✅ New session started · model: anthropic/claude-sonnet-4-5


A new session was started via /new or /reset. Greet the user in your configured persona, if one is provided. Be yourself - use your defined voice, mannerisms, and mood. Keep it to 1-3 sentences and ask what they want to do. If the runtime model differs from default_model in the system prompt, mention the default model. Do not mention internal steps, files, tools, or reasoning.


The user wants me to greet them in my configured persona as Selena, the Art Director and Creative Muse. Let me be enthusiastic and creative, keeping it to 1-3 sentences.

Hey Chad! 🎨 Selena here, ready to transform some imagination into reality — whether that's images, video, audio, or a whole new workflow pipeline. What are we creating today?


Conversation info (untrusted metadata):
```json
{
  "message_id": "144",
  "sender": "6558388607"
}
```

Run this through the pipeline


The user said "Run this through the pipeline" but didn't specify what "this" is. There's no attachment, image, file, or other content referenced in their message. I need to ask what they want to run through the pipeline.

[[reply_to_current]] Ooh, I love the enthusiasm — but I'm not seeing anything attached! 😄 What are you wanting to run through the pipeline? A prompt, an image, a video clip? Drop it here and let's get moving.


[media attached: /home/crogers2287/.openclaw/media/inbound/file_2---fa26d2fd-af78-479a-a709-f6c5e17aae7f.jpg (image/jpeg) | /home/crogers2287/.openclaw/media/inbound/file_2---fa26d2fd-af78-479a-a709-f6c5e17aae7f.jpg]
To send an image back, prefer the message tool (media/path/filePath). If you must inline, use MEDIA:https://example.com/image.jpg (spaces ok, quote if needed) or a safe relative path like MEDIA:./image.jpg. Avoid absolute paths (MEDIA:/...) and ~ paths — they are blocked for security. Keep caption in the text body.
Conversation info (untrusted metadata):
```json
{
  "message_id": "145",
  "sender": "6558388607"
}
```

<media:image>


The user sent an image of a hand holding an Olay Regenerist 10 cream jar against a marble tile background with window blind shadows. They want to "run this through the pipeline" - but I need to understand what pipeline they mean.

Looking at the image, this appears to be a product photo of an Olay Regenerist 10 face cream. The image has heavy black bars at top and bottom (letterboxing), and the photo is taken in a bathroom with marble tiles and interesting shadow patterns from window blinds.

The user said "run this through the pipeline" but didn't specify what kind of processing they want. As Selena, the Art Director, I could:

- Upscale or enhance the image quality
- Recompose it as a more professional product shot
- Remove the black letterboxing
- Strip out the background entirely

I need to ask what they're actually looking for here - whether they want image enhancement, a complete product photography redo, background removal, or something else entirely.

[[reply_to_current]] Nice shot — got the Olay Regenerist 10 with some gorgeous blind shadow texture on that marble tile.

A few questions before I fire it up:

1. **What output are you going for?** Upscale/enhance? Professional product shot? Background removal? Something else?
2. **Any style direction?** Studio white, lifestyle, luxury editorial?
3. **Crop those black bars** — want me to clean those up as part of it?

Tell me the goal and I'll dial in the right workflow! 🎨


Conversation info (untrusted metadata):
```json
{
  "message_id": "148",
  "sender": "6558388607"
}
```

All of the above. Place it on some good backgrounds


The user wants me to:
1. Upscale/enhance the image
2. Create a professional product shot
3. Remove background
4. Place it on good backgrounds
5. Clean up the black bars

This is a ComfyUI/image generation task. Let me think about what tools I have available to accomplish this.

I need to:
1. First, crop/clean the image (remove black bars)
2. Remove the background from the product
3. Generate some nice backgrounds
4. Composite the product onto those backgrounds

Let me check what tools are available on this system. I should look at what's installed. Let me check the MEMORY.md and see if there's any installed software noted.

Since the hardware info isn't documented yet, I need to check what's actually available on the system—I'll start by probing for GPU capabilities, whether ComfyUI is set up, and what image processing libraries or tools are installed.

Love it! Let me check what we're working with on this machine first — then I'll build the workflow.

exec

nvidia-smi --query-gpu=name,memory.total,memory.free --format=csv,noheader 2>/dev/null || echo "No NVIDIA GPU detected"

free -h | grep Mem && df -h /home | tail -1


NVIDIA GeForce RTX 3090, 24576 MiB, 6451 MiB
NVIDIA GeForce RTX 3090, 24576 MiB, 22058 MiB


Mem:           125Gi        36Gi        13Gi       273Mi        77Gi        89Gi
/dev/mapper/ubuntu--vg-ubuntu--lv  915G  818G   59G  94% /


Two RTX 3090s with 24GB VRAM each! That's great. And 125GB RAM with 59GB free disk. Let me check what's installed for image processing.

which python3 && python3 --version; pip list 2>/dev/null | grep -iE "rembg|diffusers|torch|PIL|pillow|transformers|comfy" | head -20

ls ~/StabilityMatrix* 2>/dev/null || echo "No StabilityMatrix"; ls ~/ComfyUI* 2>/dev/null || echo "No ComfyUI in home"; find /home -name "ComfyUI" -type d 2>/dev/null | head -5


/usr/bin/python3
Python 3.12.3
pillow                                 12.0.0
pillow-avif-plugin                     1.5.2
torch                                  2.5.1+cu121
torchvision                            0.20.1+cu121
transformers                           4.46.3
types-Pillow                           10.1


Command still running (session cool-claw, pid 1725437). Use process (list/poll/log/write/kill/clear/remove) for follow-up.

No StabilityMatrix
/home/crogers2287/ComfyUI:
CODEOWNERS
CONTRIBUTING.md
LICENSE
MUSIC_VIDEO_SETUP.md
QUANTIZATION.md
README.md
__pycache__
alembic.ini
alembic_db
api_server
app
blueprints
comfy
comfy_api
comfy_api_nodes
comfy_config
comfy_execution
comfy_extras
comfyui_version.py
cuda_malloc.py
custom_nodes
ecosystem.config.js
execution.py
extra_model_paths.yaml
extra_model_paths.yaml.example
folder_paths.py
hook_breaker_ac10a0.py
input
latent_preview.py
logs
main.py
manager_requirements.txt
middleware
models
new_updater.py
node_helpers.py
nodes.py
output
protocol.py
pyproject.toml
pytest.ini
requirements.txt
script_examples
server.py
start.sh
temp
tests
tests-unit
user
utils
venv
web

/home/crogers2287/ComfyUI-Launcher:
=7.0.0
Dockerfile
LICENSE
README.md
assets
celery_env
cloud
config.json
docker
models
nginx.conf
package-lock.json
package.json
projects
requirements.txt
run.sh
server
server.log
templates
test-workflow.json
venv
web


We have torch, transformers, and pillow. Let me check if rembg is installed and if ComfyUI is installed. Let me also check the background process.

pip show rembg 2>/dev/null && echo "rembg installed" || echo "rembg NOT installed"; pip show diffusers 2>/dev/null | head -3 || echo "diffusers NOT installed"


No StabilityMatrix
/home/crogers2287/ComfyUI:
CODEOWNERS
CONTRIBUTING.md
LICENSE
MUSIC_VIDEO_SETUP.md
QUANTIZATION.md
README.md
__pycache__
alembic.ini
alembic_db
api_server
app
blueprints
comfy
comfy_api
comfy_api_nodes
comfy_config
comfy_execution
comfy_extras
comfyui_version.py
cuda_malloc.py
custom_nodes
ecosystem.config.js
execution.py
extra_model_paths.yaml
extra_model_paths.yaml.example
folder_paths.py
hook_breaker_ac10a0.py
input
latent_preview.py
logs
main.py
manager_requirements.txt
middleware
models
new_updater.py
node_helpers.py
nodes.py
output
protocol.py
pyproject.toml
pytest.ini
requirements.txt
script_examples
server.py
start.sh
temp
tests
tests-unit
user
utils
venv
web

/home/crogers2287/ComfyUI-Launcher:
=7.0.0
Dockerfile
LICENSE
README.md
assets
celery_env
cloud
config.json
docker
models
nginx.conf
package-lock.json
package.json
projects
requirements.txt
run.sh
server
server.log
templates
test-workflow.json
venv
web
/home/crogers2287/projects/imagestuff/ComfyUI
/home/crogers2287/ComfyUI

Process exited with code 0.


rembg NOT installed


ComfyUI is installed at ~/ComfyUI. Let me check if it's running and what custom nodes are available. Also, I need to check if rembg is installed in the ComfyUI venv. Also need to check diffusers.

Let me:
1. Check if ComfyUI is running
2. Check what custom nodes are in ComfyUI
3. Check the ComfyUI venv for rembg

The plan would be:
1. Install rembg to remove the background from the product
2. Use ComfyUI to generate nice backgrounds
3. Composite the product onto the generated backgrounds

4. Actually, I could handle this entire workflow within ComfyUI itself if the right custom nodes are available, so let me first see what I'm working with.

exec

curl -s http://127.0.0.1:8188/system_stats 2>/dev/null | python3 -c "import sys,json; d=json.load(sys.stdin); print('ComfyUI RUNNING:', d.get('system',{}).get('python_version','?'))" 2>/dev/null || echo "ComfyUI not responding on 8188"


ComfyUI RUNNING: 3.12.3 (main, Jan 22 2026, 20:57:42) [GCC 13.3.0]


ComfyUI-Florence2
ComfyUI-IC-Light
ComfyUI-IC-Light-Native
ComfyUI-Image-Filters
ComfyUI-KJNodes
ComfyUI-Manager
ComfyUI-RMBG
ComfyUI_essentials
RyanOnTheInside
__pycache__
example_node.py.example
rgthree-comfy
websocket_image_save.py


ComfyUI is running and it has:
- **ComfyUI-RMBG** - Background removal! Perfect for removing the hand/background from the product
- **ComfyUI-IC-Light / ComfyUI-IC-Light-Native** - IC-Light for relighting! This is perfect for placing the product on different backgrounds with proper lighting
- **ComfyUI-Florence2** - Florence2 for image analysis/captioning
- **ComfyUI-KJNodes** - Utility nodes
- **ComfyUI-Image-Filters** - Image filtering
- **ComfyUI_essentials** - Essential nodes

-
I can build a workflow that loads the product image, removes the background and hand with RMBG, then uses IC-Light to relight it on different backgrounds before saving multiple versions. But I need to think through the actual image more carefully—there's a hand holding the product, black bars at the top and bottom, and a marble tile background with window shadow. The goal is to isolate just the product and place it on better backgrounds, so I need to check what models are available and what RMBG and IC-Light require.

Oh we're in business! ComfyUI is live with exactly the right nodes — **RMBG** for background removal and **IC-Light** for beautiful relighting. Let me check our models.

ls ~/ComfyUI/models/ && echo "---" && find ~/ComfyUI/models -name "*.safetensors" -o -name "*.ckpt" 2>/dev/null | head -30

ls ~/ComfyUI/custom_nodes/ComfyUI-RMBG/ && echo "---" && ls ~/ComfyUI/custom_nodes/ComfyUI-IC-Light-Native/ 2>/dev/null || ls ~/ComfyUI/custom_nodes/ComfyUI-IC-Light/ 2>/dev/null


LLM
RMBG
audio_encoders
checkpoints
clip
clip_vision
configs
controlnet
diffusers
diffusion_models
embeddings
gligen
hypernetworks
latent_upscale_models
loras
model_patches
photomaker
style_models
text_encoders
ultralytics
unet
upscale_models
vae
vae_approx
yolo
---
/home/crogers2287/ComfyUI/models/RMBG/BiRefNet/BiRefNet-general.safetensors


Skipped due to queued user message.


Conversation info (untrusted metadata):
```json
{
  "message_id": "150",
  "sender": "6558388607"
}
```

The workflow is already built. You did it last night, you just need to use them


The user says I already built the workflow last night. Let me check my memory and look at the workflows directory for existing workflows.

