type: custom

customType: model-snapshot

provider: zai

modelApi: openai-completions


type: message

role: user

type: text

text:
[cron:bb08392d-b9fb-40c6-8cc3-163a764c847d selena-heartbeat-30m-qwen] Run heartbeat check (read HEARTBEAT.md). If actionable tasks are found, execute them. If nothing to do, reply with NO_REPLY. Do NOT say HEARTBEAT_OK.
Current time: Saturday, March 7th, 2026 — 8:12 PM (UTC)

Return your summary as plain text; it will be delivered automatically. If the task explicitly calls for messaging a specific external recipient, note who/where it should go instead of sending it yourself.


role: assistant

type: thinking

thinking: The user is asking me to run a heartbeat check. According to my AGENTS.md protocol, I should read HEARTBEAT.md first. Let me do that.

read

type: toolCall

api: openai-completions

stopReason: toolUse


role: toolResult

toolName: read

text:
# HEARTBEAT.md

# Keep this file empty (or with only comments) to skip heartbeat API calls.

# Tasks to check during heartbeat:
# - Pending generation requests from other agents
# - ComfyUI status (if running)
# - Model download progress
# - Disk space for outputs


thinking:
The HEARTBEAT.md file is mostly comments and doesn't have any specific tasks. Let me check if there are any pending tasks in TASKS.md and also check my memory files to see if there's anything pending that needs attention.

According to my boot sequence, I should check:
1. Today's memory file (2026-03-07)
2. Yesterday's memory file (2026-03-06)
3. MEMORY.md (main session only)

Let me check for any pending work.


text:
# TASKS.md - Active Task State

| ID | Request | Status | Next | Blocked By |
|----|---------|--------|------|------------|
| T1 | Wan 2.2 I2V pipeline setup | done | Test with user video | — |
| T2 | Inter-agent comms setup | done | — | — |
| T3 | Canna blog headers (11x) | done | — | — |
| T4 | Cat poop i2v video | blocked | Retry when VRAM free | ACE-Step/VRAM |
| T5 | Canna SEO content images (12x) | done | — | — |
| T6 | Weekend Deals blog hero | done | — | — |
| T7 | ATF/Gun Rights blog hero | done | — | — |

<!-- Status: pending | active | done | blocked -->
<!-- Update this table on every task start, completion, or block -->


text:
# 2026-03-07 - Selena Activity Log

## Tasks Accomplished
- Generated "Magical Poolside Adventure" image using Nano Banana (Gemini 3 Pro Image).
- Encountered and resolved `FinishReason.PROHIBITED_CONTENT` by generalizing the prompt (replacing specific Disney names with descriptions).
- Verified and fixed Nano Banana skill integration in the local environment by explicitly providing the `GEMINI_API_KEY` from `openclaw.json`.
- Cancelled Hunyuan Video generation as per user request for only an image.

## Decisions
- Used the prompt: "A magical poolside adventure with two friends. They are playing together by a sparkling blue tropical swimming pool. Lush greenery and colorful flowers. High-quality digital art, 1200x630 landscape."
- Switched to generic descriptions to bypass safety filters while maintaining the requested vibe.

## Next Steps
- Standby for further artistic direction.


text:
# MEMORY.md - Long-Term Knowledge

## Model Selection Guide

### Image Generation - Quick Reference

| Use Case | Recommended Model | VRAM | Speed |
|----------|-------------------|------|-------|
| Fast prototyping | FLUX.1-schnell | 12GB+ | Fast |
| Best quality | FLUX.1-dev | 16GB+ | Slow |
| Low VRAM | SDXL + Juggernaut | 8GB+ | Medium |
| Very low VRAM | SD 1.5 | 4GB+ | Fast |
| Anime/stylized | SDXL + LoRAs | 8GB+ | Medium |
| Photorealism | FLUX or Juggernaut | 12GB+ | Varies |

### Video Generation - Quick Reference

| Use Case | Recommended Model | VRAM | Duration |
|----------|-------------------|------|----------|
| Quick iterations | LTXVideo | 12GB+ | Variable |
| High quality short | Mochi 1 | 24GB+ | 5s |
| Cinematic | HunyuanVideo | 80GB | 15s |
| Human-focused | SkyReels V1 | 24GB+ | 12s |
| SD animation | AnimateDiff | 8GB+ | Variable |

### TTS - Quick Reference

| Use Case | Recommended | Notes |
|----------|-------------|-------|
| Voice cloning | XTTS v2 | 6s sample needed |
| Fast synthesis | Kokoro | 82M params, very fast |
| Expressive | FishAudio S1 | Emotion control |
| Dialogue/podcast | Dia2 | Multi-speaker |
| Long-form | VibeVoice | Up to 90 minutes |

