Fal AI Generation

fal.ai Generation

Direct access to fal.ai's full model catalog (1,000+ models) using the user's own API key. The built-in image_generate tool routes through the Nous gateway and is pinned to whatever model the user configured (currently FLUX 2 Klein — a generalist that leans illustration/render, weak for strict anime). For anything model-specific, call fal.ai directly.

Auth

FAL_KEY lives in ~/.hermes/.env (line FAL_KEY=...). Parse it like ~/cheonma/scripts/fal_generate.py does. Header: Authorization: Key <FAL_KEY>.

Core call pattern (no SDK needed, stdlib only)

1. POST https://fal.run/<model-id> with JSON payload (model-specific params).
2. Response is usually the finished result synchronously (image gens ~10–60s). If it returns request_id instead, poll GET https://fal.run/<model-id>/requests/<id> until status == COMPLETED.
3. Result media are CDN URLs under *.fal.media — download to ~/Downloads/ (user convention: VisualOutput→SaveToDownloads).
4. Always record and report the returned seed for reproducibility.

Use scripts/fal_txt2img.py in this skill for text-to-image instead of hand-writing the HTTP each time.

Workflow

1. Survey models first — the user expects a shortlist with a recommendation before rendering, not an immediate generate. Present 3–5 candidates with a one-line why/why-not each (see references/model-catalog.md).
2. Confirm or pick the top recommendation, then render.
3. QA the output with vision before presenting; flag missed traits honestly and offer targeted re-rolls (same seed for composition, fresh seed for variety, or batch of 4).

Model selection (quick map — full notes in references/model-catalog.md)

• Anime / stylized illustration: fal-ai/bytedance/seedream/v4.5/text-to-image (top pick — excellent multi-trait prompt adherence, ~$0.04/img, ~17s) > Z-Image Turbo (fast/cheap, weaker detail) > Qwen-Image > Nano Banana Pro (polished digital-art lean, less "anime").
• Anime via LoRA: fal-ai/krea-2/turbo/lora or fal-ai/flux-lora with a style LoRA. User's curated LoRA skins (incl. open-sky-anime) live in the SKINS dict of ~/cheonma/scripts/fal_generate.py.
• Photoreal / flagship: FLUX.1/2 Pro, GPT-Image 1.5, Imagen 4.
• Multi-panel layouts / character sheets / structured documents: Nano Banana Pro (top pick — best instruction-following for complex layouts) > GPT-Image 1.5 (best text) > Seedream 4.5 (cheapest). See references/character-sheet-model-rankings.md.
• Editing: FLUX Kontext Pro, Nano Banana 2 Edit, Seedream (unified gen+edit).
• Upscale / bg removal / caption: SeedVR2, Topaz, ESRGAN / BiRefNet, Bria RMBG / Florence-2.
• Video / audio: Veo 3.1, Kling, Sora 2, LTX-2 / Chatterbox, MiniMax Speech, ElevenLabs Music (per-second or per-video billing).
• Text-to-music (score a video edit): fal-ai/elevenlabs/music via FAL_KEY — no local GPU needed (audiocraft/heartmula need CUDA). Queue endpoint, force_instrumental, $0.80/output-minute rounded up. Full schema, pricing, action-sports build→breakdown→drop prompting, and scripts/fal_music.py in references/elevenlabs-music.md. Generate the track FIRST, then energy-map it (see the ai-video-generation skill).

Model performance for character rendering (session-verified 2026-07)

Tested models: FLUX Pro Ultra v1.1, Ideogram V3, Recraft V4, Seedream 4.5, GPT-Image 1.5, Nano Banana Pro, FLUX.2 Pro, Recraft V4 Pro

Character sheet 6-model shootout (2026-07-29):

1. 🥇 Nano Banana Pro — 8/10 (complete layout, clean text, strong consistency, 25s)
2. 🥈 GPT-Image 1.5 — 7.5/10 (best text, missing eye panel, 52s)
3. 🥉 Seedream 4.5 — 7.5/10 (complete but typos + hair conflicts, 25s)
4. Recraft V4 Pro — 7/10 (garbled label, 3 conflicting sword designs, 24s)
5. FLUX.2 Pro — 6.5/10 (ignored multi-panel brief, only front/back + palette, 34s)
6. Ideogram V3 — 5.5/10 (garbled captions, missing palette/stats, 16s)

Key insight: Strong single-image models (FLUX.2 Pro, Ideogram V3) fail at structured multi-panel layouts. Nano Banana Pro's instruction-following is what makes it win for character sheets. Recommended workflow: draft with Seedream (cheap) → finalize with Nano Banana Pro or GPT-Image 1.5.