Question: Olay product photo pipeline workflow background removal IC-Light

ls ~/ComfyUI/workflows/ 2>/dev/null || ls /home/crogers2287/.openclaw/agents/selena/workflows/ 2>/dev/null || find ~/ComfyUI -name "*.json" | grep -v node_modules | head -20


(no output)


find ~/ComfyUI -name "*.json" 2>/dev/null | grep -v "__pycache__\|node_modules\|venv" | head -30

Question: product shot workflow background pipeline last night


- ComfyUI-RMBG (1038lab) — BiRefNet background removal
- ComfyUI-Image-Filters
- ComfyUI_essentials (cubiq)
- rgthree-comfy
- ComfyUI-IC-Light-Native (huchenlei)
- ComfyUI-IC-Light (kijai) — includes DetailTransfer
- ComfyUI-KJNodes (kijai)
- RyanOnTheInside — FAILING (permission denied on widget_hotkey.js, not critical)

### Workflows Created
- `z_image_turbo.json` — T2I with Z-Image Turbo
- `bg_remove.json` — BiRefNet background removal
- `prompt_creator.json` — Florence-2 image captioning
- `upscale.json` — 4x-UltraSharp → 2048px
- `portrait_realism.json` — DSLR-style portrait (832x1216)
- `product_hero.json` — Clean white background product shot
- `product_lifestyle.json` — Product in li

Source: memory/2026-02-18.md#L34-L69

# Session: 2026-02-18 13:13:12 UTC

- **Session Key**: agent:selena:main
- **Session ID**: e32e81a5-7e16-4bc2-a52a-cea3c00f0b24
- **Source**: telegram

## Conversation Summary

user: Conversation info (untrusted metadata):
```json
{
  "message_id": "127",
  "sender": "6558388607"
}
```

Yes
assistant: Already restarting. Let me wait for it to come up:
assistant: All nodes loaded clean — IC-Light-Native, IC-Light, KJNodes, RMBG all good. Only RyanOnTheInside still failing (permissions, not our issue). Now let me check the exact node names and build the workflow:
assistant: Got the node names. Key ones: `LoadAndApplyICLightUnet`, `ICLightConditioning`, `ICLightApplyMaskGrey`, `LightSource`. No

Source: memory/2026-02-18-ic-light-workflow.md#L1-L28

assistant: FBC model needs a background latent input too. Simpler fix: switch to the **FC model** — it generates the background itself from the text prompt, perfect for our use case:
assistant: All 3 done, no errors. Let me check them:
assistant: Scene 3 is a 7/10 — that's a massive improvement from the hard-composite approach. Scene 1 is decent at 6. Scene 2 is trash.

The FC model is doing proper lighting integration — the issue now is mainly prompt tuning for the background scene. The bokeh/candlelight one was over-stylized. Want me to run more iterations focusing on the scene 3 style (window light, natural setting) and vary the environments? That 7/10 foundation is solid to build on.
use

Source: memory/2026-02-18-ic-light-workflow.md#L25-L44

Ok next I want to see if we can get an img to video pipeline setup. Whatever is the best/fastest setup we can get working on our hardware. See what the top workflows are as of today's date, get some setup with the latest models please
user: [media attached: /home/crogers2287/.openclaw/media/inbound/file_2---916e46e9-07f0-4eb6-bad3-23debf8c33d7.jpg (image/jpeg) | /home/crogers2287/.openclaw/media/inbound/file_2---916e46e9-07f0-4eb6-bad3-23debf8c33d7.jpg]
To send an image back, prefer the message tool (media/path/filePath). If you must inline, use MEDIA:https://example.com/image.jpg (spaces ok, quote if needed) or a safe relative path like MEDIA:./image.jpg. Avoid absolute paths (MEDIA:/...) a

Source: memory/2026-02-18-ic-light-workflow.md#L43-L55

### Message Tool
- `message` tool with `filePath`/`media` parameters failing with "Cannot read properties of undefined (reading 'listAccountIds')"
- Cannot send images via Telegram from Selena session — user must view via `http://fred:8188` History tab

### llama-swap
- Running at `localhost:9069`
- Unload endpoint: `POST /unload`
- Actual llama-server process must be killed separately to free VRAM

---

## Active Projects

### NIVEA Product Placement
- Source image: `/home/crogers2287/ComfyUI/input/nivea_products.jpg`
- Products: Skin Firming lotion, Cocoa Butter lotion, Cocoa Butter jar
- IC-Light FC results: scene_3 (window light) = 7/10, scene_1 (marble) = 6/10, scene_2 (bokeh) = 3/10
-

Source: memory/2026-02-18.md#L85-L123

# Session Log — 2026-02-18

## ComfyUI Setup (Completed)

### Installation Location
- ComfyUI: `/home/crogers2287/ComfyUI`
- Models: `/mnt/storage/comfyui-models/` (mapped via `extra_model_paths.yaml`)
- Workflows: `/home/crogers2287/ComfyUI/user/default/workflows/`
- Output: `/home/crogers2287/ComfyUI/output/`

### Hardware
- GPU: 2x NVIDIA RTX 3090 (24GB VRAM each)
- RAM: 128GB
- Storage: 1.9TB /mnt/storage (716GB free at session start)
- OS: Linux 6.8.0, Python 3.12, PyTorch 2.10.0+cu128

### Models Downloaded
| Model | Path | Size |
|-------|------|------|
| z_image_turbo_bf16.safetensors | diffusion_models/ | 12.3GB |
| qwen_3_4b.safetensors | text_encoders/ | 8GB |
| z_image_turbo_ae.s

Source: memory/2026-02-18.md#L1-L43


I found the workflows! They're in `/home/crogers2287/ComfyUI/user/default/workflows/`. There are these relevant workflows:
- `bg_remove.json` — BiRefNet background removal
- `product_hero.json` — Clean white background product shot
- `product_lifestyle.json` — Product in lifestyle setting

The memory also mentions IC-Light FC model for background generation. Let me find these workflow files and use the ComfyUI API to run them.

Let me:
1. First copy the Olay image to ComfyUI's input directory
2. Check what workflows exist in the workflows directory

3. Execute the background removal workflow, then run the product hero and lifestyle workflows to generate the different image variations.

Right, we built this! Product hero and lifestyle workflows. Let me grab them and queue the jobs.


bg_remove.json
portrait_realism.json
product_composite.json
product_hero.json
product_iclight.json
product_lifestyle.json
prompt_creator.json
upscale.json
z_image_turbo.json


We have all the workflows. Let me:
1. Copy the Olay image to ComfyUI's input directory
2. Check the product_hero.json, product_lifestyle.json, and product_iclight.json to understand their structure
3. Queue the jobs via ComfyUI API

First, let me copy the image and check the workflow files. I should look at the input node in each workflow to know which filename to use.

All there. Let me copy the image in and check what input filename the workflows expect.

cp /home/crogers2287/.openclaw/media/inbound/file_2---fa26d2fd-af78-479a-a709-f6c5e17aae7f.jpg ~/ComfyUI/input/olay_product.jpg && echo "Copied OK"

cat ~/ComfyUI/user/default/workflows/product_hero.json | python3 -c "import sys,json; d=json.load(sys.stdin); [print(k, d[k].get('class_type',''), d[k].get('inputs',{}).get('image','')) for k in d if 'image' in str(d[k].get('inputs',{}))]" 2>/dev/null | head -20


Copied OK


python3 -c "
import json
with open('/home/crogers2287/ComfyUI/user/default/workflows/product_hero.json') as f:
    d = json.load(f)
for k, v in d.items():
    inp = v.get('inputs', {})
    if 'image' in inp or 'filename' in inp:
        print(f'Node {k} ({v.get(\"class_type\")}): image={inp.get(\"image\",\"\")} filename={inp.get(\"filename\",\"\")}')
"

python3 -c "
import json
with open('/home/crogers2287/ComfyUI/user/default/workflows/product_lifestyle.json') as f:
    d = json.load(f)
for k, v in d.items():
    inp = v.get('inputs', {})
    if 'image' in inp or 'filename' in inp:
        print(f'Node {k} ({v.get(\"class_type\")}): image={inp.get(\"image\",\"\")} filename={inp.get(\"filename\",\"\")}')
"


Traceback (most recent call last):
  File "<string>", line 6, in <module>
AttributeError: 'int' object has no attribute 'get'

(Command exited with code 1)


python3 -c "
import json
with open('/home/crogers2287/ComfyUI/user/default/workflows/product_hero.json') as f:
    d = json.load(f)
print(type(d))
if isinstance(d, list):
    print('list len:', len(d))
elif isinstance(d, dict):
    keys = list(d.keys())[:5]
    print('dict keys sample:', keys)
    first = d[keys[0]]
    print('first val type:', type(first))
"


<class 'dict'>
dict keys sample: ['last_node_id', 'last_link_id', 'nodes', 'links', 'groups']
first val type: <class 'int'>


The workflow is in ComfyUI's native UI format (not the API format). It has `nodes` and `links` arrays - this is the graph format ComfyUI uses. I need to either:1. Convert to API format and submit via API2. Or use the ComfyUI `/prompt` endpoint with the workflow converted to API formatLet me look at the `nodes` array to find the LoadImage node.Actually, for the ComfyUI API, I need the prompt format (not the workflow format). The workflow format stores it with nodes/links, while the API format stores it with node IDs as keys.Let me check the structure and find the LoadImage node, and also try to understand the workflow.

python3 -c "
import json
with open('/home/crogers2287/ComfyUI/user/default/workflows/product_hero.json') as f:
    d = json.load(f)
nodes = d.get('nodes', [])
for n in nodes:
    if isinstance(n, dict) and n.get('type','') in ('LoadImage', 'SaveImage', 'RMBG', 'PreviewImage'):
        print(f'id={n.get(\"id\")} type={n.get(\"type\")} title={n.get(\"title\",\"\")} widgets={n.get(\"widgets_values\",[])}')
"


id=9 type=SaveImage title= widgets=['product_hero/']


python3 -c "
import json
with open('/home/crogers2287/ComfyUI/user/default/workflows/product_hero.json') as f:
    d = json.load(f)
nodes = d.get('nodes', [])
for n in nodes:
    if isinstance(n, dict):
        print(f'id={n.get(\"id\")} type={n.get(\"type\")} widgets={n.get(\"widgets_values\",[])}')
"


id=1 type=UNETLoader widgets=['z_image_turbo_bf16.safetensors', 'default']
id=2 type=CLIPLoader widgets=['qwen_3_4b.safetensors', 'lumina2']
id=3 type=VAELoader widgets=['z_image_turbo_ae.safetensors']
id=4 type=CLIPTextEncode widgets=['professional product photography of a Nike Air Jordan 1 sneaker, centered on pure white background, hero shot, studio lighting, soft shadows, ultra sharp detail, commercial photography, 85mm lens, f/8, product catalog photo']
id=5 type=CLIPTextEncode widgets=['person, hands, blurry, dark, dirty, scratched, low quality, watermark, text, deformed']
id=6 type=EmptyLatentImage widgets=[1024, 1024, 1]
id=7 type=KSampler widgets=[42, 'randomize', 9, 1.0, 'euler', 'simple', 1.0]
id=8 type=VAEDecode widgets=[]
id=9 type=SaveImage widgets=['product_hero/']


I see - the product_hero workflow doesn't use a LoadImage node. It's a pure text-to-image workflow that generates a product shot directly from a text prompt. I need to update the prompt to be about the Olay Regenerist 10 cream jar.

Let me also check the product_lifestyle and product_iclight workflows to see what they do and if they use IC-Light (which would need the product image as input).

python3 -c "
import json
for wf in ['product_lifestyle', 'product_iclight', 'product_composite', 'bg_remove']:
    print(f'\n=== {wf} ===')
    with open(f'/home/crogers2287/ComfyUI/user/default/workflows/{wf}.json') as f:
        d = json.load(f)
    nodes = d.get('nodes', [])
    for n in nodes:
        if isinstance(n, dict):
            print(f'  id={n.get(\"id\")} type={n.get(\"type\")} widgets={n.get(\"widgets_values\",[])}')
"


=== product_lifestyle ===
  id=1 type=UNETLoader widgets=['z_image_turbo_bf16.safetensors', 'default']
  id=2 type=CLIPLoader widgets=['qwen_3_4b.safetensors', 'lumina2']
  id=3 type=VAELoader widgets=['z_image_turbo_ae.safetensors']
  id=4 type=CLIPTextEncode widgets=['product lifestyle photo, a Louis Vuitton handbag placed on a marble table in a bright luxury apartment, golden hour sunlight streaming through floor-to-ceiling windows, fresh flowers nearby, aspirational aesthetic, editorial photography, 50mm lens, shallow depth of field, warm tones']
  id=5 type=CLIPTextEncode widgets=['blurry, low quality, cluttered, dark, dirty, watermark, text, deformed, bad proportions']
  id=6 type=EmptyLatentImage widgets=[1216, 832, 1]
  id=7 type=KSampler widgets=[42, 'randomize', 9, 1.0, 'euler', 'simple', 1.0]
  id=8 type=VAEDecode widgets=[]
  id=9 type=SaveImage widgets=['product_lifestyle/']

=== product_iclight ===
  id=1 type=LoadImage widgets=['nivea_products.jpg', 'image']
  id=2 type=BiRefNetRMBG widgets=['BiRefNet-general']
  id=3 type=ICLightApplyMaskGrey widgets=[]
  id=4 type=CheckpointLoaderSimple widgets=['RealisticVision_V6_fp16.safetensors']
  id=5 type=LoadAndApplyICLightUnet widgets=['iclight_sd15_fbc_unet_ldm.safetensors']
  id=6 type=CLIPTextEncode widgets=['product on a luxury marble bathroom countertop, soft warm studio lighting, professional product photography, photorealistic, sharp detail']
  id=7 type=CLIPTextEncode widgets=['blurry, low quality, watermark, deformed, dark, ugly, shadows clipping']
  id=8 type=VAEEncode widgets=[]
  id=9 type=ICLightConditioning widgets=[1.0]
  id=10 type=KSampler widgets=[42, 'randomize', 25, 2.0, 'dpmpp_2m', 'karras', 1.0]
  id=11 type=VAEDecode widgets=[]
  id=12 type=SaveImage widgets=['product_iclight/']

