everywhere modern), AVIF for cutting edge (smaller still, support is now ubiquitous in 2026), JPG for compatibility, PNG only if you need transparency. - **Strip EXIF metadata.** Generated images sometimes include model name, prompt, and timestamps in EXIF. Strip them before shipping unless you want your prompt leaked publicly. - **Generate alt-text.** For every image you ship to a website, generate descriptive alt-text. A vision-model call with "describe this image in one sentence for a blind user" is roughly $0.001 and makes the page accessible. - **Watermark or sign if needed.** C2PA content credentials are now standard from most providers (OpenAI, Google) — they sign the image so it can be authenticated as AI-generated. Don't strip these if you want provenance. ## The pipeline as code The function shape: ```python def run_image_pipeline(brief): prompt = build_prompt(brief) # stage 2 model = pick_image_model(brief.task) # from lesson 01 candidates = generate_batch(model, prompt, n=brief.count) # stage 3 top_k = filter_with_vision(candidates, brief) # stage 4 upscaled = [upscale(img) for img in top_k] if brief.needs_4k else top_k # stage 5 return [format_for_platform(img, brief) for img in upscaled] # stage 6 ``` Six stages, six function calls. Every production image-gen feature you've ever used has roughly this shape underneath. The next step drills batch-and-filter — the stage 3+4 economics. Then a multiple choice on diagnosing a broken pipeline. Then format-and-platform. Then the fill step where you wire up a dispatch. Then the write step: `cost_for_batch(model, count, options)` at real 2026 rates. Then the checkpoint plans a full shoot end-to-end.37:T1340,# Batch and filter — where the math actually works The single biggest 2026 unlock in image generation isn't a better model. It's the **collapse in per-image cost** at the Flash tier. nano banana 2 at $0.067 (1K) changed the economics of "how should I get to a usable image" from "spend a lot, generate one, hope" to "spend a little, generate many, filter." ## The math, walked through Suppose you need one shipping-ready image. Three strategies: ### Strategy A — One-shot premium Send one prompt to Midjourney Pro tier or gpt-image-2 quality=high ($0.211/image). Per-image cost: ~$0.10-0.21. Hit rate (image is usable as-is, no broken hands, on-brief): assume 30-40%. So you regenerate, on average, 2-3 times to get a winner. **Real cost per shipping-ready image: ~$0.25-0.60, plus operator time.** ### Strategy B — Batch and filter Send the same prompt to nano banana 2 at 1K 10 times. Cost: $0.067 × 10 = $0.67. Each individual image has a lower hit rate (~20%) because the Flash tier isn't as polished as the premium models. But you have 10 candidates. Probability that AT LEAST ONE is shipping-ready: ``` P(none) = (1 - 0.2) ^ 10 = 0.8 ^ 10 ≈ 0.107 P(at least one) ≈ 89% ``` **Real cost per shipping-ready image: ~$0.67 with 89% confidence on first batch.** If the first batch produces nothing usable (~11% of the time), you batch again. Two batches gets you to 98.8% confidence at ~$1.34 total. That's still cheaper than strategy A's expected cost. ### Strategy C — Self-hosted Flux Rent an H100 GPU at ~$2/hour. Run FLUX.2. A well-tuned setup generates ~1 image/second at 1024x1024. That's 3,600 images/hour at $2/hour, so ~$0.00056 per image. klein (from $0.014) is the API version of that floor. Below ~50,000 images/month, the API cost wins. Above that, the self-hosted economics win and the gap widens fast. ## When strategy B (batch-and-filter) is wrong A few cases where you DON'T want batch-and-filter: - **Highest possible quality required.** Hero shot for a billboard. Flagship product launch image. Use `flux-2-pro` or `gemini-3-pro-image` and review it yourself, don't try to filter 25 mediocre nano banana 2 renders. - **Specific text required.** Ideogram 4.0. Generate 3-5, pick the one with crispest text. The filter step is text-readability check, not aesthetic. - **Tight subject-identity requirement across multiple outputs.** FLUX.2 editing with a reference. Batch is the wrong frame — you want consistent outputs, not varied ones. - **Single one-off where operator time is cheap.** Personal project, internal use. The math collapses; just pick whichever model you like and run it once. ## The filter step in production Three ways to filter, in order of how often you'll actually use them: ### Vision-model judge (the default) ```python # pseudocode — real call shape varies by provider # import anthropic # client = anthropic.Anthropic() # resp = client.messages.create( # model="claude-sonnet-4-7", # messages=[{ # "role": "user", # "content": [ # {"type": "text", "text": f"Brief: {brief}. Rate each on 0-10 for fit."}, # *[{"type": "image", "source": {...}} for img in candidates], # ], # }], # ) ``` Cost: ~$0.001-0.005 per candidate evaluated. Speed: ~3-10 seconds for a batch of 10. Quality: very good — the model catches broken hands, off-brief composition, AI face. The 2026 default. ### CLIP similarity (the deterministic option) Embed the brief in CLIP-space, embed each candidate, rank by cosine similarity. Fast and free. The downside: CLIP only knows "does this image match this text," not "is this beautiful" or "does this have broken hands." ### Aesthetic scorer Open-source models like `LAION-Aesthetics-V2-Predictor` give an aesthetic score 0-10. Combine with CLIP for a composite "matches the brief AND looks good" rank. Useful when you don't want vision-model latency or cost. In practice, most production pipelines use the vision-model judge. The cost is in the noise compared to generation, and the quality of the rank is the bottleneck for shipping confidence. ## The batch size question How many candidates is the right batch size? It's a function of three things: - **Per-image cost.** Cheaper model → bigger batches make sense. - **Per-image hit rate.** Lower hit rate → bigger batches needed for high confidence. - **Filter cost.** The filter step costs money/time too. At some point adding more candidates costs more than it's worth. The 2026 default heuristic: **batch of 10 for nano banana 2, 5 for `flux-2-pro`, 3 for premium tiers.