[media attached: /home/crogers2287/.openclaw/media/inbound/file_1---68a68f22-7ac2-4cc5-83c1-53539ab4e8da.jpg (image/jpeg) | /home/crogers2287/.openclaw/media/inbound/file_1---68a68f22-7ac2-4cc5-83c1-53539ab4e8da.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.
System: [2026-02-18 13:50:01 UTC] Model switched to anthropic/claude-sonnet-4-6.

Conversation info (untrusted metadata):
```json
{
  "message_id": "189",
  "conversation_label": "Skinny, Selena and Chelsea id:-5037974429",
  "sender": "6558388607",
  "group_subject": "Skinny, Selena and Chelsea"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Skinny",
  "name": "Skinny",
  "username": "skinnyc813"
}
```

<media:image>


The user "Skinny" sent an image of Nivea products (Skin Firming lotion, Cocoa Butter lotion, and Cocoa Butter cream jar) in a shopping cart at what appears to be a pharmacy/drugstore. Based on my memory, Chelsea will request product placement images in group chat with Chad, and the workflow involves asking about product type/image + scene style, then running IC-Light FC.

This looks like Chelsea or Skinny is sharing a product image - possibly wanting product placement work done. Let me respond naturally as Selena would, acknowledging the products and asking if they want some placement magic done with them.

Wait, the sender is "Skinny" (6558388607) - that's Chad's number based on USER.md (owner numbers: 8138102287, 6558388607).

Chad's sending this over, so he probably wants me to help with product placement on these Nivea items. I should respond enthusiastically and ask if he's looking to get some placement work done with them.

Ooh, Nivea trio! 🛒 Love those cocoa butter bottles — that warm golden packaging photographs beautifully.

Are you thinking product placement magic with these? I can drop them into a luxe scene — marble vanity, spa setting, maybe a moody dark background to make that gold pop. Just say the word and I'll fire up IC-Light! ✨


Conversation info (untrusted metadata):
```json
{
  "message_id": "191",
  "conversation_label": "Skinny, Selena and Chelsea id:-5037974429",
  "sender": "6558388607",
  "group_subject": "Skinny, Selena and Chelsea"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Skinny",
  "name": "Skinny",
  "username": "skinnyc813"
}
```

Show me what you got, send them here. Full professionalism


Chad (Skinny) wants me to do product placement on those Nivea products and send the result here. Let me run the IC-Light FC workflow on the product image.

Based on my memory, I need to use the `product_iclight_fc.json` workflow and the `submit_iclight.py` script. Let me check the current state of things first.

The workflow uses IC-Light FC to properly integrate lighting. Let me look at the submit script and then run it with the Nivea image.

The image is at `/home/crogers2287/.openclaw/media/inbound/file_1---68a68f22-7ac2-4cc5-83c1-53539ab4e8da.jpg`

I'll examine the submit script to see how it works, then execute it with a good scene description for the Nivea products.

Let me pull up the workflow and get these beauties looking editorial! 🎨


#!/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")


Now I understand the pattern. I need to:
1. Copy the Nivea image to the ComfyUI input folder
2. Submit IC-Light jobs with Nivea-appropriate scene prompts
3. Check the output and send them

Let me first check the ComfyUI input directory and then set up the Nivea jobs.

Got it! Let me copy the Nivea image into ComfyUI and queue up some cinematic scenes. 🔥

exec

cp "/home/crogers2287/.openclaw/media/inbound/file_1---68a68f22-7ac2-4cc5-83c1-53539ab4e8da.jpg" /home/crogers2287/ComfyUI/input/nivea_products.jpg && echo "Copied!" && curl -s http://127.0.0.1:8188/system_stats | python3 -c "import sys,json; d=json.load(sys.stdin); print('ComfyUI up:', d.get('system',{}).get('comfyui_version','?'))"


Copied!
ComfyUI up: 0.13.0


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[: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}},
    }

nivea_scenes = [
    ("NIVEA skincare bottles on luxury white marble bathroom countertop, soft natural window light, elegant skincare display, warm golden tones, photorealistic editorial cosmetics photography", "nivea/icl_marble", 301),
    ("NIVEA skincare bottles on wooden spa shelf surrounded by tropical green plants and white towels, warm diffused light, zen wellness aesthetic, photorealistic", "nivea/icl_spa", 302),
    ("NIVEA skincare bottles on dark velvet luxury surface, dramatic rim lighting, deep navy and gold tones, premium brand aesthetic, photorealistic editorial", "nivea/icl_moody", 303),
    ("NIVEA skincare bottles outdoor lifestyle scene, morning sunlight on white linen cloth, fresh clean aesthetic, summer beauty photography, photorealistic", "nivea/icl_outdoor", 304),
]

print("=== Nivea IC-Light FC ===")
for pos, prefix, seed in nivea_scenes:
    submit(make_iclight_prompt("nivea_products.jpg", pos, prefix, seed), prefix.split("/")[1])

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


=== Nivea IC-Light FC ===
✅ [icl_marble]: 6ecfdd57-b9f4-4703-b207-261af303c12f
✅ [icl_spa]: 28b871dc-0c8b-43d0-9511-829311bd8b42
✅ [icl_moody]: a3c9d1c0-330e-4ee1-8bf4-f824bc9e11bc
✅ [icl_outdoor]: 8ff71c70-dcdf-462c-bc00-4cb8bee0c76c

All queued — view at http://fred:8188


All 4 jobs are queued. Now I need to wait for them to complete and then grab the output files and send them. Let me poll for completion.

4 scenes queued! Now let me wait for them to render and grab the outputs. 🎬

echo "Waiting for renders..." && sleep 60 && ls -la /home/crogers2287/ComfyUI/output/nivea/ 2>/dev/null || echo "Not done yet, checking queue..." && curl -s http://127.0.0.1:8188/queue | python3 -c "import sys,json; d=json.load(sys.stdin); print('Running:', len(d.get('queue_running',[])), '| Pending:', len(d.get('queue_pending',[])))"


Command still running (session vivid-comet, pid 2412845). Use process (list/poll/log/write/kill/clear/remove) for follow-up.


Waiting for renders...
total 7172
drwxr-xr-x 2 crogers2287 crogers2287    4096 Feb 18 13:52 .
drwxrwxr-x 7 crogers2287 crogers2287    4096 Feb 18 13:35 ..
-rw-r--r-- 1 crogers2287 crogers2287  657132 Feb 18 13:52 icl_marble_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287  635274 Feb 18 13:52 icl_moody_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287  714185 Feb 18 13:52 icl_outdoor_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287  651450 Feb 18 13:52 icl_spa_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 1140433 Feb 18 05:34 scene_1__00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 1070319 Feb 18 05:34 scene_2__00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 1212493 Feb 18 05:35 scene_3__00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 1230193 Feb 18 05:35 scene_4__00001_.png
Running: 0 | Pending: 0

Process exited with code 0.


All 4 renders are done! Now I need to send them to the group chat using the message tool. Let me send each image with a caption.

All 4 are done! Sending them now! 🔥

message

🏛 **Marble Luxury** — soft window light on a pristine marble countertop. Clean, high-end, editorial.


Cannot read properties of undefined (reading 'listAccountIds')


🏛 **Marble Luxury** — soft window light on a pristine marble countertop. Clean, high-end, editorial.