=== product_composite ===
  id=1 type=LoadImage widgets=['your_product.png', 'image']
  id=2 type=BiRefNetRMBG widgets=['BiRefNet-general', True, 'RGBA']
  id=10 type=UNETLoader widgets=['z_image_turbo_bf16.safetensors', 'default']
  id=11 type=CLIPLoader widgets=['qwen_3_4b.safetensors', 'lumina2']
  id=12 type=VAELoader widgets=['z_image_turbo_ae.safetensors']
  id=13 type=CLIPTextEncode widgets=['luxury lifestyle background, marble countertop, soft morning light, minimalist interior, warm tones, shallow depth of field, no product, empty surface, editorial photography']
  id=14 type=CLIPTextEncode widgets=['blurry, dark, cluttered, low quality, watermark, people, hands']
  id=15 type=EmptyLatentImage widgets=[1024, 1024, 1]
  id=16 type=KSampler widgets=[42, 'randomize', 9, 1.0, 'euler', 'simple', 1.0]
  id=17 type=VAEDecode widgets=[]
  id=18 type=ImageCompositeMasked widgets=[312, 312, False]
  id=19 type=SaveImage widgets=['product_composite/']

=== bg_remove ===
  id=1 type=LoadImage widgets=['example.png', 'image']
  id=2 type=BiRefNetRMBG widgets=['BiRefNet-general', True, 'RGBA']
  id=3 type=SaveImage widgets=['bg_remove/']


Now I understand the workflows:

1. **product_hero.json** - Pure T2I, generates a hero shot from a text prompt, no input image needed. I need to update the prompt to describe the Olay jar.

2. **product_lifestyle.json** - Pure T2I, generates a lifestyle shot from text, no input image needed. I need to update the prompt.

3. **product_iclight.json** - Uses IC-Light with an input image (currently set to `nivea_products.jpg`). I need to change the input to `olay_product.jpg`. This uses IC-Light FBC (foreground/background conditioned).

4. **product_composite.json** - Removes the background from a product image using BiRefNet, generates a new background with Z-Image Turbo, then composites the product back on top. I need to update the input to `olay_product.jpg`.

5. **bg_remove.json** - Handles background removal only.

For the Olay product, my approach is to run product_iclight and product_composite with the Olay image to leverage the actual product photo with relighting and background replacement, then run product_hero with an updated prompt describing the Olay jar.

I should also run product_lifestyle with an updated prompt for the lifestyle shot. The workflows that use the actual image are most valuable here since the text-to-image ones would just generate an imagined product rather than work with the real photo.

Now I need to convert these workflows from ComfyUI's UI format to the API prompt format so I can submit them as jobs. Now I'm looking at the specific workflows and what needs to change. For the product_iclight workflow, I need to swap the input image from nivea_products.jpg to olay_product.jpg, and do the same for product_composite. The text-to-image workflows also need their prompts updated to focus on Olay Regenerist 10. My strategy is to run product_iclight first with the Olay image on a marble bathroom countertop background, then generate multiple background variations for the other workflows.

I need to convert these workflows to ComfyUI's API format and submit them via Python script, since the API expects a different structure than the UI workflow format.

Looking at the product_iclight workflow more carefully, I see it uses IC-Light FBC which requires both a foreground product image and a background. The pipeline removes the background from the product, applies masking, then uses the IC-Light model to relight it against different generated backgrounds depending on the prompt I provide.

For product_composite, the approach is simpler - it removes the background from the product image, generates a new background using a text-to-image model, then composites the product back onto that generated background.

Now I'm thinking through how to actually submit these workflows to ComfyUI. I could convert the graph format to the API format, but that's complex with all the node connections. Let me just write a Python script that builds the API prompts directly instead.

I need to understand the link structure - each link connects an output from one node to an input on another, and I'll need to map those connections when building the inputs dictionary for each node.

Building the conversion script... I'm mapping out the node connections for the product_iclight workflow: the image loader feeds into both the background removal model and the mask application step, which then passes to the VAE encoder, while the checkpoint loader branches into the text encoders for positive and negative prompts and the ICLight unet loader that will guide the generation.

Now I need to trace through the remaining nodes—the conditioning setup, sampler, and final decode steps—to understand how everything connects, so I'm going to write a conversion script that can parse the workflow structure directly rather than trying to manually track each link.

Right — IC-Light, composite, hero, and lifestyle. Let me build a converter and queue all four against the Olay image.

python3 -c "
import json

def graph_to_api(graph):
    nodes_list = graph.get('nodes', [])
    links = graph.get('links', [])

    # Build link lookup: {link_id: [from_node, from_slot]}
    link_map = {}
    for lnk in links:
        # [link_id, from_node_id, from_output_slot, to_node_id, to_input_slot, type]
        link_map[lnk[0]] = [str(lnk[1]), lnk[2]]

    api = {}
    for node in nodes_list:
        if not isinstance(node, dict):
            continue
        nid = str(node['id'])
        class_type = node.get('type', '')
        inputs_def = node.get('inputs', [])  # list of {name, type, link}
        outputs_def = node.get('outputs', [])
        widgets_values = node.get('widgets_values', [])

        # Separate connected inputs from widget inputs
        connected_inputs = {}
        for inp in inputs_def:
            if isinstance(inp, dict) and inp.get('link') is not None:
                lnk_id = inp['link']
                if lnk_id in link_map:
                    connected_inputs[inp['name']] = link_map[lnk_id]

        # Widget inputs: names from inputs_def where link is None
        widget_inputs = {}
        wi = 0
        for inp in inputs_def:
            if isinstance(inp, dict) and inp.get('link') is None and inp.get('widget'):
                if wi < len(widgets_values):
                    widget_inputs[inp['name']] = widgets_values[wi]
                    wi += 1

        # If no widget key map, just assign by remaining widget values
        # For nodes with no input connections for widgets, use widgets directly
        # This is a simplified approach
        all_inputs = {**widget_inputs, **connected_inputs}

        # For simple cases: if no widget inputs were mapped, try mapping directly
        # using the widgets_values to the widget-type inputs
        if not widget_inputs and widgets_values:
            # Find inputs that have widget: true or are not linked
            wdg_inputs = [i for i in inputs_def if isinstance(i, dict) and i.get('link') is None]
            for idx, wdg in enumerate(wdg_inputs):
                if idx < len(widgets_values):
                    all_inputs[wdg['name']] = widgets_values[idx]

        api[nid] = {
            'class_type': class_type,
            'inputs': all_inputs
        }

    return api

# Test with product_iclight
with open('/home/crogers2287/ComfyUI/user/default/workflows/product_iclight.json') as f:
    g = json.load(f)

api = graph_to_api(g)
print(json.dumps(api, indent=2))
" 2>&1 | head -80


{
  "1": {
    "class_type": "LoadImage",
    "inputs": {}
  },
  "2": {
    "class_type": "BiRefNetRMBG",
    "inputs": {
      "image": [
        "1",
        0
      ]
    }
  },
  "3": {
    "class_type": "ICLightApplyMaskGrey",
    "inputs": {
      "image": [
        "2",
        0
      ],
      "mask": [
        "2",
        1
      ]
    }
  },
  "4": {
    "class_type": "CheckpointLoaderSimple",
    "inputs": {}
  },
  "5": {
    "class_type": "LoadAndApplyICLightUnet",
    "inputs": {
      "model": [
        "4",
        0
      ]
    }
  },
  "6": {
    "class_type": "CLIPTextEncode",
    "inputs": {
      "clip": [
        "4",
        1
      ]
    }
  },
  "7": {
    "class_type": "CLIPTextEncode",
    "inputs": {
      "clip": [
        "4",
        1
      ]
    }
  },
  "8": {
    "class_type": "VAEEncode",
    "inputs": {
      "pixels": [
        "3",
        0
      ],
      "vae": [
        "4",
        2
      ]
    }
  },
  "9": {
    "class_type": "ICLightConditioning",
    "inputs": {
      "positive": [
        "6",
        0
      ],
      "negative": [
        "7",


The converter is working but it's not mapping the widget values for nodes like LoadImage, CheckpointLoaderSimple, etc. where the inputs are just widgets (no connected inputs). I need to look at the raw node data to understand how to properly handle those.

Let me look at the actual node data for LoadImage to understand the input/widget structure.

Good skeleton, but widget values aren't mapping on loader nodes. Let me inspect the raw node structure and patch it.

python3 -c "
import json
with open('/home/crogers2287/ComfyUI/user/default/workflows/product_iclight.json') as f:
    g = json.load(f)
# Print nodes 1, 4, 6 in full detail
for n in g['nodes']:
    if n['id'] in [1, 4, 6, 7, 10]:
        print(json.dumps(n, indent=2))
        print('---')
"


{
  "id": 1,
  "type": "LoadImage",
  "pos": [
    50,
    80
  ],
  "size": [
    300,
    100
  ],
  "order": 0,
  "mode": 0,
  "outputs": [
    {
      "name": "IMAGE",
      "type": "IMAGE",
      "links": [
        1
      ],
      "slot_index": 0
    },
    {
      "name": "MASK",
      "type": "MASK",
      "links": [],
      "slot_index": 1
    }
  ],
  "widgets_values": [
    "nivea_products.jpg",
    "image"
  ]
}
---
{
  "id": 4,
  "type": "CheckpointLoaderSimple",
  "pos": [
    50,
    280
  ],
  "size": [
    360,
    80
  ],
  "order": 3,
  "mode": 0,
  "outputs": [
    {
      "name": "MODEL",
      "type": "MODEL",
      "links": [
        5
      ],
      "slot_index": 0
    },
    {
      "name": "CLIP",
      "type": "CLIP",
      "links": [
        6,
        7
      ],
      "slot_index": 1
    },
    {
      "name": "VAE",
      "type": "VAE",
      "links": [
        8
      ],
      "slot_index": 2
    }
  ],
  "widgets_values": [
    "RealisticVision_V6_fp16.safetensors"
  ]
}
---
{
  "id": 6,
  "type": "CLIPTextEncode",
  "pos": [
    470,
    400
  ],
  "size": [
    500,
    160
  ],
  "order": 5,
  "mode": 0,
  "inputs": [
    {
      "name": "clip",
      "type": "CLIP",
      "link": 6
    }
  ],
  "outputs": [
    {
      "name": "CONDITIONING",
      "type": "CONDITIONING",
      "links": [
        10
      ],
      "slot_index": 0
    }
  ],
  "widgets_values": [
    "product on a luxury marble bathroom countertop, soft warm studio lighting, professional product photography, photorealistic, sharp detail"
  ]
}
---
{
  "id": 7,
  "type": "CLIPTextEncode",
  "pos": [
    470,
    590
  ],
  "size": [
    500,
    100
  ],
  "order": 6,
  "mode": 0,
  "inputs": [
    {
      "name": "clip",
      "type": "CLIP",
      "link": 7
    }
  ],
  "outputs": [
    {
      "name": "CONDITIONING",
      "type": "CONDITIONING",
      "links": [
        11
      ],
      "slot_index": 0
    }
  ],
  "widgets_values": [
    "blurry, low quality, watermark, deformed, dark, ugly, shadows clipping"
  ]
}
---
{
  "id": 10,
  "type": "KSampler",
  "pos": [
    1290,
    380
  ],
  "size": [
    360,
    480
  ],
  "order": 9,
  "mode": 0,
  "inputs": [
    {
      "name": "model",
      "type": "MODEL",
      "link": 9
    },
    {
      "name": "positive",
      "type": "CONDITIONING",
      "link": 13
    },
    {
      "name": "negative",
      "type": "CONDITIONING",
      "link": 14
    },
    {
      "name": "latent_image",
      "type": "LATENT",
      "link": 15
    }
  ],
  "outputs": [
    {
      "name": "LATENT",
      "type": "LATENT",
      "links": [
        16
      ],
      "slot_index": 0
    }
  ],
  "widgets_values": [
    42,
    "randomize",
    25,
    2.0,
    "dpmpp_2m",
    "karras",
    1.0
  ]
}
---


Now I understand the structure. Nodes that are pure "loader" nodes (like LoadImage, CheckpointLoaderSimple) have no `inputs` key - they only have `widgets_values`. Nodes that have connections have `inputs` with `link` values, plus `widgets_values` for their non-connected inputs.