** Adjust based on the hit rate you observe on your specific use case. ## What the next step asks You'll see five pipelines, each broken in one specific stage. The drill is identifying which stage broke. Then you'll read about format-and-platform, fill in a dispatch pattern, write the cost function, and plan a full shoot.38:T119b,# Format and platform — the stage everyone skips The final stage of the pipeline is the one nobody respects. The image came out of the model. It looks great. Ship it. You can do that. You will pay for it in three places: 1. **Page load time.** A 1024x1024 PNG is 1-2MB. The same image as a 1024x1024 WebP at quality 85 is 80-200KB. On mobile, that's 1-2 seconds vs. 100-200ms. Conversion rates move on that gap. 2. **Privacy leaks.** AI-generated images sometimes include the prompt in EXIF. Ship one to your competitor and they have your prompt. 3. **Accessibility.** No alt text means screen readers say "image" and move on. Anyone using a screen reader gets nothing from your gorgeous hero shot. ## The four format jobs Every image that leaves your pipeline does four things at stage 6: ### 1. Convert to platform format The 2026 defaults: - **WebP** for web. Universal support, 25-35% smaller than JPG at equivalent quality. - **AVIF** for cutting-edge web. 50% smaller than JPG. Support is now ubiquitous (Chrome, Firefox, Safari all support it). - **JPG** as fallback when you need maximum compatibility (email clients, older platforms). - **PNG** only if you need transparency. Almost never for photographs. The pseudocode shape: ```python # pseudocode — using Pillow stdlib # from PIL import Image # img = Image.open(input_path) # img.save(output_path, format="WEBP", quality=85) # img.save(avif_path, format="AVIF", quality=80) ``` ### 2. Resize per channel You generated at 1024x1024. The actual destinations are: | Channel | Target size | |---|---| | Instagram square | 1080×1080 | | Instagram story | 1080×1920 | | TikTok 9:16 | 1080×1920 | | Twitter / X post | 1200×675 | | LinkedIn post | 1200×627 | | Email header | 600×300 | | Web hero | 1920×1080 (with 2x retina at 3840×2160) | | Thumbnail | 400×400 | Generating once and resizing six times beats generating six times. The image-model bill is the expensive line; resize is essentially free with `Pillow`. ### 3. Strip EXIF metadata Every generated image potentially contains: prompt text, model name, generation timestamp, sometimes a "made with X" tag. Strip them unless you have a reason to keep them. The exception: C2PA content credentials. As of 2026, OpenAI, Google, and most major providers attach C2PA signatures to generated images. These are designed to be preserved — they let downstream viewers verify "this is AI-generated, this is the provenance." Strip EXIF but not C2PA, if you care about content authentication. ### 4. Generate alt text For every image going to a website, generate descriptive alt text. The pseudocode: ```python # pseudocode — vision model call for alt text # resp = vision_client.describe(image, prompt="One-sentence alt text for a screen reader. Describe what's visually present.") # alt = resp.text ``` Cost: ~$0.001 per image. Quality: 80-90% as good as a human writer for descriptive alt text. Strongly recommended for any production web pipeline. ## The platform routing function The dispatch shape, in pseudocode: ```python PLATFORM_CONFIGS = { "instagram_square": {"size": (1080, 1080), "format": "JPEG", "quality": 90}, "instagram_story": {"size": (1080, 1920), "format": "JPEG", "quality": 90}, "tiktok": {"size": (1080, 1920), "format": "JPEG", "quality": 90}, "twitter": {"size": (1200, 675), "format": "JPEG", "quality": 85}, "web_hero": {"size": (1920, 1080), "format": "WEBP", "quality": 85}, "web_hero_retina": {"size": (3840, 2160), "format": "WEBP", "quality": 80}, "thumbnail": {"size": (400, 400), "format": "WEBP", "quality": 75}, "email_header": {"size": (600, 300), "format": "JPEG", "quality": 80}, } def format_for_platform(img, platform): cfg = PLATFORM_CONFIGS[platform] # resize, convert format, strip EXIF, return ... ``` The next step has you fill in this exact dispatch. After that, you'll write the cost function that estimates the API spend across nano banana 2 / Flux / Ideogram / gpt-image-2 for a given batch — the function you'd actually call before kicking off a job to make sure it fits your budget. ## What this means for your workflow Stop thinking of stage 6 as "save the file." Think of it as "the image now has to go through six platforms, each with different requirements, and the original 1024 PNG is the wrong shape for every single one." Generate once. Format six times. Strip metadata. Add alt text. Then ship.39:T452,PRICING = { "gemini-3.1-flash-image": 0.067, "gemini-3.1-flash-lite-image": 0.0336, "gemini-3-pro-image": 0.134, "gpt-image-2": 0.053, "ideogram-v4": 0.06, "flux-2-pro": 0.03, "flux-2-klein": 0.014, "recraftv4_1": 0.035, } NB2_4K = 0.151 PRO_4K = 0.24 def cost_for_batch(model, count, options=None): options = options or {} if options.get("resolution") == "4K" and model == "gemini-3.1-flash-image": unit = NB2_4K elif options.get("resolution") == "4K" and model == "gemini-3-pro-image": unit = PRO_4K else: unit = PRICING[model] return round(unit * count, 2) print(f"nb2 1K x 100: ${cost_for_batch('gemini-3.1-flash-image', 100):.2f}") print(f"lite 1K x 100: ${cost_for_batch('gemini-3.1-flash-lite-image', 100):.2f}") print(f"flux-2-pro x 25: ${cost_for_batch('flux-2-pro', 25):.2f}") print(f"nb2 4K x 50: ${cost_for_batch('gemini-3.1-flash-image', 50, {'resolution': '4K'}):.2f}") 3a:T496,Final image drill. Wire all three lessons together. Write `plan_shoot(brief)` that takes a brief dict and returns a plan dict: ```python {"model": str, "count": int, "total_cost": float, "formats": list[str]} ``` The brief may contain: - `channel`: one of `"instagram"`, `"web"`, `"email"`, `"tiktok"` - `batch_count`: int (default 1) - `budget_per_image`: float (default 1.0) - `needs_text_in_image`: bool - `needs_vector`: bool - `photoreal_art_directed`: bool - `instruction_heavy`: bool - `google_premium`: bool - `needs_4k`: bool (uses the 4K row for nano banana 2 / Pro) Use the routing from lesson 01 (`pick_image_model`), the pricing table from lesson 03 step 06, and the `PLATFORM_FORMATS` table (provided) to pick output formats by channel. Four briefs run. Expected output: ``` B1: model=ideogram-v4 count= 5 total=$ 0.30 formats=['WEBP_1920', 'WEBP_400_thumb'] B2: model=gpt-image-2 count= 4 total=$ 0.21 formats=['JPEG_1080'] B3: model=gemini-3.1-flash-image count= 50 total=$ 3.35 formats=['JPEG_1080_vertical'] B4: model=gemini-3-pro-image count= 10 total=$ 2.40 formats=['WEBP_1920', 'WEBP_400_thumb'] ``` 3b:T4ed,PRICING = { "gemini-3.1-flash-image": 0.067, "gemini-3.1-flash-lite-image": 0.0336, "gemini-3-pro-image": 0.134, "gpt-image-2": 0.053, "ideogram-v4": 0.06, "flux-2-pro": 0.03, "flux-2-klein": 0.014, "recraftv4_1": 0.035, } NB2_4K = 0.151 PRO_4K = 0.24 PLATFORM_FORMATS = { "instagram": ["JPEG_1080"], "web": ["WEBP_1920", "WEBP_400_thumb"], "email": ["JPEG_600"], "tiktok": ["JPEG_1080_vertical"], } briefs = [ ("B1", {"channel": "web", "needs_text_in_image": True, "batch_count": 5, "budget_per_image": 0.10}), ("B2", {"channel": "instagram", "instruction_heavy": True, "batch_count": 4, "budget_per_image": 0.15}), ("B3", {"channel": "tiktok", "batch_count": 50, "budget_per_image": 0.07}), ("B4", {"channel": "web", "google_premium": True, "batch_count": 10, "needs_4k": True, "budget_per_image": 0.30}), ] # define pick_image_model(task), cost_for_batch(model, count, options=None), # and plan_shoot(brief) below for name, b in briefs: p = plan_shoot(b) print(f"{name}: model={p['model']:<24} count={p['count']:>3} total=${p['total_cost']:>6.2f} formats={p['formats']}") 3c:T994,PRICING = { "gemini-3.1-flash-image": 0.067, "gemini-3.1-flash-lite-image": 0.0336, "gemini-3-pro-image": 0.134, "gpt-image-2": 0.053, "ideogram-v4": 0.06, "flux-2-pro": 0.03, "flux-2-klein": 0.014, "recraftv4_1": 0.035, } NB2_4K = 0.151 PRO_4K = 0.24 PLATFORM_FORMATS = { "instagram": ["JPEG_1080"], "web": ["WEBP_1920", "WEBP_400_thumb"], "email": ["JPEG_600"], "tiktok": ["JPEG_1080_vertical"], } briefs = [ ("B1", {"channel": "web", "needs_text_in_image": True, "batch_count": 5, "budget_per_image": 0.10}), ("B2", {"channel": "instagram", "instruction_heavy": True, "batch_count": 4, "budget_per_image": 0.15}), ("B3", {"channel": "tiktok", "batch_count": 50, "budget_per_image": 0.07}), ("B4", {"channel": "web", "google_premium": True, "batch_count": 10, "needs_4k": True, "budget_per_image": 0.30}), ] def pick_image_model(task): if task.get("needs_vector"): return "recraftv4_1" if task.get("needs_text_in_image"): return "ideogram-v4" if task.get("budget_per_image", 1.0) < 0.04: return "gemini-3.1-flash-lite-image" if task.get("instruction_heavy"): return "gpt-image-2" if task.get("google_premium"): return "gemini-3-pro-image" if task.get("photoreal_art_directed"): return "flux-2-pro" return "gemini-3.1-flash-image" def cost_for_batch(model, count, options=None): options = options or {} if options.get("resolution") == "4K" and model == "gemini-3.1-flash-image": unit = NB2_4K elif options.get("resolution") == "4K" and model == "gemini-3-pro-image": unit = PRO_4K else: unit = PRICING[model] return round(unit * count, 2) def plan_shoot(brief): model = pick_image_model(brief) count = brief.get("batch_count", 1) options = {} if brief.get("needs_4k"): options["resolution"] = "4K" total_cost = cost_for_batch(model, count, options) formats = PLATFORM_FORMATS.get(brief.get("channel", "web"), ["WEBP_1920"]) return { "model": model, "count": count, "total_cost": total_cost, "formats": formats, } for name, b in briefs: p = plan_shoot(b) print(f"{name}: model={p['model']:<24} count={p['count']:>3} total=${p['total_cost']:>6.2f} formats={p['formats']}") 3d:I[93250,["/_next/static/chunks/2rjtqver_q2gs.js","/_next/static/chunks/0mg7io3h2u8wq.js","/_next/static/chunks/2axu7w_5i8n32.js","/_next/static/chunks/3he5zzx1k7c6r.js","/_next/static/chunks/0h0bavt00zz2v.js","/_next/static/chunks/3s-oul0007que.js"],"IconMark"] 1c:[["$","$L24",null,{"payload":{"catalog":"The image pipeline — turning one idea into a hundred shipped assets (step 3/7) · ai image generation · promptdojo","byPath":{"developer":"The image pipeline — 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,"lessons":[{"slug":"the-tool-contract","steps":[{"id":"agent-studio/the-tool-contract/s01-01-the-feature-you-ship-today"},{"id":"agent-studio/the-tool-contract/s02-02-the-schema-is-the-blast-radius"},{"id":"agent-studio/the-tool-contract/s03-03-spot-the-loose-tool"},{"id":"agent-studio/the-tool-contract/s04-04-predict-the-validation"},{"id":"agent-studio/the-tool-contract/s05-05-fix-the-yolo-rerun"},{"id":"agent-studio/the-tool-contract/s06-06-write-validate-call"},{"id":"agent-studio/the-tool-contract/s07-07-checkpoint"}]},{"slug":"the-loop-with-a-meter","steps":[{"id":"agent-studio/the-loop-with-a-meter/s01-01-a-loop-that-cannot-stop-is-an-invoice"},{"id":"agent-studio/the-loop-with-a-meter/s02-02-caps-are-part-of-the-loop"},{"id":"agent-studio/the-loop-with-a-meter/s03-03-why-did-it-stop"},{"id":"agent-studio/the-loop-with-a-meter/s04-04-predict-the-stop-reason"},{"id":"agent-studio/the-loop-with-a-meter/s05-05-fill-the-budget-check"},{"id":"agent-studio/the-loop-with-a-meter/s06-06-the-trace-is-the-receipt"},{"id":"agent-studio/the-loop-with-a-meter/s07-07-write-run-loop"},{"id":"agent-studio/the-loop-with-a-meter/s08-08-checkpoint"}]},{"slug":"evals-before-it-works","steps":[{"id":"agent-studio/evals-before-it-works/s01-01-green-before-it-runs"},{"id":"agent-studio/evals-before-it-works/s02-02-a-golden-case-is-a-past-incident"},{"id":"agent-studio/evals-before-it-works/s03-03-which-case-catches-it"},{"id":"agent-studio/evals-before-it-works/s04-04-predict-the-eval-run"},{"id":"agent-studio/evals-before-it-works/s05-05-fix-the-router-to-green"},{"id":"agent-studio/evals-before-it-works/s06-06-the-ratchet-entry"},{"id":"agent-studio/evals-before-it-works/s07-07-write-lint-the-ratchet"},{"id":"agent-studio/evals-before-it-works/s08-08-checkpoint"}]}]}]},"detail":{"number":27,"slug":"ai-image-generation","title":"ai image generation — from prompt to production asset","blurb":"the 2026 image model landscape, the prompts that work, and the pipeline that turns one good idea into a hundred ready-to-ship images. nano banana 2 for volume, nano banana pro for fidelity, flux, midjourney, ideogram, gpt-image-2. when each wins and what they cost.","overview":"$27","status":"live","lessons":[{"slug":"the-image-model-landscape","title":"The