Output:
invoke_url 102350
qwen/image -1
qwen-image 14983
bearer 102468
NVIDIA_API_KEY 102476
https://images.nvidia.com/aem-dam/Solutions/ot-js/ot-custom.js
https://brev.nvidia.com/environment/new/public
https://docs.api.nvidia.com/
https://brev.nvidia.com/environment/new/public
https://docs.api.nvidia.com/
https://images.nvidia.com/aem-dam/Solutions/ot-js/ot-custom.js
https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA_Technology_Access_TOU.pdf
https://www.nvidia.com/en-us/about-nvidia/privacy-policy/
https://www.nvidia.com/en-us/about-nvidia/privacy-center/
https://developer.nvidia.com/contact
https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA_Technology_Access_TOU.pdf
https://www.nvidia.com/en-us/about-nvidia/privacy-policy/
https://www.nvidia.com/en-us/about-nvidia/privacy-center/
https://developer.nvidia.com/contact
https://brev.nvidia.com/environment/new/public
https://docs.api.nvidia.com/
https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA_Technology_Access_TOU.pdf
https://www.nvidia.com/en-us/about-nvidia/privacy-policy/
https://www.nvidia.com/en-us/about-nvidia/privacy-center/
https://developer.nvidia.com/contact
https://build.nvidia.com/qwen/qwen-image
https://build.nvidia.com/qwen/qwen-image
https://assets.ngc.nvidia.com/products/api-catalog/images/qwen-image.jpg
https://build.nvidia.com/twitter-image.jpg
https://build.nvidia.com/qwen/qwen-image
https://build.nvidia.com/qwen/qwen-image
https://assets.ngc.nvidia.com/products/api-catalog/images/qwen-image.jpg
https://build.nvidia.com/twitter-image.jpg
https://assets.ngc.nvidia.com/products/api-catalog/images/qwen-image.jpg
https://www.nvidia.com/en-us/ai/nim-notifyme/
https://catalog.ngc.nvidia.com/orgs/nim/teams/qwen/containers/qwen-image
https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf); and use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/e
https://build.nvidia.com/qwen/qwen-image](https://build.nvidia.com/qwen/qwen-image)
https://huggingface.co/nvidia/Cosmos-1.0-Guardrail).
https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
https://docs.nvidia.com/nim/visual-genai/latest/overview.html) for release documentation, deployment guides and more.
https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).
https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev'
https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev
https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev
https://docs.nvidia.com/nim/visual-genai/latest/getting-started.html).
https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nvidia.com%2Fen-us%2Fagreements%2Fenterprise-software%2Fnvidia-software-license-agreement%2F
https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nvidia.com%2Fen-us%2Fagreements%2Fenterprise-software%2Fproduct-specific-terms-for-ai-products%2F
https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nvidia.com%2Fen-us%2Fagreements%2Fenterprise-software%2Fnvidia-open-model-license%2F
https://assets.ngc.nvidia.com/products/api-catalog/images/qwen-image.jpg
https://help.nvidia.com/
https://docs.api.nvidia.com/nim for more details.
https://nvidia.com/legal/terms-of-use
https://ai.api.nvidia.com/v1/
https://assets.ngc.nvidia.com/products/api-catalog/flux/base/output/1.jpg
https://assets.ngc.nvidia.com/products/api-catalog/flux/base/output/2.jpg
https://assets.ngc.nvidia.com/products/api-catalog/flux/base/output/3.jpg
https://assets.ngc.nvidia.com/products/api-catalog/flux/base/output/4.jpg
https://assets.ngc.nvidia.com/products/api-catalog/flux/canny/input/1.jpg
https://assets.ngc.nvidia.com/products/api-catalog/flux/canny/output/1.jpg
https://assets.ngc.nvidia.com/products/api-catalog/flux/canny/input/1.jpg
https://assets.ngc.nvidia.com/products/api-catalog/flux/canny/output/2.jpg
https://assets.ngc.nvidia.com/products/api-catalog/flux/canny/input/1.jpg
https://assets.ngc.nvidia.com/products/api-catalog/flux/canny/output/3.jpg
https://assets.ngc.nvidia.com/products/api-catalog/flux/depth/input/1.jpg
https://assets.ngc.nvidia.com/products/api-catalog/flux/depth/output/1.jpg
https://assets.ngc.nvidia.com/products/api-catalog/flux/depth/input/3.jpg
https://assets.ngc.nvidia.com/products/api-catalog/flux/depth/output/3.jpg
https://assets.ngc.nvidia.com/products/api-catalog/flux/depth/input/4.jpg
https://assets.ngc.nvidia.com/products/api-catalog/flux/depth/output/4.jpg
https://docs.nvidia.com/nim/visual-genai/latest/getting-started.html
https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf
https://docs.nvidia.com/ai-foundation-models-community-license.pdf
https://docs.nvidia.com/nim/visual-genai/latest/getting-started.html
https://www.nvidia.com/en-us/data-center/dgx-cloud/
https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf
https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
https://modelscope.cn/models/Qwen/Qwen-Image-2512

build.nvidia.com April 30, 2026 via <a target=
https://build.nvidia.com/qwen/qwen-image
https://build.nvidia.com/qwen/qwen-image

<h2 class=
https://huggingface.co/nvidia/Cosmos-1.0-Guardrail
https://www.nvidia.com/en-us/support/submit-security-vulnerability/
https://docs.nvidia.com/nim/visual-genai/latest/overview.html
https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/


Explanation:
**Reviewing flux URL**

I think I need to check the HTML for the correct flux URL since it seems like there might be a problem with it. It looks like I need to extract the exact information surrounding the invoke_url to get it right. Let's make sure everything is accurate to avoid any issues down the line. It's all about getting the details straightened out, so I can proceed smoothly!

terminal

python - <<'PY'
import requests
html=requests.get('https://build.nvidia.com/qwen/qwen-image',timeout=20).text
i=html.find('invoke_url')
print(html[i-1000:i+2000])
PY


Output: d will not blur or maintain proportions of image subjects included.\n\nUsers are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n# Get Help\n\n## Getting started with the NIM\nDeploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the [Visual Generative AI NIM page](https://docs.nvidia.com/nim/visual-genai/latest/overview.html) for release documentation, deployment guides and more.\n\n## Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/)."])</script><script>self.__next_f.push([1,"39:T557,"])</script><script>self.__next_f.push([1,"invoke_url='https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev'\n\nauthorization_header=\"Authorization: Bearer $NVIDI...ept: application/json'\ncontent_type_header='Content-Type: application/json'\n\ndata='{\n  \"prompt\": \u003c%- JSON.stringify(request.prompt).replaceAll('\\'', '\\'\\\\\\'\\'') %\u003e,\n  \u003c% if (request.mode !== \"base\") { %\u003e\"mode\": \u003c%- JSON.stringify(request.mode) %\u003e,\n  \"image\": \"\u003c%- request.image %\u003e\",\u003c% } else { %\u003e\"mode\": \u003c%- JSON.stringify(request.mode) %\u003e,\u003c% } %\u003e\n  \"cfg_scale\": \u003c%- request.cfg_scale %\u003e,\n  \u003c% switch(request.ratio) {\n    case \"16:9\": %\u003e\"width\": 768,\n  \"height\": 1344,\u003c% break; \n    case \"9:16\": %\u003e\"width\": 1344,\n  \"height\": 768,\u003c% break;\n    case \"5:4\": %\u003e\"width\": 896,\n  \"height\": 1152,\u003c% break;\n    case \"4:5\": %\u003e\"width\": 1152,\n  \"height\": 896,\u003c% break;\n    case \"3:2\": %\u003e\"width\": 832,\n  \"height\": 1216,\u003c% break;\n    case \"2:3\": %\u003e\"width\": 1216,\n  \"height\": 832,\u003c% break;\n    case \"1:1\":\n    default: %\u003e\"width\": 1024,\n  \"height\": 1024,\u003c% break; } %\u003e\n  \"seed\": \u003c%- request.seed %\u003e,\n  \"steps\": \u003c%- request.steps %\u003e\n}'\n\nresponse=$(curl --silent -i -w \"\\n%{http_code}\" --request POST \\\n  --url \"$invoke_url\" \\\n  --header \"$authorization_header\" \\\n  --header \"$accept_header\" \\\n  --header \"$content_type_header\" \\\n  --data \"$data\"\n)\n\nhttp_code=$(echo \"$response\" | tail -n 1)\n\necho \"$response\" | awk '/{/,EOF-1'\n"])</script><script>self.__next_f.push([1,"3a:T5c9,"])</script><script>self.__next_f.push([1,"import fetch from \"node-fetch\";\n\nconst invokeUrl = \"https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev\"\n\nconst headers = {\n    \"Authorization\": \"Bearer $NVIDIA_API_KEY\",\n    \"Accept\": \"application/


python - <<'PY'
import requests,re
html=requests.get('https://build.nvidia.com/qwen/qwen-image',timeout=20).text
for m in re.finditer(r'ai\.api\.nvidia\.com/v1/genai/[^\'"\\ ]+', html):
    print(m.group(0)[:160])
PY


Output:
ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev
ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev
ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev


Ja, Sir — **NVIDIA/Qwen-Image ist genau der richtige Weg** für diese Keyframes.

## Warum ich gerade keine Bilder „direkt“ generieren konnte

Kurzfassung:

- **Codex ≠ Bildgenerator.**
  Codex kann Code schreiben, Dateien bearbeiten, APIs aufrufen und Pipelines bauen — aber es hat nicht automatisch Zugriff auf OpenAI Images oder irgendeinen Bildgenerator.

- Das Hermes-Tool `image_generate` ist bei uns offenbar auf einen Backend-Provider konfiguriert, der aktuell **FAL** erwartet.

- In der laufenden Umgebung fehlt aber:

```text
FAL_KEY
```

- Ich habe außerdem geprüft, ob ein NVIDIA-Key für diesen Hermes-Prozess sichtbar ist. Aktuell sichtbar:

```text
NVIDIA_API_KEY: absent
NGC_API_KEY: absent
NVIDIA_NIM_API_KEY: absent
FAL_KEY: absent
```

Heisst: Der Key mag bei dir im NVIDIA-Account / Browser / Build-Portal hinterlegt sein, aber **nicht in dieser Hermes/Gateway-Umgebung**. Klassischer „ist hinterlegt, aber nicht da wo der Roboter die Finger hat“-Fall. Sehr 2026.

## NVIDIA Qwen-Image verwenden?

Ja. Ich würde das sogar bevorzugen.

Für unsere Pipeline ist Qwen-Image über NVIDIA API sinnvoll, weil wir genau diese Art Assets brauchen:

- fotorealistische 9:16 Keyframes
- keine Texte / Logos / Kennzeichen
- kontrollierte Komposition
- danach Wan2.2 I2V als Animation
- Renderer macht Captions/Zahlen/Overlays deterministisch

Also:

```text
Qwen-Image / NVIDIA API
→ scene_01.png / scene_02.png
→ Wan2.2 I2V
→ Clip Selection
→ Hybrid Renderer
```

Das ist sauberer als reparierte Stock-/Web-Bilder.

## Was wir dafür brauchen

Der Key muss für Hermes lokal verfügbar sein, idealerweise als:

```bash
NVIDIA_API_KEY=...
```

in:

```text
/home/agent/.hermes/.env
```

oder als Secret-Datei, z.B.:

```text
~/.hermes/secrets/nvidia_api_key
```

Wichtig: **Key nicht in Telegram posten.**
Am besten lokal in `.env` oder Secret-Datei eintragen, dann Gateway neu starten bzw. Env reloaden.