The message tool is failing. Let me try a different approach - copy the images to a location that can be served, or try the message tool with a different format. Let me check if there's a different way to send images.

Actually, looking at SOUL.md again:
"For nano-banana-pro (Gemini image gen):
- Read the skill at /usr/lib/node_modules/openclaw/skills/nano-banana-pro/SKILL.md
- It outputs images that you can then send via the message tool"

And also:
"When you generate or have an image to share, use the message tool:
message tool:

action: send
media: /path/to/image.png  (absolute path to the file)
message: "Your caption here""

The message tool keeps failing with an error about undefined properties. Let me try using exec to copy the files and serve them instead, or maybe the canvas tool would work better. I'm also noticing there's an inline image format mentioned in the system prompt that uses URLs, so maybe I need to get the images accessible via HTTP first.

Actually, looking at the format requirements more carefully, I should avoid absolute paths and use relative paths instead. Let me copy the images to my workspace first and reference them with a relative path like `./image.jpg`.

exec

cp /home/crogers2287/ComfyUI/output/nivea/icl_marble_00001_.png /home/crogers2287/.openclaw/agents/selena/nivea_marble.png && cp /home/crogers2287/ComfyUI/output/nivea/icl_spa_00001_.png /home/crogers2287/.openclaw/agents/selena/nivea_spa.png && cp /home/crogers2287/ComfyUI/output/nivea/icl_moody_00001_.png /home/crogers2287/.openclaw/agents/selena/nivea_moody.png && cp /home/crogers2287/ComfyUI/output/nivea/icl_outdoor_00001_.png /home/crogers2287/.openclaw/agents/selena/nivea_outdoor.png && echo "Done"


Done


🏛 **Marble Luxury** — soft window light on a pristine marble countertop:

MEDIA:./nivea_marble.png

🌿 **Spa Wellness** — tropical plants, white towels, zen vibes:

MEDIA:./nivea_spa.png

🌙 **Moody Premium** — dark velvet, dramatic rim lighting, navy & gold:

MEDIA:./nivea_moody.png

☀ **Outdoor Fresh** — morning sunlight on white linen, summer beauty:

MEDIA:./nivea_outdoor.png

---

Four scenes, full professional IC-Light relit — pick your favorite or I can tweak any of them! 🎨


Conversation info (untrusted metadata):
```json
{
  "message_id": "197",
  "conversation_label": "Skinny, Selena and Chelsea id:-5037974429",
  "sender": "6558388607",
  "group_subject": "Skinny, Selena and Chelsea"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Skinny",
  "name": "Skinny",
  "username": "skinnyc813"
}
```

Those look good but you completely mangled the text and you need to run it through the upscaler


Chad is saying the text on the products got mangled (which is common with IC-Light/SD 1.5 - it can't preserve text well) and wants me to upscale the images.

The text issue is a fundamental limitation of IC-Light with SD 1.5 - it regenerates the entire image including the text on the bottles, which gets distorted. I should acknowledge this and think about solutions.

Options:
1. Upscale first (what he asked for)
2. Try to fix the text issue - could use ControlNet to better preserve the product, or use inpainting to restore the text after generation

3. The upscaler might help slightly with text clarity but won't solve the core problem of mangled text.

I should be upfront about this limitation while running the upscaler anyway, and explore whether ControlNet or img2img with compositing could better preserve the product details and text during generation.

My best plan is to upscale the current outputs first using RealESRGAN, then explain that proper text handling would require either generating the background separately and compositing it back, or using a different approach like ControlNet canny edge detection to maintain product fidelity.

Yeah, text mangling is a known pain with SD 1.5 — it can't faithfully reproduce product labels, it just "halluccinates" similar-looking text. The upscaler won't fix that part, but I want to be straight with you on a better solution:

**Real fix for text**: Instead of relighting the whole product, I should:
1. Generate just the **background scene**
2. **Composite the original cutout** back on top with proper shadow/lighting blend

That preserves the real labels perfectly. But first — let me run the upscaler on what we have as you asked, then we can do a proper round 2 with the compositing approach.

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[:400]}")

def make_upscale_prompt(input_image, save_prefix):
    return {
        "1": {"class_type": "LoadImage",            "inputs": {"image": input_image}},
        "2": {"class_type": "UpscaleModelLoader",   "inputs": {"model_name": "4x-UltraSharp.pth"}},
        "3": {"class_type": "ImageUpscaleWithModel","inputs": {"upscale_model": ["2",0], "image": ["1",0]}},
        "4": {"class_type": "SaveImage",            "inputs": {"images": ["3",0], "filename_prefix": save_prefix}},
    }

scenes = [
    ("nivea_marble.png",  "nivea/up_marble"),
    ("nivea_spa.png",     "nivea/up_spa"),
    ("nivea_moody.png",   "nivea/up_moody"),
    ("nivea_outdoor.png", "nivea/up_outdoor"),
]

# Copy outputs back to ComfyUI input dir for upscaling
import shutil, os
base = "/home/crogers2287/ComfyUI/output/nivea"
inp  = "/home/crogers2287/ComfyUI/input"
for fname, _ in scenes:
    src = f"{base}/icl_{fname.replace('nivea_','').replace('.png','')}_00001_.png"
    dst = f"{inp}/{fname}"
    shutil.copy(src, dst)
    print(f"Staged: {fname}")

print("\n=== Upscaling ===")
for fname, prefix in scenes:
    submit(make_upscale_prompt(fname, prefix), prefix.split("/")[1])

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


Staged: nivea_marble.png
Staged: nivea_spa.png
Staged: nivea_moody.png
Staged: nivea_outdoor.png

=== Upscaling ===
✅ [up_marble]: 505ea3a9-4d0e-4579-986a-052e0fd7c9bd
✅ [up_spa]: 495ea218-2cff-41c4-94bc-5f143484ecd0
✅ [up_moody]: 2829fa69-0faf-4e64-9e89-a6628ea6246e
✅ [up_outdoor]: b779637b-574e-4986-9e2d-50b16970f235

All queued — http://fred:8188


4x upscales queued! Polling for completion...

sleep 45 && ls -lah /home/crogers2287/ComfyUI/output/nivea/up_* 2>/dev/null && curl -s http://127.0.0.1:8188/queue | python3 -c "import sys,json; d=json.load(sys.stdin); print('Running:', len(d.get('queue_running',[])), '| Pending:', len(d.get('queue_pending',[])))"


Command still running (session quiet-dune, pid 2477547). Use process (list/poll/log/write/kill/clear/remove) for follow-up.


-rw-r--r-- 1 crogers2287 crogers2287 13M Feb 18 13:55 /home/crogers2287/ComfyUI/output/nivea/up_marble_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 11M Feb 18 13:56 /home/crogers2287/ComfyUI/output/nivea/up_moody_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 12M Feb 18 13:56 /home/crogers2287/ComfyUI/output/nivea/up_outdoor_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 13M Feb 18 13:55 /home/crogers2287/ComfyUI/output/nivea/up_spa_00001_.png
Running: 0 | Pending: 0

Process exited with code 0.


Conversation info (untrusted metadata):
```json
{
  "message_id": "200",
  "conversation_label": "Skinny, Selena and Chelsea id:-5037974429",
  "sender": "6558388607",
  "group_subject": "Skinny, Selena and Chelsea"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Skinny",
  "name": "Skinny",
  "username": "skinnyc813"
}
```

That's how you were supposed to do it, you were supposed to remove the background using that workflow, each workflow has a purpose.


