88 lines
3.5 KiB
Markdown
88 lines
3.5 KiB
Markdown
# 05 — Quality Improvement Levers
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Three levers control FLUX output quality, in order of impact:
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## 1. Prompt (highest impact, zero cost)
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Incoherent objects in the frame are almost always prompt bleed — the model fills
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empty or ambiguous space with training-data defaults. Fix by naming every part of
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the frame explicitly.
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**Background** — name it, don't imply it:
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- Bad: "living room" (model invents furniture, decor, wall art)
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- Good: "plain cream painted wall with a single frosted sliding glass door"
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**Floor material** — always explicit:
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- "plush cream berber carpet" or "light oak hardwood floor"
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- Ambiguous floor → random floor type generated
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**Ceiling** — if visible, name it; if not wanted, push it out of frame with a
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lower camera angle:
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- "white drop ceiling with recessed can lights"
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- Or: lower the angle until ceiling exits the frame entirely
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**Negative scene elements** — add inline, not as a separate negative prompt
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(FLUX Schnell ignores negative prompts):
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- "no furniture clutter, no decorative objects, no picture frames, no signage"
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- "no cleaning equipment, no machines, no people"
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**What not to use:**
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- "wide shot" without a camera angle qualifier — produces flat frontal views
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- Vague room names ("office", "lobby") without specifying what fills the space
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## 2. Steps (marginal gain, 2x slower)
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FLUX Schnell is distilled to 4 steps. The distillation process compresses
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a full diffusion model's quality into very few steps.
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| Steps | Quality change | Time impact |
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|---|---|---|
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| 4 (default) | Baseline | ~4 min/image |
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| 6 | Slightly sharper edges, cleaner fine detail | ~6 min/image |
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| 8 | Diminishing returns past 6 | ~8 min/image |
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Not recommended as a first fix. The distillation ceiling is the constraint,
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not step count. Step increases help texture detail but will not fix scene
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incoherence — that requires prompt changes.
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KSampler in `gen-images-flux.py`:
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```python
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"steps": 4, # increase to 6 for detail passes
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```
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## 3. Model size (real quality jump, 6x slower on CPU)
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| Model | Steps | Quality | CPU time/image |
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|---|---|---|---|
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| FLUX.1 Schnell (current) | 4 | Good depth, some coherence gaps | ~4 min |
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| FLUX.1 Dev (full, non-distilled) | 20-30 | Better coherence, sharper geometry | ~20-30 min |
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FLUX Dev would fix most coherence issues. At current CPU-only speed (2GB VRAM
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insufficient), a full 28-image batch would take 9+ hours.
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**Practical path to FLUX Dev:**
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- Cloud GPU: RunPod or Vast.ai A100 runs FLUX Dev in ~90 seconds/image
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- Same prompts, same ComfyUI workflow — only model file and step count change
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- Switch `flux1-schnell-Q8_0.gguf` → FLUX Dev GGUF, set steps to 20, cfg to 3.5
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## Decision matrix
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| Issue | Fix |
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| Objects that shouldn't be in frame | Prompt: name every surface explicitly |
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| Wrong floor/wall material | Prompt: be specific about material |
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| Flat angle despite prompt | Prompt: add "low-angle", lens mm, "foreground sharp" |
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| Soft edges on carpet fibers | Steps: increase 4 → 6 |
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| Incoherent room geometry | Model: switch to FLUX Dev on cloud GPU |
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| Overall composition wrong | Prompt: camera position + lens + foreground/bokeh split |
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## Re-running specific images
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To re-run only the problem frames without regenerating all 28:
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1. Edit `tools/gen-images-flux.py`
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2. Change the `IMAGES` list to include only the failed image keys
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3. Run: `python3 tools/gen-images-flux.py 2>&1 | tee tools/flux-gen.log`
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4. Run: `python3 tools/convert-to-webp.py` (converts only new JPGs)
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5. Rebuild: `docker compose build --no-cache web && docker compose up -d`
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