## Nächster technischer Schritt

Ich würde jetzt im AutoShortsBot ein eigenes Modul bauen:

```text
autoshorts/nvidia_image_client.py
scripts/generate_keyframes_nvidia.py
tests/test_nvidia_image_client.py
```

Funktion:

```text
Keyframe Request Package
→ NVIDIA Qwen-Image API
→ PNG speichern unter data/keyframes/...
→ Acceptance Checks
→ danach I2V Runner starten
```

Dann ist Bildgenerierung nicht mehr manuell oder improvisiert, sondern Teil der Produktionspipeline.

## Kleine Warnung

Die NVIDIA Build-Seite für `qwen/qwen-image` zeigt in ihrem eingebetteten Beispiel aktuell teilweise noch einen `flux.1-dev` Invoke-URL-Snippet. Das muss ich beim Implementieren sauber verifizieren, statt blind zu kopieren.

Vermutete Richtung:

```text
https://ai.api.nvidia.com/v1/genai/qwen/qwen-image
```

Aber ich würde den Adapter so bauen, dass Endpoint/Model konfigurierbar ist.

## Meine Empfehlung

Ja: **NVIDIA Qwen-Image als Standard-Keyframe-Generator verwenden.**

Damit ersetzen wir den bisherigen Notbehelf:

```text
Web/Stock-Bild reparieren
```

durch:

```text
sauber generierter Keyframe → Wan2.2 I2V
```

Das ist für den Qualitätsanspruch deutlich besser.


[M G] NVIDIA API Key abgelegt unter: ~/.hermes/secrets/nvidia_api_key
- Format: NVIDIA_API_KEY=...
- Rechte gesetzt: 0600 . ok, bau ketzt weiter. teste ob die Imagegeneration funktioniert, oder wechsel anderenfalls das model. auf build.nvidia.com sind verschiedene verfügbar. https://build.nvidia.com/stabilityai/stable-diffusion-3_5-large/deploy oder eben https://build.nvidia.com/qwen/qwen-image . aber muss man dieses selber hosten?


Explanation:
**Looking into relevant skills**

I need to take action and think about which skills to load. Maybe I should load the image generation skill since Hermes is already loaded. The social shorts skill could also be relevant. I should inspect the repository to check what’s available and determine the best tools to use. Let’s make sure I’m thorough in figuring this out! It’s important to get the right resources ready for the task.


social-media-shorts-automation

---
name: social-media-shorts-automation
description: Design, generate, review, publish, and optimize automated short-form social videos across YouTube Shorts, TikTok, Instagram Reels, and related channels.
version: 1.0.0
tags: [shorts, tiktok, youtube-shorts, reels, creator-monetization, content-automation, social-media]
---

# Social-Media Shorts Automation

Use this skill when building or operating systems that generate short-form social videos, especially faceless or semi-automated pipelines for YouTube Shorts, TikTok, Instagram Reels, Facebook Reels, Pinterest video pins, X video, or LinkedIn video.

## Core principle

Treat the system as a **Shorts Growth Engine** / **semi-automatic Retention Studio**, not merely a renderer. A working video generator can still fail if it produces generic, low-retention, AI-slop content. Optimize for audience identity, hook quality, retention mechanics, visual mechanism reveals, platform fit, and analytics feedback. For the user's strongest strategic direction, see `references/retention-studio-strategy.md`.

## Retention-Studio / Mechanism-Reveal Pipeline

For quality-first automated Shorts, especially Hidden Money & AI Systems concepts, prefer the pipeline documented in `references/retention-studio-pipeline-pattern.md`:

```text
strategy → scored ideas → mechanism explainer → shot plan → keyframe/I2V prompt plan → seeded clip jobs → clip review → deterministic final renderer
```

Hard rule for this class: AI-video clips provide photorealistic texture and motion only. Captions, numbers, labels, arrows, UI text, price/risk/margin layers, and factual claims are renderer-owned. Final content should keep `static_card_limit = 0`, use visual changes every 1–2 seconds, and reject technically successful clips that do not serve the retention beat.

## Recommended workflow

1. **Define channel position**
   - Niche(s): e.g. life optimization, AI life systems, Christian faith/FaithTok.
   - Target audience and emotional problem.
   - Language and tone.
   - Whether the brand is faceless, AI-persona-led, or creator-led.
   - No-go topics and theological/ethical boundaries if faith-related.

2. **Generate test ideas before code-heavy automation**
   - Create 20–30 candidate ideas across 2–4 content pillars.
   - For each: hook, script kernel, visual concept, caption, CTA, risk/quality score.
   - Select a small first batch (e.g. 3 per pillar) for real testing.

3. **Use hook-first, retention-led scripting**
   - First 1–2 seconds must contain a clear tension, identity statement, pain point, contrarian line, or immediate self-recognition.
   - Prefer one idea per video over generic “4 tips” unless the list format is the explicit series.
   - Avoid generic advice viewers have already heard unless framed with a new angle, concrete payoff, or strong story.
   - Reward hooks with direct address, specificity, open loops, tension, and a visible payoff; reject slow intros such as “In this video...”.
   - See `references/retention-led-shorts.md` for scoring signals, penalties, and voiceover finalization rules.

4. **Quality gate before rendering**
   Score drafts on:
   - hook strength
   - novelty
   - emotional pull
   - clarity
   - retention risk
   - comment/share potential
   - AI-slop risk
   - platform/policy risk
   Render only drafts above the chosen threshold, e.g. 8/10.

5. **Visual strategy**
   - Avoid obvious AI artifacts, fake UI text, illegible generated lettering, generic charts, and soulless stock visuals.
   - Prefer high-contrast kinetic captions, visible hook text in first seconds, relatable B-roll, screen recordings for AI/tutorial content, phone/screen close-ups, before/after transformations, cards, and timer visuals.
   - Use pattern interrupts every 2–3 seconds for retention.
   - Before rendering, generate a shot plan from the finalized voiceover: first-frame hook, pattern interrupt, core explanation, screen/card visual, CTA, caption beats, and asset type.
   - Before writing a renderer, convert shot plans into deterministic render manifests: machine-readable timeline items, asset placeholders, caption tracks, style constraints, and approval constraints. See `references/render-manifest-pipeline.md`.
   - Treat this as a deterministic intermediate artifact, not optional prose. See `references/retention-render-planning.md` for the five-scene shot-plan pattern and renderer-facing fields.
   - For the first video output, build a manifest-driven preview renderer before any AI-video or polished asset pipeline: render vertical scene/card frames from manifest fields, assemble them with FFmpeg, then use the MP4 for timing/approval review. See `references/manifest-preview-renderer.md`.
   - When the user rejects static/slideshow output, add an `asset_plan.v2` layer that maps each scene to motion-led local asset modes such as `cinematic_ai_video`, `screen_recording_simulation`, `mechanism_animation`, or `kinetic_text_overlay` with no baked text. If the user requires a free workflow, do not depend on paid video-credit tools; use local procedural motion and optional free NVIDIA LLM text refinement. See `references/ai-video-asset-planning.md`.
   - For hybrid workflows, add an imported-clip assembly layer: provider handoff prompts create short manual AI-video clips, humans drop them into deterministic `data/imported_ai_clips/<slug>/scene_XX.mp4` paths, and the final renderer combines imported clips with local mechanism animations/captions. See `references/imported-ai-clip-assembly.md`.
   - For local Wan2.2 quality workflows, prefer keyframe/image-to-video over raw text-to-video: generate/select a clean keyframe, run native Wan2.2 I2V variants, review clips bluntly for subject clarity/motion/artifacts, then add all labels/numbers/captions via deterministic renderer. See `references/wan-i2v-retention-studio.md`.

6. **Human approval loop before posting**
   - Send preview to the user through the requested channel, usually Telegram.
   - Include: video, title, caption, hashtags, platform recommendation, duration, quality/risk score.
   - Wait for explicit approval such as `Freigabe alle`, `Freigabe YouTube`, `Freigabe TikTok`, `Ablehnen`, or `Ändern: ...`.
   - Do not publish automatically unless the user has explicitly authorized that workflow.

7. **Publish and measure**
   Track at minimum:
   - platform, video id/url, niche, format, hook, duration
   - views at 2h/24h/7d
   - likes, comments, shares, saves, follows
   - retention metrics if available
   Use results to generate the next batch.

## Platform duration/monetization notes

See `references/monetization-and-approval-notes.md` for current working notes captured from official YouTube/TikTok docs and the recommended approval/posting setup. See `references/retention-led-shorts.md` for retention scoring signals, no-faith/adjacent-pillar handling, and voiceover finalization rules. See `references/render-manifest-pipeline.md` for the script → shot plan → render manifest → renderer sequence that prevents generic AI slideshow output. See `references/manifest-preview-renderer.md` for the first deterministic Pillow/FFmpeg preview-renderer pattern. See `references/ai-video-asset-planning.md` for motion-led final-render asset planning with Kling/Hailuo/Runway-style clips and screen simulations. See `references/imported-ai-clip-assembly.md` for hybrid assembly where manually generated provider clips are imported into a deterministic local renderer. Re-check official docs before making business-critical decisions because platform programs change often.

## Project/repo hygiene for automated video bots

- Keep source code in Git; keep generated videos, assets, `.env`, OAuth tokens, API keys, and downloaded secrets out of Git.
- Use `.gitignore` aggressively for outputs, media caches, local credentials, and temp files.
- Store platform credentials as runtime secrets; never print raw tokens.
- Build the repo as modular components: idea generation, scripting, rendering, review, publishing, analytics.

## Sensitive-content positioning

When AI/productivity automation is the primary brand, be cautious about mixing in religious/FaithTok, health, finance, or other high-trust themes. If the user flags the combination as sensitive, pause that pillar rather than forcing it into the same testbatch. Replace it with a safer adjacent pillar (for example `attention_design`) and document the paused pillar in config so it can be revisited only with explicit approval and a separate brand strategy.

## Pitfalls

- Do not over-optimize renderer quality before testing whether the content format earns retention.
- Do not assume TikTok monetization rules match YouTube: TikTok Creator Rewards emphasizes 1min+ eligible videos, while shorter videos may still be better for growth.
- Do not publish religious or health-adjacent content with overconfident claims; preserve humility, nuance, and platform safety.
- Do not rely on platform payout as the only monetization path; plan indirect monetization such as newsletter, templates, affiliates, digital products, or services.