For the API format, I need:
- For LoadImage: `{"image": "olay_product.jpg", "upload": "image"}`
- For CheckpointLoaderSimple: `{"ckpt_name": "RealisticVision_V6_fp16.safetensors"}`

- For CLIPTextEncode: `{"text": "...", "clip": ["4", 1]}`
- For KSampler: `{"seed": 42, "control_after_generate": "randomize", "steps": 25, "cfg": 2.0, "sampler_name": "dpmpp_2m", "scheduler": "karras", "denoise": 1.0, "model": [...], "positive": [...], "negative": [...], "latent_image": [...]}`

The real challenge is that each node type has its own specific input parameter names, and I can't construct the API calls without knowing what those parameters are for each node.

I should query the ComfyUI API's object_info endpoint to retrieve the complete input specifications for all available node types.

curl -s http://127.0.0.1:8188/object_info/LoadImage 2>/dev/null | python3 -c "import sys,json; d=json.load(sys.stdin); print(json.dumps(list(d['LoadImage']['input']['required'].keys())))"

curl -s http://127.0.0.1:8188/object_info/KSampler 2>/dev/null | python3 -c "import sys,json; d=json.load(sys.stdin); print(json.dumps(list(d['KSampler']['input']['required'].keys())))"


image


model seed steps cfg sampler_name scheduler positive negative latent_image denoise


Conversation info (untrusted metadata):
```json
{
  "message_id": "156",
  "sender": "6558388607"
}
```

Well?


Conversation info (untrusted metadata):
```json
{
  "message_id": "157",
  "sender": "6558388607"
}
```

Well?


The user is impatient - I've been doing a lot of exploration/investigation. Let me just write a comprehensive Python script that submits the jobs to ComfyUI properly and get them running. I know enough to build the API format now. Let me just do it.

The key insight is:
- For nodes with NO `inputs` key (pure widget nodes like LoadImage, CheckpointLoaderSimple), all values come from `widgets_values`
- For nodes WITH `inputs` key, connected inputs come from `link` references, and widget values fill the remaining non-linked inputs

Let me get the input names for all the node types I need, then build and submit the prompts.

I'll use the ComfyUI `/prompt` endpoint directly to submit the workflows. For the product_iclight workflow, I'll swap in the olay_product.jpg image and run it with different scene prompts like marble, kitchen, and outdoor settings. For product_composite, I'll do the same with various background prompts. Now I'm writing a script that queries the ComfyUI API for input names across all the node types I need, then constructs and submits the properly formatted prompts.

Sorry — enough inspection, let's run it. Writing the submit script now.

curl -s "http://127.0.0.1:8188/object_info/CheckpointLoaderSimple" | python3 -c "import sys,json; d=json.load(sys.stdin); k=list(d.keys())[0]; print(list(d[k]['input']['required'].keys()))"
curl -s "http://127.0.0.1:8188/object_info/CLIPTextEncode" | python3 -c "import sys,json; d=json.load(sys.stdin); k=list(d.keys())[0]; print(list(d[k]['input']['required'].keys()))"
curl -s "http://127.0.0.1:8188/object_info/VAEEncode" | python3 -c "import sys,json; d=json.load(sys.stdin); k=list(d.keys())[0]; print(list(d[k]['input']['required'].keys()))"
curl -s "http://127.0.0.1:8188/object_info/SaveImage" | python3 -c "import sys,json; d=json.load(sys.stdin); k=list(d.keys())[0]; print(list(d[k]['input']['required'].keys()))"
curl -s "http://127.0.0.1:8188/object_info/EmptyLatentImage" | python3 -c "import sys,json; d=json.load(sys.stdin); k=list(d.keys())[0]; print(list(d[k]['input']['required'].keys()))"
curl -s "http://127.0.0.1:8188/object_info/ImageCompositeMasked" | python3 -c "import sys,json; d=json.load(sys.stdin); k=list(d.keys())[0]; req=d[k]['input']['required']; opt=d[k]['input'].get('optional',{}); print('req:', list(req.keys()), 'opt:', list(opt.keys()))"


['ckpt_name']
['text', 'clip']
['pixels', 'vae']
['images', 'filename_prefix']
['width', 'height', 'batch_size']
req: ['destination', 'source', 'x', 'y', 'resize_source'] opt: ['mask']


write

#!/usr/bin/env python3
"""Submit Olay product placement jobs to ComfyUI."""

import json, requests, uuid, sys

API = "http://127.0.0.1:8188"
INPUT_IMAGE = "olay_product.jpg"
CLIENT_ID = str(uuid.uuid4())

def submit(prompt_dict, label=""):
    payload = {"prompt": prompt_dict, "client_id": CLIENT_ID}
    r = requests.post(f"{API}/prompt", json=payload)
    if r.status_code == 200:
        pid = r.json().get("prompt_id", "?")
        print(f"✅ Queued [{label}]: {pid}")
        return pid
    else:
        print(f"❌ Failed [{label}]: {r.status_code} {r.text[:200]}")
        return None

# ── 1. IC-Light scenes (uses actual product image + IC-Light relighting) ──────

ICLIGHT_BASE = {
    "1": {"class_type": "LoadImage",             "inputs": {"image": INPUT_IMAGE}},
    "2": {"class_type": "BiRefNetRMBG",          "inputs": {"image": ["1",0], "model": "BiRefNet-general"}},
    "3": {"class_type": "ICLightApplyMaskGrey",  "inputs": {"image": ["2",0], "mask": ["2",1]}},
    "4": {"class_type": "CheckpointLoaderSimple","inputs": {"ckpt_name": "RealisticVision_V6_fp16.safetensors"}},
    "5": {"class_type": "LoadAndApplyICLightUnet","inputs": {"model_name": "iclight_sd15_fbc_unet_ldm.safetensors", "model": ["4",0]}},
    "7": {"class_type": "CLIPTextEncode",        "inputs": {"text": "blurry, low quality, watermark, deformed, dark, ugly, shadows clipping", "clip": ["4",1]}},
    "8": {"class_type": "VAEEncode",             "inputs": {"pixels": ["3",0], "vae": ["4",2]}},
    "9": {"class_type": "ICLightConditioning",   "inputs": {"positive": ["6",0], "negative": ["7",0], "vae": ["4",2], "foreground": ["8",0], "multiplier": 1.0}},
    "10":{"class_type": "KSampler",              "inputs": {"model": ["5",0], "positive": ["9",0], "negative": ["9",1], "latent_image": ["9",2], "seed": 42, "steps": 25, "cfg": 2.0, "sampler_name": "dpmpp_2m", "scheduler": "karras", "denoise": 1.0}},
    "11":{"class_type": "VAEDecode",             "inputs": {"samples": ["10",0], "vae": ["4",2]}},
}

scenes = [
    ("luxury marble bathroom countertop, soft warm studio lighting, elegant skincare display, white marble, gold accents, photorealistic", "iclight_marble"),
    ("spa wooden shelf surrounded by green plants and eucalyptus, natural light, zen aesthetic, photorealistic product photography", "iclight_spa"),
    ("modern minimalist bathroom, concrete grey surface, dramatic side lighting, editorial cosmetics photography, sharp detail", "iclight_editorial"),
    ("rustic wooden table with morning coffee and flowers, warm golden hour light streaming through window, cozy lifestyle photography", "iclight_morning"),
]

for pos_text, label in scenes:
    p = json.loads(json.dumps(ICLIGHT_BASE))  # deep copy
    p["6"] = {"class_type": "CLIPTextEncode", "inputs": {"text": pos_text, "clip": ["4",1]}}
    p["12"]= {"class_type": "SaveImage",      "inputs": {"images": ["11",0], "filename_prefix": f"olay/{label}"}}
    submit(p, label)

# ── 2. Composite scenes (RMBG cutout composited onto generated background) ───

COMPOSITE_BASE = {
    "1": {"class_type": "LoadImage",            "inputs": {"image": INPUT_IMAGE}},
    "2": {"class_type": "BiRefNetRMBG",         "inputs": {"image": ["1",0], "model": "BiRefNet-general"}},
    "10":{"class_type": "UNETLoader",           "inputs": {"unet_name": "z_image_turbo_bf16.safetensors", "weight_dtype": "default"}},
    "11":{"class_type": "CLIPLoader",           "inputs": {"clip_name": "qwen_3_4b.safetensors", "type": "lumina2"}},
    "12":{"class_type": "VAELoader",            "inputs": {"vae_name": "z_image_turbo_ae.safetensors"}},
    "14":{"class_type": "CLIPTextEncode",       "inputs": {"text": "blurry, dark, cluttered, low quality, watermark, people, hands", "clip": ["11",0]}},
    "15":{"class_type": "EmptyLatentImage",     "inputs": {"width": 1024, "height": 1024, "batch_size": 1}},
    "16":{"class_type": "KSampler",             "inputs": {"model": ["10",0], "positive": ["13",0], "negative": ["14",0], "latent_image": ["15",0], "seed": 99, "steps": 9, "cfg": 1.0, "sampler_name": "euler", "scheduler": "simple", "denoise": 1.0}},
    "17":{"class_type": "VAEDecode",            "inputs": {"samples": ["16",0], "vae": ["12",0]}},
    "18":{"class_type": "ImageCompositeMasked", "inputs": {"destination": ["17",0], "source": ["2",0], "x": 312, "y": 312, "resize_source": False, "mask": ["2",1]}},
}

bg_scenes = [
    ("luxury lifestyle background, white marble countertop, soft morning light, minimalist interior, warm tones, shallow depth of field, no product, empty surface, editorial photography", "composite_marble"),
    ("outdoor garden terrace, stone surface, lush greenery blurred in background, natural daylight, fresh and clean aesthetic, no product", "composite_garden"),
    ("dark moody luxury, black granite surface, dramatic rim lighting, dark background with subtle bokeh, premium brand aesthetic, no product", "composite_moody"),
]

for pos_text, label in bg_scenes:
    p = json.loads(json.dumps(COMPOSITE_BASE))
    p["13"] = {"class_type": "CLIPTextEncode", "inputs": {"text": pos_text, "clip": ["11",0]}}
    p["19"] = {"class_type": "SaveImage",       "inputs": {"images": ["18",0], "filename_prefix": f"olay/{label}"}}
    submit(p, label)

print("\nAll jobs queued. View at http://fred:8188")


Successfully wrote 5298 bytes to /home/crogers2287/.openclaw/agents/selena/submit_olay.py


exec

cd /home/crogers2287/.openclaw/agents/selena && python3 submit_olay.py 2>&1


❌ Failed [iclight_marble]: 400 {"error": {"type": "prompt_outputs_failed_validation", "message": "Prompt outputs failed validation", "details": "", "extra_info": {}}, "node_errors": {"5": {"errors": [{"type": "required_input_missin
❌ Failed [iclight_spa]: 400 {"error": {"type": "prompt_outputs_failed_validation", "message": "Prompt outputs failed validation", "details": "", "extra_info": {}}, "node_errors": {"5": {"errors": [{"type": "required_input_missin
❌ Failed [iclight_editorial]: 400 {"error": {"type": "prompt_outputs_failed_validation", "message": "Prompt outputs failed validation", "details": "", "extra_info": {}}, "node_errors": {"5": {"errors": [{"type": "required_input_missin
❌ Failed [iclight_morning]: 400 {"error": {"type": "prompt_outputs_failed_validation", "message": "Prompt outputs failed validation", "details": "", "extra_info": {}}, "node_errors": {"5": {"errors": [{"type": "required_input_missin
✅ Queued [composite_marble]: ac9e551c-0a72-499c-b999-97b0c9d0130a
✅ Queued [composite_garden]: ff6ac609-b91e-4500-9118-aac4da567960
✅ Queued [composite_moody]: 206bb668-640c-4f3f-9a69-2de68eb100d0

All jobs queued. View at http://fred:8188


The composite jobs are running. The IC-Light jobs are failing because node 5 (LoadAndApplyICLightUnet) has a wrong input name. Let me check what inputs it expects.

Composites are queued. Let me fix the IC-Light input name real quick.

curl -s "http://127.0.0.1:8188/object_info/LoadAndApplyICLightUnet" | python3 -c "
import sys,json
d=json.load(sys.stdin)
k=list(d.keys())[0]
print('required:', list(d[k]['input']['required'].keys()))
print('optional:', list(d[k]['input'].get('optional',{}).keys()))
"


required: ['model', 'model_path']
optional: []


Conversation info (untrusted metadata):
```json
{
  "message_id": "160",
  "sender": "6558388607"
}
```

Try this one


The user said "try this one" but there's no attachment or image with the message. Let me check if there's something I'm missing...

Wait, looking at the message - there's no media attached. The user might be referring to the IC-Light fix I was about to make, or they might be attaching something and it didn't come through. Let me ask.

Actually wait - the message_id is 160. Let me re-read the context. The user said "Try this one" - but there's no attachment shown. This is a different context - maybe they're referring to trying the `product_composite` workflow that just got queued? Or maybe an image is coming separately?

I need to ask them what they want me to try since nothing's actually attached.

[[reply_to_current]] Nothing came through on my end — no image or file attached. Try sending it again?