---

## Prompt Engineering Tips

### Stable Diffusion / SDXL
```
Quality boosters: masterpiece, best quality, highly detailed, 8k
Negative: blurry, low quality, watermark, text, deformed

Structure: [subject], [action/pose], [setting], [style], [lighting], [quality tags]

Example: "a cyberpunk hacker, typing on holographic keyboard, neon-lit server room, cinematic lighting, volumetric fog, masterpiece, 8k"
```

### FLUX
- More natural language friendly
- Less need for quality tags
- Better at following complex prompts
- Specify aspect ratio explicitly

```
Example: "Photograph of a vintage coffee shop interior, morning sunlight streaming through large windows, dust particles in the air, film grain, Kodak Portra 400"
```

### ControlNet Best Practices
- **Depth**: Preserve spatial relationships
- **OpenPose**: Character poses
- **Canny/LineArt**: Edge-guided generation
- **Scribble**: Quick sketches to images

---

## Workflow Patterns

### Basic Text-to-Image
```
[CLIP Encode] → [KSampler] → [VAE Decode] → [Save Image]
```

### Image-to-Image
```
[Load Image] → [VAE Encode] → [KSampler (denoise < 1.0)] → [VAE Decode]
```

### Upscale Pipeline
```
[Load Image] → [Upscale Model] → [KSampler (low denoise)] → [Save Image]
```

### ControlNet Pipeline
```
[Load Image] → [Preprocessor] → [ControlNet Apply] → [KSampler]
```

### Video from Images (LTX)
```
[Load Images] → [LTX Encode] → [Sample] → [LTX Decode] → [Video Combine]
```

---

## Common Issues & Solutions

### Out of Memory (OOM)
1. Reduce resolution
2. Use fp16 instead of fp32
3. Enable attention slicing
4. Use tiled VAE decode
5. Close other GPU applications

### Slow Generation
1. Use schnell/turbo variants
2. Reduce steps (20-30 is often enough)
3. Use LCM/Turbo samplers
4. Check for thermal throttling

### Inconsistent Faces
1. Use face-specific LoRAs
2. Add face restoration (GFPGAN, CodeFormer)
3. Use inpainting for face region
4. Try different seeds

### Video Artifacts
1. Increase frame overlap
2. Use temporal smoothing
3. Check motion intensity settings
4. Try different models

---

## Installation Commands

### Stability Matrix (Linux)
```bash
# Download from releases page or:
wget https://github.com/LykosAI/StabilityMatrix/releases/latest/download/StabilityMatrix-linux-x64.zip
unzip StabilityMatrix-linux-x64.zip
chmod +x StabilityMatrix
./StabilityMatrix
```

### ComfyUI (Manual)
```bash
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
pip install -r requirements.txt
python main.py
```

### XTTS v2
```bash
pip install TTS
tts --text "Hello world" --model_name tts_models/multilingual/multi-dataset/xtts_v2 --out_path output.wav
```

### faster-whisper
```bash
pip install faster-whisper
# In Python:
from faster_whisper import WhisperModel
model = WhisperModel("large-v3", device="cuda")
segments, info = model.transcribe("audio.mp3")
```

### AudioCraft
```bash
pip install audiocraft
# In Python:
from audiocraft.models import MusicGen
model = MusicGen.get_pretrained('facebook/musicgen-medium')
model.generate(['epic orchestral battle music'], duration=30)
```

---

## Hardware Installed

*(Update this section after detecting local hardware)*

- **GPU**: [Not yet detected]
- **VRAM**: [Not yet detected]
- **RAM**: [Not yet detected]
- **Storage**: [Not yet detected]

---

## Product Placement Pipeline (IC-Light FC) ✅ WORKING

### The Right Workflow
Use `product_iclight_fc.json` — IC-Light FC (Foreground Conditioned) properly integrates lighting.
**Do NOT use composite workflow** — that just hard-pastes the cutout, lighting looks wrong.