To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'


Explanation:
**Considering hosting and testing**

I need to find an answer about hosting while also focusing on building and testing. First, I’ll inspect the API docs because that’s crucial. I need to use the key but remember not to print it. I think searching the web for a page that might include snippets for sd3.5 and Qwen would be helpful. Plus, gathering some code is essential. Alright, time to start my todo list!

todo

NVIDIA API-Key/Endpoints ohne Secret-Ausgabe prüfen

NVIDIA Image Client + CLI in AutoShortsBot bauen

Qwen-Image testen, bei Bedarf auf SD3.5 Large wechseln

Keyframes erzeugen/ablegen und Tests/Commit/Push


terminal

python - <<'PY'
from pathlib import Path
p=Path.home()/'.hermes/secrets/nvidia_api_key'
print('exists', p.exists())
if p.exists():
    st=p.stat()
    print('mode', oct(st.st_mode & 0o777))
    txt=p.read_text().strip()
    print('format_ok', txt.startswith('NVIDIA_API_KEY=') and len(txt.split('=',1)[1].strip())>10)
PY


Foreground command uses '&' backgrounding. Use terminal(background=true) for long-lived processes, then run health checks and tests in follow-up terminal calls.


python - <<'PY'
from pathlib import Path
import stat
p=Path.home()/'.hermes/secrets/nvidia_api_key'
print('exists', p.exists())
if p.exists():
    st=p.stat()
    print('mode', stat.filemode(st.st_mode))
    txt=p.read_text().strip()
    ok=txt.startswith('NVIDIA_API_KEY=') and len(txt.split('=',1)[1].strip())>10
    print('format_ok', ok)
PY


Output:
exists True
mode -rw-------
format_ok True


python - <<'PY'
import requests,re
for url in ['https://build.nvidia.com/qwen/qwen-image','https://build.nvidia.com/stabilityai/stable-diffusion-3_5-large/deploy']:
    print('\nURL',url)
    html=requests.get(url,timeout=25).text
    for term in ['invoke_url','ai.api.nvidia.com','NVIDIA_API_KEY','stable-diffusion','qwen-image']:
        print(term, html.lower().find(term.lower()))
    endpoints=sorted(set(re.findall(r'https://ai\.api\.nvidia\.com/v1/genai/[^\'"\\\s<]+', html)))
    print('endpoints', endpoints[:10])
PY


Output:
URL https://build.nvidia.com/qwen/qwen-image
invoke_url 102350
ai.api.nvidia.com 102370
NVIDIA_API_KEY 102476
stable-diffusion -1
qwen-image 14983
endpoints ['https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev']

URL https://build.nvidia.com/stabilityai/stable-diffusion-3_5-large/deploy
invoke_url 97533
ai.api.nvidia.com 97553
NVIDIA_API_KEY 97659
stable-diffusion 7101
qwen-image -1
endpoints ['https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev']

Command required approval (Security scan — [HIGH] Invalid characters in hostname: Hostname 'ai\.api\.nvidia\.com' contains characters that are never valid in DNS names) and was approved by the user.


Explanation:
**Testing API endpoints**

I think I need to check if I built the snippets correctly. I'll look at the API documentation and test the endpoints, ensuring I don’t reveal the key. Maybe I’ll try the OPTIONS method or a specific endpoint for the qwen API. The payload likely includes parameters like prompt, width, height, cfg, seed, and steps. I'll want to keep costs in check while testing, especially since the user requested this. I'll inspect the package too.


Explanation:
**Testing API Implementation**

I need to implement this, so the first step is to test the API manually to understand the endpoint. I wonder if the qwen endpoint is unavailable unless it's specified in some way. I’m planning to use Python calls with the appropriate key. Should I look for the endpoints in the official documentation? It might also help to search the internet for more information on the docs related to the API.

python - <<'PY'
import requests,re
urls=['https://docs.api.nvidia.com/','https://docs.nvidia.com/nim/visual-genai/latest/getting-started.html']
for url in urls:
    print('URL',url)
    r=requests.get(url,timeout=20)
    print(r.status_code, r.url, r.text[:300])
    print('qwen', 'qwen' in r.text.lower(), 'sd3', 'stable-diffusion' in r.text.lower())
PY


Output:
URL https://docs.api.nvidia.com/
200 https://docs.api.nvidia.com/ <!DOCTYPE html><html data-color-mode="dark" class="useReactApp"><head><meta charset="utf-8"><meta name="readme-deploy" content="5.745.0"><meta name="readme-subdomain" content="nvidia-group"><title>NVIDIA</title><meta name="description" property="og:description" content="NVIDIA APIs for Artificial In
qwen True sd3 True
URL https://docs.nvidia.com/nim/visual-genai/latest/getting-started.html
200 https://docs.nvidia.com/nim/visual-genai/latest/getting-started.html

<!DOCTYPE html>

<html lang="en" data-content_root="./" >

  <head>
    <meta charset="utf-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />

    <title>Getting Started — NVIDIA NIM for Vi
qwen True sd3 True


python - <<'PY'
import requests,re
html=requests.get('https://docs.nvidia.com/nim/visual-genai/latest/getting-started.html',timeout=20).text
for term in ['stable-diffusion-3_5-large','qwen-image','ai.api.nvidia.com/v1/genai']:
 print(term, html.find(term))
for m in re.finditer(r'ai\.api\.nvidia\.com/v1/genai/[^\s"\']+', html):
 print(m.group(0)[:200])
PY


Output:
stable-diffusion-3_5-large -1
qwen-image 14399
ai.api.nvidia.com/v1/genai -1


python - <<'PY'
import requests,re
html=requests.get('https://docs.api.nvidia.com/',timeout=20).text
for term in ['stable-diffusion-3_5-large','qwen-image','qwen/image','flux.1']:
 print(term, html.lower().find(term.lower()))
# save small?
print(len(html))
PY


Output:
stable-diffusion-3_5-large -1
qwen-image -1
qwen/image -1
flux.1 -1
1054142


python - <<'PY'
import requests,json,os
from pathlib import Path
secret=Path.home()/'.hermes/secrets/nvidia_api_key'
key=secret.read_text().strip().split('=',1)[1].strip()
endpoints=[
 'https://ai.api.nvidia.com/v1/genai/qwen/qwen-image',
 'https://ai.api.nvidia.com/v1/genai/stabilityai/stable-diffusion-3_5-large',
 'https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev',
]
payload={"prompt":"photorealistic vertical image of a red used compact car in a bright European inspection garage, no text, no logos, no watermark","width":768,"height":1344,"steps":10,"cfg_scale":4,"seed":123,"mode":"base"}
for ep in endpoints:
    print('\nEP',ep)
    try:
        r=requests.post(ep,headers={'Authorization':f'Bearer {key}','Accept':'application/json','Content-Type':'application/json'},json=payload,timeout=90)
        print('status',r.status_code,'ct',r.headers.get('content-type'),'len',len(r.content))
        txt=r.text[:600]
        # redact any accidental key
        print(txt.replace(key,'[REDACTED]'))
    except Exception as e:
        print(type(e).__name__,e)
PY


Output:
EP https://ai.api.nvidia.com/v1/genai/qwen/qwen-image
status 404 ct text/plain; charset=utf-8 len 19
404 page not found

EP https://ai.api.nvidia.com/v1/genai/stabilityai/stable-diffusion-3_5-large
status 404 ct text/plain; charset=utf-8 len 19
404 page not found

EP https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev
status 200 ct application/json len 136437
{"artifacts":[{"base64":"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


Explanation:
**Evaluating endpoint options**

It seems only the flux endpoint is functional right now, but the page snippets I've built are wrong. I need to save the output for future use. Maybe I could implement a generic NVIDIA image client as a default fallback for flux with configurable endpoints. The user mentioned something about qwen/sd and possibly no hosting. I should clarify that hosted endpoints are from NVIDIA, and self-hosting is an option for the deployed page. I'll keep parsing the output and use keyframe requests to read.


     1|{
     2|  "schema_version": "keyframe_requests.v1",
     3|  "video": {
     4|    "id": "hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann",
     5|    "title": "Warum ein billiges Auto teuer sein kann",
     6|    "series": "Unsichtbare Systeme in 30 Sekunden",
     7|    "pillar": "hidden_money_systems"
     8|  },
     9|  "style_profile": "photorealistic_macro_investigation",
    10|  "workflow": "ChatGPT Pro keyframe -> Wan2.2 I2V -> deterministic renderer overlays",
    11|  "fps_policy": {
    12|    "generation_fps": 12,
    13|    "generation_frames": 61,
    14|    "generation_duration_seconds": 5.08,
    15|    "final_delivery_fps": 24,
    16|    "interpolation": "renderer_or_ffmpeg_optical_flow_to_24fps",
    17|    "reason": "12fps/61f is a smoother local Wan2.2 generation target on 16GB VRAM; final Shorts should be delivered at 24fps after interpolation/compositing."
    18|  },
    19|  "requests": [
    20|    {
    21|      "scene_id": "scene_01",
    22|      "purpose": "pattern_interrupt",
    23|      "target_path": "data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png",
    24|      "generator": "ChatGPT Pro image generation or equivalent photorealistic image model",
    25|      "generator_prompt": "Photorealistic vertical image of a red used compact car in a bright European vehicle inspection garage, full front three-quarter view, clean empty license plate area, visible wheels and body panels, soft overhead lights, realistic reflections, shallow depth of field, balanced exposure, high quality inspection-bay photography. Leave clean negative space for captions in the upper third and lower third. No text, no logos, no watermark.",
    26|      "negative_requirements": "cartoon, animation, deformed subject, duplicated subject, disappearing subject, melting body, distorted geometry, blurry, low detail, flickering colors, abstract, static image, camera shake, overexposed, underexposed, dark frame, text, subtitles, captions, watermark, logo, unreadable letters, fake UI text, unreadable license plate, fake license plate text, brand logos, distorted wheels, melted car body, broken windshield geometry, fake numbers, fake labels",
    27|      "required_format": {
    28|        "width": 416,
    29|        "height": 736,
    30|        "format": "png"
    31|      },
    32|      "acceptance_checklist": [
    33|        "subject clearly visible in the first frame",
    34|        "full object visible with clean silhouette",
    35|        "bright balanced exposure, not dark cinematic mush",
    36|        "no readable text/logos/license plates",
    37|        "enough negative space in upper and lower thirds for captions",
    38|        "photorealistic texture, no cartoon or fantasy styling",
    39|        "movement implied by pose/composition where possible"
    40|      ],
    41|      "downstream_i2v_prompt": "Photorealistic vertical video of the same red used compact car in a bright inspection garage. Camera slowly pushes in from a front three-quarter angle, then lands on a subtle freeze-like emphasis. Subtle light sweep over the hood and windshield, realistic reflections, stable composition, balanced exposure, high quality automotive inspection footage. Preserve the same car and garage. No text, no logo, no watermark."
    42|    },
    43|    {
    44|      "scene_id": "scene_02",
    45|      "purpose": "problem",
    46|      "target_path": "data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png",
    47|      "generator": "ChatGPT Pro image generation or equivalent photorealistic image model",
    48|      "generator_prompt": "Photorealistic vertical image of a red used compact car in a bright European vehicle inspection garage, full front three-quarter view, clean empty license plate area, visible wheels and body panels, soft overhead lights, realistic reflections, shallow depth of field, balanced exposure, high quality inspection-bay photography. Leave clean negative space for captions in the upper third and lower third. No text, no logos, no watermark.",
    49|      "negative_requirements": "cartoon, animation, deformed subject, duplicated subject, disappearing subject, melting body, distorted geometry, blurry, low detail, flickering colors, abstract, static image, camera shake, overexposed, underexposed, dark frame, text, subtitles, captions, watermark, logo, unreadable letters, fake UI text, unreadable license plate, fake license plate text, brand logos, distorted wheels, melted car body, broken windshield geometry, fake numbers, fake labels",
    50|      "required_format": {
    51|        "width": 416,
    52|        "height": 736,
    53|        "format": "png"
    54|      },
    55|      "acceptance_checklist": [
    56|        "subject clearly visible in the first frame",
    57|        "full object visible with clean silhouette",
    58|        "bright balanced exposure, not dark cinematic mush",
    59|        "no readable text/logos/license plates",
    60|        "enough negative space in upper and lower thirds for captions",
    61|        "photorealistic texture, no cartoon or fantasy styling",
    62|        "movement implied by pose/composition where possible"
    63|      ],
    64|      "downstream_i2v_prompt": "Photorealistic vertical video of the same red used compact car in a bright inspection garage. Camera slowly pushes in from a front three-quarter angle with gentle parallax. A subtle surface layer starts to peel away visually, hinting that the visible price is not the full system. Subtle light sweep over the hood and windshield, realistic reflections, stable composition, balanced exposure, high quality automotive inspection footage. Preserve the same car and garage. No text, no logo, no watermark."
    65|    }
    66|  ]
    67|}
    68|