[media attached: /home/crogers2287/.openclaw/media/inbound/file_3---a1eea367-f87b-41e5-90e7-ebd684de991b.jpg (image/jpeg) | /home/crogers2287/.openclaw/media/inbound/file_3---a1eea367-f87b-41e5-90e7-ebd684de991b.jpg]
To send an image back, prefer the message tool (media/path/filePath). If you must inline, use MEDIA:https://example.com/image.jpg (spaces ok, quote if needed) or a safe relative path like MEDIA:./image.jpg. Avoid absolute paths (MEDIA:/...) and ~ paths — they are blocked for security. Keep caption in the text body.
Conversation info (untrusted metadata):
```json
{
  "message_id": "161",
  "sender": "6558388607"
}
```

Forwarded message context (untrusted metadata):
```json
{
  "from": "Chelsea Schnepp",
  "type": "user",
  "title": "Chelsea Schnepp",
  "date_ms": 1771420281000
}
```

<media:image>


The user sent a new product image - a PGVTBI power bank/portable charger with a red handle, black body, LCD display showing 100%, and multiple USB ports. It already has a white background and appears to be a product shot. They want to run this through the same pipeline - place it on good backgrounds.

Let me copy this image to ComfyUI input and queue it up. I'll use the composite workflow since that one was already working (3 jobs queued successfully). Let me also fix the IC-Light issue and run that too.

The image already has a clean white background which is perfect for the composite workflow.

Let me:
1. Copy the new image to ComfyUI input

2. Queue it through the composite workflow with multiple backgrounds and fix the IC-Light workflow to process it as well.

Nice — PGVTBI power bank. Already has a clean white bg which is perfect for the composite pipeline. Copying it in and queuing now.

cp /home/crogers2287/.openclaw/media/inbound/file_3---a1eea367-f87b-41e5-90e7-ebd684de991b.jpg ~/ComfyUI/input/powerbank.jpg && echo "Copied"


Copied


python3 - <<'EOF'
import json, requests, uuid

API = "http://127.0.0.1:8188"
CLIENT_ID = str(uuid.uuid4())

def submit(prompt_dict, label=""):
    r = requests.post(f"{API}/prompt", json={"prompt": prompt_dict, "client_id": CLIENT_ID})
    if r.status_code == 200:
        pid = r.json().get("prompt_id","?")
        print(f"✅ [{label}]: {pid}")
        return pid
    else:
        print(f"❌ [{label}]: {r.status_code} {r.text[:300]}")

INPUT = "powerbank.jpg"

COMPOSITE_BASE = {
    "1": {"class_type": "LoadImage",            "inputs": {"image": INPUT}},
    "2": {"class_type": "BiRefNetRMBG",         "inputs": {"image": ["1",0], "model": "BiRefNet-general"}},
    "10":{"class_type": "UNETLoader",           "inputs": {"unet_name": "z_image_turbo_bf16.safetensors", "weight_dtype": "default"}},
    "11":{"class_type": "CLIPLoader",           "inputs": {"clip_name": "qwen_3_4b.safetensors", "type": "lumina2"}},
    "12":{"class_type": "VAELoader",            "inputs": {"vae_name": "z_image_turbo_ae.safetensors"}},
    "14":{"class_type": "CLIPTextEncode",       "inputs": {"text": "blurry, dark, cluttered, low quality, watermark, people, hands, product", "clip": ["11",0]}},
    "15":{"class_type": "EmptyLatentImage",     "inputs": {"width": 1024, "height": 1024, "batch_size": 1}},
    "16":{"class_type": "KSampler",             "inputs": {"model": ["10",0], "positive": ["13",0], "negative": ["14",0], "latent_image": ["15",0], "seed": 77, "steps": 9, "cfg": 1.0, "sampler_name": "euler", "scheduler": "simple", "denoise": 1.0}},
    "17":{"class_type": "VAEDecode",            "inputs": {"samples": ["16",0], "vae": ["12",0]}},
    "18":{"class_type": "ImageCompositeMasked", "inputs": {"destination": ["17",0], "source": ["2",0], "x": 262, "y": 162, "resize_source": False, "mask": ["2",1]}},
}

scenes = [
    ("outdoor adventure campsite, rocky surface, pine forest background, golden hour light, rugged tech aesthetic, no product, empty ground", "pb_outdoor", 11),
    ("modern desk setup, dark carbon fiber surface, soft blue LED accent lighting, tech workspace, premium gadget photography, empty surface, no product", "pb_desk", 22),
    ("travel flat lay, world map, passport, coffee cup, wooden table, warm light, wanderlust aesthetic, no product", "pb_travel", 33),
    ("studio hero shot, pure gradient dark background, dramatic spotlight, professional tech product photography, empty stage, no product", "pb_hero", 44),
]

for pos_text, label, seed in scenes:
    p = json.loads(json.dumps(COMPOSITE_BASE))
    p["16"]["inputs"]["seed"] = seed
    p["13"] = {"class_type": "CLIPTextEncode", "inputs": {"text": pos_text, "clip": ["11",0]}}
    p["19"] = {"class_type": "SaveImage",       "inputs": {"images": ["18",0], "filename_prefix": f"powerbank/{label}"}}
    submit(p, label)

print("\nDone. View at http://fred:8188")
EOF


✅ [pb_outdoor]: 0ffbe6dd-3bc1-4281-8ee2-c371d21d085c
✅ [pb_desk]: 5b291987-85c7-4fc5-b17b-5a6372afbff4
✅ [pb_travel]: 8cdd5cfd-0230-410c-95bd-09651e5915c7
✅ [pb_hero]: b8111f88-49ee-471f-ad74-4dde2fe44c5e

Done. View at http://fred:8188


All 4 queued — outdoor/camping, tech desk, travel flat lay, and dark studio hero. Let me check if the Olay composites from before finished too.

ls -lh ~/ComfyUI/output/olay/ 2>/dev/null && echo "---" && ls -lh ~/ComfyUI/output/powerbank/ 2>/dev/null || echo "Still processing..."


total 2.8M
-rw-r--r-- 1 crogers2287 crogers2287 1.1M Feb 18 13:33 composite_garden_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 804K Feb 18 13:32 composite_marble_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 972K Feb 18 13:33 composite_moody_00001_.png
---
Still processing...


The Olay composites are done! Let me send them to the user and then wait for the powerbank ones to finish.

Olay ones are done already! Let me send them.

message

Olay — Marble countertop 🤍


Cannot read properties of undefined (reading 'listAccountIds')


The message tool is failing - this is the same issue noted in the memory. The memory says "Cannot send images via Telegram from Selena session — user must view via `http://fred:8188` History tab". Let me tell the user to check the ComfyUI output tab.

[[reply_to_current]] Known snag — can't push images from this session directly. But your **Olay results are ready** (3 scenes: marble, garden, moody) and the **power bank is queued** (4 scenes).

Check them at: **http://fred:8188** → History tab in the top right, or browse directly:
- `/ComfyUI/output/olay/` — Olay composites ✅ done
- `/ComfyUI/output/powerbank/` — power bank generating now 🔄


Conversation info (untrusted metadata):
```json
{
  "message_id": "167",
  "sender": "6558388607"
}
```

Lighting looks terrible on all of those, none of them look like they're supposed to go in the image. We had a nice workflow set up last night. What happened?


The user is saying the lighting looks bad and that we had a better workflow last night. Let me check the memory more carefully to understand what workflow we had that was working well. The memory mentioned IC-Light which does proper relighting. Let me look at the session memory in more detail.

From what I found earlier:
- `product_iclight.json` — uses IC-Light FBC model with actual input image
- Last night we got scene_3 (window light) = 7/10, scene_1 (marble) = 6/10

The issue is:
1. I was running the `composite` workflow which just does a hard composite without relighting - no wonder the lighting looks wrong!

2. The `product_iclight.json` workflow is what I should be using since it has the actual IC-Light model for proper relighting, but I ran into an input naming issue last night where `LoadAndApplyICLightUnet` expects `model_path` instead of `model_name`. Let me pull up the exact workflow configuration and check which IC-Light models are available to fix this properly.

exec

curl -s "http://127.0.0.1:8188/object_info/LoadAndApplyICLightUnet" | python3 -c "
import sys,json; d=json.load(sys.stdin); k=list(d.keys())[0]
req=d[k]['input']['required']
opt=d[k]['input'].get('optional',{})
print('required:', json.dumps(req, indent=2))
print('optional:', list(opt.keys()))
"


### Message Tool
- `message` tool with `filePath`/`media` parameters failing with "Cannot read properties of undefined (reading 'listAccountIds')"
- Cannot send images via Telegram from Selena session — user must view via `http://fred:8188` History tab

### llama-swap
- Running at `localhost:9069`
- Unload endpoint: `POST /unload`
- Actual llama-server process must be killed separately to free VRAM

---

## Active Projects

### NIVEA Product Placement
- Source image: `/home/crogers2287/ComfyUI/input/nivea_products.jpg`
- Products: Skin Firming lotion, Cocoa Butter lotion, Cocoa Butter jar
- IC-Light FC results: scene_3 (window light) = 7/10, scene_1 (marble) = 6/10, scene_2 (bokeh) = 3/10
- Next step: iterate on scene 3 style with varied natural-light environments

### Alice in Wonderland / Witcher Images
- Generated in `/home/crogers2287/ComfyUI/output/alice_witcher/`
- v4 batch was last run — prompt includes explicit decapitation action
- Quality improved but model still softens violence

---

## ComfyUI API Pattern
```python
# Queue a job
payload = {"prompt": { "1": {"class_type": "...", "inputs": {...}}, ... }}
req = urllib.request.Request("http://127.0.0.1:8188/prompt", data=json.dumps(payload).encode(), headers={"Content-Type": "application/json"})
res = urllib.request.urlopen(req)

# Check queue
curl -s http://127.0.0.1:8188/queue
# Get node signatures
curl -s http://localhost:8188/object_info/NodeName
```


required: {
  "model": [
    "MODEL"
  ],
  "model_path": [
    [
      "iclight_sd15_fbc_unet_ldm.safetensors",
      "iclight_sd15_fc_unet_ldm.safetensors",
      "z_image_turbo_bf16.safetensors"
    ]
  ]
}
optional: []


I see the issue now. The `LoadAndApplyICLightUnet` requires `model_path` not `model_name`. And last night we used the FC model (`iclight_sd15_fc_unet_ldm.safetensors`) which generates the background itself from a text prompt, whereas the FBC model needs a background latent input too.

Also, the composite workflow doesn't use IC-Light at all - it's just a hard composite of the product cutout onto a generated background, which is why the lighting looks terrible.

The IC-Light FC model is the right one - it takes the foreground product (with bg removed) and generates a properly lit scene around it. The memory says FC results were 6-7/10.

Let me check what the ICLightConditioning node actually expects as inputs to make sure I'm wiring this up correctly.

[[reply_to_current]] My fault — I ran the basic composite workflow (just a hard paste with no relighting). Last night's good results used IC-Light FC which actually integrates the lighting. The input name was also wrong. Let me fix it properly right now.

curl -s "http://127.0.0.1:8188/object_info/ICLightConditioning" | python3 -c "
import sys,json; d=json.load(sys.stdin); k=list(d.keys())[0]
req=d[k]['input']['required']
opt=d[k]['input'].get('optional',{})
print('required:', list(req.keys()))
print('optional:', list(opt.keys()))
"

curl -s "http://127.0.0.1:8188/object_info/ICLightApplyMaskGrey" | python3 -c "
import sys,json; d=json.load(sys.stdin); k=list(d.keys())[0]
req=d[k]['input']['required']
opt=d[k]['input'].get('optional',{})
print('required:', list(req.keys()))
print('optional:', list(opt.keys()))
"


required: ['positive', 'negative', 'vae', 'foreground', 'multiplier']
optional: ['opt_background']


required: ['image', 'alpha']
optional: []


curl -s "http://127.0.0.1:8188/object_info/BiRefNetRMBG" | python3 -c "
import sys,json; d=json.load(sys.stdin); k=list(d.keys())[0]
req=d[k]['input']['required']
opt=d[k]['input'].get('optional',{})
print('required:', list(req.keys()))
print('outputs:', d[k].get('output_name', d[k].get('output',[])))
"


required: ['image', 'model']
outputs: ['IMAGE', 'MASK', 'MASK_IMAGE']


Now I understand the node signatures:

- `BiRefNetRMBG`: inputs: `image`, `model` → outputs: IMAGE (slot 0), MASK (slot 1), MASK_IMAGE (slot 2)
- `ICLightApplyMaskGrey`: inputs: `image`, `alpha` → this takes the product image and the alpha/mask
- `ICLightConditioning`: inputs: `positive`, `negative`, `vae`, `foreground`, `multiplier`, optional: `opt_background` → outputs: positive, negative, latent_image

So the flow for IC-Light FC is:

1. Load the image and run it through BiRefNetRMBG to extract the foreground and mask
2. Apply the mask to create a grey-masked version of the product image
3. Load the checkpoint and apply the IC-Light UNet model, then encode both positive and negative prompts with CLIP
4. Encode the masked image to latent space, then pass everything through ICLightConditioning to prepare the conditioning and latent inputs
5. Run KSampler with the conditioned inputs, decode the result, and save the final image

Now I'm clarifying the node structure—the ICLightApplyMaskGrey node takes an image and alpha (which is the mask), and I need to use the FC model variant since we're just providing text descriptions without a background image. For the Olay product shot, I should verify that BiRefNet can properly isolate the jar from the hand in the image when generating the mask.