image model landscape — six families and what each is for","estMinutes":18,"prerequisites":[],"status":"live","steps":[{"type":"read","id":"ai-image-generation/the-image-model-landscape/s01-01-the-six-families","xp":1,"hint":[],"personalize":false,"phase":"warmup","estSeconds":180,"concept":"six-image-model-families","body":"$28","cta":"Got it","runnable":true},{"type":"read","id":"ai-image-generation/the-image-model-landscape/s02-02-flux-vs-midjourney","xp":1,"hint":[],"personalize":false,"phase":"warmup","estSeconds":200,"concept":"flux-vs-midjourney","body":"$29","cta":"Got it","runnable":true},{"type":"mc","id":"ai-image-generation/the-image-model-landscape/s03-03-pick-the-model","xp":2,"hint":[{"level":1,"body":"Midjourney's home turf is 'mood-driven, taste-heavy, no specific art direction.' Of the five briefs, exactly one fits that profile.","cost":0}],"personalize":false,"phase":"build","estSeconds":60,"concept":"flux-vs-midjourney-pick","prompt":"Five briefs. Which one is the BEST fit for Midjourney?\n(Reminder: Midjourney wins on subjective taste, loses on text-in-image,\ncharacter consistency across renders, and high-volume API workloads.)\n","options":[{"id":"brief_2","label":"2 — concept-art moodboard, aesthetic-driven, no specific composition"},{"id":"brief_1","label":"1 — photoreal earbud, art-directed","explain":"Art-directed + photoreal is Flux territory (`flux-2-pro`). Midjourney will style it, but won't follow the composition as literally as Flux does."},{"id":"brief_3","label":"3 — banner with 'Summer Sale — 30% off' rendered crisply","explain":"Text in image. V8.x finally renders quoted text legibly, but reviewers still put it below Ideogram 4.0 (which claims 90-95%) for letter-perfect copy. When the letters must be exact, use the specialist."},{"id":"brief_4","label":"4 — twelve variants of the same product, identical-looking","explain":"Character/object consistency across renders. FLUX.2 editing is built for exactly this. Midjourney's current Edit Model is lossy by comparison, and the V7 `--oref` flags are not the brief to send on V8.x."},{"id":"brief_5","label":"5 — 100 thumbnails for $50 total","explain":"Volume economics. Midjourney has no per-image API, no pro-rata pricing for an app. nano banana 2 at $0.067/image (1K) fits the budget; Lite is $0.0336 at 1K. Midjourney doesn't have the right billing surface."}],"answerIds":["brief_2"],"shuffle":false,"code":"# five real briefs. one model fits each best.\nbriefs = [\n \"Photoreal hero shot of a wireless earbud on a marble surface, top-down, soft window light, art-directed.\",\n \"Concept-art moodboard for a fantasy novel cover. Aesthetic-driven, no specific composition required.\",\n \"Lifestyle banner with the headline 'Summer Sale — 30% off' rendered crisply across the image.\",\n \"Twelve variants of the same product on twelve different backgrounds, keeping the product visually identical.\",\n \"100 thumbnail variants for an A/B test, $50 total budget.\",\n]\nfor i, b in enumerate(briefs, 1):\n print(f\"{i}. {b}\")\n","runnable":true},{"type":"read","id":"ai-image-generation/the-image-model-landscape/s04-04-nano-banana-and-the-batch-economics","xp":1,"hint":[],"personalize":false,"phase":"build","estSeconds":200,"concept":"nano-banana-batch-economics","body":"$2a","cta":"Got it","runnable":true},{"type":"read","id":"ai-image-generation/the-image-model-landscape/s05-05-text-in-image","xp":1,"hint":[],"personalize":false,"phase":"build","estSeconds":170,"concept":"text-in-image-and-specialists","body":"$2b","cta":"Got it","runnable":true},{"type":"mc","id":"ai-image-generation/the-image-model-landscape/s06-06-which-model-for-text","xp":2,"hint":[{"level":1,"body":"The brief specifies a literal text string that must render legibly. There's exactly one model family in 2026 designed for that job.","cost":0}],"personalize":false,"phase":"build","estSeconds":50,"concept":"text-in-image-pick","prompt":"The brief: legible \"LAUNCHING JUNE 1\" headline rendered inside the\nimage, budget $0.10/image, one image needed. Which model wins?\n","options":[{"id":"ideogram_quality","label":"Ideogram 4.0 (Quality tier, $0.10/image, POST /v1/ideogram-v4/generate)"},{"id":"midjourney","label":"Midjourney","explain":"V8.x made quoted text legible at last — but reviewers still put it below Ideogram 4.0 for letter-perfect copy, and this brief's headline must be exact. Spend the $0.10 on the specialist."},{"id":"flux_pro","label":"flux-2-pro","explain":"FLUX.2 improved on text but still loses to Ideogram. Use it for photorealism without specific text demands."},{"id":"nano_banana","label":"nano banana 2 (gemini-3.1-flash-image, $0.067/image at 1K)","explain":"Cheaper, but Ideogram is the specialist for text. With a $0.10 budget and only one image needed, you pay for the accuracy premium."}],"answerIds":["ideogram_quality"],"shuffle":false,"code":"# the brief: a banner for an upcoming product launch.\n# the headline 'LAUNCHING JUNE 1' must render legibly.\n# budget is $0.10 per generation. one image needed.\nbrief = {\n \"needs_text_in_image\": True,\n \"text_string\": \"LAUNCHING JUNE 1\",\n \"budget_per_image\": 0.10,\n \"count\": 1,\n}\nprint(brief)\n","runnable":true},{"type":"read","id":"ai-image-generation/the-image-model-landscape/s07-07-the-decision-tree","xp":2,"hint":[],"personalize":false,"phase":"build","estSeconds":150,"concept":"image-model-decision-tree","body":"$2c","cta":"Got it","code":"# the routing logic for picking an image model.\n# the order matters: specialty rules first, then defaults.\n\nRULES = [\n (\"needs_vector\", \"recraftv4_1\"),\n (\"needs_text_in_image\", \"ideogram-v4\"),\n (\"cheap_floor\", \"gemini-3.1-flash-lite-image\"),\n (\"instruction_heavy\", \"gpt-image-2\"),\n (\"google_premium\", \"gemini-3-pro-image\"),\n (\"photoreal_art_directed\", \"flux-2-pro\"),\n (\"default\", \"gemini-3.1-flash-image\"),\n]\n\nfor flag, model in RULES:\n print(f\"{flag:24} → {model}\")\n","runnable":true},{"type":"write","id":"ai-image-generation/the-image-model-landscape/s08-08-write-pick-image-model","xp":5,"hint":[{"level":1,"body":"Seven `if` checks in priority order. Use `task.get(key, default)` everywhere so missing fields don't crash. Vector and text fire first. The budget check (`< 0.04`) returns `gemini-3.1-flash-lite-image`. High `batch_count` is not its own branch — it falls through to `gemini-3.1-flash-image`.","cost":0},{"level":2,"body":"The trap is the `batch` task. It has `batch_count=50` but `budget_per_image=0.07`, so the cheap-floor check does not fire. Same family as the default → `gemini-3.1-flash-image`. If your version returns Lite for that task, you treated 'batch' as 'cheap'.","cost":0}],"personalize":false,"phase":"build","estSeconds":220,"concept":"route-image-task-to-model","prompt":"Write `pick_image_model(task)` that routes an image-generation\ntask to the right 2026 model.