Group shot reality: 5+ distinct characters in one frame is hard for most models. Seedream was the only one that got all 5 identifiable. FLUX and Ideogram dropped to 4 or merged characters. Consider per-character renders + composite if strict accuracy needed.

Payload format differences (verified endpoints)

Each model uses different parameter names — don't assume uniformity:

# FLUX Pro Ultra v1.1
{"prompt": "...", "image_size": {"width": 1024, "height": 1536}, "num_images": 1, "scheduler": "k_euler"}

# Ideogram V3
{"prompt": "...", "resolution": "1024x1536"}  # string format, not dict

# Recraft V4 / V4 Pro
{"prompt": "...", "image_size": {"width": 1024, "height": 1536}}  # dict format

# Seedream 4.5
{"prompt": "...", "image_size": {"width": 1024, "height": 1536}}  # dict format

# GPT-Image 1.5
{"prompt": "...", "image_size": "1536x1024"}  # STRING, not dict! Only accepts: "1024x1024", "1536x1024", "1024x1536"

# Nano Banana Pro
{"prompt": "...", "image_size": {"width": 1536, "height": 1024}}  # dict format

# FLUX.2 Pro
{"prompt": "...", "image_size": {"width": 1536, "height": 1024}}  # dict format

Endpoint patterns:

• fal-ai/flux-pro/v1.1-ultra ✅
• fal-ai/ideogram/v3 ✅ (NOT /v3/text-to-image)
• fal-ai/recraft/v4/text-to-image ✅ (NOT /v4)
• fal-ai/recraft/v4/pro/text-to-image ✅
• fal-ai/bytedance/seedream/v4.5/text-to-image ✅
• fal-ai/gpt-image-1.5 ✅ — 422 if image_size is a dict; must be string
• fal-ai/nano-banana-pro ✅
• fal-ai/flux-2-pro ✅

Batch rendering workflow

For 10+ images, foreground execute_code hits the 300s timeout. Use background process:

# Write script to /tmp, run with background=true + notify_on_complete=true
terminal("python3 /tmp/batch_render.py", background=True, notify_on_complete=True, timeout=600)

Typical throughput: ~15-20s per image. 24 images ≈ 6-8 minutes total.

Troubleshooting Authentication & 403 Errors

When encountering 403 Forbidden errors during image generation:

0. Read the error body FIRST — a 403 is not always a key problem. If the JSON detail reads "User is locked. Reason: Exhausted balance. Top up your balance at fal.ai/dashboard/billing.", the account BALANCE is empty — the key is valid and nothing is misconfigured. Diagnose with a tiny probe (a minimal payload returns the JSON detail instantly, no generation cost):

   curl -s -X POST "https://fal.run/fal-ai/bytedance/seedream/v4.5/text-to-image" \
     -H "Authorization: Key $(grep '^FAL_KEY' ~/.hermes/.env | cut -d'=' -f2-)" \
     -H "Content-Type: application/json" -d '{"prompt":"x","num_images":1}'
   

   On exhausted balance: don't burn retries (the lock is account-wide and persists until billing is resolved at fal.ai/dashboard/billing). Route work elsewhere — Suno web UI for music, local ComfyUI/HeartMuLa, or the gateway image_generate tool. The gateway image_generate bills through the Nous subscription, NOT the FAL_KEY balance, so it keeps working for image work when direct fal.run calls are locked. Ask the user to top up before relying on fal.ai again.

1. Verify API Key Scope: If the detail is NOT an exhausted-balance lock, ensure your FAL_KEY has access to the specific model you're using. Check https://fal.ai/docs/api-reference/platform-apis/authentication for required scopes.