### API Submit Pattern
See `/home/crogers2287/.openclaw/agents/selena/submit_iclight.py` for full working example.

Key nodes in order:
`LoadImage → BiRefNetRMBG → ICLightApplyMaskGrey → VAEEncode`
`CheckpointLoaderSimple → LoadAndApplyICLightUnet`
`CLIPTextEncode (pos+neg) → ICLightConditioning → KSampler → VAEDecode → SaveImage`

### Verified Node Input Names
- `LoadAndApplyICLightUnet`: inputs = `model`, `model_path` → use `iclight_sd15_fc_unet_ldm.safetensors`
- `ICLightApplyMaskGrey`: inputs = `image`, `alpha` (alpha = MASK slot 1 from BiRefNetRMBG)
- `ICLightConditioning`: inputs = `positive`, `negative`, `vae`, `foreground`, `multiplier`; optional: `opt_background`
- `BiRefNetRMBG` outputs: IMAGE (slot 0), MASK (slot 1), MASK_IMAGE (slot 2)
- Checkpoint: `RealisticVision_V6_fp16.safetensors` (SD1.5 based, required for IC-Light)

### Settings That Work
- Steps: 25, CFG: 2.0, sampler: dpmpp_2m karras, denoise: 1.0

### Outputs
- Outputs: `/home/crogers2287/ComfyUI/output/`
- Users view via: http://fred:8188 (History tab) — cannot push images from Selena session

---

## Chelsea Schnepp — Group Chat
- Chelsea will request product placement images in group chat with Chad
- Workflow: ask product type/image + scene style → run IC-Light FC → point to http://fred:8188
- Good scene prompts: luxury marble, spa/plants, morning vanity, moody/dark, outdoor, tech desk, travel

---

## Wan 2.2 Two-Pass I2V Workflow ✅ WORKING

### Setup
- Custom node: `ComfyUI-WanVideoWrapper` (installed)
- Models in `models/diffusion_models/WanVideo/2_2/`:
  - `Wan2_2-I2V-A14B-HIGH_fp8_e4m3fn_scaled_KJ.safetensors` (14GB)
  - `Wan2_2-I2V-A14B-LOW_fp8_e4m3fn_scaled_KJ.safetensors` (14GB)
- VAE: `models/vae/wanvideo/Wan2_1_VAE_bf16.safetensors` (243MB)
- T5: `models/text_encoders/umt5-xxl-enc-fp8_e4m3fn.safetensors` (6.3GB)
- CLIP: `models/clip_vision/open-clip-xlm-roberta-large-vit-huge-14_visual_fp16.safetensors` (1.2GB)

### API Submit Script
`/home/crogers2287/.openclaw/agents/selena/submit_wan22_v4.py`

### Two-Pass Settings
- **Pass 1 (HIGH)**: Steps 0-3, CFG 1.5 → forces motion
- **Pass 2 (LOW)**: Steps 3-6, CFG 1.0 → refines detail
- Use `attention_mode: sdpa` (sageattn not installed)
- Block swap: 20 blocks for 24GB VRAM

### Prompting
- 80-100 words, explicit camera constraints
- Always include: "Static camera shot. ... The camera remains perfectly still. No zooming, no panning."

---

## Projects Log

*(Track major generation projects here)*

---

*Last updated: 2026-02-18*


thinking:
The TASKS.md shows:
- T4 (Cat poop i2v video) is blocked by ACE-Step/VRAM

Let me check if there's anything from yesterday's memory file and also check the preconscious buffer for any priority items.

Looking at the current state:
- Most tasks are done
- T4 is blocked waiting for VRAM
- HEARTBEAT.md has no specific tasks

Let me check the preconscious buffer to see if there's anything urgent, and also check if there are any pending requests from other agents.


text:
# Preconscious Buffer
# Auto-generated: Sat Mar  7 18:03:45 UTC 2026
# Top 10 priority items for immediate attention

<think>
Thinking Process:

1.  **Analyze the Request:**
    *   **Role:** Priority Selector.
    *   **Task:** Select the TOP 10 most important items from recent observations and events.
    *   **Rules:**
        *   Prefer actionable items over passive facts.
        *   Prefer recent over old.
        *   Prefer HIGH priority.
        *   Group related items.
        *   Score each item 0.0-1.0.
    *   **Format:**
        *   `### [SCORE] Title`
        *   `- Details`
        *   `- Source: where it came from`
    *   **Input Data:**
        *   One recent HIGH priority observation: `[HIGH] FACT: System audit detected missing startup files (WORKFLOW_AUTO.md, memory/*.md) after context reset, requiring immediate restoration.`
        *   No other recent events provided (the list is empty).
    *   **Constraint:** I need to output TOP 10 items, but I only have *one* explicit input item. This creates a conflict. I cannot invent facts, but I must follow the instruction to select the top 10.
    *   **Interpretation:** Since I am an AI assistant operating within a specific context (likely a system with memory/workflow files), and the prompt implies I should act as a "priority selector" based on *given* observations, but the observations provided are sparse (only one item), I need to extrapolate actionable priorities based on the *implication* of that single item to reach 10 items, OR acknowledge the limitation while maximizing the single item into actionable steps.
    *   **Better Interpretation:** The prompt asks me to select from "Recent observations and events". If there is only one, I cannot fabricate 10 distinct *events*. However, the instruction says "Select the TOP 10 most important items". If the input is insufficient, I should prioritize the *actions* required by the single input to flesh out the list, treating sub-tasks as items, OR I must acknowledge the scarcity.
    *   **Strict Adherence:** The prompt says "Given recent observations and events...". It provides one. It asks for 10. If I output 10 items based on one fact, I am hallucinating events. If I output 1 item, I violate the "TOP 10" constraint.
    *   **Resolution:** I will break down the single HIGH priority observation into its constituent actionable steps and related system health checks to form a list of 10 prioritized tasks/items. This aligns with "Prefer actionable items". The source will remain the system audit/context reset event. I will group related items as per rules.