     1|"""Small ComfyUI REST client used by AutoShortsBot clip jobs."""
     2|
     3|from __future__ import annotations
     4|
     5|import json
     6|import time
     7|import urllib.parse
     8|import urllib.request
     9|from pathlib import Path
    10|from typing import Any
    11|
    12|
    13|class UrllibComfyUITransport:
    14|    """HTTP transport for local/Tailscale ComfyUI instances."""
    15|
    16|    def upload_image(self, base_url: str, path: Path, *, remote_name: str, overwrite: bool = True) -> dict[str, Any]:
    17|        boundary = "----AutoShortsBotBoundary"
    18|        body: list[bytes] = []
    19|
    20|        def add(part: str | bytes) -> None:
    21|            body.append(part.encode() if isinstance(part, str) else part)
    22|
    23|        add(
    24|            f'--{boundary}\r\nContent-Disposition: form-data; name="image"; filename="{remote_name}"\r\n'
    25|            "Content-Type: image/png\r\n\r\n"
    26|        )
    27|        add(path.read_bytes())
    28|        add("\r\n")
    29|        add(f'--{boundary}\r\nContent-Disposition: form-data; name="type"\r\n\r\ninput\r\n')
    30|        add(f'--{boundary}\r\nContent-Disposition: form-data; name="overwrite"\r\n\r\n{str(overwrite).lower()}\r\n')
    31|        add(f"--{boundary}--\r\n")
    32|        request = urllib.request.Request(
    33|            f"{base_url}/upload/image",
    34|            data=b"".join(body),
    35|            headers={"Content-Type": f"multipart/form-data; boundary={boundary}"},
    36|        )
    37|        return json.loads(urllib.request.urlopen(request, timeout=120).read().decode())
    38|
    39|    def post_json(self, base_url: str, endpoint: str, payload: dict[str, Any]) -> dict[str, Any]:
    40|        request = urllib.request.Request(
    41|            f"{base_url}{endpoint}",
    42|            data=json.dumps(payload).encode(),
    43|            headers={"Content-Type": "application/json"},
    44|        )
    45|        raw = urllib.request.urlopen(request, timeout=120).read().decode()
    46|        return json.loads(raw) if raw else {}
    47|
    48|    def wait_for_completion(
    49|        self,
    50|        base_url: str,
    51|        prompt_id: str,
    52|        *,
    53|        poll_seconds: float,
    54|        timeout_seconds: int,
    55|    ) -> dict[str, Any]:
    56|        deadline = time.monotonic() + timeout_seconds
    57|        while time.monotonic() < deadline:
    58|            queue = json.loads(urllib.request.urlopen(f"{base_url}/queue", timeout=30).read().decode())
    59|            running = queue.get("queue_running", [])
    60|            pending = queue.get("queue_pending", [])
    61|            if not running and not pending:
    62|                history = json.loads(urllib.request.urlopen(f"{base_url}/history/{prompt_id}", timeout=60).read().decode())
    63|                return history.get(prompt_id, {})
    64|            time.sleep(poll_seconds)
    65|        raise TimeoutError(f"ComfyUI prompt did not finish within {timeout_seconds}s: {prompt_id}")
    66|
    67|    def download_view(self, base_url: str, output: dict[str, Any], destination: Path) -> Path:
    68|        params = urllib.parse.urlencode(
    69|            {
    70|                "filename": output["filename"],
    71|                "subfolder": output.get("subfolder", ""),
    72|                "type": output.get("type", "output"),
    73|            }
    74|        )
    75|        destination.parent.mkdir(parents=True, exist_ok=True)
    76|        destination.write_bytes(urllib.request.urlopen(f"{base_url}/view?{params}", timeout=300).read())
    77|        return destination
    78|
    79|
    80|class ComfyUIClient:
    81|    """Minimal client for uploading keyframes, running workflows, and downloading MP4 outputs."""
    82|
    83|    def __init__(self, base_url: str, *, transport: Any | None = None):
    84|        self.base_url = base_url.rstrip("/")
    85|        self.transport = transport or UrllibComfyUITransport()
    86|
    87|    def run_i2v_workflow(
    88|        self,
    89|        *,
    90|        workflow: dict[str, Any],
    91|        keyframe_path: Path,
    92|        remote_image_name: str,
    93|        output_path: Path,
    94|        poll_seconds: float = 5,
    95|        timeout_seconds: int = 900,
    96|    ) -> dict[str, Any]:
    97|        upload = self.transport.upload_image(self.base_url, keyframe_path, remote_name=remote_image_name, overwrite=True)
    98|        prompt_response = self.transport.post_json(
    99|            self.base_url,
   100|            "/prompt",
   101|            {"prompt": workflow, "client_id": "autoshorts-i2v"},
   102|        )
   103|        prompt_id = prompt_response["prompt_id"]
   104|        history = self.transport.wait_for_completion(
   105|            self.base_url,
   106|            prompt_id,
   107|            poll_seconds=poll_seconds,
   108|            timeout_seconds=timeout_seconds,
   109|        )
   110|        status = history.get("status", {})
   111|        if status.get("status_str") != "success":
   112|            return {"status": "failed", "prompt_id": prompt_id, "history": history, "upload": upload}
   113|        output = _first_video_output(history)
   114|        self.transport.download_view(self.base_url, output, output_path)
   115|        return {
   116|            "status": "success",
   117|            "prompt_id": prompt_id,
   118|            "output_path": str(output_path),
   119|            "remote_output": output,
   120|            "upload": upload,
   121|        }
   122|
   123|
   124|def _first_video_output(history: dict[str, Any]) -> dict[str, Any]:
   125|    for node_output in history.get("outputs", {}).values():
   126|        for item in node_output.get("images", []):
   127|            if str(item.get("filename", "")).lower().endswith(".mp4"):
   128|                return item
   129|    raise ValueError("No MP4 output found in ComfyUI history")
   130|


python - <<'PY'
import requests
from pathlib import Path
key=(Path.home()/'.hermes/secrets/nvidia_api_key').read_text().split('=',1)[1].strip()
endpoints=['stabilityai/stable-diffusion-3.5-large','stabilityai/stable-diffusion-3_5-large','stabilityai/stable-diffusion-3-5-large','stabilityai/sdxl-turbo']
payload={"prompt":"red car","width":512,"height":512,"steps":4,"cfg_scale":4,"seed":1,"mode":"base"}
for e in endpoints:
 ep='https://ai.api.nvidia.com/v1/genai/'+e
 r=requests.post(ep,headers={'Authorization':f'Bearer {key}','Accept':'application/json','Content-Type':'application/json'},json=payload,timeout=30)
 print(e, r.status_code, r.headers.get('content-type'), r.text[:120].replace(key,'[REDACTED]'))
PY


Output:
stabilityai/stable-diffusion-3.5-large 404 text/plain; charset=utf-8 404 page not found

stabilityai/stable-diffusion-3_5-large 404 text/plain; charset=utf-8 404 page not found

stabilityai/stable-diffusion-3-5-large 404 text/plain; charset=utf-8 404 page not found

stabilityai/sdxl-turbo 404 application/problem+json {"status":404,"title":"Not Found","detail":"Function 'f886140c-424e-4c82-a841-99e23f9ae35d': Not found for account '4_qQ


Explanation:
**Implementing file writing**

I need to implement a file-writing function. I think I should use write_file for this task, but I'm wondering if I need to update the .gitignore file as well since the media files aren't being tracked. It's important to keep unnecessary files out of version control, so I want to make sure I'm being thorough about that. Let's see what the best approach is to handle both the writing and the .gitignore update!


     1|# Python
     2|__pycache__/
     3|*.py[cod]
     4|.pytest_cache/
     5|.ruff_cache/
     6|.mypy_cache/
     7|.venv/
     8|venv/
     9|
    10|# Secrets
    11|.env
    12|.env.*
    13|*.token
    14|*token*
    15|secrets/
    16|credentials/
    17|
    18|# Generated media / heavy local outputs
    19|assets/
    20|output/
    21|output_videos/
    22|input_videos/
    23|*.mp4
    24|*.mov
    25|*.mp3
    26|*.wav
    27|*.vtt
    28|*.jpg
    29|*.jpeg
    30|*.png
    31|
    32|# Local runtime data
    33|data/performance/*.local.json
    34|data/generated/
    35|data/local_motion_renders/
    36|data/imported_ai_clips/
    37|


"""NVIDIA NIM image-generation client for AutoShortsBot keyframes."""

from __future__ import annotations

import base64
import json
import os
import urllib.error
import urllib.request
from dataclasses import dataclass
from pathlib import Path
from typing import Any

NVIDIA_IMAGE_ENDPOINTS: dict[str, str] = {
    "qwen-image": "https://ai.api.nvidia.com/v1/genai/qwen/qwen-image",
    "stable-diffusion-3.5-large": "https://ai.api.nvidia.com/v1/genai/stabilityai/stable-diffusion-3_5-large",
    # The two Build pages above currently expose this hosted endpoint in their embedded snippets.
    # Keep it as the production fallback because it is available through NVIDIA API Catalog.
    "flux.1-dev": "https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev",
}

@dataclass(frozen=True)
class NvidiaImageRequest:
    """Provider-neutral request for a single keyframe image."""

    prompt: str
    output_path: Path
    width: int = 768
    height: int = 1344
    seed: int = 101
    steps: int = 28
    cfg_scale: float = 4.0
    mode: str = "base"

@dataclass(frozen=True)
class NvidiaImageResult:
    """Structured result for one provider attempt."""

    status: str
    model: str
    endpoint: str
    output_path: Path | None = None
    http_status: int | None = None
    error: str | None = None
    bytes_written: int = 0

class UrllibNvidiaImageTransport:
    """Small urllib transport so tests can swap the network layer."""

    def post_json(self, endpoint: str, api_key: str, payload: dict[str, Any], *, timeout_seconds: int) -> tuple[int, bytes, str]:
        request = urllib.request.Request(
            endpoint,
            data=json.dumps(payload).encode("utf-8"),
            headers={
                "Authorization": f"Bearer {api_key}",
                "Accept": "application/json",
                "Content-Type": "application/json",
            },
            method="POST",
        )
        try:
            with urllib.request.urlopen(request, timeout=timeout_seconds) as response:
                return response.status, response.read(), response.headers.get("Content-Type", "")
        except urllib.error.HTTPError as exc:
            return exc.code, exc.read(), exc.headers.get("Content-Type", "")

class NvidiaImageClient:
    """Client for hosted NVIDIA image-generation endpoints.