2. Test Key Validity: Run this diagnostic command:

   curl -H "Authorization: Key $(grep '^FAL_KEY' ~/.hermes/.env | cut -d'=' -f2-)" https://fal.run/fal-ai/bytedance/seedream/v4.5/text-to-image -I
   

   Should return 200 OK. A 403 means the key lacks model access.

3. Model-Specific Access: Some models require additional permissions (e.g., "image generation" scope). Check the model's documentation on fal.ai.

4. Common Fixes:

   • Regenerate API key with proper scopes
   • Wait 5-10 minutes for key propagation
   • Use fallback models that don't require premium access
   • Check for key expiration (fal.ai keys don't expire but can be revoked)

5. Debug Workflow:

   • First: Check ~/.hermes/.env for valid FAL_KEY
   • Second: Test key with curl command above
   • Third: Try model access verification via browser at https://fal.ai/models
   • Fourth: Consult skill's references/model-catalog.md for model-specific permissions

Character sheet / reference sheet workflow

User keeps prompt templates at ~/character designs/ (original ChatGPT template + fal-optimized version). Test renders go under ~/character designs/model-test-*/ with filenames labeled by model.

Prompt adaptation for fal.ai models:

• Front-load layout structure — early tokens carry more weight on fal models
• Convert negation to positive directives ("show X" not "no Y") — Seedream especially mishandles negation
• Use explicit spatial language ("top left", "stacked vertically", "side by side") for panel placement
• Consolidate redundant quality tokens ("8k" + "ultra-high detail" → pick one; dilution hurts)

Recommended sizes: Landscape sheet 1536×1024 or 1920×1440; portrait 1440×1920; square 1536×1536.

3-model comparison test: Use scripts/character_sheet_test.py — renders the same prompt on GPT-Image 1.5, Ideogram V3, and Seedream 4.5 sequentially, saves labeled PNGs to an output dir. Run via background terminal (~2–3 min total).

See references/character-sheet-prompts.md for the full original and optimized prompt templates.

Prompt-Craft Pitfalls (Updated)

• Body types need explicit weight language. "Plump" alone collapses to standard anime proportions — write "soft round belly, full hips, thick thighs, plump figure" and avoid hourglass-implying words. (Confirmed: Seedream 4.5 rendered "plump" as merely voluptuous without this.)
• Multi-trait specificity works on Seedream 4.5 — split-tone hair ("left half jet black, right half snow white") and heterochromia ("one amber-orange eye, one sapphire-blue eye") landed cleanly when spelled out spatially.
• Keep suggestive-but-tasteful phrasing ("draped off shoulders, tastefully hinting at curves") to stay inside safety checkers; Seedream's enable_safety_checker defaults true.
• Seedream image_size accepts {"width": W, "height": H} in the 1280–4096 range per side; 1440×1920 is a good portrait default (2.8MP).

Support files

• references/model-catalog.md — condensed fal.ai coverage: top models per category, calling patterns (run/subscribe/submit/stream/realtime), pricing normalization.
• references/character-sheet-model-rankings.md — 6-model shootout results for character sheets: per-model QA notes, scores, payload quirks, recommended workflow.
• references/character-sheet-prompts.md — original ChatGPT character sheet prompt + fal-optimized variant with model-specific notes and recommended sizes.
• scripts/fal_txt2img.py — CLI: --model --prompt --width --height --seed --out; handles sync result or request_id polling; downloads image.
• scripts/character_sheet_test.py — 3-model comparison runner (GPT-Image 1.5, Ideogram V3, Seedream 4.5) for character sheets; saves labeled PNGs to an output dir.
• scripts/fal_music.py — CLI text-to-music via fal-ai/elevenlabs/music (queue submit→poll→download): --prompt --length-ms --out --instrumental --format. See references/elevenlabs-music.md.

Related skills

Sibling provider umbrellas: comfyui (local/cloud ComfyUI pipelines), higgsfield-generate (Higgsfield credits/MCP). Route: quick gateway render → built-in image_generate; specific fal.ai model or LoRA → this skill; full pipeline control → comfyui; Higgsfield account features → higgsfield-*.