PRD.md

PRD — Shopfront (Product Photo Studio)

Turn a tiny business's phone snapshots into clean, professional product photos. Upload a plain phone photo of a handmade product; FLUX.2 [klein] re-lights and re-stages it on preset backgrounds (marble, linen, a sunlit windowsill) while keeping the product itself intact — and returns a grid of variations to pick from.

Field	Value
Hackathon	Build Small (Hugging Face × Gradio)
Track	🏡 Backyard AI (practical — solve a real problem for someone you know)
Partner kit	Black Forest Labs — FLUX.2 [klein]
Model	black-forest-labs/FLUX.2-klein-4B (4B, Apache 2.0); optional brand LoRA on FLUX.2-klein-base-4B
Badges targeted	🏅 Tiny Titan (≤4B), 🎨 Off Brand (custom UI), 🎬 Best Demo, 🧩 Bonus Quest Champion
Deadline	June 15, 2026 · 23:59 UTC
Starter	Forked from stephenbtl/klein-build-small-starter (this repo)

---

1. Summary

Shopfront is a single-purpose image-editing app for one very real user: a friend who sells something handmade (jewelry, candles, baked goods) and whose product photos are phone snaps on a cluttered table. Good product photography is expensive; Shopfront does the re-lighting and staging with klein's image-editing, keeping the actual product recognizable. The user never writes a prompt — they upload a photo and click a scene preset.

This is the most directly "Backyard AI" of the three concept branches (lifted from STARTER_IDEAS.md #1, "Shopfront"). It's a focused tool for one user with preset styling — explicitly not a generic prompt box.

2. The problem & user

User: a small/handmade seller (Etsy-scale) with no photography budget. Job-to-be-done: "make my product look like it was shot for a catalogue, without a studio." Constraint that makes it a product: the product must stay intact and recognizable — only the lighting/background/staging changes.

3. Why it fits "Build Small" (rule → how we satisfy it)

Rule / badge	How this app delivers it
REQ-01 ≤ 32B	klein 4B = 4B params (+ optional small brand LoRA). ✅
REQ-02 Gradio Space in org	Forked Gradio Space; deploy into the Build Small HF org. ✅
REQ-03 Demo video	Phone snap → 3 staged scenes → 4-variation grid, end to end.
REQ-04 Social post	Before/after of a real product, linked from README.
REQ-05 ZeroGPU ≤10 apps/user	Single Space on ZeroGPU (zero-a10g). ✅
REQ-06 README tags + write-up	See §9 for the exact YAML block.
🏅 Tiny Titan (≤4B)	Runs entirely on klein 4B — qualifies for the ≤4B badge.
🎨 Off Brand	Replace the dev tabs with a "studio counter" UI: upload, scene chips, variation grid.
🎬 Best Demo	Real product + real seller story sells hard on video.

4. Scope

MVP (must ship by deadline)

1. Upload → scene presets → result. A row of named scene chips (e.g. "White Marble", "Linen Flat-lay", "Sunlit Windowsill", "Soft Studio Grey").
2. Each scene = a curated edit prompt that re-lights/re-stages while preserving the product. One click; no prompt writing.
3. Generate 4 variations (4 seeds) shown as a grid so the seller picks the best one.
4. Clear before → after so the value is obvious.

Stretch

• Brand LoRA: train on ~20 of the seller's existing on-brand shots so every generated scene matches their aesthetic (the "push it further" in idea #1).
• Aspect presets for marketplace formats (1:1 for Etsy/IG, 4:5 portrait) via the starter's SIZE_PRESETS.
• Light "keep product, change only background" guidance text + a strength control.

Out of scope

• True background segmentation/compositing (klein edits holistically; we rely on prompt + low change). Pixel-perfect product masking is a later iteration.
• In-Space training (offline via AI Toolkit — §6).

5. Technical design (grounded in the starter app.py)

This is the starter's Image → Image path, specialized and re-skinned.