    The Build portal has both hosted API endpoints and deploy/self-host pages. This client only uses
    the hosted API path under ``https://ai.api.nvidia.com/v1/genai/...`` and does not require hosting
    a NIM container locally.
    """

    def __init__(
        self,
        *,
        api_key: str | None = None,
        endpoints: dict[str, str] | None = None,
        transport: Any | None = None,
        timeout_seconds: int = 180,
    ) -> None:
        self.api_key = api_key or load_nvidia_api_key()
        self.endpoints = endpoints or NVIDIA_IMAGE_ENDPOINTS
        self.transport = transport or UrllibNvidiaImageTransport()
        self.timeout_seconds = timeout_seconds

    def generate(
        self,
        request: NvidiaImageRequest,
        *,
        model_order: list[str] | None = None,
    ) -> NvidiaImageResult:
        """Generate one image, trying models in order until one succeeds."""

        order = model_order or ["qwen-image", "stable-diffusion-3.5-large", "flux.1-dev"]
        last_result: NvidiaImageResult | None = None
        for model in order:
            endpoint = self.endpoints[model]
            result = self._generate_with_model(request, model=model, endpoint=endpoint)
            if result.status == "success":
                return result
            last_result = result
        if last_result is None:
            raise ValueError("model_order is empty")
        return last_result

    def _generate_with_model(self, request: NvidiaImageRequest, *, model: str, endpoint: str) -> NvidiaImageResult:
        payload = {
            "prompt": request.prompt,
            "mode": request.mode,
            "cfg_scale": request.cfg_scale,
            "width": request.width,
            "height": request.height,
            "seed": request.seed,
            "steps": request.steps,
        }
        status_code, body, _content_type = self.transport.post_json(
            endpoint,
            self.api_key,
            payload,
            timeout_seconds=self.timeout_seconds,
        )
        if status_code < 200 or status_code >= 300:
            return NvidiaImageResult(
                status="failed",
                model=model,
                endpoint=endpoint,
                http_status=status_code,
                error=_safe_error_text(body),
            )

        try:
            image_bytes = _extract_image_bytes(json.loads(body.decode("utf-8")))
        except Exception as exc:  # noqa: BLE001 - convert provider quirks into reportable failures.
            return NvidiaImageResult(
                status="failed",
                model=model,
                endpoint=endpoint,
                http_status=status_code,
                error=f"could not parse image response: {exc}",
            )

        request.output_path.parent.mkdir(parents=True, exist_ok=True)
        request.output_path.write_bytes(image_bytes)
        return NvidiaImageResult(
            status="success",
            model=model,
            endpoint=endpoint,
            output_path=request.output_path,
            http_status=status_code,
            bytes_written=len(image_bytes),
        )

def load_nvidia_api_key(path: Path | None = None) -> str:
    """Load NVIDIA API key from env or Hermes secret file without printing it."""

    for env_name in ("NVIDIA_API_KEY", "NGC_API_KEY", "NVIDIA_NIM_API_KEY"):
        value = os.getenv(env_name)
        if value:
            return value.strip()

    secret_path = path or Path.home() / ".hermes" / "secrets" / "nvidia_api_key"
    raw = secret_path.read_text(encoding="utf-8").strip()
    if "=" in raw:
        name, value = raw.split("=", 1)
        if name.strip() not in {"NVIDIA_API_KEY", "NGC_API_KEY", "NVIDIA_NIM_API_KEY"}:
            raise ValueError(f"Unsupported NVIDIA secret key name: {name.strip()}")
        return value.strip()
    return raw.strip()

def _extract_image_bytes(payload: dict[str, Any]) -> bytes:
    """Extract image bytes from common NVIDIA Visual GenAI response shapes."""

    if artifacts := payload.get("artifacts"):
        first = artifacts[0]
        if encoded := first.get("base64"):
            return base64.b64decode(encoded)

    for key in ("image", "b64_json", "base64"):
        encoded = payload.get(key)
        if isinstance(encoded, str) and encoded:
            return base64.b64decode(encoded)

    if data := payload.get("data"):
        first = data[0]
        for key in ("b64_json", "base64", "image"):
            encoded = first.get(key)
            if isinstance(encoded, str) and encoded:
                return base64.b64decode(encoded)

    raise ValueError(f"no base64 image field found; keys={sorted(payload.keys())}")

def _safe_error_text(body: bytes, *, limit: int = 500) -> str:
    text = body.decode("utf-8", errors="replace").strip()
    return text[:limit]


#!/usr/bin/env python3
"""Generate pilot keyframes through NVIDIA hosted image APIs."""

from __future__ import annotations

import argparse
import json
from pathlib import Path
from typing import Any

from PIL import Image

from autoshorts.nvidia_image_client import NvidiaImageClient, NvidiaImageRequest

DEFAULT_REQUESTS = Path(
    "data/keyframe_requests/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json"
)
DEFAULT_REPORT = Path(
    "data/keyframe_generation_reports/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json"
)

def main() -> int:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--requests", type=Path, default=DEFAULT_REQUESTS)
    parser.add_argument("--report", type=Path, default=DEFAULT_REPORT)
    parser.add_argument(
        "--models",
        default="qwen-image,stable-diffusion-3.5-large,flux.1-dev",
        help="Comma-separated NVIDIA model order. The first working model is used per scene.",
    )
    parser.add_argument("--width", type=int, default=768)
    parser.add_argument("--height", type=int, default=1344)
    parser.add_argument("--steps", type=int, default=28)
    parser.add_argument("--cfg-scale", type=float, default=4.0)
    parser.add_argument("--seed-base", type=int, default=7000)
    parser.add_argument("--overwrite", action="store_true")
    args = parser.parse_args()

    package = json.loads(args.requests.read_text(encoding="utf-8"))
    model_order = [item.strip() for item in args.models.split(",") if item.strip()]
    client = NvidiaImageClient()
    results: list[dict[str, Any]] = []

    for index, item in enumerate(package["requests"], start=1):
        output_path = Path(item["target_path"])
        if output_path.exists() and not args.overwrite:
            results.append(
                {
                    "scene_id": item["scene_id"],
                    "status": "skipped",
                    "reason": "output_exists",
                    "output_path": str(output_path),
                }
            )
            continue

        prompt = _build_prompt(item)
        request = NvidiaImageRequest(
            prompt=prompt,
            output_path=output_path,
            width=args.width,
            height=args.height,
            seed=args.seed_base + index,
            steps=args.steps,
            cfg_scale=args.cfg_scale,
        )
        result = client.generate(request, model_order=model_order)
        row: dict[str, Any] = {
            "scene_id": item["scene_id"],
            "status": result.status,
            "model": result.model,
            "endpoint": result.endpoint,
            "http_status": result.http_status,
            "output_path": str(result.output_path) if result.output_path else str(output_path),
            "bytes_written": result.bytes_written,
        }
        if result.error:
            row["error"] = result.error
        if result.status == "success":
            row["image_check"] = _inspect_image(output_path)
        results.append(row)

    report = {
        "schema_version": "nvidia_keyframe_generation_report.v1",
        "source_requests": str(args.requests),
        "model_order": model_order,
        "requested_size": {"width": args.width, "height": args.height},
        "total": len(results),
        "succeeded": sum(1 for item in results if item["status"] == "success"),
        "skipped": sum(1 for item in results if item["status"] == "skipped"),
        "failed": sum(1 for item in results if item["status"] == "failed"),
        "results": results,
    }
    args.report.parent.mkdir(parents=True, exist_ok=True)
    args.report.write_text(json.dumps(report, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
    print(json.dumps(report, indent=2, ensure_ascii=False))
    return 0 if report["failed"] == 0 else 1

def _build_prompt(item: dict[str, Any]) -> str:
    prompt = item["generator_prompt"]
    negative = item.get("negative_requirements", "")
    scene_id = item.get("scene_id", "")
    if scene_id == "scene_02":
        prompt += " Slightly closer composition than the first shot, with subtle investigative mood and clean caption space."
    return (
        f"{prompt}\n\nStrict requirements: vertical 9:16 photorealistic keyframe, no readable text, "
        f"no brand logos, no watermark, no readable license plate, no fake UI, no captions. "
        f"Avoid: {negative}"
    )

def _inspect_image(path: Path) -> dict[str, Any]:
    with Image.open(path) as image:
        converted = image.convert("RGB")
        if path.suffix.lower() != ".png":
            converted.save(path.with_suffix(".png"))
        return {"width": image.width, "height": image.height, "mode": image.mode, "format": image.format}

if __name__ == "__main__":
    raise SystemExit(main())


from __future__ import annotations

import base64
from pathlib import Path
from typing import Any

from autoshorts.nvidia_image_client import NvidiaImageClient, NvidiaImageRequest, load_nvidia_api_key

ONE_PIXEL_PNG = base64.b64decode(
    "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mP8/x8AAwMCAO+/p9sAAAAASUVORK5CYII="
)

class FakeTransport:
    def __init__(self, statuses: list[int] | None = None):
        self.statuses = statuses or [200]
        self.calls: list[dict[str, Any]] = []

    def post_json(self, endpoint: str, api_key: str, payload: dict[str, Any], *, timeout_seconds: int) -> tuple[int, bytes, str]:
        self.calls.append({"endpoint": endpoint, "api_key": api_key, "payload": payload, "timeout_seconds": timeout_seconds})
        status = self.statuses[min(len(self.calls) - 1, len(self.statuses) - 1)]
        if status != 200:
            return status, b"404 page not found", "text/plain"
        encoded = base64.b64encode(ONE_PIXEL_PNG).decode()
        return 200, (f'{{"artifacts":[{{"base64":"{encoded}"}}]}}').encode(), "application/json"

def test_load_nvidia_api_key_supports_env_file_format(tmp_path: Path) -> None:
    secret = tmp_path / "nvidia_api_key"
    secret.write_text("NVIDIA_API_KEY=test-key\n", encoding="utf-8")

    assert load_nvidia_api_key(secret) == "test-key"

def test_client_writes_image_from_artifacts_response(tmp_path: Path) -> None:
    transport = FakeTransport()
    client = NvidiaImageClient(api_key="secret", transport=transport)
    output = tmp_path / "scene_01.png"

    result = client.generate(NvidiaImageRequest(prompt="red car", output_path=output, width=416, height=736), model_order=["flux.1-dev"])

    assert result.status == "success"
    assert result.model == "flux.1-dev"
    assert output.read_bytes() == ONE_PIXEL_PNG
    assert transport.calls[0]["payload"]["width"] == 416
    assert transport.calls[0]["payload"]["height"] == 736
    assert transport.calls[0]["payload"]["mode"] == "base"

def test_client_falls_back_when_first_model_is_unavailable(tmp_path: Path) -> None:
    transport = FakeTransport(statuses=[404, 200])
    client = NvidiaImageClient(api_key="secret", transport=transport)
    output = tmp_path / "scene_01.png"

    result = client.generate(
        NvidiaImageRequest(prompt="red car", output_path=output),
        model_order=["qwen-image", "flux.1-dev"],
    )

    assert result.status == "success"
    assert result.model == "flux.1-dev"
    assert len(transport.calls) == 2
    assert transport.calls[0]["endpoint"].endswith("/qwen/qwen-image")
    assert transport.calls[1]["endpoint"].endswith("/black-forest-labs/flux.1-dev")


python -m pytest tests/test_nvidia_image_client.py -q


Output:
...                                                                      [100%]
3 passed in 0.05s


patch

def _inspect_image(path: Path) -> dict[str, Any]:
    with Image.open(path) as image:
        converted = image.convert("RGB")
        if path.suffix.lower() != ".png":
            converted.save(path.with_suffix(".png"))
        return {"width": image.width, "height": image.height, "mode": image.mode, "format": image.format}

def _inspect_image(path: Path) -> dict[str, Any]:
    with Image.open(path) as image:
        original = {"width": image.width, "height": image.height, "mode": image.mode, "format": image.format}
        converted = image.convert("RGB")
        converted.save(path, format="PNG")
        original["normalized_format"] = "PNG"
        return original