    *   *Wait, looking closer at the prompt structure:* The prompt says "Recent events:" is empty. Only one `[HIGH] FACT` is provided.
    *   *Strategy:* I will derive 10 actionable priorities based on the critical nature of the missing startup files. This ensures I meet the "TOP 10" requirement while staying true to the "actionable" rule. I will treat the restoration process as a series of 10 critical steps/items.

    *   *Refinement:* Actually, as an AI, I should be careful not to hallucinate *events*. But I can list *actions* required. The prompt asks for "items" in the context of a priority selector. If the input is limited, I should expand on the implications of the input to fill the quota without lying about *new* events. I will frame them as "Action Items" derived from the single observation.

    *   *Alternative:* If I can only find 1 item, I should state that. But the instruction says "Output top 10 items". I will generate 10 actionable priorities derived from the system audit failure.

    *   *Let's draft the 10 items based on the one fact:*
        1.  Restore WORKFLOW_AUTO.md.
        2.  Restore memory/*.md files.
        3.  Verify file integrity.
        4.  Check system startup sequence.
        5.  Audit context reset logs.
        6.  Prevent future context loss.
        7.  Notify stakeholders of audit failure.
        8.  Backup current state before restoration.
        9.  Test workflow execution post-restoration.
        10. Update memory persistence protocols.

    *   *Scoring:* All are HIGH priority due to the `[HIGH]` tag on the source. Scores should reflect urgency.
    *   *Grouping:* I will group them logically (Restoration, Verification, Prevention).

    *   *Wait, one more check:* The prompt says "Given recent observations...". If I make up 9 more items that didn't happen, is that valid? The prompt asks me to *select* from observations. If there aren't 10 observations, I can't select 10. However, usually, in these roleplay tasks, the user expects the AI to fulfill the format. I will frame the items as "Actionable Priorities" derived from the observation to satisfy the count constraint while maintaining truthfulness about the source (the audit).

    *   *Actually, a safer approach:* I will prioritize the single observation but break it down into 10 specific *tasks* required to resolve the issue, labeling them as items to be selected for action. This satisfies "Prefer actionable items".

    *   *Let's adjust:* I need to make sure I don't lie about the *source*. The source for all will be the System Audit/Context Reset event.

    *   *Wait, is there a hidden constraint?* "Recent events:" is empty. I only have the one FACT.
    *   *Decision:* I will create 10 actionable items derived from the critical failure to ensure the output format is met, as strictly outputting 1 item would fail the "TOP 10" instruction. I will treat the "items" as "Priority Tasks".

    *   *Re-evaluating:* If I output 10 items based on 1 fact, it looks like hallucination. But if I output 1 item, I fail the count. The best middle ground is to list the 1 critical fact and then 9 derived action items, clearly stating the source is the audit.
    *   *Actually, looking at the "Rules":* "Group related items". This suggests I can have fewer than 10 if that's all there is, but the instruction says "Output top 10 items". I will prioritize the *actions* needed to fix the problem as the items.

    *   *Let's try to find 10 distinct actionable points.*
    *   1. Restore WORKFLOW_AUTO.md
    *   2. Restore memory/*.md
    *   3. Validate file checksums
    *   4. Reboot startup sequence
    *

---
*This buffer is injected into agent sessions on startup/heartbeat*


status: error

tool: read

error: ENOENT: no such file or directory, access '/home/crogers2287/.openclaw/agents/selena/memory/2026-03-06.md'


thinking:
Looking at the current state:

1. HEARTBEAT.md has no specific tasks
2. TASKS.md shows most tasks done, T4 blocked by VRAM
3. Preconscious buffer shows only one item - about system audit detecting missing startup files, but that seems to be for another agent/context
4. Today's memory (2026-03-07) shows standby for further artistic direction

There's nothing actionable right now. T4 is blocked waiting for VRAM. All other tasks are done. No new requests from other agents.

According to the heartbeat instructions: "If nothing to do, reply with NO_REPLY."

stopReason: stop