\n\n`task` is a dict with optional keys:\n\n- `budget_per_image`: float (default 1.0)\n- `needs_text_in_image`: bool\n- `needs_vector`: bool\n- `photoreal_art_directed`: bool\n- `batch_count`: int (default 1)\n- `instruction_heavy`: bool\n- `google_premium`: bool\n\nReturn one of the model name strings, in this priority order:\n\n1. `needs_vector` → `\"recraftv4_1\"`\n2. `needs_text_in_image` → `\"ideogram-v4\"`\n3. `budget_per_image < 0.04` → `\"gemini-3.1-flash-lite-image\"`\n4. `instruction_heavy` → `\"gpt-image-2\"`\n5. `google_premium` → `\"gemini-3-pro-image\"`\n6. `photoreal_art_directed` → `\"flux-2-pro\"`\n7. default (including high `batch_count`) → `\"gemini-3.1-flash-image\"`\n\nFour tasks run. Expected output:\n```\nposter: ideogram-v4\nhero: flux-2-pro\nbatch: gemini-3.1-flash-image\ncheap: gemini-3.1-flash-lite-image\n```\n","starter":"poster = {\"needs_text_in_image\": True, \"budget_per_image\": 0.10}\nhero = {\"photoreal_art_directed\": True, \"budget_per_image\": 0.08}\nbatch = {\"batch_count\": 50, \"budget_per_image\": 0.07}\ncheap = {\"budget_per_image\": 0.03}\n\n# define pick_image_model(task) below\n\nprint(f\"poster: {pick_image_model(poster)}\")\nprint(f\"hero: {pick_image_model(hero)}\")\nprint(f\"batch: {pick_image_model(batch)}\")\nprint(f\"cheap: {pick_image_model(cheap)}\")\n","grader":{"kind":"stdout-equality","expected":"poster: ideogram-v4\nhero: flux-2-pro\nbatch: gemini-3.1-flash-image\ncheap: gemini-3.1-flash-lite-image\n","normalize":"collapse-trailing-newline"},"solution":"poster = {\"needs_text_in_image\": True, \"budget_per_image\": 0.10}\nhero = {\"photoreal_art_directed\": True, \"budget_per_image\": 0.08}\nbatch = {\"batch_count\": 50, \"budget_per_image\": 0.07}\ncheap = {\"budget_per_image\": 0.03}\n\ndef pick_image_model(task):\n if task.get(\"needs_vector\"):\n return \"recraftv4_1\"\n if task.get(\"needs_text_in_image\"):\n return \"ideogram-v4\"\n if task.get(\"budget_per_image\", 1.0) < 0.04:\n return \"gemini-3.1-flash-lite-image\"\n if task.get(\"instruction_heavy\"):\n return \"gpt-image-2\"\n if task.get(\"google_premium\"):\n return \"gemini-3-pro-image\"\n if task.get(\"photoreal_art_directed\"):\n return \"flux-2-pro\"\n return \"gemini-3.1-flash-image\"\n\nprint(f\"poster: {pick_image_model(poster)}\")\nprint(f\"hero: {pick_image_model(hero)}\")\nprint(f\"batch: {pick_image_model(batch)}\")\nprint(f\"cheap: {pick_image_model(cheap)}\")\n","hiddenTests":[]},{"type":"checkpoint","id":"ai-image-generation/the-image-model-landscape/s09-09-checkpoint","xp":8,"hint":[],"personalize":false,"phase":"check","estSeconds":260,"concept":"audit-an-image-pipeline","prompt":"Final image drill. You're auditing a real image pipeline at an agency.\nWrite `audit_image_pipeline(tasks)` that takes a list of task\ndicts (same shape as the previous step) and returns a list of\ndicts, one per task, in the same order:\n\n```python\n{\"task_id\": ..., \"model\": ..., \"specialist_used\": bool}\n```\n\n`model` comes from your `pick_image_model(task)` routing.\n`specialist_used` is `True` if the chosen model is one of the\nthree specialty picks: `ideogram-v4`, `recraftv4_1`, or\n`flux-2-pro`. Everything else is False (including the default\n`gemini-3.1-flash-image`, Lite, `gpt-image-2`, and\n`gemini-3-pro-image`).\n\nEach task carries a `task_id` field (string). Preserve it in\nthe output.\n\nFive tasks run. Expected output:\n```\nT1: ideogram-v4 specialist=True\nT2: gpt-image-2 specialist=False\nT3: gemini-3.1-flash-image specialist=False\nT4: recraftv4_1 specialist=True\nT5: gemini-3-pro-image specialist=False\n```\n","starter":"tasks = [\n {\"task_id\": \"T1\", \"needs_text_in_image\": True, \"budget_per_image\": 0.10},\n {\"task_id\": \"T2\", \"instruction_heavy\": True, \"budget_per_image\": 0.15},\n {\"task_id\": \"T3\", \"batch_count\": 50, \"budget_per_image\": 0.07},\n {\"task_id\": \"T4\", \"needs_vector\": True, \"budget_per_image\": 0.10},\n {\"task_id\": \"T5\", \"google_premium\": True, \"budget_per_image\": 0.20},\n]\n\n# define pick_image_model and audit_image_pipeline below\n\nfor row in audit_image_pipeline(tasks):\n print(f\"{row['task_id']}: {row['model']:<26} specialist={row['specialist_used']}\")\n","grader":{"kind":"stdout-equality","expected":"T1: ideogram-v4 specialist=True\nT2: gpt-image-2 specialist=False\nT3: gemini-3.1-flash-image specialist=False\nT4: recraftv4_1 specialist=True\nT5: gemini-3-pro-image specialist=False\n","normalize":"collapse-trailing-newline"},"solution":"$2d","passThreshold":0.66}],"xpTotal":23},{"slug":"prompting-for-real-output","title":"Prompting for real output — composition, control, and the eight knobs that matter","estMinutes":20,"prerequisites":["the-image-model-landscape"],"status":"live","steps":[{"type":"read","id":"ai-image-generation/prompting-for-real-output/s01-01-why-the-girl-with-red-hair-fails","xp":1,"hint":[],"personalize":false,"phase":"warmup","estSeconds":160,"concept":"vague-prompts-fail","body":"$2e","cta":"Got it","runnable":true},{"type":"read","id":"ai-image-generation/prompting-for-real-output/s02-02-the-eight-knobs","xp":2,"hint":[],"personalize":false,"phase":"warmup","estSeconds":200,"concept":"eight-prompt-knobs","body":"$2f","cta":"Got it","code":"# the eight knobs, in the order they go in a prompt.\n# naming each one narrows the distribution the model samples from.