--- a//home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_keyframes_nvidia.py
+++ b//home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_keyframes_nvidia.py
@@ -113,10 +113,11 @@

 def _inspect_image(path: Path) -> dict[str, Any]:
     with Image.open(path) as image:
+        original = {"width": image.width, "height": image.height, "mode": image.mode, "format": image.format}
         converted = image.convert("RGB")
-        if path.suffix.lower() != ".png":
-            converted.save(path.with_suffix(".png"))
-        return {"width": image.width, "height": image.height, "mode": image.mode, "format": image.format}
+        converted.save(path, format="PNG")
+        original["normalized_format"] = "PNG"
+        return original

 if __name__ == "__main__":


terminal

python scripts/generate_keyframes_nvidia.py --overwrite --models qwen-image,stable-diffusion-3.5-large,flux.1-dev --steps 28 --seed-base 7200


Output:
Traceback (most recent call last):
  File "/home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_keyframes_nvidia.py", line 13, in <module>
    from autoshorts.nvidia_image_client import NvidiaImageClient, NvidiaImageRequest
ModuleNotFoundError: No module named 'autoshorts'


     1|#!/usr/bin/env python3
     2|"""Generate executable Wan2.2 I2V clip-job packages from I2V prompt plans."""
     3|
     4|from __future__ import annotations
     5|
     6|import argparse
     7|import json
     8|import sys
     9|from pathlib import Path
    10|
    11|PROJECT_ROOT = Path(__file__).resolve().parents[1]
    12|if str(PROJECT_ROOT) not in sys.path:
    13|    sys.path.insert(0, str(PROJECT_ROOT))
    14|
    15|from autoshorts.i2v_clip_jobs import write_i2v_clip_jobs
    16|
    17|
    18|DEFAULT_PROMPT_PLAN = Path(
    19|    "data/i2v_prompt_plans/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json"
    20|)
    21|
    22|
    23|def _parse_seeds(value: str) -> list[int]:
    24|    seeds = [int(part.strip()) for part in value.split(",") if part.strip()]
    25|    if not seeds:
    26|        raise argparse.ArgumentTypeError("Provide at least one seed")
    27|    return seeds
    28|
    29|
    30|def main() -> int:
    31|    parser = argparse.ArgumentParser(description=__doc__)
    32|    parser.add_argument("--prompt-plan", type=Path, default=DEFAULT_PROMPT_PLAN)
    33|    parser.add_argument("--output-file", type=Path, default=Path("data/i2v_clip_jobs/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json"))
    34|    parser.add_argument("--keyframe-root", type=Path, default=Path("data/keyframes"))
    35|    parser.add_argument("--generated-clip-root", type=Path, default=Path("data/generated_clips"))
    36|    parser.add_argument("--seeds", type=_parse_seeds, default=[101, 202, 303])
    37|    args = parser.parse_args()
    38|
    39|    prompt_plan = json.loads(args.prompt_plan.read_text(encoding="utf-8"))
    40|    path = write_i2v_clip_jobs(
    41|        prompt_plan,
    42|        output_file=args.output_file,
    43|        keyframe_root=args.keyframe_root,
    44|        generated_clip_root=args.generated_clip_root,
    45|        seeds=args.seeds,
    46|    )
    47|    print(path)
    48|    return 0
    49|
    50|
    51|

Use offset=51 to continue reading (showing 1-50 of 52 lines)


patch

import argparse
import json
import sys
from pathlib import Path
from typing import Any

from PIL import Image

PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))

from autoshorts.nvidia_image_client import NvidiaImageClient, NvidiaImageRequest

import argparse
import json
from pathlib import Path
from typing import Any

from PIL import Image

from autoshorts.nvidia_image_client import NvidiaImageClient, NvidiaImageRequest


--- a//home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_keyframes_nvidia.py
+++ b//home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_keyframes_nvidia.py
@@ -5,10 +5,15 @@

 import argparse
 import json
+import sys
 from pathlib import Path
 from typing import Any

 from PIL import Image
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))

 from autoshorts.nvidia_image_client import NvidiaImageClient, NvidiaImageRequest


terminal

Output:
{
  "schema_version": "nvidia_keyframe_generation_report.v1",
  "source_requests": "data/keyframe_requests/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json",
  "model_order": [
    "qwen-image",
    "stable-diffusion-3.5-large",
    "flux.1-dev"
  ],
  "requested_size": {
    "width": 768,
    "height": 1344
  },
  "total": 2,
  "succeeded": 2,
  "skipped": 0,
  "failed": 0,
  "results": [
    {
      "scene_id": "scene_01",
      "status": "success",
      "model": "flux.1-dev",
      "endpoint": "https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev",
      "http_status": 200,
      "output_path": "data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png",
      "bytes_written": 135897,
      "image_check": {
        "width": 768,
        "height": 1344,
        "mode": "RGB",
        "format": "JPEG",
        "normalized_format": "PNG"
      }
    },
    {
      "scene_id": "scene_02",
      "status": "success",
      "model": "flux.1-dev",
      "endpoint": "https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev",
      "http_status": 200,
      "output_path": "data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png",
      "bytes_written": 107046,
      "image_check": {
        "width": 768,
        "height": 1344,
        "mode": "RGB",
        "format": "JPEG",
        "normalized_format": "PNG"
      }
    }
  ]
}


Explanation:
**Considering image dimensions**

I’m looking into the needed image dimensions—768x1344, while my I2V workflow expects 416x736. It might just scale, but I'm noticing the keyframe package needs that specific size. I’m thinking I should downscale to the required format after generation since we normalized but didn't resize. Maybe the CLI should read the required format and perform the resize. I could also consider checking how ImageScale is working in this job upload process.


File unchanged since last read. The content from the earlier read_file result in this conversation is still current — refer to that instead of re-reading.


     1|"""Build executable Wan2.2 I2V clip jobs from prompt plans."""
     2|
     3|from __future__ import annotations
     4|
     5|import json
     6|from pathlib import Path
     7|from typing import Any
     8|
     9|
    10|NATIVE_WAN22_CLIENT_ID = "autoshorts-i2v"
    11|
    12|
    13|def _video_slug(prompt_plan: dict[str, Any]) -> str:
    14|    return prompt_plan["video"]["id"]
    15|
    16|
    17|def _scene_keyframe_path(keyframe_root: Path, video_slug: str, scene_id: str) -> Path:
    18|    return keyframe_root / video_slug / f"{scene_id}.png"
    19|
    20|
    21|def _scene_output_path(output_root: Path, video_slug: str, scene_id: str, seed: int) -> Path:
    22|    return output_root / video_slug / scene_id / f"seed_{seed}.mp4"
    23|
    24|
    25|def build_i2v_clip_jobs(
    26|    prompt_plan: dict[str, Any],
    27|    keyframe_root: Path,
    28|    output_root: Path,
    29|    seeds: list[int],
    30|) -> list[dict[str, Any]]:
    31|    """Create seeded clip jobs for each Wan2.2 I2V scene."""
    32|    video_slug = _video_slug(prompt_plan)
    33|    jobs: list[dict[str, Any]] = []
    34|    for scene in prompt_plan["scenes"]:
    35|        for seed in seeds:
    36|            scene_id = scene["scene_id"]
    37|            keyframe_path = _scene_keyframe_path(keyframe_root, video_slug, scene_id)
    38|            output_path = _scene_output_path(output_root, video_slug, scene_id, seed)
    39|            jobs.append(
    40|                {
    41|                    "schema_version": "i2v_clip_job.v1",
    42|                    "job_id": f"{scene_id}_seed_{seed}",
    43|                    "video_id": video_slug,
    44|                    "scene_id": scene_id,
    45|                    "purpose": scene["purpose"],
    46|                    "seed": seed,
    47|                    "status": "ready" if keyframe_path.exists() else "needs_keyframe",
    48|                    "keyframe_path": str(keyframe_path),
    49|                    "output_path": str(output_path),
    50|                    "remote_image_name": f"{video_slug}_{scene_id}.png",
    51|                    "prompt_spec": scene,
    52|                    "review": {
    53|                        "human_review_required": True,
    54|                        "criteria": [
    55|                            "subject clear in first 0.5s",
    56|                            "motion supports retention beat",
    57|                            "no baked text/logos/watermarks",
    58|                            "no distracting deformation/flicker",
    59|                            "clean space remains for captions",
    60|                        ],
    61|                    },
    62|                }
    63|            )
    64|    return jobs
    65|
    66|
    67|def _filename_prefix(job: dict[str, Any]) -> str:
    68|    return f"autoshorts_{job['video_id']}_{job['scene_id']}_seed_{job['seed']}"
    69|
    70|
    71|def build_native_wan22_i2v_workflow(job: dict[str, Any], uploaded_image_name: str) -> dict[str, Any]:
    72|    """Build an API-format native ComfyUI Wan2.2 I2V workflow for a clip job."""
    73|    spec = job["prompt_spec"]
    74|    comfy = spec["comfyui"]
    75|    width = int(comfy["resolution"]["width"])
    76|    height = int(comfy["resolution"]["height"])
    77|    return {
    78|        "1": {
    79|            "class_type": "UNETLoader",
    80|            "inputs": {"unet_name": comfy["model"], "weight_dtype": "default"},
    81|        },
    82|        "2": {
    83|            "class_type": "CLIPLoader",
    84|            "inputs": {"clip_name": comfy["text_encoder"], "type": "wan", "device": "default"},
    85|        },
    86|        "3": {
    87|            "class_type": "CLIPTextEncode",
    88|            "inputs": {"clip": ["2", 0], "text": spec["i2v_motion_prompt"]},
    89|        },
    90|        "4": {
    91|            "class_type": "CLIPTextEncode",
    92|            "inputs": {"clip": ["2", 0], "text": spec["negative_prompt"]},
    93|        },
    94|        "5": {"class_type": "VAELoader", "inputs": {"vae_name": comfy["vae"]}},
    95|        "6": {"class_type": "LoadImage", "inputs": {"image": uploaded_image_name}},
    96|        "7": {
    97|            "class_type": "ImageScale",
    98|            "inputs": {
    99|                "image": ["6", 0],
   100|                "upscale_method": "lanczos",
   101|                "width": width,
   102|                "height": height,
   103|                "crop": "disabled",
   104|            },
   105|        },
   106|        "8": {
   107|            "class_type": "Wan22ImageToVideoLatent",
   108|            "inputs": {
   109|                "vae": ["5", 0],
   110|                "width": width,
   111|                "height": height,
   112|                "length": int(comfy["frames"]),
   113|                "batch_size": 1,
   114|                "start_image": ["7", 0],
   115|            },
   116|        },
   117|        "9": {
   118|            "class_type": "KSampler",
   119|            "inputs": {
   120|                "model": ["1", 0],
   121|                "seed": int(job["seed"]),
   122|                "steps": int(comfy["steps"]),
   123|                "cfg": float(comfy["cfg"]),
   124|                "sampler_name": "uni_pc",
   125|                "scheduler": "simple",
   126|                "positive": ["3", 0],
   127|                "negative": ["4", 0],
   128|                "latent_image": ["8", 0],
   129|                "denoise": 1.0,
   130|            },
   131|        },
   132|        "10": {"class_type": "VAEDecode", "inputs": {"samples": ["9", 0], "vae": ["5", 0]}},
   133|        "11": {"class_type": "CreateVideo", "inputs": {"images": ["10", 0], "fps": float(comfy["fps"])}},
   134|        "12": {
   135|            "class_type": "SaveVideo",
   136|            "inputs": {
   137|                "video": ["11", 0],
   138|                "filename_prefix": _filename_prefix(job),
   139|                "format": "mp4",
   140|                "codec": "h264",
   141|            },
   142|        },
   143|    }
   144|
   145|
   146|def write_i2v_clip_jobs(
   147|    prompt_plan: dict[str, Any],
   148|    output_file: Path,
   149|    keyframe_root: Path,
   150|    generated_clip_root: Path,
   151|    seeds: list[int],
   152|) -> Path:
   153|    """Write a clip-job package JSON."""
   154|    output_file.parent.mkdir(parents=True, exist_ok=True)
   155|    package = {
   156|        "schema_version": "i2v_clip_jobs.v1",
   157|        "video": prompt_plan["video"],
   158|        "strategy": prompt_plan["strategy"],
   159|        "keyframe_root": str(keyframe_root),
   160|        "generated_clip_root": str(generated_clip_root),
   161|        "jobs": build_i2v_clip_jobs(prompt_plan, keyframe_root, generated_clip_root, seeds),
   162|    }
   163|    output_file.write_text(json.dumps(package, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
   164|    return output_file
   165|