Chad is telling me that I should have used the background removal + compositing approach from the start - use IC-Light to remove the background and then composite the original product back on top with proper lighting/background. He's right - the IC-Light FC workflow does handle the background removal via BiRefNetRMBG, but the issue is that the KSampler still regenerates everything including the product itself.

Wait, let me re-read what he said: "That's how you were supposed to do it, you were supposed to remove the background using that workflow, each workflow has a purpose."

I think he's saying I should have:
1. Used BiRefNet to remove the background from the product image

2. Then placed it on a new generated background rather than running the whole IC-Light regeneration that mangled the text

Looking back at the IC-Light FC workflow, I see it's actually designed to isolate the foreground through background removal and masking, then encode just that masked foreground - so the intent is to preserve the product while only regenerating the background and lighting context around it.

But the problem is that even with the foreground conditioning, the KSampler still runs diffusion across the entire image, which causes SD 1.5 to partially regenerate and mangle the text details. The better approach would be to keep the original product cutout completely untouched and only generate a fresh background separately, then composite them together.

I'm realizing Chad probably has specific workflows in mind for each step—background removal, background generation, and compositing—rather than trying to do everything in one pass. I should ask if he wants me to rebuild this using that proper pipeline now, or if there's an existing workflow I should reference. than running IC-Light which regenerates everything. Chad is frustrated because I used IC-Light to regenerate the whole image, mangling the text, when I should have followed a proper pipeline instead: remove the background cleanly, generate a new background separately, composite them together, then upscale. Each workflow has its own purpose, and I need to respect that structure rather than taking shortcuts. Let me rebuild this the right way—extracting the product with BiRefNet, generating a quality background with Stable Diffusion, compositing them together, making subtle adjustments, and finally upscaling.

You're right, I skipped the proper pipeline. IC-Light rerenders the whole image — that's why the text got destroyed. The correct flow should have been:

1. **BiRefNet** → clean product cutout (preserving original labels)
2. **Generate background** scene separately
3. **Composite** original cutout onto the new background
4. **Upscale** the final result

Let me redo this the right way — original Nivea text intact. Running it now.

exec

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

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_composite_prompt(product_image, bg_prompt, save_prefix, seed=42):
    return {
        # --- Load & extract product ---
        "1":  {"class_type": "LoadImage",               "inputs": {"image": product_image}},
        "2":  {"class_type": "BiRefNetRMBG",             "inputs": {"image": ["1",0], "model": "BiRefNet-general"}},

        # --- Generate background ---
        "3":  {"class_type": "CheckpointLoaderSimple",   "inputs": {"ckpt_name": "RealisticVision_V6_fp16.safetensors"}},
        "4":  {"class_type": "CLIPTextEncode",           "inputs": {"text": bg_prompt, "clip": ["3",1]}},
        "5":  {"class_type": "CLIPTextEncode",           "inputs": {"text": "people, hands, products, bottles, text, watermark, blurry, deformed", "clip": ["3",1]}},
        "6":  {"class_type": "EmptyLatentImage",         "inputs": {"width": 768, "height": 512, "batch_size": 1}},
        "7":  {"class_type": "KSampler",                 "inputs": {"model": ["3",0], "positive": ["4",0], "negative": ["5",0], "latent_image": ["6",0], "seed": seed, "steps": 25, "cfg": 7.0, "sampler_name": "dpmpp_2m", "scheduler": "karras", "denoise": 1.0}},
        "8":  {"class_type": "VAEDecode",                "inputs": {"samples": ["7",0], "vae": ["3",2]}},

        # --- Resize bg to match product image ---
        "9":  {"class_type": "ImageScale",               "inputs": {"image": ["8",0], "upscale_method": "lanczos", "width": 768, "height": 512, "crop": "disabled"}},

        # --- Composite original product cutout onto background ---
        "10": {"class_type": "ImageScale",               "inputs": {"image": ["2",0], "upscale_method": "lanczos", "width": 768, "height": 512, "crop": "disabled"}},
        "11": {"class_type": "MaskToImage",              "inputs": {"mask": ["2",1]}},
        "12": {"class_type": "ImageScale",               "inputs": {"image": ["11",0], "upscale_method": "lanczos", "width": 768, "height": 512, "crop": "disabled"}},
        "13": {"class_type": "ImageToMask",              "inputs": {"image": ["12",0], "channel": "red"}},
        "14": {"class_type": "ImageCompositeMasked",     "inputs": {"destination": ["9",0], "source": ["10",0], "mask": ["13",0], "x": 0, "y": 0, "resize_source": False}},

        # --- Upscale final ---
        "15": {"class_type": "UpscaleModelLoader",       "inputs": {"model_name": "4x-UltraSharp.pth"}},
        "16": {"class_type": "ImageUpscaleWithModel",    "inputs": {"upscale_model": ["15",0], "image": ["14",0]}},

        # --- Save ---
        "17": {"class_type": "SaveImage",                "inputs": {"images": ["16",0], "filename_prefix": save_prefix}},
    }

scenes = [
    ("luxury white marble bathroom countertop, soft natural morning window light, clean minimal backdrop, elegant spa atmosphere, photorealistic",   "nivea/comp_marble",  401),
    ("wooden spa shelf with lush tropical green plants and white folded towels, warm diffused ambient light, zen wellness studio, photorealistic",   "nivea/comp_spa",     402),
    ("dark navy velvet surface, dramatic soft rim lighting from behind, premium luxury backdrop, moody editorial atmosphere, photorealistic",        "nivea/comp_moody",   403),
    ("white linen cloth on outdoor wooden table, bright summer morning sunlight, fresh air and natural beauty lifestyle setting, photorealistic",    "nivea/comp_outdoor", 404),
]

print("=== Nivea: BG Remove → BG Gen → Composite → Upscale ===")
for bg_prompt, prefix, seed in scenes:
    submit(make_composite_prompt("nivea_products.jpg", bg_prompt, prefix, seed), prefix.split("/")[1])

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


=== Nivea: BG Remove → BG Gen → Composite → Upscale ===
✅ [comp_marble]: a10a8f69-f9d7-46b8-ba4e-400cd27403ed
✅ [comp_spa]: 00895a75-d161-4cc7-be3a-f3bfd351083f
✅ [comp_moody]: effb7dcf-f301-45be-a724-a2063587f945
✅ [comp_outdoor]: 2227fe15-fdf8-42f9-813a-1187f718233f

All queued — http://fred:8188


All 4 queued. Polling for renders...

sleep 90 && ls -lah /home/crogers2287/ComfyUI/output/nivea/comp_* 2>/dev/null && curl -s http://127.0.0.1:8188/queue | python3 -c "import sys,json; d=json.load(sys.stdin); print('Running:', len(d.get('queue_running',[])), '| Pending:', len(d.get('queue_pending',[])))"


Command still running (session rapid-reef, pid 2506133). Use process (list/poll/log/write/kill/clear/remove) for follow-up.


-rw-r--r-- 1 crogers2287 crogers2287 7.5M Feb 18 13:57 /home/crogers2287/ComfyUI/output/nivea/comp_marble_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 7.8M Feb 18 13:57 /home/crogers2287/ComfyUI/output/nivea/comp_moody_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 9.6M Feb 18 13:57 /home/crogers2287/ComfyUI/output/nivea/comp_outdoor_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 8.4M Feb 18 13:57 /home/crogers2287/ComfyUI/output/nivea/comp_spa_00001_.png
Running: 0 | Pending: 0

Process exited with code 0.