Got it — FC model, correct input names. Writing and submitting now for both products.

write

#!/usr/bin/env python3
"""IC-Light FC pipeline — proper lighting integration for product shots."""
import json, requests, uuid

API = "http://127.0.0.1:8188"
CLIENT_ID = str(uuid.uuid4())

def submit(prompt_dict, label=""):
    r = requests.post(f"{API}/prompt", json={"prompt": prompt_dict, "client_id": CLIENT_ID})
    if r.status_code == 200:
        pid = r.json().get("prompt_id","?")
        print(f"✅ [{label}]: {pid}")
        return pid
    else:
        print(f"❌ [{label}]: {r.status_code} {r.text[:400]}")

def make_iclight_prompt(input_image, pos_text, save_prefix, seed=42):
    return {
        "1":  {"class_type": "LoadImage",               "inputs": {"image": input_image}},
        "2":  {"class_type": "BiRefNetRMBG",             "inputs": {"image": ["1",0], "model": "BiRefNet-general"}},
        "3":  {"class_type": "ICLightApplyMaskGrey",     "inputs": {"image": ["2",0], "alpha": ["2",1]}},
        "4":  {"class_type": "CheckpointLoaderSimple",   "inputs": {"ckpt_name": "RealisticVision_V6_fp16.safetensors"}},
        "5":  {"class_type": "LoadAndApplyICLightUnet",  "inputs": {"model": ["4",0], "model_path": "iclight_sd15_fc_unet_ldm.safetensors"}},
        "6":  {"class_type": "CLIPTextEncode",           "inputs": {"text": pos_text, "clip": ["4",1]}},
        "7":  {"class_type": "CLIPTextEncode",           "inputs": {"text": "blurry, low quality, watermark, deformed, dark, ugly, harsh shadows, flat lighting", "clip": ["4",1]}},
        "8":  {"class_type": "VAEEncode",                "inputs": {"pixels": ["3",0], "vae": ["4",2]}},
        "9":  {"class_type": "ICLightConditioning",      "inputs": {"positive": ["6",0], "negative": ["7",0], "vae": ["4",2], "foreground": ["8",0], "multiplier": 1.0}},
        "10": {"class_type": "KSampler",                 "inputs": {"model": ["5",0], "positive": ["9",0], "negative": ["9",1], "latent_image": ["9",2], "seed": seed, "steps": 25, "cfg": 2.0, "sampler_name": "dpmpp_2m", "scheduler": "karras", "denoise": 1.0}},
        "11": {"class_type": "VAEDecode",                "inputs": {"samples": ["10",0], "vae": ["4",2]}},
        "12": {"class_type": "SaveImage",                "inputs": {"images": ["11",0], "filename_prefix": save_prefix}},
    }

# ── Olay Regenerist 10 ────────────────────────────────────────────────────────
olay_scenes = [
    ("Olay Regenerist cream jar, luxury marble bathroom countertop, soft natural window light, elegant skincare display, photorealistic", "olay/icl_marble",  101),
    ("Olay Regenerist cream jar, wooden spa shelf surrounded by green eucalyptus plants, warm diffused light, zen wellness aesthetic, photorealistic", "olay/icl_spa",     102),
    ("Olay Regenerist cream jar, modern bathroom vanity, bright morning sunlight, clean minimal aesthetic, editorial cosmetics photography", "olay/icl_morning", 103),
    ("Olay Regenerist cream jar, dark moody luxury surface, dramatic soft rim lighting, premium skincare brand aesthetic, photorealistic", "olay/icl_moody",   104),
]

print("=== Olay ===")
for pos, prefix, seed in olay_scenes:
    submit(make_iclight_prompt("olay_product.jpg", pos, prefix, seed), prefix.split("/")[1])

# ── Power Bank ────────────────────────────────────────────────────────────────
pb_scenes = [
    ("portable power bank charger, outdoor rocky mountain trail, dramatic golden hour sunlight, adventure travel photography, photorealistic", "powerbank/icl_outdoor", 201),
    ("portable power bank charger, dark carbon fiber desk, blue LED accent lighting, tech workspace, premium gadget photography", "powerbank/icl_desk",    202),
    ("portable power bank charger, travel flat lay on world map, warm cafe light, passport and coffee nearby, wanderlust lifestyle photography", "powerbank/icl_travel",  203),
    ("portable power bank charger, studio gradient background, dramatic centred spotlight, clean hero product shot, photorealistic", "powerbank/icl_hero",   204),
]

print("=== Power Bank ===")
for pos, prefix, seed in pb_scenes:
    submit(make_iclight_prompt("powerbank.jpg", pos, prefix, seed), prefix.split("/")[1])

print("\nAll queued — view at http://fred:8188")


Successfully wrote 4141 bytes to /home/crogers2287/.openclaw/agents/selena/submit_iclight.py


exec

cd /home/crogers2287/.openclaw/agents/selena && python3 submit_iclight.py 2>&1


=== Olay ===
✅ [icl_marble]: 8d05a4ff-50c4-4ca2-b974-997335edf447
✅ [icl_spa]: 43784146-2241-462f-a6ac-39482e947940
✅ [icl_morning]: 7f54c11c-26d9-4b37-b0ec-db6ee9e14bdc
✅ [icl_moody]: 31576655-d44f-4a65-9a11-3e897a71b1c7
=== Power Bank ===
✅ [icl_outdoor]: 5fece449-a7ae-406f-a5bd-c2b4c35b0ab6
✅ [icl_desk]: 0709b991-6cf0-43a3-beb6-6f9bdead3d0c
✅ [icl_travel]: f1123fe2-ae73-4a29-9ddb-202c5654a340
✅ [icl_hero]: 5dd5ebac-c658-4554-a5c8-03c9948210ed

All queued — view at http://fred:8188


All 8 queued — 4 Olay, 4 power bank — all running through IC-Light FC this time so the lighting will actually integrate properly. Check **http://fred:8188** → History as they complete. Should take a few minutes each.


Conversation info (untrusted metadata):
```json
{
  "message_id": "171",
  "sender": "6558388607"
}
```

Save those workflows I'm going to invite you to a group chat with Chelsea you can generate whatever she needs


Chad wants me to save these workflows (the IC-Light pipeline) and also prepare for a group chat with Chelsea where I can generate content on demand. Let me:

1. Save the IC-Light workflow as a proper JSON file in the workflows directory
2. Update MEMORY.md with notes about the group chat and Chelsea
3. Maybe also update the workflows directory with the new working pipeline

Let me save the workflow properly to the ComfyUI workflows directory, and also update my memory about Chelsea and the upcoming group chat.

python3 - <<'EOF'
import json

def make_iclight_workflow(input_image="your_product.jpg", pos_text="product on a luxury marble surface, soft natural light, photorealistic", save_prefix="output/iclight", seed=42):
    return {
        "last_node_id": 12, "last_link_id": 12, "version": 0.4,
        "nodes": [
            {"id":1,  "type":"LoadImage",              "pos":[50,80],   "size":[300,100],"order":0,"mode":0,"outputs":[{"name":"IMAGE","type":"IMAGE","links":[1],"slot_index":0},{"name":"MASK","type":"MASK","links":[],"slot_index":1}],"widgets_values":[input_image,"image"]},
            {"id":2,  "type":"BiRefNetRMBG",            "pos":[420,80],  "size":[300,80], "order":1,"mode":0,"inputs":[{"name":"image","type":"IMAGE","link":1}],"outputs":[{"name":"IMAGE","type":"IMAGE","links":[2],"slot_index":0},{"name":"MASK","type":"MASK","links":[3],"slot_index":1},{"name":"MASK_IMAGE","type":"IMAGE","links":[],"slot_index":2}],"widgets_values":["BiRefNet-general"]},
            {"id":3,  "type":"ICLightApplyMaskGrey",    "pos":[790,80],  "size":[300,80], "order":2,"mode":0,"inputs":[{"name":"image","type":"IMAGE","link":2},{"name":"alpha","type":"MASK","link":3}],"outputs":[{"name":"IMAGE","type":"IMAGE","links":[8],"slot_index":0}],"widgets_values":[]},
            {"id":4,  "type":"CheckpointLoaderSimple",  "pos":[50,280],  "size":[360,80], "order":3,"mode":0,"outputs":[{"name":"MODEL","type":"MODEL","links":[5],"slot_index":0},{"name":"CLIP","type":"CLIP","links":[6,7],"slot_index":1},{"name":"VAE","type":"VAE","links":[9,11],"slot_index":2}],"widgets_values":["RealisticVision_V6_fp16.safetensors"]},
            {"id":5,  "type":"LoadAndApplyICLightUnet", "pos":[470,280], "size":[360,80], "order":4,"mode":0,"inputs":[{"name":"model","type":"MODEL","link":5}],"outputs":[{"name":"MODEL","type":"MODEL","links":[10],"slot_index":0}],"widgets_values":["iclight_sd15_fc_unet_ldm.safetensors"]},
            {"id":6,  "type":"CLIPTextEncode",          "pos":[470,400], "size":[500,160],"order":5,"mode":0,"inputs":[{"name":"clip","type":"CLIP","link":6}],"outputs":[{"name":"CONDITIONING","type":"CONDITIONING","links":[12],"slot_index":0}],"widgets_values":[pos_text]},
            {"id":7,  "type":"CLIPTextEncode",          "pos":[470,590], "size":[500,100],"order":6,"mode":0,"inputs":[{"name":"clip","type":"CLIP","link":7}],"outputs":[{"name":"CONDITIONING","type":"CONDITIONING","links":[13],"slot_index":0}],"widgets_values":["blurry, low quality, watermark, deformed, dark, ugly, harsh shadows, flat lighting"]},
            {"id":8,  "type":"VAEEncode",               "pos":[1160,80], "size":[300,100],"order":7,"mode":0,"inputs":[{"name":"pixels","type":"IMAGE","link":8},{"name":"vae","type":"VAE","link":9}],"outputs":[{"name":"LATENT","type":"LATENT","links":[14],"slot_index":0}],"widgets_values":[]},
            {"id":9,  "type":"ICLightConditioning",     "pos":[1160,280],"size":[360,160],"order":8,"mode":0,"inputs":[{"name":"positive","type":"CONDITIONING","link":12},{"name":"negative","type":"CONDITIONING","link":13},{"name":"vae","type":"VAE","link":11},{"name":"foreground","type":"LATENT","link":14}],"outputs":[{"name":"positive","type":"CONDITIONING","links":[15],"slot_index":0},{"name":"negative","type":"CONDITIONING","links":[16],"slot_index":1},{"name":"latent","type":"LATENT","links":[17],"slot_index":2}],"widgets_values":[1.0]},
            {"id":10, "type":"KSampler",                "pos":[1600,280],"size":[360,480],"order":9,"mode":0,"inputs":[{"name":"model","type":"MODEL","link":10},{"name":"positive","type":"CONDITIONING","link":15},{"name":"negative","type":"CONDITIONING","link":16},{"name":"latent_image","type":"LATENT","link":17}],"outputs":[{"name":"LATENT","type":"LATENT","links":[18],"slot_index":0}],"widgets_values":[seed,"randomize",25,2.0,"dpmpp_2m","karras",1.0]},
            {"id":11, "type":"VAEDecode",               "pos":[2020,280],"size":[300,100],"order":10,"mode":0,"inputs":[{"name":"samples","type":"LATENT","link":18},{"name":"vae","type":"VAE","link":19}],"outputs":[{"name":"IMAGE","type":"IMAGE","links":[20],"slot_index":0}],"widgets_values":[]},
            {"id":12, "type":"SaveImage",               "pos":[2380,280],"size":[300,100],"order":11,"mode":0,"inputs":[{"name":"images","type":"IMAGE","link":20}],"widgets_values":[save_prefix]},
        ],
        "links": [
            [1,1,0,2,0,"IMAGE"],[2,2,0,3,0,"IMAGE"],[3,2,1,3,1,"MASK"],
            [5,4,0,5,0,"MODEL"],[6,4,1,6,0,"CLIP"],[7,4,1,7,0,"CLIP"],
            [8,3,0,8,0,"IMAGE"],[9,4,2,8,1,"VAE"],[10,5,0,10,0,"MODEL"],
            [11,4,2,9,2,"VAE"],[12,6,0,9,0,"CONDITIONING"],[13,7,0,9,1,"CONDITIONING"],
            [14,8,0,9,3,"LATENT"],[15,9,0,10,1,"CONDITIONING"],[16,9,1,10,2,"CONDITIONING"],
            [17,9,2,10,3,"LATENT"],[18,10,0,11,0,"LATENT"],[19,4,2,11,1,"VAE"],
            [20,11,0,12,0,"IMAGE"],
        ],
        "groups": []
    }

wf = make_iclight_workflow()
out = "/home/crogers2287/ComfyUI/user/default/workflows/product_iclight_fc.json"
with open(out, "w") as f:
    json.dump(wf, f, indent=2)
print(f"Saved: {out}")
EOF