Explanation:
**Evaluating scaling requirements**

I'm considering the scaling options and it seems there’s a report mismatch regarding what’s required. Maybe I need to ensure that 416 is accepted as it seems okay. For keyframes, the original request was for 416x736, while NVIDIA supports 416. I should check if it tests well at that size. I wonder if resizing to the required dimensions after generating might be necessary, using LANCZOS for the resizing. I’ll need to patch that in the code.

patch

        if result.status == "success":
            required = item.get("required_format", {})
            row["image_check"] = _inspect_image(
                output_path,
                target_size=(int(required.get("width", args.width)), int(required.get("height", args.height))),
            )

        if result.status == "success":
            row["image_check"] = _inspect_image(output_path)


--- a//home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_keyframes_nvidia.py
+++ b//home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_keyframes_nvidia.py
@@ -83,7 +83,11 @@
         if result.error:
             row["error"] = result.error
         if result.status == "success":
-            row["image_check"] = _inspect_image(output_path)
+            required = item.get("required_format", {})
+            row["image_check"] = _inspect_image(
+                output_path,
+                target_size=(int(required.get("width", args.width)), int(required.get("height", args.height))),
+            )
         results.append(row)

     report = {

LSP diagnostics introduced by this edit:
<diagnostics file="/home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_keyframes_nvidia.py">
ERROR [89:17] No parameter named "target_size" [reportCallIssue] (Pyright)
</diagnostics>


def _inspect_image(path: Path, *, target_size: tuple[int, int]) -> dict[str, Any]:
    with Image.open(path) as image:
        original = {"width": image.width, "height": image.height, "mode": image.mode, "format": image.format}
        converted = image.convert("RGB").resize(target_size, Image.Resampling.LANCZOS)
        converted.save(path, format="PNG")
        original["normalized_format"] = "PNG"
        original["normalized_width"] = target_size[0]
        original["normalized_height"] = target_size[1]
        return original

def _inspect_image(path: Path) -> dict[str, Any]:
    with Image.open(path) as image:
        original = {"width": image.width, "height": image.height, "mode": image.mode, "format": image.format}
        converted = image.convert("RGB")
        converted.save(path, format="PNG")
        original["normalized_format"] = "PNG"
        return original


--- a//home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_keyframes_nvidia.py
+++ b//home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_keyframes_nvidia.py
@@ -120,12 +120,14 @@
     )

-def _inspect_image(path: Path) -> dict[str, Any]:
+def _inspect_image(path: Path, *, target_size: tuple[int, int]) -> dict[str, Any]:
     with Image.open(path) as image:
         original = {"width": image.width, "height": image.height, "mode": image.mode, "format": image.format}
-        converted = image.convert("RGB")
+        converted = image.convert("RGB").resize(target_size, Image.Resampling.LANCZOS)
         converted.save(path, format="PNG")
         original["normalized_format"] = "PNG"
+        original["normalized_width"] = target_size[0]
+        original["normalized_height"] = target_size[1]
         return original


Explanation:
**Considering image processing**

I think I need to rerun the process since there might not be a new API. I’m concerned that overwriting could be costly. What if I just run it with --models flux only? It seems like it might only use the API. I also need to normalize the existing images without regenerating them, so calling the PIL resize script could be a good plan. But then again, the report still reflects the old data. Maybe using --overwrite would be okay. I have two images to consider here.

terminal

python scripts/generate_keyframes_nvidia.py --overwrite --models qwen-image,stable-diffusion-3.5-large,flux.1-dev --steps 28 --seed-base 7300


Output:
{
  "schema_version": "nvidia_keyframe_generation_report.v1",
  "source_requests": "data/keyframe_requests/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json",
  "model_order": [
    "qwen-image",
    "stable-diffusion-3.5-large",
    "flux.1-dev"
  ],
  "requested_size": {
    "width": 768,
    "height": 1344
  },
  "total": 2,
  "succeeded": 2,
  "skipped": 0,
  "failed": 0,
  "results": [
    {
      "scene_id": "scene_01",
      "status": "success",
      "model": "flux.1-dev",
      "endpoint": "https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev",
      "http_status": 200,
      "output_path": "data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png",
      "bytes_written": 119654,
      "image_check": {
        "width": 768,
        "height": 1344,
        "mode": "RGB",
        "format": "JPEG",
        "normalized_format": "PNG",
        "normalized_width": 416,
        "normalized_height": 736
      }
    },
    {
      "scene_id": "scene_02",
      "status": "success",
      "model": "flux.1-dev",
      "endpoint": "https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev",
      "http_status": 200,
      "output_path": "data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png",
      "bytes_written": 115730,
      "image_check": {
        "width": 768,
        "height": 1344,
        "mode": "RGB",
        "format": "JPEG",
        "normalized_format": "PNG",
        "normalized_width": 416,
        "normalized_height": 736
      }
    }
  ]
}


Question: Assess this keyframe for a vertical short: photorealistic red used compact car in bright European inspection garage, no text/logos/license plates, caption negative space. Mention visible issues.


Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.

Question: Assess this keyframe for a vertical short: photorealistic red used compact car in bright European inspection garage, no text/logos/license plates, caption negative space. Mention visible issues.


Question: Assess this keyframe for a vertical short: photorealistic red used compact car in bright European inspection garage, no text/logos/license plates, caption negative space. Mention visible issues.


Explanation:
**Considering image generation cleanup**

I'm thinking about needing a logo or license plate for an EU blue background. It's possible the prompt "blank plate" didn't help. Should I post-process to neutralize? I could still see a logo. I might need to build in a cleanup method, maybe in the CLI. The user wants image generation, but I should ensure no visible logos. If I generate the image, I can mask the plate and badge using nearby colors while keeping it simple.
**Refining the car prompt**

I'm planning to add a prompt that specifies a "generic badge-free car" with a smooth blank grille and no manufacturer badge, plus a blank rectangular white plate without a blue strip. I think running the model with these adjustments will help. Also, I want to ensure the scenes are in a 3/4 view but still frontal. I might need to tweak the _build_prompt to include the badge-free details and then rerun it to see if it improves the outcome.

patch

    return (
        f"{prompt}\n\nStrict requirements: vertical 9:16 photorealistic keyframe, generic unbranded used compact car, "
        f"smooth badge-free grille and hood, blank neutral license plate area without blue EU strip, "
        f"no readable text, no brand logos, no manufacturer emblem, no watermark, no fake UI, no captions. "
        f"Avoid: {negative}"
    )

    return (
        f"{prompt}\n\nStrict requirements: vertical 9:16 photorealistic keyframe, no readable text, "
        f"no brand logos, no watermark, no readable license plate, no fake UI, no captions. "
        f"Avoid: {negative}"
    )


--- a//home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_keyframes_nvidia.py
+++ b//home/agent/jarvis_runtime/AutoShortsBot/scripts/generate_keyframes_nvidia.py
@@ -114,8 +114,9 @@
     if scene_id == "scene_02":
         prompt += " Slightly closer composition than the first shot, with subtle investigative mood and clean caption space."
     return (
-        f"{prompt}\n\nStrict requirements: vertical 9:16 photorealistic keyframe, no readable text, "
-        f"no brand logos, no watermark, no readable license plate, no fake UI, no captions. "
+        f"{prompt}\n\nStrict requirements: vertical 9:16 photorealistic keyframe, generic unbranded used compact car, "
+        f"smooth badge-free grille and hood, blank neutral license plate area without blue EU strip, "
+        f"no readable text, no brand logos, no manufacturer emblem, no watermark, no fake UI, no captions. "
         f"Avoid: {negative}"
     )


terminal

python scripts/generate_keyframes_nvidia.py --overwrite --models qwen-image,stable-diffusion-3.5-large,flux.1-dev --steps 28 --seed-base 7400


Output:
{
  "schema_version": "nvidia_keyframe_generation_report.v1",
  "source_requests": "data/keyframe_requests/mechanism_pilots/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann.json",
  "model_order": [
    "qwen-image",
    "stable-diffusion-3.5-large",
    "flux.1-dev"
  ],
  "requested_size": {
    "width": 768,
    "height": 1344
  },
  "total": 2,
  "succeeded": 2,
  "skipped": 0,
  "failed": 0,
  "results": [
    {
      "scene_id": "scene_01",
      "status": "success",
      "model": "flux.1-dev",
      "endpoint": "https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev",
      "http_status": 200,
      "output_path": "data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png",
      "bytes_written": 122189,
      "image_check": {
        "width": 768,
        "height": 1344,
        "mode": "RGB",
        "format": "JPEG",
        "normalized_format": "PNG",
        "normalized_width": 416,
        "normalized_height": 736
      }
    },
    {
      "scene_id": "scene_02",
      "status": "success",
      "model": "flux.1-dev",
      "endpoint": "https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev",
      "http_status": 200,
      "output_path": "data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png",
      "bytes_written": 133734,
      "image_check": {
        "width": 768,
        "height": 1344,
        "mode": "RGB",
        "format": "JPEG",
        "normalized_format": "PNG",
        "normalized_width": 416,
        "normalized_height": 736
      }
    }
  ]
}


Question: Assess visible logos/license plate/text and overall suitability. Be concise.


Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.

Question: Assess visible logos/license plate/text and overall suitability. Be concise.


Question: Assess visible logos/license plate/text and overall suitability. Be concise.


Explanation:
**Planning logo cleanup**

I need to create a function to blur or paint over logos in certain images—specifically a blue strip across the central logo. I'm thinking of setting up a script, maybe called autoshorts/keyframe_cleanup.py. I want to use some kind of inpainting for the region around the logo, focusing on dimensions like 416x736. For scene coordinates, I'll need to note the locations for different car logos, like the VW and Ford ones. Overall, it seems like a lot of manual work.
**Setting up image details**

I'm considering displaying an image sized 416x736. For scene 1, I should mark the license plate rectangle from x143 to x273 and y447 to y475. The logo placement should be around x191 to x225 and y383. After resizing, I should approximate those coordinates for scene 2, where the plate goes from x145 to x275 and y443 to y470, and the logo from x190 to x225 and y322 to y340. I could also draw a red badge on the hood with a script, ignoring other background logos.

python - <<'PY'
from PIL import Image, ImageDraw, ImageFilter
from pathlib import Path
base=Path('data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann')
ops={
 'scene_01.png': {
   'plate': (142,445,275,475),
   'badge': (187,374,229,405),
   'badge_color_at': (208,369),
 },
 'scene_02.png': {
   'plate': (141,442,275,470),
   'badge': (185,317,230,342),
   'badge_color_at': (208,315),
 },
}
for name,cfg in ops.items():
    p=base/name
    im=Image.open(p).convert('RGB')
    draw=ImageDraw.Draw(im)
    # neutral blank plate
    draw.rounded_rectangle(cfg['plate'], radius=3, fill=(198,207,205))
    # remove badge/emblem with local red body patch, then light blur for less pasted look
    color=im.getpixel(cfg['badge_color_at'])
    x1,y1,x2,y2=cfg['badge']
    draw.ellipse((x1,y1,x2,y2), fill=color)
    patch=im.crop((x1-4,y1-4,x2+4,y2+4)).filter(ImageFilter.GaussianBlur(radius=1.2))
    im.paste(patch,(x1-4,y1-4))
    im.save(p, format='PNG')
    print(name, im.size)
PY


Output:
scene_01.png (416, 736)
scene_02.png (416, 736)


Question: After cleanup, assess visible logos/license plate/text and overall suitability. Be concise.


Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.

Question: After cleanup, assess visible logos/license plate/text and overall suitability. Be concise.


Explanation:
**Evaluating grille design**

I’m thinking I need to create a black grille cover that will go over the upper gray area. Maybe I can draw a black rectangle starting from coordinates x143, y445 to represent the actual grille. I want to inspect these coordinates visually. Next, for the license plate area, I'll set x143-275 and y489-519. I’ll draw the upper bad gray part with a dark grille and add a red fill for the hood blob. The license plate will be filled in gray instead of blue. I might need to check Scene2 afterward.

Question: After cleanup, assess visible logos/license plate/text and overall suitability. Be concise.


python - <<'PY'
from autoshorts.nvidia_image_client import NvidiaImageClient,NvidiaImageRequest
from pathlib import Path
prompt='''Photorealistic vertical image of a generic unbranded red used compact hatchback in a bright European vehicle inspection garage, front-left three-quarter view from slightly above, smooth blank front grille with no emblem, no manufacturer badge, no license plate, no text, no logos, no watermark, visible wheels and body panels, soft overhead lights, realistic reflections, shallow depth of field, balanced exposure, high quality inspection-bay photography, clean negative space in upper and lower thirds for captions.'''
client=NvidiaImageClient()
for i,seed in enumerate([7501,7502,7503],1):
    out=Path(f'data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/variant_{i}.png')
    r=client.generate(NvidiaImageRequest(prompt=prompt,output_path=out,width=768,height=1344,steps=28,seed=seed),model_order=['flux.1-dev'])
    print(seed,r.status,r.model,r.bytes_written,out)
PY


Output:
7501 success flux.1-dev 125800 data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/variant_1.png
7502 success flux.1-dev 121354 data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/variant_2.png
7503 success flux.1-dev 117388 data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/variant_3.png


Question: Assess whether visible brand logos/emblems/license plates/text are present; suitability as clean keyframe.


Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.

Question: Assess whether visible brand logos/emblems/license plates/text are present; suitability as clean keyframe.


Question: Assess whether visible brand logos/emblems/license plates/text are present; suitability as clean keyframe.


Question: Assess whether visible brand logos/emblems/license plates/text are present; suitability as clean keyframe.


Explanation:
**Planning logo variations**

I'm considering a plate design with coordinates x150-266 and y475-505, and for the v2 logo, it might go at x208 and y369. There’s a plate with coordinates x144-270 and y465-494 that features a blue strip. I think about scripting the variants cleanly without using the current designs. I want to draw an ellipse on the hood logo with a red sample and a full gray plate. Also, a tiny grille logo above the grille at x205 and y380 might work.

python - <<'PY'
from PIL import Image, ImageDraw, ImageFilter
from pathlib import Path
base=Path('data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann')
configs=[
 ('variant_1.png','scene_01.png', {'badge':(190,360,226,382), 'badge_sample':(208,355), 'plate':(145,472,272,504)}),
 ('variant_2.png','scene_02.png', {'badge':(189,350,227,374), 'badge_sample':(208,345), 'plate':(143,464,273,495)}),
]
for src_name,dst_name,cfg in configs:
    im=Image.open(base/src_name).convert('RGB').resize((416,736), Image.Resampling.LANCZOS)
    draw=ImageDraw.Draw(im)
    # remove hood badge with local red; make it broad enough to cover synthetic emblem text.
    color=im.getpixel(cfg['badge_sample'])
    draw.rounded_rectangle(cfg['badge'], radius=8, fill=color)
    x1,y1,x2,y2=cfg['badge']
    patch=im.crop((x1-3,y1-3,x2+3,y2+3)).filter(ImageFilter.GaussianBlur(0.8))
    im.paste(patch,(x1-3,y1-3))
    # blank license plate and any blue strip.
    draw.rounded_rectangle(cfg['plate'], radius=3, fill=(199,208,207))
    im.save(base/dst_name, format='PNG')
    print(dst_name, im.size)
PY


Output:
scene_01.png (416, 736)
scene_02.png (416, 736)


Question: Assess visible logos/license plate/text and overall suitability after final cleanup. Be concise.


Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.

Question: Assess visible logos/license plate/text and overall suitability after final cleanup. Be concise.


Explanation:
**Clarifying logo details**

I'm working on the logo design and noticed that the badge coordinates are too high. It looks like the actual position should be at 397? I need some coverage for it. I'm thinking about whether to draw it in red or black. In scene 1, the logo should be positioned at x208, y403 on the grille. I wonder if I should fill it in black or red, keeping the hood patch less visible with a smooth red fill at specific coordinates. Should I discuss scene 2 next?

Question: Assess visible logos/license plate/text and overall suitability after final cleanup. Be concise.


Explanation:
**Considering logo positioning**

I need to use Python to work with coordinates. I'm thinking about whether I can output a crop. It seems a straightforward task, but I have to ensure the Scene2 logo stays at x207 and y384 and fills with red. There's also a smaller grille badge at x293, but I'm not sure if I need to include that. I’ll need to address these design elements carefully. Let's see how it all comes together!

python - <<'PY'
from PIL import Image, ImageDraw, ImageFilter
from pathlib import Path
base=Path('data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann')
# manual cleanup based on visual review
ops={
 'scene_01.png': [
   ('red',(190,362,226,383),(208,356)),
   ('black',(190,397,226,416),None),
   ('plate',(145,472,272,504),None),
 ],
 'scene_02.png': [
   ('red',(190,382,226,402),(208,378)),
   ('black',(285,411,305,424),None),
   ('plate',(143,464,273,495),None),
 ],
}
for name,items in ops.items():
    im=Image.open(base/name).convert('RGB')
    draw=ImageDraw.Draw(im)
    for kind,box,sample in items:
        if kind=='red':
            color=im.getpixel(sample)
            draw.rounded_rectangle(box, radius=8, fill=color)
            x1,y1,x2,y2=box
            patch=im.crop((x1-2,y1-2,x2+2,y2+2)).filter(ImageFilter.GaussianBlur(0.7))
            im.paste(patch,(x1-2,y1-2))
        elif kind=='black':
            draw.rounded_rectangle(box, radius=5, fill=(9,13,15))
        elif kind=='plate':
            draw.rounded_rectangle(box, radius=3, fill=(199,208,207))
    im.save(base/name,format='PNG')
    print(name)
PY


Output:
scene_01.png
scene_02.png


Question: Final check: visible logos/license plate/text? Suitability? Be concise.


Image loaded into your context — you can see it natively now. Use your built-in vision to answer the user.

Question: Final check: visible logos/license plate/text? Suitability? Be concise.


Explanation:
**Considering design changes**

I'm noticing that the black looks a bit ugly, especially without a logo. Maybe it's not acceptable to leave it like this. I think using a black patch in the grille should be hidden, but it's positioned above. It might be better to use red in that area instead. Using a red cover over the black could help, especially if I adjust where the grille logo sits. I'll consider patching the black to red for a cleaner look.

python - <<'PY'
from PIL import Image, ImageDraw, ImageFilter
from pathlib import Path
p=Path('data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_01.png')
im=Image.open(p).convert('RGB')
d=ImageDraw.Draw(im)
# cover accidental black box with surrounding red hood paint
box=(190,391,226,413); color=im.getpixel((208,386))
d.rounded_rectangle(box,radius=7,fill=color)
# subtle blur area
x1,y1,x2,y2=box
patch=im.crop((x1-3,y1-3,x2+3,y2+3)).filter(ImageFilter.GaussianBlur(0.9))
im.paste(patch,(x1-3,y1-3))
im.save(p,format='PNG')
PY


Question: Final check: visible logos/license plate/text? Suitability? Be concise.


Question: Final check: visible logos/license plate/text? Suitability? Be concise.


Explanation:
**Adjusting logo visibility**

I noticed the Ford logo is still visible on the hood, which is a bit concerning. It seems our coordinates are off. Plus, there's a black patch that needs addressing. I’m thinking about manually masking the hood logo at coordinates x207 and y380 to cover it properly. I also need to extend the black area slightly on the right and use red over the coordinates x190, y379 to x226, y394 to blend things better.

python - <<'PY'
from PIL import Image, ImageDraw, ImageFilter
from pathlib import Path
p=Path('data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png')
im=Image.open(p).convert('RGB')
d=ImageDraw.Draw(im)
for box,sample in [((187,374,229,397),(208,370)), ((284,399,306,416),(280,398))]:
    color=im.getpixel(sample)
    d.rounded_rectangle(box,radius=7,fill=color)
    x1,y1,x2,y2=box
    patch=im.crop((x1-2,y1-2,x2+2,y2+2)).filter(ImageFilter.GaussianBlur(0.8))
    im.paste(patch,(x1-2,y1-2))
im.save(p,format='PNG')
PY


Question: Final check: visible logos/license plate/text? Suitability? Be concise.


Explanation:
**Designing logo elements**

I’m thinking about the Ford logo at coordinates y392. It looks like I need a cover between x197-220 and y389-400. Maybe a black patch? Could it work if it’s mirrored? I also wonder if adding some red would improve the design. I'll have to keep in mind how everything balances out visually. It’s important for the elements to fit well together and create a cohesive look!

python - <<'PY'
from PIL import Image, ImageDraw
from pathlib import Path
p=Path('data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png')
im=Image.open(p).convert('RGB')
d=ImageDraw.Draw(im)
d.rounded_rectangle((193,386,224,402),radius=6,fill=im.getpixel((205,380)))
d.rounded_rectangle((282,399,307,416),radius=6,fill=im.getpixel((279,397)))
im.save(p,format='PNG')
PY


Question: Final check: visible logos/license plate/text? Suitability? Be concise.


Explanation:
**Considering logo placement**

I’m thinking about the logo and whether it’s aligned correctly. It looks like there's a small oval in the grille, but my patch doesn’t seem to match. I might need to update to the current logo at coordinates (208, 400). I want to ensure it covers the area between x190-225 and y390-410 in red. It’s essential to get this spot on for the best visual impact!

python - <<'PY'
from PIL import Image, ImageDraw
from pathlib import Path
p=Path('data/keyframes/hidden-money-systems-warum-ein-billiges-auto-teuer-sein-kann/scene_02.png')
im=Image.open(p).convert('RGB')
d=ImageDraw.Draw(im)
# broad red overlay over remaining hood/grille badge area; rough but logo-free for I2V input
d.rounded_rectangle((188,384,228,414),radius=8,fill=(199,15,20))
d.rounded_rectangle((281,397,309,418),radius=7,fill=(170,18,24))
im.save(p,format='PNG')
PY


Question: Final check: visible logos/license plate/text? Suitability? Be concise.