Done! Copying to workspace and sending...

exec

cp /home/crogers2287/ComfyUI/output/nivea/comp_marble_00001_.png /home/crogers2287/.openclaw/agents/selena/nivea_comp_marble.png && cp /home/crogers2287/ComfyUI/output/nivea/comp_spa_00001_.png /home/crogers2287/.openclaw/agents/selena/nivea_comp_spa.png && cp /home/crogers2287/ComfyUI/output/nivea/comp_moody_00001_.png /home/crogers2287/.openclaw/agents/selena/nivea_comp_moody.png && cp /home/crogers2287/ComfyUI/output/nivea/comp_outdoor_00001_.png /home/crogers2287/.openclaw/agents/selena/nivea_comp_outdoor.png && echo "Done"


Conversation info (untrusted metadata):
```json
{
  "message_id": "204",
  "conversation_label": "Skinny, Selena and Chelsea id:-5037974429",
  "sender": "6558388607",
  "group_subject": "Skinny, Selena and Chelsea"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Skinny",
  "name": "Skinny",
  "username": "skinnyc813"
}
```

?


Chad is asking "?" - probably wondering where the images are. Let me send them now using the MEDIA inline format since the message tool isn't working.

Sorry! Here they are — original labels preserved this time 🎨

🏛 **Marble Luxury**
MEDIA:./nivea_comp_marble.png

🌿 **Spa Wellness**
MEDIA:./nivea_comp_spa.png

🌙 **Moody Premium**
MEDIA:./nivea_comp_moody.png

☀ **Outdoor Fresh**
MEDIA:./nivea_comp_outdoor.png


Conversation info (untrusted metadata):
```json
{
  "message_id": "206",
  "conversation_label": "Skinny, Selena and Chelsea id:-5037974429",
  "sender": "6558388607",
  "group_subject": "Skinny, Selena and Chelsea"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Skinny",
  "name": "Skinny",
  "username": "skinnyc813"
}
```

That looks terrible. The ones you created last night looked decent. I feel like you're not using the workflows correctly


Chad is saying the composite approach looks terrible and that the ones from last night looked decent. He thinks I'm not using the workflows correctly. Let me look at what workflows exist and what was done last night. Let me check the memory files and look at the existing workflows.

Looking at MEMORY.md, it mentions:
- `product_iclight_fc.json` — the IC-Light FC workflow that was verified working
- The "Product Placement Pipeline (IC-Light FC) ✅ WORKING" section

So the IC-Light FC workflow IS the correct working workflow, and "last night" the outputs were decent. The issue isn't the workflow choice - it was my composite approach that looked bad.

I need to review what workflows are actually available and compare them to what produced those decent results previously.

You're right — let me stop guessing and look at exactly what was used last night that worked.

ls /home/crogers2287/.openclaw/agents/selena/workflows/ 2>/dev/null && ls /home/crogers2287/ComfyUI/output/nivea/ 2>/dev/null


comp_marble_00001_.png
comp_moody_00001_.png
comp_outdoor_00001_.png
comp_spa_00001_.png
icl_marble_00001_.png
icl_moody_00001_.png
icl_outdoor_00001_.png
icl_spa_00001_.png
scene_1__00001_.png
scene_2__00001_.png
scene_3__00001_.png
scene_4__00001_.png
up_marble_00001_.png
up_moody_00001_.png
up_outdoor_00001_.png
up_spa_00001_.png


ls /home/crogers2287/.openclaw/agents/selena/workflows/ 2>/dev/null || echo "No workflows dir"
ls /home/crogers2287/ComfyUI/output/olay/ 2>/dev/null | head -20
ls /home/crogers2287/ComfyUI/user/default/workflows/ 2>/dev/null | head -20


composite_garden_00001_.png
composite_marble_00001_.png
composite_moody_00001_.png
icl_marble_00001_.png
icl_moody_00001_.png
icl_morning_00001_.png
icl_spa_00001_.png
bg_remove.json
portrait_realism.json
product_composite.json
product_hero.json
product_iclight.json
product_iclight_fc.json
product_lifestyle.json
prompt_creator.json
upscale.json
z_image_turbo.json


ls /home/crogers2287/ComfyUI/output/olay/
echo "---"
# Check what the scene_ files from last night were - those were the Olay ones that looked decent
ls -lah /home/crogers2287/ComfyUI/output/nivea/scene_*


composite_garden_00001_.png
composite_marble_00001_.png
composite_moody_00001_.png
icl_marble_00001_.png
icl_moody_00001_.png
icl_morning_00001_.png
icl_spa_00001_.png
---
-rw-r--r-- 1 crogers2287 crogers2287 1.1M Feb 18 05:34 /home/crogers2287/ComfyUI/output/nivea/scene_1__00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 1.1M Feb 18 05:34 /home/crogers2287/ComfyUI/output/nivea/scene_2__00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 1.2M Feb 18 05:35 /home/crogers2287/ComfyUI/output/nivea/scene_3__00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 1.2M Feb 18 05:35 /home/crogers2287/ComfyUI/output/nivea/scene_4__00001_.png


There were scene_1 through scene_4 files generated at 05:34-05:35 last night. These must have been done with a different workflow. Let me check what workflow was used and look at the memory for that session. Also there are olay composite files that looked decent. Let me look at the actual workflow files to understand the correct approach.

Let me check the memory from last night and look at what workflows were used.

cat /home/crogers2287/.openclaw/agents/selena/memory/2026-02-18.md 2>/dev/null || echo "No daily file"


{
  "last_node_id": 16,
  "last_link_id": 20,
  "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": ["your_product.png", "image"]
    },
    {
      "id": 2,
      "type": "BiRefNetRMBG",

[153 more lines in file. Use offset=21 to continue.]


# 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.safetensors | vae/ | 320MB |
| z_image_turbo_distill_patch_lora_bf16.safetensors | loras/z_image/ | 152MB |
| 4x-UltraSharp.pth | upscale_models/ | 64MB |
| florence2-base/model.safetensors | LLM/florence2-base/ | 442MB |
| BiRefNet-general.safetensors | BiRefNet/ | 885MB |
| iclight_sd15_fc_unet_ldm.safetensors | diffusion_models/ | 1.7GB |
| iclight_sd15_fbc_unet_ldm.safetensors | diffusion_models/ | 1.7GB |
| RealisticVision_V6_fp16.safetensors | checkpoints/ | 2GB |

### Custom Nodes Installed
- ComfyUI-Manager
- ComfyUI-Florence2
- 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 lifestyle scene (1216x832)
- `product_composite.json` — Load product → BiRefNet → composite onto generated BG
- `product_iclight.json` — IC-Light relighting pipeline (FC model)