\n\nKNOBS = [\n (\"subject\", \"young woman with curly auburn hair, looking off-camera\"),\n (\"composition\", \"rule-of-thirds, subject in left third, blurred bookshelves background\"),\n (\"lighting\", \"golden-hour side-lighting from a window on her left, soft shadows\"),\n (\"style\", \"editorial portrait, film grain, Kodak Portra 400 palette\"),\n (\"camera\", \"shot on 35mm film camera, eye-level perspective\"),\n (\"lens\", \"85mm f/1.4 lens, shallow depth of field\"),\n (\"palette\", \"muted earth tones, terracotta sweater, warm shadows\"),\n (\"negatives\", \"no text, no watermark, no harsh studio lighting\"),\n]\nfor name, example in KNOBS:\n print(f\"{name:12} → {example}\")\n","runnable":true},{"type":"fix","id":"ai-image-generation/prompting-for-real-output/s03-03-fix-the-vague-prompt","xp":3,"hint":[{"level":1,"body":"Rewrite the return string to mention at least one phrase from each of the eight keyword lists. The grader case-insensitively substring-matches, so you don't need exact capitalization.","cost":0},{"level":2,"body":"Something like:\n\n```python\nreturn (\n \"Portrait of a young woman with auburn hair. \"\n \"Rule of thirds, subject in left third. \"\n \"Golden hour side-lighting from a window. \"\n \"Editorial portrait style. \"\n \"Shot on 35mm camera, eye level. \"\n \"85mm f/1.4 lens, shallow depth of field. \"\n \"Muted earth tones, Kodak Portra palette. \"\n \"No text, no watermark, no harsh studio light.\"\n)\n```\n","cost":0}],"personalize":false,"phase":"build","estSeconds":90,"concept":"rewrite-vague-into-specific","brokenCode":"KNOB_KEYWORDS = {\n \"subject\": [\"woman\", \"man\", \"person\", \"subject\", \"portrait\"],\n \"composition\": [\"rule of thirds\", \"centered\", \"third\", \"frame\", \"composition\"],\n \"lighting\": [\"golden hour\", \"rim light\", \"side-lighting\", \"window light\", \"natural light\"],\n \"style\": [\"editorial\", \"cinematic\", \"documentary\", \"fashion\", \"fine art\"],\n \"camera\": [\"35mm camera\", \"Leica\", \"shot on\", \"eye level\", \"low angle\"],\n \"lens\": [\"85mm\", \"50mm\", \"f/1.4\", \"f/1.8\", \"shallow depth\", \"telephoto\"],\n \"palette\": [\"earth tones\", \"muted\", \"Kodak Portra\", \"warm shadows\", \"monochrome\"],\n \"negatives\": [\"no text\", \"no watermark\", \"no harsh\", \"no extra\", \"no smile\"],\n}\n\ndef build_prompt():\n # bug: vague one-liner, hits zero knobs cleanly\n return \"a girl with red hair\"\n\nprompt = build_prompt()\nfor knob, keywords in KNOB_KEYWORDS.items():\n hit = any(k.lower() in prompt.lower() for k in keywords)\n mark = \"✓\" if hit else \"✗\"\n print(f\"{knob:12} {mark}\")\n","prompt":"A function `build_prompt()` is supposed to return a production-grade\nprompt with all eight knobs named (subject, composition, lighting,\nstyle, camera, lens, palette, negatives). The current return value\nis a vague one-liner. Fix it to hit all eight knobs.\n\nThe grader is lenient: it checks that the returned string contains\nat least one phrase for each of the eight knob categories\n(keyword-match), and the call site joins them with newlines.\n\nExpected output (eight checkmarks):\n```\nsubject ✓\ncomposition ✓\nlighting ✓\nstyle ✓\ncamera ✓\nlens ✓\npalette ✓\nnegatives ✓\n```\n","grader":{"kind":"stdout-equality","expected":"subject ✓\ncomposition ✓\nlighting ✓\nstyle ✓\ncamera ✓\nlens ✓\npalette ✓\nnegatives ✓\n","normalize":"collapse-trailing-newline"},"bugLines":[13,14],"revealAfter":6},{"type":"read","id":"ai-image-generation/prompting-for-real-output/s04-04-controlnet-and-reference","xp":1,"hint":[],"personalize":false,"phase":"build","estSeconds":180,"concept":"reference-images-and-controlnet","body":"$30","cta":"Got it","runnable":true},{"type":"mc","id":"ai-image-generation/prompting-for-real-output/s05-05-which-knob-is-missing","xp":2,"hint":[{"level":1,"body":"Walk down the eight: subject, composition, lighting, style, camera, lens, palette, negatives. Read each phrase in the prompt and check it off. The unchecked one is the answer.","cost":0}],"personalize":false,"phase":"build","estSeconds":70,"concept":"missing-knob-in-prompt","prompt":"The prompt above names seven of the eight knobs (subject,\ncomposition, lighting, style, camera, palette, negatives — in some\norder). One is missing. Which one?\n","options":[{"id":"lens","label":"lens (no aperture/focal length specified — e.g., 85mm f/1.4, shallow depth)"},{"id":"subject","label":"subject","explain":"The 32-year-old man with a salt-and-pepper beard, looking off-camera, IS the subject. Specific enough to narrow the distribution. Not missing."},{"id":"lighting","label":"lighting","explain":"'Soft window light from camera-left, deep shadows on the right' covers the lighting knob clearly."},{"id":"palette","label":"palette","explain":"'Muted earth tones, slight film grain' is the palette. Color direction is specified."}],"answerIds":["lens"],"shuffle":false,"code":"# the brief — every knob named EXCEPT one.\n# which one is missing?\nprompt = (\n \"Portrait of a 32-year-old man with a salt-and-pepper beard, \"\n \"looking off-camera. Centered composition, subject filling the \"\n \"middle third of the frame. Soft window light from camera-left, \"\n \"deep shadows on the right. Editorial documentary style, \"\n \"muted earth tones, slight film grain. Shot on 35mm film camera, \"\n \"eye level. No text, no watermark, no harsh studio lighting, \"\n \"no plastic skin.\"\n)\nfor line in prompt.split(\". \"):\n print(f\"- {line.strip()}\")\n","runnable":true},{"type":"read","id":"ai-image-generation/prompting-for-real-output/s06-06-failure-modes","xp":1,"hint":[],"personalize":false,"phase":"build","estSeconds":200,"concept":"image-failure-modes","body":"$31","cta":"Got it","runnable":true},{"type":"write","id":"ai-image-generation/prompting-for-real-output/s07-07-write-score-image-prompt","xp":5,"hint":[{"level":1,"body":"Lowercase the prompt once. Then iterate the KNOB_KEYWORDS dict in order (Python 3.7+ dicts preserve insertion order). For each knob, if `any(k in low for k in keywords)` then increment score, else append the knob name to missing. Return both.","cost":0},{"level":2,"body":"The trap is iteration order — `missing` must preserve the dict's insertion order so the output matches. `dict.items()` does this in CPython 3.7+. Don't sort the missing list.","cost":0}],"personalize":false,"phase":"build","estSeconds":240,"concept":"score-prompt-by-knobs","prompt":"Write `score_image_prompt(prompt)` that takes a prompt string and\nreturns a dict:\n\n```python\n{\"score\": int (0-8), \"missing\": list[str]}\n```\n\nUse this exact `KNOB_KEYWORDS` table (provided in the starter).