• Pipeline: keep the starter's ZeroGPU setup verbatim — import spaces before torch; Flux2KleinPipeline.from_pretrained("black-forest-labs/FLUX.2-klein-4B", torch_dtype=torch.bfloat16) built on CPU at module scope; get_pipe() moves it to cuda inside the @GPU call.
• Edit call: reuse img2img(). Footgun: call pipe(prompt=…, image=…) by keyword (image is the first positional arg).
• Prompts (from PROMPTING.md): describe the change, not the whole scene — e.g. "Place the product on white marble in soft daylight, clean studio background, gentle reflections" rather than re-describing the product. Avoid "for a mug / as a logo" phrasing (the model would draw the mug).
• Sizing: klein_size() snaps input to a legal size; offer marketplace aspect presets via SIZE_PRESETS.
• Variation grid: call the edit 4× with different seeds; return a gr.Gallery.
• Steps/guidance: distilled 4B → num_inference_steps=4, guidance_scale=1.0 (starter defaults). Optional "High quality" path → FLUX.2-klein-base-4B, 50 steps, guidance 4.0.

Files to change

File	Change
app.py	Collapse to one editing screen; add SCENES = {name: edit_prompt}; add 4-seed variation grid; wire scene chips → img2img. Keep ZeroGPU + klein_size.
README.md	New frontmatter + submission write-up + demo/social links (§9).
configs/my_lora_klein_4b.yaml	(Stretch) brand LoRA — trigger word, dataset path.
examples/	Add real before/after product pairs for the demo + gr.Examples.

6. Optional brand LoRA (stretch — earns the fine-tuning angle)

Per TRAIN_A_LORA.md: train on FLUX.2-klein-base-4B with 15–40 of the seller's on-brand photos; caption content only, coined trigger (e.g. SHOPBRAND); keep arch: "flux2_klein_4b"; $0.50 / ~30 min on RunPod; pick the best sample checkpoint (step 750–1500), not the last. Load with pipe.load_lora_weights(...) so scenes inherit the brand look.

7. Demo & social plan (REQ-03 / REQ-04)

• Demo video (2–4 min): real friend's product, real phone snap → three scene presets → variation grid → "the photo they'll actually post." Tell the seller's story.
• Social post: side-by-side before/after + Space link; link it back from the README.

8. Risks & mitigations

Risk	Mitigation
Product identity drifts during edit	Prompt the change only; keep edits restrained; expose a strength control; offer base-4B/50-step "High quality".
Hallucinated text/labels on packaging	PROMPTING.md: klein text is unreliable — avoid label-dependent products in the demo; add "no text, no logos" to prompts.
ZeroGPU cold start / 75s budget	Keep the starter's module-scope CPU build; distilled 4-step default; @GPU(duration=75).
Deadline today	MVP = upload + scenes + 4-grid. Brand LoRA + aspect presets are stretch.

9. Submission checklist (REQ-01 → REQ-06)

• REQ-01 Every model ≤32B — klein 4B (+ optional small LoRA). ✅ (≤4B → Tiny Titan)
• REQ-02 Gradio Space uploaded into the Build Small HF org.
• REQ-03 Demo video recorded and linked.
• REQ-04 One social post, linked from README.
• REQ-05 ≤10 ZeroGPU apps for this user.
• REQ-06 README YAML tagged + idea/tech write-up.

README frontmatter to apply at submission (REQ-06):

title: Shopfront — Product Photo Studio
short_description: Turn phone snaps into clean product photos on klein 4B
sdk: gradio
app_file: app.py
license: apache-2.0
suggested_hardware: zero-a10g
models:
  - black-forest-labs/FLUX.2-klein-4B
  - black-forest-labs/FLUX.2-klein-base-4B
tags:
  - build-small-hackathon
  - backyard-ai          # track
  - tiny-titan           # ≤4B badge
  - off-brand            # custom UI badge
  - best-demo            # badge
  - flux
  - image-to-image

10. Definition of done

• The Space loads on ZeroGPU (no token), accepts a product photo, and returns a re-staged result for at least 3 scene presets, plus a 4-variation grid.
• Before → after is obvious in one screen.
• README has track + badge tags, an idea/tech write-up, and links to the demo video and social post.
• Submitted into the Build Small org before 23:59 UTC.