---

## 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
- 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
```


Now I have the full picture. The daily memory is very clear:

1. The IC-Light FC results from last night: **scene_3 (window light) = 7/10, scene_1 (marble) = 6/10, scene_2 (bokeh) = 3/10**
2. The "scene_" prefix files are what looked decent
3. The CORRECT approach is `product_iclight.json` or the IC-Light FC workflow I was using — but those still have the text mangling issue

Wait, but Chad said "The ones you created last night looked decent" - meaning the IC-Light FC ones from last night (scene_1, scene_2, scene_3, scene_4 in the nivea folder). And those were scored as decent.

So the IC-Light FC approach definitely worked last night and produced decent results. The plan is to iterate on scene 3's natural-light style with different environments. I also need to figure out why today's run didn't match that quality — either the prompts shifted or something broke in the workflow. There's also that message tool issue with the `filePath`/`media` parameters that's been failing.

Looking back at Chad's feedback, he's saying the first batch looked good and last night's output was decent, but my composite approach today was terrible. He's not criticizing the IC-Light FC method itself — he's saying I'm not executing the workflows correctly. I need to figure out which workflow is actually the right one to use. Looking at what's available, there's `product_composite.json` and I should probably check what the actual correct workflow setup is rather than guessing. composite approach I just tried
- `product_iclight.json` / `product_iclight_fc.json` - IC-Light approach (what was done last night)

The issue is that IC-Light DOES mangle text, but it still looked decent because the overall composition/lighting was good. The composite approach I did today looked bad because the compositing was poor.

Wait, let me re-read the memory more carefully: "IC-Light FC results: scene_3 (window light) = 7/10" - so IC-Light FC IS what gave good results last night.

Now the key issue is: the `product_iclight.json` workflow in ComfyUI is probably what was used. Let me read that workflow to understand it properly, and then use it correctly for Nivea.

Also important: the memory says "Message Tool failing" so I should NOT be trying to send via message tool.

Looking back at what Chad said - he approved the first IC-Light batch I showed him, but noted the text was mangled and needed upscaling. After the upscaler ran, he clarified that I should've been using that workflow to remove the background from the IC-Light outputs, which is exactly what happened. So my composite approach was the mistake - I need to stick with the IC-Light workflow and understand how it actually works versus what I submitted through Python.


product on a luxury marble bathroom countertop, soft warm studio lighting, professional product photography, photorealistic, sharp detail

blurry, low quality, watermark, deformed, dark, ugly, shadows clipping

STEP 1 — Remove BG + prep for IC-Light

STEP 2 — IC-Light relighting pipeline


Now I can see the key differences:

1. The workflow uses `iclight_sd15_fbc_unet_ldm.safetensors` (FBC - foreground/background conditioned) NOT the FC model I was using
2. The `ICLightApplyMaskGrey` node receives inputs named `image` and `mask` (not `alpha` like I was using)
3. The input is "nivea_products.jpg" - already set correctly

The critical difference is: I was using `iclight_sd15_fc_unet_ldm.safetensors` but the workflow uses `iclight_sd15_fbc_unet_ldm.safetensors`.

FC means foreground conditioned only, while FBC handles both foreground and background conditioning. I'm also noticing a discrepancy - my notes say `ICLightApplyMaskGrey` takes an `alpha` input, but the workflow JSON shows `mask` instead. Let me verify which parameter name the actual node expects.

Looking at the workflow, node 3 clearly uses `mask` as the input name, not `alpha` - that's likely why my runs today differed from last night. I also need to verify the VAE routing through the nodes to make sure the connections are correct, then fix my submit script to use the right parameter names and confirm whether I'm using the FC or FBC model variant.

The cleanest approach is to submit the workflow JSON directly through the API's `/prompt` endpoint, just updating the text prompts and seed rather than trying to reconstruct the entire workflow programmatically.

Found it. Two bugs in my script vs the actual saved workflow:
1. **Wrong IC-Light model** — I used `fc` but the workflow uses `fbc` (foreground+background conditioned)
2. **Wrong input name** — I passed `alpha` but node expects `mask`

Let me run it the right way using the actual workflow as the base:

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

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]}")

# Load the actual working workflow
with open("/home/crogers2287/ComfyUI/user/default/workflows/product_iclight.json") as f:
    wf = json.load(f)

# Build node id → node map
nodes = {str(n["id"]): n for n in wf["nodes"]}

def make_prompt(pos_text, save_prefix, seed):
    # Deep copy the workflow nodes into API format
    p = {}
    for n in wf["nodes"]:
        nid = str(n["id"])
        p[nid] = {
            "class_type": n["type"],
            "inputs": {}
        }
    # Rebuild inputs from links
    link_map = {l[0]: l for l in wf["links"]}  # link_id -> [id, src_node, src_slot, dst_node, dst_slot, type]
    for n in wf["nodes"]:
        nid = str(n["id"])
        if "inputs" in n:
            for inp in n["inputs"]:
                if inp.get("link") is not None:
                    l = link_map[inp["link"]]
                    src_node = str(l[1])
                    src_slot = l[2]
                    p[nid]["inputs"][inp["name"]] = [src_node, src_slot]
        if "widgets_values" in n:
            # Map widget values to input names based on node type
            wv = n["widgets_values"]
            t = n["type"]
            if t == "LoadImage":
                p[nid]["inputs"]["image"] = "nivea_products.jpg"
                p[nid]["inputs"]["upload"] = "image"
            elif t == "BiRefNetRMBG":
                p[nid]["inputs"]["model"] = wv[0]
            elif t == "CheckpointLoaderSimple":
                p[nid]["inputs"]["ckpt_name"] = wv[0]
            elif t == "CLIPTextEncode":
                p[nid]["inputs"]["text"] = wv[0]  # override below for pos
            elif t == "KSampler":
                p[nid]["inputs"]["seed"] = seed
                p[nid]["inputs"]["control_after_generate"] = wv[1]
                p[nid]["inputs"]["steps"] = wv[2]
                p[nid]["inputs"]["cfg"] = wv[3]
                p[nid]["inputs"]["sampler_name"] = wv[4]
                p[nid]["inputs"]["scheduler"] = wv[5]
                p[nid]["inputs"]["denoise"] = wv[6]
            elif t == "SaveImage":
                p[nid]["inputs"]["filename_prefix"] = save_prefix
            elif t == "ICLightConditioning":
                p[nid]["inputs"]["multiplier"] = wv[0]

    # Override positive prompt (node 6)
    p["6"]["inputs"]["text"] = pos_text
    return p

scenes = [
    ("NIVEA skincare bottles on luxury white marble bathroom countertop, soft warm studio lighting, professional product photography, photorealistic, sharp detail",  "nivea/v2_marble",  501),
    ("NIVEA skincare bottles on wooden spa shelf with tropical green plants, warm diffused ambient light, zen wellness aesthetic, professional product photography",   "nivea/v2_spa",     502),
    ("NIVEA skincare bottles on dark velvet surface, dramatic rim lighting, moody editorial beauty photography, deep shadows, professional product shot",             "nivea/v2_moody",   503),
    ("NIVEA skincare bottles on white linen in bright morning sunlight, fresh outdoor lifestyle beauty photography, airy and clean, photorealistic",                 "nivea/v2_outdoor", 504),
]

print("=== Nivea v2 — correct FBC model + mask input ===")
for pos, prefix, seed in scenes:
    p = make_prompt(pos, prefix, seed)
    submit(p, prefix.split("/")[1])

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


=== Nivea v2 — correct FBC model + mask input ===
❌ [v2_marble]: 400 {"error": {"type": "prompt_outputs_failed_validation", "message": "Prompt outputs failed validation", "details": "", "extra_info": {}}, "node_errors": {"5": {"errors": [{"type": "required_input_missing", "message": "Required input is missing", "details": "model_path", "extra_info": {"input_name": "model_path"}}], "dependent_outputs": ["12"], "class_type": "LoadAndApplyICLightUnet"}, "3": {"errors"
❌ [v2_spa]: 400 {"error": {"type": "prompt_outputs_failed_validation", "message": "Prompt outputs failed validation", "details": "", "extra_info": {}}, "node_errors": {"5": {"errors": [{"type": "required_input_missing", "message": "Required input is missing", "details": "model_path", "extra_info": {"input_name": "model_path"}}], "dependent_outputs": ["12"], "class_type": "LoadAndApplyICLightUnet"}, "3": {"errors"
❌ [v2_moody]: 400 {"error": {"type": "prompt_outputs_failed_validation", "message": "Prompt outputs failed validation", "details": "", "extra_info": {}}, "node_errors": {"5": {"errors": [{"type": "required_input_missing", "message": "Required input is missing", "details": "model_path", "extra_info": {"input_name": "model_path"}}], "dependent_outputs": ["12"], "class_type": "LoadAndApplyICLightUnet"}, "3": {"errors"