\nLowercase the prompt and check if ANY keyword for a knob appears\nas a substring. If yes, that knob is hit. If no, add the knob's\nname to `missing`. `score` is the number of knobs hit (0-8).\n`missing` keeps the original knob-table order.\n\nThree prompts run: `good`, `mid`, `bad`. Expected output:\n```\ngood: {'score': 8, 'missing': []}\nmid: {'score': 5, 'missing': ['lighting', 'lens', 'negatives']}\nbad: {'score': 0, 'missing': ['subject', 'composition', 'lighting', 'style', 'camera', 'lens', 'palette', 'negatives']}\n```\n","starter":"$32","grader":{"kind":"stdout-equality","expected":"good: {'score': 8, 'missing': []}\nmid: {'score': 5, 'missing': ['lighting', 'lens', 'negatives']}\nbad: {'score': 0, 'missing': ['subject', 'composition', 'lighting', 'style', 'camera', 'lens', 'palette', 'negatives']}\n","normalize":"collapse-trailing-newline"},"solution":"$33","hiddenTests":[]},{"type":"checkpoint","id":"ai-image-generation/prompting-for-real-output/s08-08-checkpoint","xp":8,"hint":[],"personalize":false,"phase":"check","estSeconds":240,"concept":"rank-prompts-by-completeness","prompt":"Final image drill. Reuse `score_image_prompt` and write\n`rank_prompts(prompts)` that takes a dict of `{id: prompt_string}`\nand returns a list of `(id, score)` tuples sorted by score\nDESCENDING, ties broken by id ASCENDING (so the result is\ndeterministic).\n\nFive prompts run, ranging from \"a girl with red hair\" (zero knobs)\nto a full 8-knob prompt. Expected output:\n```\nP5: score=8\nP4: score=7\nP3: score=5\nP2: score=3\nP1: score=0\n```\n","starter":"$34","grader":{"kind":"stdout-equality","expected":"P5: score=8\nP4: score=7\nP3: score=5\nP2: score=3\nP1: score=0\n","normalize":"collapse-trailing-newline"},"solution":"$35","passThreshold":0.66}],"xpTotal":23},{"slug":"the-image-pipeline","title":"The image pipeline — turning one idea into a hundred shipped assets","estMinutes":22,"prerequisites":["the-image-model-landscape","prompting-for-real-output"],"status":"live","steps":[{"type":"read","id":"ai-image-generation/the-image-pipeline/s01-01-the-six-stages","xp":1,"hint":[],"personalize":false,"phase":"warmup","estSeconds":200,"concept":"six-stage-image-pipeline","body":"$36","cta":"Got it","code":"# the six stages of a 2026 image pipeline.\n\nSTAGES = [\n (\"1. brief\", \"what is the image FOR — channel, intent, audience, dimensions\"),\n (\"2. prompt\", \"the eight knobs (subject, composition, lighting, style, camera, lens, palette, negatives)\"),\n (\"3. generate\", \"send the prompt to the chosen model — usually N times, not once\"),\n (\"4. filter\", \"score candidates, drop the bad ones, keep the top K\"),\n (\"5. upscale\", \"boost resolution for hero/print use (native 4K on nano banana 2 / Pro, dedicated upscalers)\"),\n (\"6. format\", \"convert to platform formats (WebP, AVIF, JPG), strip EXIF, generate alt-text\"),\n]\nfor name, desc in STAGES:\n print(f\"{name:14} {desc}\")\n","runnable":true},{"type":"read","id":"ai-image-generation/the-image-pipeline/s02-02-batch-and-filter","xp":1,"hint":[],"personalize":false,"phase":"build","estSeconds":200,"concept":"batch-and-filter-economics","body":"$37","cta":"Got it","runnable":true},{"type":"mc","id":"ai-image-generation/the-image-pipeline/s03-03-which-stage-is-broken","xp":2,"hint":[{"level":1,"body":"Map each symptom to the stage that would have caught it. Broken hands shipping to prod = no filter (stage 4) caught them. 1024 PNG on mobile = no format (stage 6) sized/converted them.","cost":0}],"personalize":false,"phase":"build","estSeconds":70,"concept":"diagnose-broken-pipeline-stage","prompt":"Six pipeline stages: brief, prompt, generate, filter, upscale,\nformat. The symptoms above point at which TWO broken stages?\n","options":[{"id":"filter_and_format","label":"filter + format (broken hands = no filter step; 1024 PNG on mobile = no format step)"},{"id":"brief_and_prompt","label":"brief + prompt","explain":"The brief and prompt could be fine. The issue is downstream: nothing catches the broken hands (filter), and nothing reformats for the channel (format)."},{"id":"generate_and_upscale","label":"generate + upscale","explain":"Generation is producing the images correctly (Flux Pro is fine). Upscale isn't the issue — the problem is the OUTPUT format, not the resolution."},{"id":"prompt_and_upscale","label":"prompt + upscale","explain":"The prompt issue would be 'images don't match the brief.' That's not the symptom here. The symptoms are post-generation: no filtering, no formatting."}],"answerIds":["filter_and_format"],"shuffle":false,"code":"# the symptom: the team is generating beautiful Flux Pro images\n# at $0.04/image. they ship one per request. but the customer\n# complains the images sometimes have six-fingered hands AND\n# they're served as 1024x1024 PNG even when the customer is on\n# mobile where a 400x400 WebP would load 8x faster.\nsymptoms = [\n \"occasional broken hands shipped to production\",\n \"1024x1024 PNG served regardless of viewport\",\n \"no resize, no format conversion, no EXIF stripping\",\n]\nfor s in symptoms:\n print(f\"- {s}\")\n","runnable":true},{"type":"read","id":"ai-image-generation/the-image-pipeline/s04-04-format-and-platform","xp":1,"hint":[],"personalize":false,"phase":"build","estSeconds":180,"concept":"format-and-platform-output","body":"$38","cta":"Got it","runnable":true},{"type":"fill","id":"ai-image-generation/the-image-pipeline/s05-05-fill-the-pipeline","xp":3,"hint":[{"level":1,"body":"Same shape as the harness tool dispatch (@@pd-path:path.ref:agent-harnesses@@ step 05): registry lookup `HANDLERS[stage]`, then call with the running state. The handler returns a new state dict (immutable update), which becomes the input to the next stage.","cost":0},{"level":2,"body":"`HANDLERS[stage](state)` — exact line. Each handler returns a NEW dict spread from the old one plus its addition. That's the immutable update pattern from @@pd-path:path.ref:mutation-and-state@@.","cost":0}],"personalize":false,"phase":"build","estSeconds":75,"concept":"pipeline-stage-dispatch","prompt":"The pipeline runs six stages in order: brief, prompt, generate,\nfilter, upscale, format. The dispatch function takes a brief\nthrough each stage by looking up the stage handler in a registry.