Saved: /home/crogers2287/ComfyUI/user/default/workflows/product_iclight_fc.json


# 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 |

[179 more lines in file. Use offset=11 to continue.]


edit

## 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
```python
make_iclight_prompt(input_image, pos_text, save_prefix, seed)
# Nodes: LoadImage → BiRefNetRMBG → ICLightApplyMaskGrey → VAEEncode
#        CheckpointLoaderSimple → LoadAndApplyICLightUnet
#        CLIPTextEncode (pos/neg) → ICLightConditioning → KSampler → VAEDecode → SaveImage
```

### Key Node Input Names (verified)
- `LoadAndApplyICLightUnet`: `model`, `model_path` (use `iclight_sd15_fc_unet_ldm.safetensors`)
- `ICLightApplyMaskGrey`: `image`, `alpha` (alpha = MASK slot 1 from BiRefNetRMBG)
- `ICLightConditioning`: `positive`, `negative`, `vae`, `foreground`, `multiplier`, optional: `opt_background`
- `BiRefNetRMBG` outputs: IMAGE (slot 0), MASK (slot 1), MASK_IMAGE (slot 2)

### Submit Scripts
- `/home/crogers2287/.openclaw/agents/selena/submit_iclight.py` — multi-scene batch submitter
- Workflows dir: `/home/crogers2287/ComfyUI/user/default/workflows/`
- Outputs: `/home/crogers2287/ComfyUI/output/`

### Chelsea Schnepp — Group Chat
- Chelsea will request product placement images in a group chat
- Run products through IC-Light FC pipeline with varied scene prompts
- Ask: product name/type, style preference (luxury/lifestyle/moody/clean)
- Send results: user views via http://fred:8188 (can't push images from Selena session directly)

---

## Active Projects


Could not find the exact text in /home/crogers2287/.openclaw/agents/selena/MEMORY.md. The old text must match exactly including all whitespace and newlines.


exec

grep -n "Active Projects\|Projects Log" /home/crogers2287/.openclaw/agents/selena/MEMORY.md


182:## Projects Log


edit

## 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

---

## Projects Log

*(Track major generation projects here)*


Successfully replaced text in /home/crogers2287/.openclaw/agents/selena/MEMORY.md.

     ...
 178 - **Storage**: [Not yet detected]
 179
 180 ---
 181
+182 ## Product Placement Pipeline (IC-Light FC) ✅ WORKING
+183
+184 ### The Right Workflow
+185 Use `product_iclight_fc.json` — IC-Light FC (Foreground Conditioned) properly integrates lighting.
+186 **Do NOT use composite workflow** — that just hard-pastes the cutout, lighting looks wrong.
+187
+188 ### API Submit Pattern
+189 See `/home/crogers2287/.openclaw/agents/selena/submit_iclight.py` for full working example.
+190
+191 Key nodes in order:
+192 `LoadImage → BiRefNetRMBG → ICLightApplyMaskGrey → VAEEncode`
+193 `CheckpointLoaderSimple → LoadAndApplyICLightUnet`
+194 `CLIPTextEncode (pos+neg) → ICLightConditioning → KSampler → VAEDecode → SaveImage`
+195
+196 ### Verified Node Input Names
+197 - `LoadAndApplyICLightUnet`: inputs = `model`, `model_path` → use `iclight_sd15_fc_unet_ldm.safetensors`
+198 - `ICLightApplyMaskGrey`: inputs = `image`, `alpha` (alpha = MASK slot 1 from BiRefNetRMBG)
+199 - `ICLightConditioning`: inputs = `positive`, `negative`, `vae`, `foreground`, `multiplier`; optional: `opt_background`
+200 - `BiRefNetRMBG` outputs: IMAGE (slot 0), MASK (slot 1), MASK_IMAGE (slot 2)
+201 - Checkpoint: `RealisticVision_V6_fp16.safetensors` (SD1.5 based, required for IC-Light)
+202
+203 ### Settings That Work
+204 - Steps: 25, CFG: 2.0, sampler: dpmpp_2m karras, denoise: 1.0
+205
+206 ### Outputs
+207 - Outputs: `/home/crogers2287/ComfyUI/output/`
+208 - Users view via: http://fred:8188 (History tab) — cannot push images from Selena session
+209
+210 ---
+211
+212 ## Chelsea Schnepp — Group Chat
+213 - Chelsea will request product placement images in group chat with Chad
+214 - Workflow: ask product type/image + scene style → run IC-Light FC → point to http://fred:8188
+215 - Good scene prompts: luxury marble, spa/plants, morning vanity, moody/dark, outdoor, tech desk, travel
+216
+217 ---
+218
 182 ## Projects Log
 183
 184 *(Track major generation projects here)*
 185
     ...


Done! Here's what I saved:

- **`product_iclight_fc.json`** — the working IC-Light FC workflow in your ComfyUI workflows folder
- **`submit_iclight.py`** — the batch submitter script for any product image
- **MEMORY.md** — updated with the correct pipeline, verified node signatures, and a note about Chelsea

When she joins the group, just have her send a product image and tell me what vibe she wants — I'll queue it right through IC-Light. 🎨


System: [2026-02-18 16:22:05 UTC] Model switched to anthropic/claude-sonnet-4-6.

Conversation info (untrusted metadata):
```json
{
  "message_id": "236",
  "sender": "6558388607"
}
```

what happened with the i2v workflows?


The user is asking about image-to-video workflows. Let me search my memory for what happened with those.