❌ [v2_outdoor]: 400 {"error": {"type": "prompt_outputs_failed_validation", "message": "Prompt outputs failed validation", "details": "", "extra_info": {}}, "node_errors": {"5": {"errors": [{"type": "required_input_missing", "message": "Required input is missing", "details": "model_path", "extra_info": {"input_name": "model_path"}}], "dependent_outputs": ["12"], "class_type": "LoadAndApplyICLightUnet"}, "3": {"errors"

All queued — http://fred:8188


Two errors: `model_path` missing for `LoadAndApplyICLightUnet` and an error on node 3 (ICLightApplyMaskGrey). Let me check the node info to see the exact input names.

curl -s http://localhost:8188/object_info/LoadAndApplyICLightUnet | python3 -c "import sys,json; d=json.load(sys.stdin); print(json.dumps(d['LoadAndApplyICLightUnet']['input'], indent=2))"
echo "---"
curl -s http://localhost:8188/object_info/ICLightApplyMaskGrey | python3 -c "import sys,json; d=json.load(sys.stdin); print(json.dumps(d['ICLightApplyMaskGrey']['input'], indent=2))"


{
  "required": {
    "model": [
      "MODEL"
    ],
    "model_path": [
      [
        "iclight_sd15_fbc_unet_ldm.safetensors",
        "iclight_sd15_fc_unet_ldm.safetensors",
        "z_image_turbo_bf16.safetensors"
      ]
    ]
  }
}
---
{
  "required": {
    "image": [
      "IMAGE"
    ],
    "alpha": [
      "MASK"
    ]
  }
}


Okay, so:
1. `LoadAndApplyICLightUnet` requires `model_path` as a widget (string from an enum), not linked - it's a dropdown value
2. `ICLightApplyMaskGrey` requires `alpha` not `mask` - so my original script was CORRECT on this one!

The workflow JSON shows the node input as "mask" but the actual node expects "alpha". The workflow JSON might have a display name vs actual name mismatch.

And `model_path` is a widget value (enum), not a linked input. So for `LoadAndApplyICLightUnet`, `model_path` should be in `widgets_values`, not in `inputs` as a link.

Now I need to update my prompt builder to properly handle widget values. For node 5, the `model_path` widget should be set to `iclight_sd15_fbc_unet_ldm.safetensors` in the widgets array, while the `model` input gets linked from node 4.

I also realized that node 3 uses `alpha` as the actual input parameter name, even though the workflow JSON displays it as `mask` - I need to use the correct parameter name when submitting through the API.

Got it — `model_path` is a widget (not a link), and `ICLightApplyMaskGrey` actually uses `alpha` after all. Let me submit this cleanly:

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[:500]}")

def make_prompt(pos_text, save_prefix, seed):
    return {
        "1":  {"class_type": "LoadImage",
               "inputs": {"image": "nivea_products.jpg", "upload": "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_fbc_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, 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": 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}},
    }

scenes = [
    ("NIVEA skincare bottles on luxury white marble bathroom countertop, soft warm studio lighting, professional product photography, photorealistic, sharp detail",  "nivea/v2_marble",  501),
    ("NIVEA skincare bottles on wooden spa shelf with tropical green plants, warm diffused ambient light, zen wellness aesthetic, professional product photography",   "nivea/v2_spa",     502),
    ("NIVEA skincare bottles on dark velvet surface, dramatic rim lighting, moody editorial beauty photography, deep shadows, professional product shot",             "nivea/v2_moody",   503),
    ("NIVEA skincare bottles on white linen in bright morning sunlight, fresh outdoor lifestyle beauty photography, airy and clean, photorealistic",                 "nivea/v2_outdoor", 504),
]

print("=== Nivea v2 — FBC + correct inputs ===")
for pos, prefix, seed in scenes:
    submit(make_prompt(pos, prefix, seed), prefix.split("/")[1])

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


=== Nivea v2 — FBC + correct inputs ===
✅ [v2_marble]: addb98b5-1b2b-4889-8bcf-12e90c8e0b9b
✅ [v2_spa]: eab8ba65-1cc9-4657-b19a-573d6eba1709
✅ [v2_moody]: 442dacec-8a9a-4b8f-8a72-31adbf91e87a
✅ [v2_outdoor]: d67b1f6a-9e9d-4236-ac58-346e833cb4cb

All queued — http://fred:8188


All 4 queued correctly this time. Polling...

sleep 90 && ls -lah /home/crogers2287/ComfyUI/output/nivea/v2_* 2>/dev/null && curl -s http://127.0.0.1:8188/queue | python3 -c "import sys,json; d=json.load(sys.stdin); print('Running:', len(d.get('queue_running',[])), '| Pending:', len(d.get('queue_pending',[])))"


Command still running (session tidy-coral, pid 2863716). Use process (list/poll/log/write/kill/clear/remove) for follow-up.


(no new output)

Process exited with code 2.


ls -lah /home/crogers2287/ComfyUI/output/nivea/v2_* 2>/dev/null && curl -s http://127.0.0.1:8188/queue | python3 -c "import sys,json; d=json.load(sys.stdin); print('Running:', len(d.get('queue_running',[])), '| Pending:', len(d.get('queue_pending',[])))"


(Command exited with code 2)


curl -s http://127.0.0.1:8188/queue | python3 -c "import sys,json; d=json.load(sys.stdin); print(json.dumps(d, indent=2))"
echo "---"
ls /home/crogers2287/ComfyUI/output/nivea/ 2>/dev/null


{
  "queue_running": [],
  "queue_pending": []
}
---
comp_marble_00001_.png
comp_moody_00001_.png
comp_outdoor_00001_.png
comp_spa_00001_.png
icl_marble_00001_.png
icl_moody_00001_.png
icl_outdoor_00001_.png
icl_spa_00001_.png
scene_1__00001_.png
scene_2__00001_.png
scene_3__00001_.png
scene_4__00001_.png
up_marble_00001_.png
up_moody_00001_.png
up_outdoor_00001_.png
up_spa_00001_.png


The v2 outputs didn't generate - they're not in the directory even though the queue shows empty (no running/pending). This means either the jobs failed silently or they didn't complete. Let me check the ComfyUI logs for errors.

Jobs disappeared from queue but no output files. Checking ComfyUI logs for errors:

tail -50 /home/crogers2287/ComfyUI/user/comfyui_8188.log 2>/dev/null || tail -50 /tmp/comfyui.log 2>/dev/null || journalctl -u comfyui --no-pager -n 50 2>/dev/null || tail -50 /home/crogers2287/ComfyUI/comfyui.log 2>/dev/null


return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/crogers2287/ComfyUI/execution.py", line 308, in _async_map_node_over_list
    await process_inputs(input_dict, i)
  File "/home/crogers2287/ComfyUI/execution.py", line 296, in process_inputs
    result = f(**inputs)
             ^^^^^^^^^^^
  File "/home/crogers2287/ComfyUI/nodes.py", line 1590, in sample
    return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/crogers2287/ComfyUI/nodes.py", line 1555, in common_ksampler
    samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/crogers2287/ComfyUI/comfy/sample.py", line 66, in sample
    samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/crogers2287/ComfyUI/comfy/samplers.py", line 1177, in sample
    return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/crogers2287/ComfyUI/comfy/samplers.py", line 1067, in sample
    return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/crogers2287/ComfyUI/comfy/samplers.py", line 1049, in sample
    output = executor.execute(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/crogers2287/ComfyUI/comfy/patcher_extension.py", line 112, in execute
    return self.original(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/crogers2287/ComfyUI/comfy/samplers.py", line 993, in outer_sample
    output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed, latent_shapes=latent_shapes)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/crogers2287/ComfyUI/comfy/samplers.py", line 968, in inner_sample
    self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed, latent_shapes=latent_shapes)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/crogers2287/ComfyUI/comfy/samplers.py", line 794, in process_conds
    conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed, latent_shapes=latent_shapes)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/crogers2287/ComfyUI/comfy/samplers.py", line 704, in encode_model_conds
    out = model_function(**params)
          ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/crogers2287/ComfyUI/custom_nodes/ComfyUI-IC-Light/nodes.py", line 81, in bound_extra_conds
    return ICLight.extra_conds(self, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/crogers2287/ComfyUI/custom_nodes/ComfyUI-IC-Light/nodes.py", line 103, in extra_conds
    raise Exception(f"Input channels {input_channels} does not match model in_channels {model_in_channels}, 'opt_background' latent input should be used with the IC-Light 'fbc' model, and only with it")
Exception: Input channels 8 does not match model in_channels 12, 'opt_background' latent input should be used with the IC-Light 'fbc' model, and only with it

[2026-02-18 14:16:54.455] Prompt executed in 0.09 seconds
[2026-02-18 14:17:58.456] WARNING path /mnt/storage/comfyui-models/loras/wan/Wan2.2-Lightning_I2V-A14B-4steps-lora_HIGH_fp16.safetensors exists but doesn't link anywhere, skipping.
[2026-02-18 14:17:58.457] WARNING path /mnt/storage/comfyui-models/loras/wan/Wan2.2-Lightning_I2V-A14B-4steps-lora_LOW_fp16.safetensors exists but doesn't link anywhere, skipping.


The error is very clear now:

```
Exception: Input channels 8 does not match model in_channels 12, 'opt_background' latent input should be used with the IC-Light 'fbc' model, and only with it
```

The FBC (Foreground + Background Conditioned) model REQUIRES an `opt_background` latent input. Without it, it fails. The FC (Foreground Conditioned) model works WITHOUT a background - that's what I was using before and it worked (the original scene_1 through scene_4 from last night).

So:
- **FC model** (`iclight_sd15_fc_unet_ldm.safetensors`) = Foreground only conditioning, generates background from scratch based on text prompt → This is what actually worked before

- **FBC model** (`iclight_sd15_fbc_unet_ldm.safetensors`) = Foreground + Background conditioning, REQUIRES `opt_background` latent → Needs a pre-encoded background image

The workflow is using the FBC model but doesn't have the background input connected, which is why it's failing. The `opt_background` parameter shows as optional in the node definition, but the FBC model actually requires it to function properly.

Actually, looking back at my notes—the FC model is what was working all along. My original submit_iclight.py script used the FC model with the alpha mask approach, and those scene files from last night turned out well. The batch I ran today with those same settings (icl_marble, icl_spa, icl_moody, icl_outdoor) are the ones Chad approved, so I should stick with that approach instead of switching to FBC. might mean he was satisfied with the upscaler step and that IC-Light → Upscale is the correct pipeline. But his comment about removing the background suggests he wants a separate background removal step first, then composite onto a new background, then upscale. The problem is IC-Light FC already does background removal internally through BiRefNet, then regenerates everything through Stable Diffusion, which is what mangled the text. Maybe the right approach is to extract the product with BiRefNet separately, generate a new background without the product, composite the original cutout back onto it, and then upscale—which is essentially what I tried with the composite approach, so maybe my implementation was just buggy rather than the concept being wrong.

Actually, thinking about it differently—maybe Chad wants me to use the FBC model with a properly generated background. The FC model approach actually worked and got a 7/10 rating, so I should stick with that and just add the upscaler afterward like he asked. But if I'm going to use FBC properly, I need to generate a background image first, encode it as a latent, and pass it to the IC-Light conditioning. Let me try implementing the proper FBC workflow since that's what the file specifies. background generation step.

Actually, wait. Let me re-read Chad's message: "I feel like you're not using the workflows correctly." He's referring to the existing workflow files. The workflow `product_iclight.json` uses FBC model. Maybe the intended use case is:
1. Generate a background
2. Feed it to IC-Light FBC
3. The FBC model then relights the product to match the background

Let me try that properly - generate a background, encode it, pass to ICLightConditioning as opt_background.

Let me build this properly:
1. Generate background scene → VAEEncode → opt_background for ICLightConditioning
2. BiRefNet extract product → ICLightApplyMaskGrey → VAEEncode → foreground for ICLightConditioning
3. ICLightConditioning with FBC model → KSampler → VAEDecode → Save
4. Then upscale

This approach should work better since the FBC model can properly integrate the product's lighting with the actual background.

The error makes it clear: **FBC model requires an `opt_background` latent** — you have to generate a background first, then feed it in. That's the proper FBC pipeline. Let me do it right — background generation → encode → feed to IC-Light FBC → upscale:

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[:500]}")

def make_fbc_prompt(product_img, bg_text, pos_text, save_prefix, seed):
    return {
        # === STEP 1: Extract product foreground ===
        "1":  {"class_type": "LoadImage",
               "inputs": {"image": product_img, "upload": "image"}},
        "2":  {"class_type": "BiRefNetRMBG",
               "inputs": {"image": ["1",0], "model": "BiRefNet-general"}},
        "3":  {"class_type": "ICLightApplyMaskGrey",
               "inputs": {"image": ["2",0], "alpha": ["2",1]}},

        # === STEP 2: Load checkpoint ===
        "4":  {"class_type": "CheckpointLoaderSimple",
               "inputs": {"ckpt_name": "RealisticVision_V6_fp16.safetensors"}},
        "5":  {"class_type": "LoadAndApplyICLightUnet",
               "inputs": {"model": ["4",0], "model_path": "iclight_sd15_fbc_unet_ldm.safetensors"}},

        # === STEP 3: Generate background ===
        "6":  {"class_type": "CLIPTextEncode",
               "inputs": {"text": bg_text, "clip": ["4",1]}},
        "7":  {"class_type": "CLIPTextEncode",
               "inputs": {"text": "people, hands, bottles, products, watermark, blurry, deformed", "clip": ["4",1]}},
        "8":  {"class_type": "EmptyLatentImage",
               "inputs": {"width": 512, "height": 512, "batch_size": 1}},
        "9":  {"class_type": "KSampler",
               "inputs": {"model": ["4",0], "positive": ["6",0], "negative": ["7",0],
                          "latent_image": ["8",0], "seed": seed+1000, "steps": 25,
                          "cfg": 7.0, "sampler_name": "dpmpp_2m", "scheduler": "karras", "denoise": 1.0}},
        "10": {"class_type": "VAEDecode",
               "inputs": {"samples": ["9",0], "vae": ["4",2]}},

        # === STEP 4: IC-Light FBC — rlight product on background ===
        "11": {"class_type": "CLIPTextEncode",
               "inputs": {"text": pos_text, "clip": ["4",1]}},
        "12": {"class_type": "CLIPTextEncode",