\n\nFill in the dispatch line: look up the handler for the current\nstage name and call it with the running `state` dict.\n\nExpected output:\n```\ndone: {'brief': 'ok', 'prompt': '8/8 knobs', 'generated': 10, 'filtered': 3, 'upscaled': 3, 'formatted': 3}\n```\n","code":"STAGES = [\"brief\", \"prompt\", \"generate\", \"filter\", \"upscale\", \"format\"]\n\nHANDLERS = {\n \"brief\": lambda s: {**s, \"brief\": \"ok\"},\n \"prompt\": lambda s: {**s, \"prompt\": \"8/8 knobs\"},\n \"generate\": lambda s: {**s, \"generated\": 10},\n \"filter\": lambda s: {**s, \"filtered\": 3},\n \"upscale\": lambda s: {**s, \"upscaled\": s[\"filtered\"]},\n \"format\": lambda s: {**s, \"formatted\": s[\"upscaled\"]},\n}\n\ndef run_pipeline():\n state = {}\n for stage in STAGES:\n state = ___\n return state\n\nprint(f\"done: {run_pipeline()}\")\n","blanks":[{"id":"dispatch","accept":["HANDLERS[stage](state)"],"caseSensitive":true,"normalize":"trim"}]},{"type":"write","id":"ai-image-generation/the-image-pipeline/s06-06-write-cost-for-batch","xp":5,"hint":[{"level":1,"body":"Look up `PRICING[model]`, unless `options` asks for 4K on nano banana 2 or Pro — then use `NB2_4K` ($0.151) or `PRO_4K` ($0.24). Multiply by `count`. Return `round(total, 2)`. The `options or {}` idiom handles the None default cleanly.","cost":0},{"level":2,"body":"The trap is `options=None` as default — never use a mutable default. Convert with `options = options or {}` at the top of the function, then branch on `options.get('resolution') == '4K'` before the table lookup.","cost":0}],"personalize":false,"phase":"build","estSeconds":220,"concept":"image-batch-cost-math","prompt":"Write `cost_for_batch(model, count, options=None)` that returns\nthe total batch cost in USD, rounded to 2 decimals.\n\nUse this exact `PRICING` table (real per-image rates as of\nSeptember 2026, provided in the starter):\n\n- `gemini-3.1-flash-image`: $0.067 (Nano Banana 2 at 1K)\n- `gemini-3.1-flash-lite-image`: $0.0336 (1K only)\n- `gemini-3-pro-image`: $0.134 (1K/2K)\n- `gpt-image-2`: $0.053 (square, quality=medium)\n- `ideogram-v4`: $0.06 (Default tier)\n- `flux-2-pro`: $0.03 (from $0.03/MP)\n- `flux-2-klein`: $0.014\n- `recraftv4_1`: $0.035 (raster)\n\n`options` may include `{\"resolution\": \"4K\"}`. If the model is\n`gemini-3.1-flash-image`, use $0.151 per image. If the model is\n`gemini-3-pro-image`, use $0.24 per image. Other models ignore\nthe flag and stay on the table.\n\nReturn a float, rounded to 2 decimals via `round(x, 2)`.\n\nFour cases run. Expected output:\n```\nnb2 1K x 100: $6.70\nlite 1K x 100: $3.36\nflux-2-pro x 25: $0.75\nnb2 4K x 50: $7.55\n```\n","starter":"PRICING = {\n \"gemini-3.1-flash-image\": 0.067,\n \"gemini-3.1-flash-lite-image\": 0.0336,\n \"gemini-3-pro-image\": 0.134,\n \"gpt-image-2\": 0.053,\n \"ideogram-v4\": 0.06,\n \"flux-2-pro\": 0.03,\n \"flux-2-klein\": 0.014,\n \"recraftv4_1\": 0.035,\n}\n\nNB2_4K = 0.151\nPRO_4K = 0.24\n\n# define cost_for_batch(model, count, options=None) below\n\nprint(f\"nb2 1K x 100: ${cost_for_batch('gemini-3.1-flash-image', 100):.2f}\")\nprint(f\"lite 1K x 100: ${cost_for_batch('gemini-3.1-flash-lite-image', 100):.2f}\")\nprint(f\"flux-2-pro x 25: ${cost_for_batch('flux-2-pro', 25):.2f}\")\nprint(f\"nb2 4K x 50: ${cost_for_batch('gemini-3.1-flash-image', 50, {'resolution': '4K'}):.2f}\")\n","grader":{"kind":"stdout-equality","expected":"nb2 1K x 100: $6.70\nlite 1K x 100: $3.36\nflux-2-pro x 25: $0.75\nnb2 4K x 50: $7.55\n","normalize":"collapse-trailing-newline"},"solution":"$39","hiddenTests":[]},{"type":"checkpoint","id":"ai-image-generation/the-image-pipeline/s07-07-checkpoint","xp":8,"hint":[],"personalize":false,"phase":"check","estSeconds":280,"concept":"plan-a-shoot-end-to-end","prompt":"$3a","starter":"$3b","grader":{"kind":"stdout-equality","expected":"B1: model=ideogram-v4 count= 5 total=$ 0.30 formats=['WEBP_1920', 'WEBP_400_thumb']\nB2: model=gpt-image-2 count= 4 total=$ 0.21 formats=['JPEG_1080']\nB3: model=gemini-3.1-flash-image count= 50 total=$ 3.35 formats=['JPEG_1080_vertical']\nB4: model=gemini-3-pro-image count= 10 total=$ 2.40 formats=['WEBP_1920', 'WEBP_400_thumb']\n","normalize":"collapse-trailing-newline"},"solution":"$3c","passThreshold":0.66}],"xpTotal":21}],"liveLessonCount":3,"liveStepCount":24,"xpTotal":67}},"chapter":"$1c:2:props:tree:detail","lesson":"$1c:2:props:tree:detail:lessons:2","step":"$1c:2:props:tree:detail:lessons:2:steps:2","stepIndex":2,"nav":{"tocNext":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":3},"tocPrev":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":1},"nextByPath":{"developer":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":3},"marketer":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":3},"designer":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":3},"customer-service":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":3},"copywriter":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":3},"data-analyst":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":3},"project-manager":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":3},"hr-specialist":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":3},"operations":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":3},"lawyer":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":3}},"prevByPath":{"developer":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":1},"marketer":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":1},"designer":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":1},"customer-service":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":1},"copywriter":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":1},"data-analyst":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":1},"project-manager":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":1},"hr-specialist":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":1},"operations":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":1},"lawyer":{"chapterSlug":"ai-image-generation","lessonSlug":"the-image-pipeline","stepIndex":1}}}}]]

The image pipeline — turning one idea into a hundred shipped assets — step 3 of 7