Question: image to video i2v workflow setup installation


[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

Source: MEMORY.md#L82-L154

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)
```

--

Source: MEMORY.md#L139-L196

| 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 i

Source: MEMORY.md#L33-L99


Ok next I want to see if we can get an img to video pipeline setup. Whatever is the best/fastest setup we can get working on our hardware. See what the top workflows are as of today's date, get some setup with the latest models please
user: [media attached: /home/crogers2287/.openclaw/media/inbound/file_2---916e46e9-07f0-4eb6-bad3-23debf8c33d7.jpg (image/jpeg) | /home/crogers2287/.openclaw/media/inbound/file_2---916e46e9-07f0-4eb6-bad3-23debf8c33d7.jpg]
To send an image back, prefer the message tool (media/path/filePath). If you must inline, use MEDIA:https://example.com/image.jpg (spaces ok, quote if needed) or a safe relative path like MEDIA:./image.jpg. Avoid absolute paths (MEDIA:/...) and ~ paths — they are blocked for security. Keep caption in the text body.
Conversation info (untrusted metadata):
```json
{
  "message_id": "139",
  "sender": "6558388607"
}
```

<media:image>


## Critical Technical Notes

### Z-Image Turbo Correct Settings
- CLIPLoader type: **`lumina2`** (NOT sd3 — caused terrible quality)
- Sampler: `euler`
- Scheduler: `simple`
- Steps: 9
- CFG: 1.0
- Wrong settings produced heavily pixelated/blocky output

### ComfyUI Startup Issues
- **Must use `--gpu-only` flag** — without it, runs on CPU (0% GPU utilization) despite model being in VRAM
- llama-server (PID varies) eats ~16GB across both GPUs — use `/unload` endpoint at `localhost:9069` or kill the process before heavy generation
- Multiple ComfyUI instances cause port 8188 conflicts — always `pkill -9 -f "python main.py"` before restarting
- Real log file: `/home/crogers2287/ComfyUI/user/comfyui_8188.log` (not /tmp/)

### BiRefNetRMBG Node Bug Fix
- Node: `ComfyUI-RMBG/py/AILab_BiRefNet.py`
- Bug: optional params (`mask_blur`, `mask_offset`, etc.) not defaulted when not passed
- Fix applied: added `params.setdefault(...)` calls after `params["process_res"] = process_res`
- Node call: only pass `image` and `model` as required inputs

### IC-Light Setup
- FC model: foreground only → generates background from text prompt ✅ Working
- FBC model: requires `opt_background` latent — needs a pre-encoded background image passed via `opt_background` in ICLightConditioning
- Node name: `ICLightApplyMaskGrey` — input param is `alpha` not `mask`
- IC-Light uses SD1.5 architecture (RealisticVision checkpoint for CLIP/VAE)
- Best results: scene 3 (window light) scored 7/10 realism — massive improvement over hard composite

### Message Tool
- `message` tool with `filePath`/`media` parameters failing with "Cannot read properties of undefined (reading 'listAccountIds')"
- Cannot send images via Telegram from Selena session — user must view via `http://fred:8188` History tab

### llama-swap


The session log was cut off after mentioning "Ok next I want to see if we can get an img to video pipeline setup." - it seems the conversation ended there or the memory wasn't captured beyond that point. Let me check if there's any more context in the session log file or any i2v specific notes.

The user asked for i2v setup but it seems the session ended before it was completed (or the memory doesn't capture what happened after). Let me check what's actually installed in ComfyUI for video generation.

Looks like last session you asked for i2v right at the end, but we never actually got to it — the session log cuts off right after you requested it. Let me see what's actually on disk.

exec

ls ~/ComfyUI/custom_nodes/ | grep -i -E "video|ltx|wan|mochi|hunyuan|anim|diffusion"
echo "---models---"
find /mnt/storage/comfyui-models ~/ComfyUI/models -name "*.safetensors" 2>/dev/null | grep -i -E "ltx|wan|mochi|hunyuan|skyreels|video|i2v" | head -20


find /mnt/storage/comfyui-models ~/ComfyUI/models -type d 2>/dev/null | grep -i -E "wan|ltx|video|diffusion_model" | head -20
echo "---"
find /mnt/storage/comfyui-models -name "*.safetensors" 2>/dev/null | grep -iv "lora\|wan2.2" | grep -i "wan\|video" | head -10
echo "---wan nodes---"
ls ~/ComfyUI/custom_nodes/ | grep -i wan


/mnt/storage/comfyui-models/diffusion_models
/mnt/storage/comfyui-models/diffusion_models/wan
/mnt/storage/comfyui-models/diffusion_models/wan/.cache
/mnt/storage/comfyui-models/diffusion_models/wan/.cache/huggingface
/mnt/storage/comfyui-models/diffusion_models/wan/.cache/huggingface/download
/mnt/storage/comfyui-models/diffusion_models/wan/.cache/huggingface/download/I2V
/mnt/storage/comfyui-models/loras/wan
/mnt/storage/comfyui-models/loras/wan/.cache
/mnt/storage/comfyui-models/loras/wan/.cache/huggingface
/mnt/storage/comfyui-models/loras/wan/.cache/huggingface/download
/mnt/storage/comfyui-models/loras/wan/.cache/huggingface/download/Wan2.2-I2V-A14B-4steps-lora-rank64-Seko-V1
/mnt/storage/comfyui-models/loras/wan/.cache/huggingface/download/Wan2.2-T2V-A14B-4steps-250928-dyno
/mnt/storage/comfyui-models/loras/wan/.cache/huggingface/download/Wan2.2-T2V-A14B-4steps-lora-250928
/mnt/storage/comfyui-models/loras/wan/.cache/huggingface/download/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V1
/mnt/storage/comfyui-models/loras/wan/.cache/huggingface/download/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V1.1
/mnt/storage/comfyui-models/loras/wan/.cache/huggingface/download/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V2.0
/mnt/storage/comfyui-models/loras/wan/Wan2.2-I2V-A14B-4steps-lora-rank64-Seko-V1
/mnt/storage/comfyui-models/loras/wan/Wan2.2-T2V-A14B-4steps-250928-dyno
/mnt/storage/comfyui-models/loras/wan/Wan2.2-T2V-A14B-4steps-lora-250928
/mnt/storage/comfyui-models/loras/wan/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V1
---
/mnt/storage/comfyui-models/vae/wan_2.1_vae.safetensors
---wan nodes---

(Command exited with code 1)


ls /mnt/storage/comfyui-models/diffusion_models/wan/
echo "---text encoders---"
find /mnt/storage/comfyui-models/text_encoders -name "*.safetensors" 2>/dev/null | grep -i wan | head -10
echo "---clip vision---"
find /mnt/storage/comfyui-models /home/crogers2287/ComfyUI/models -name "*.safetensors" 2>/dev/null | grep -i "clip_vision\|siglip" | head -5


---text encoders---
---clip vision---


find /mnt/storage/comfyui-models/diffusion_models/wan -type f | head -20
echo "---"
ls /mnt/storage/comfyui-models/text_encoders/ 2>/dev/null | head -20
echo "---"
ls /mnt/storage/comfyui-models/clip_vision/ 2>/dev/null | head -10


Found the problem — Wan 2.2 I2V models are stuck as `.lock` and `.metadata` files only. The actual model downloads never completed. Let me check what did actually land on disk.

find /mnt/storage/comfyui-models -name "*.safetensors" ! -name "*.lock" ! -name "*.metadata" 2>/dev/null | sort
echo "---disk---"
df -h /mnt/storage


/mnt/storage/comfyui-models/BiRefNet/BiRefNet-general.safetensors
/mnt/storage/comfyui-models/LLM/florence2-base/model.safetensors
/mnt/storage/comfyui-models/checkpoints/RealisticVision_V6_fp16.safetensors
/mnt/storage/comfyui-models/diffusion_models/iclight_sd15_fbc_unet_ldm.safetensors
/mnt/storage/comfyui-models/diffusion_models/iclight_sd15_fc_unet_ldm.safetensors
/mnt/storage/comfyui-models/diffusion_models/z_image_turbo_bf16.safetensors
/mnt/storage/comfyui-models/loras/wan/Wan2.2-Lightning_I2V-A14B-4steps-lora_HIGH_fp16.safetensors
/mnt/storage/comfyui-models/loras/wan/Wan2.2-Lightning_I2V-A14B-4steps-lora_LOW_fp16.safetensors
/mnt/storage/comfyui-models/loras/wan/Wan2.2-T2V-A14B-4steps-lora-250928/high_noise_model.safetensors
/mnt/storage/comfyui-models/loras/wan/Wan2.2-T2V-A14B-4steps-lora-250928/low_noise_model.safetensors
/mnt/storage/comfyui-models/loras/wan/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V1.1/high_noise_model.safetensors
/mnt/storage/comfyui-models/loras/wan/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V1.1/low_noise_model.safetensors
/mnt/storage/comfyui-models/loras/wan/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V1/high_noise_model.safetensors
/mnt/storage/comfyui-models/loras/wan/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V1/low_noise_model.safetensors
/mnt/storage/comfyui-models/loras/wan/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V2.0/high_noise_model.safetensors
/mnt/storage/comfyui-models/loras/wan/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V2.0/low_noise_model.safetensors
/mnt/storage/comfyui-models/loras/z_image/z_image_turbo_distill_patch_lora_bf16.safetensors
/mnt/storage/comfyui-models/text_encoders/qwen_3_4b.safetensors
/mnt/storage/comfyui-models/vae/wan_2.1_vae.safetensors
/mnt/storage/comfyui-models/vae/z_image_turbo_ae.safetensors
---disk---
Filesystem      Size  Used Avail Use% Mounted on
/dev/sda2       1.9T  1.2T  690G  64% /mnt/storage


There's the story — the **Wan 2.2 I2V base models never finished downloading**. All we have are the LoRAs and the VAE, but the actual diffusion model files (those big `Wan2_2-I2V-A14B` safetensors) are just empty `.lock`/`.metadata` placeholders. The downloads stalled or got interrupted.

We also have no ComfyUI video nodes installed (no VideoHelperSuite, no WanVideoWrapper, etc.) and no CLIP vision model which I2V needs for image conditioning.

So we got sidetracked by the IC-Light product shots and never completed the i2v setup. Want me to kick off the downloads now and get the nodes installed? The Wan 2.2 I2V fp8 models are ~14GB each — with 690GB free we're good on space.


Conversation info (untrusted metadata):
```json
{
  "message_id": "240",
  "sender": "6558388607"
}
```

do it


The user wants me to set up the i2v pipeline. Let me figure out what's needed:

1. Install ComfyUI video nodes (VideoHelperSuite, WanVideoWrapper or similar)
2. Download the Wan 2.2 I2V base models
3. Download CLIP vision model for image conditioning
4. Build a workflow

Let me first check what custom nodes are available and what the current state of things is, then figure out the best approach.

For Wan 2.2 I2V, I need:
- The base diffusion model (Wan2_2-I2V-A14B fp8 - ~14GB)

- Text encoder and CLIP vision model for conditioning
- The VAE I already have
- ComfyUI nodes to run the Wan video pipeline

I should check what's already installed and whether I can use ComfyUI Manager to grab the necessary nodes, plus figure out how to download the models.

ls ~/ComfyUI/custom_nodes/ | grep -iv "ic-light\|rmbg\|florence\|filters\|kjnodes\|essentials\|rgthree\|manager\|ryan\|example\|pycache\|websocket"
echo "---hf cli---"
which huggingface-cli 2>/dev/null || echo "no hf cli"
which aria2c 2>/dev/null || echo "no aria2c"
echo "---pip in venv---"
~/ComfyUI/venv/bin/pip list 2>/dev/null | grep -i "hugging\|hf_\|datasets" | head -5


---hf cli---
/usr/local/bin/huggingface-cli
/usr/bin/aria2c
---pip in venv---
huggingface_hub                        1.4.1


Good — `huggingface-cli` and `aria2c` both available. Let me check what video nodes exist in ComfyUI Manager and what the Wan 2.2 I2V model repo looks like.

# Check what text encoders Wan needs
curl -s "https://huggingface.co/api/models/Wan-AI/Wan2.2-I2V-A14B-480P" 2>/dev/null | python3 -c "
import sys, json
d = json.load(sys.stdin)
siblings = d.get('siblings', [])
for s in siblings:
    print(s.get('rfilename',''))
" 2>/dev/null | head -30 || echo "API check failed"

# Check ComfyUI Manager's node registry for wan video nodes
find ~/ComfyUI/custom_nodes/ComfyUI-Manager -name "*.json" 2>/dev/null | head -5
ls ~/ComfyUI/custom_nodes/ComfyUI-Manager/ | head -10


(no output)


/home/crogers2287/ComfyUI/custom_nodes/ComfyUI-Manager/extension-node-map.json
/home/crogers2287/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/extension-node-map.json
/home/crogers2287/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/model-list.json
/home/crogers2287/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/custom-node-list.json
/home/crogers2287/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/legacy/extension-node-map.json
LICENSE.txt
README.md
__init__.py
__pycache__
alter-list.json
channels.list.template
check.bat
check.sh
cm-cli.py
cm-cli.sh


python3 -c "
import json
with open('/home/crogers2287/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/custom-node-list.json') as f:
    d = json.load(f)
nodes = d.get('custom_nodes', [])
for n in nodes:
    title = n.get('title','')
    if any(k in title.lower() for k in ['wan', 'video helper', 'videohel', 'wanvideo']):
        print(title, '|', n.get('reference','')[:60])
" | head -20


python3 -c "
import json
with open('/home/crogers2287/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/forked/custom-node-list.json') as f:
    d = json.load(f)
nodes = d.get('custom_nodes', [])
for n in nodes:
    title = n.get('title','').lower()
    ref = n.get('reference','').lower()
    if any(k in title+ref for k in ['wan', 'video', 'kijai']):
        print(n.get('title'), '|', n.get('reference','')[:80])
" | head -30


# Try the legacy db
python3 -c "
import json
with open('/home/crogers2287/ComfyUI/custom_nodes/ComfyUI-Manager/node_db/legacy/extension-node-map.json') as f:
    d = json.load(f)
for url, info in list(d.items())[:3]:
    print(url, type(info))
" 2>/dev/null | head -5
echo "---"
# Search directly for wan/video repos
python3 -c "
import json
with open('/home/crogers2287/ComfyUI/custom_nodes/ComfyUI-Manager/extension-node-map.json') as f:
    d = json.load(f)
for url in d:
    if any(k in url.lower() for k in ['wan', 'video', 'kijai']):
        print(url)
" | head -20


---
https://github.com/AIFSH/FancyVideo-ComfyUI
https://github.com/AIFSH/VideoSys-ComfyUI
https://github.com/Baverne/comfyUI-TiledWan
https://github.com/BetaDoggo/ComfyUI-VideoPlayer
https://github.com/ComfyUI-WanVideoWrapper/QQ.copied
https://github.com/CrateTools/comfyui-preview-video-monitor
https://github.com/DarioFT/ComfyUI-VideoDirCombiner
https://github.com/DebugPadawan/DebugPadawans-ComfyUI-Essentials
https://github.com/DenRakEiw/WAN_NN_Latent_Upscale
https://github.com/Fannovel16/ComfyUI-MagickWand
https://github.com/Fannovel16/ComfyUI-Video-Matting
https://github.com/Flow-two/ComfyUI-WanStartEndFramesNative
https://github.com/Fossiel/ComfyUI-Fossiel-WAN-Additional-Tilers
https://github.com/GiusTex/ComfyUI-Wan-TimeToMove
https://github.com/GonDesign/ComfyUI-HaoranWanxImageEdit
https://github.com/Granddyser/wan-video-extender
https://github.com/HM-RunningHub/ComfyUI_RH_VideoAsPrompt
https://github.com/HellerCommaA/ComfyUI-VideoResolutions
https://github.com/HoangYell/comfyui-hoangyell-video
https://github.com/IDGallagher/MotionVideoSearch
Exception ignored in: <_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>
BrokenPipeError: [Errno 32] Broken pipe


python3 -c "
import json
with open('/home/crogers2287/ComfyUI/custom_nodes/ComfyUI-Manager/extension-node-map.json') as f:
    d = json.load(f)
for url in sorted(d):
    if any(k in url.lower() for k in ['wanvideo', 'wan-video', 'videohel', 'video-helper']):
        print(url)
"


https://github.com/ComfyUI-WanVideoWrapper/QQ.copied
https://github.com/Granddyser/wan-video-extender
https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite
https://github.com/ShmuelRonen/ComfyUI-WanVideoKsampler
https://github.com/SquirrelRat/WanVideoLooper
https://github.com/Wakapedia/ComfyUI-WanVideoWakawave
https://github.com/jajos12/Comfyui-WanVideo-Context
https://github.com/karthikg-09/ComfyUI-WanVideoAutoResize
https://github.com/kijai/ComfyUI-WanVideoWrapper
https://github.com/raindrop313/ComfyUI-WanVideoStartEndFrames
https://github.com/siraxe/ComfyUI-WanVideoWrapper_QQ
https://github.com/vantagewithai/VantageLongWanVideo


`kijai/ComfyUI-WanVideoWrapper` — that's the one. Same author as the IC-Light and KJNodes we already use. Let me install it + VideoHelperSuite, then pull the models in parallel.

cd ~/ComfyUI/custom_nodes && git clone https://github.com/kijai/ComfyUI-WanVideoWrapper.git 2>&1 | tail -3 &
git clone https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite.git 2>&1 | tail -3 &
wait
echo "done"


Cloning into 'ComfyUI-VideoHelperSuite'...
Cloning into 'ComfyUI-WanVideoWrapper'...
done


# Install deps for both nodes
cd ~/ComfyUI && venv/bin/pip install -q -r custom_nodes/ComfyUI-WanVideoWrapper/requirements.txt 2>&1 | tail -5 &
venv/bin/pip install -q -r custom_nodes/ComfyUI-VideoHelperSuite/requirements.txt 2>&1 | tail -5 &
wait
echo "deps done"


/bin/bash: line 3: venv/bin/pip: No such file or directory
deps done


# Find the right pip
find ~/ComfyUI -name "pip" -o -name "pip3" 2>/dev/null | grep bin | head -5
cat ~/ComfyUI/start.sh 2>/dev/null | head -10


/home/crogers2287/ComfyUI/venv/bin/pip3
/home/crogers2287/ComfyUI/venv/bin/pip
#!/bin/bash
cd /home/crogers2287/ComfyUI
source venv/bin/activate
python main.py --listen 0.0.0.0 --port 8188


cd ~/ComfyUI && source venv/bin/activate && \
pip install -q -r custom_nodes/ComfyUI-WanVideoWrapper/requirements.txt 2>&1 | tail -5 && \
pip install -q -r custom_nodes/ComfyUI-VideoHelperSuite/requirements.txt 2>&1 | tail -5 && \
echo "deps OK"


ERROR: Could not open requirements file: [Errno 2] No such file or directory: 'custom_nodes/ComfyUI-VideoHelperSuite/requirements.txt'
deps OK


ls ~/ComfyUI/custom_nodes/ComfyUI-WanVideoWrapper/ | head -15
ls ~/ComfyUI/custom_nodes/ComfyUI-VideoHelperSuite/ | head -10


ATI
FlashVSR
HuMo
LICENSE
LongCat
LongVie2
MTV
Ovi
SCAIL
WanMove
__init__.py
cache_methods
configs
context_windows
controlnet
ls: cannot access '/home/crogers2287/ComfyUI/custom_nodes/ComfyUI-VideoHelperSuite/': No such file or directory


ls ~/ComfyUI/custom_nodes/ && echo "---" && cd ~/ComfyUI/custom_nodes && git clone https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite.git 2>&1 | tail -3