               "inputs": {"text": "blurry, low quality, watermark, deformed, ugly, shadows clipping", "clip": ["4",1]}},
        "13": {"class_type": "VAEEncode",
               "inputs": {"pixels": ["3",0], "vae": ["4",2]}},
        "14": {"class_type": "VAEEncode",
               "inputs": {"pixels": ["10",0], "vae": ["4",2]}},
        "15": {"class_type": "ICLightConditioning",
               "inputs": {"positive": ["11",0], "negative": ["12",0], "vae": ["4",2],
                          "foreground": ["13",0], "opt_background": ["14",0], "multiplier": 1.0}},
        "16": {"class_type": "KSampler",
               "inputs": {"model": ["5",0], "positive": ["15",0], "negative": ["15",1],
                          "latent_image": ["15",2], "seed": seed, "steps": 25,
                          "cfg": 2.0, "sampler_name": "dpmpp_2m", "scheduler": "karras", "denoise": 1.0}},
        "17": {"class_type": "VAEDecode",
               "inputs": {"samples": ["16",0], "vae": ["4",2]}},

        # === STEP 5: Upscale ===
        "18": {"class_type": "UpscaleModelLoader",
               "inputs": {"model_name": "4x-UltraSharp.pth"}},
        "19": {"class_type": "ImageUpscaleWithModel",
               "inputs": {"upscale_model": ["18",0], "image": ["17",0]}},
        "20": {"class_type": "SaveImage",
               "inputs": {"images": ["19",0], "filename_prefix": save_prefix}},
    }

scenes = [
    (
        "luxury white marble bathroom countertop, soft warm window light, clean minimal elegant surface, no objects",
        "NIVEA skincare bottles on luxury marble countertop, soft warm studio lighting, professional cosmetics photography, photorealistic, sharp detail",
        "nivea/fbc_marble", 501
    ),
    (
        "wooden spa shelf with tropical green leaves and white towels, warm diffused ambient light, zen wellness studio, no products",
        "NIVEA skincare bottles on wooden spa shelf with plants, warm glowing ambient light, zen beauty photography, photorealistic",
        "nivea/fbc_spa", 502
    ),
    (
        "dark navy velvet surface, dramatic soft rim lighting from behind, premium luxury backdrop, moody atmosphere, no objects",
        "NIVEA skincare bottles on dark velvet, dramatic rim lighting, moody premium beauty editorial, photorealistic sharp",
        "nivea/fbc_moody", 503
    ),
    (
        "white linen cloth on outdoor table, bright summer morning sunlight, airy fresh lifestyle setting, no products",
        "NIVEA skincare bottles on white linen outdoors, bright morning sunlight, fresh lifestyle beauty photography, airy clean photorealistic",
        "nivea/fbc_outdoor", 504
    ),
]

print("=== Nivea FBC — BG gen → IC-Light relight → Upscale ===")
for bg_text, pos_text, prefix, seed in scenes:
    submit(make_fbc_prompt("nivea_products.jpg", bg_text, pos_text, prefix, seed), prefix.split("/")[1])

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


=== Nivea FBC — BG gen → IC-Light relight → Upscale ===
✅ [fbc_marble]: 1e71efd2-dcc5-4d26-81de-876b35ce6988
✅ [fbc_spa]: a3687203-e174-4bfa-aa43-364380278d56
✅ [fbc_moody]: c46407b1-df0f-4864-a8e2-e383daa74828
✅ [fbc_outdoor]: d00f7260-b93a-4203-82ee-a8916c6ba676

All queued — http://fred:8188


sleep 120 && ls -lah /home/crogers2287/ComfyUI/output/nivea/fbc_* 2>/dev/null && curl -s http://127.0.0.1:8188/queue | python3 -c "import sys,json; d=json.load(sys.stdin); print('Running:', len(d.get('queue_running',[])), '| Pending:', len(d.get('queue_pending',[])))"


Conversation info (untrusted metadata):
```json
{
  "message_id": "213",
  "conversation_label": "Skinny, Selena and Chelsea id:-5037974429",
  "sender": "6558388607",
  "group_subject": "Skinny, Selena and Chelsea"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Skinny",
  "name": "Skinny",
  "username": "skinnyc813"
}
```

Well?


Chad is asking "Well?" - probably wondering what happened with the renders. Let me check the status.

ls -lah /home/crogers2287/ComfyUI/output/nivea/fbc_* 2>/dev/null && curl -s http://127.0.0.1:8188/queue | python3 -c "import sys,json; d=json.load(sys.stdin); print('Running:', len(d.get('queue_running',[])), '| Pending:', len(d.get('queue_pending',[])))"


-rw-r--r-- 1 crogers2287 crogers2287 8.8M Feb 18 14:19 /home/crogers2287/ComfyUI/output/nivea/fbc_marble_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 9.2M Feb 18 14:20 /home/crogers2287/ComfyUI/output/nivea/fbc_moody_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287  11M Feb 18 14:20 /home/crogers2287/ComfyUI/output/nivea/fbc_outdoor_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 9.0M Feb 18 14:20 /home/crogers2287/ComfyUI/output/nivea/fbc_spa_00001_.png
Running: 0 | Pending: 0


Done and upscaled! Check them at **http://fred:8188** → History tab. All 4 are there — marble, spa, moody, outdoor.

These used the proper FBC pipeline: background generated first → IC-Light relights the product to match → 4x upscaled. Labels should be intact. Let me know how they look!


Conversation info (untrusted metadata):
```json
{
  "message_id": "215",
  "conversation_label": "Skinny, Selena and Chelsea id:-5037974429",
  "sender": "6558388607",
  "group_subject": "Skinny, Selena and Chelsea"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Skinny",
  "name": "Skinny",
  "username": "skinnyc813"
}
```

Send here


Chad wants me to send the images here in the chat. The message tool has been failing. Let me try copying to workspace and using MEDIA inline paths, which is what worked before (somewhat).

cp /home/crogers2287/ComfyUI/output/nivea/fbc_marble_00001_.png /home/crogers2287/.openclaw/agents/selena/fbc_marble.png && cp /home/crogers2287/ComfyUI/output/nivea/fbc_spa_00001_.png /home/crogers2287/.openclaw/agents/selena/fbc_spa.png && cp /home/crogers2287/ComfyUI/output/nivea/fbc_moody_00001_.png /home/crogers2287/.openclaw/agents/selena/fbc_moody.png && cp /home/crogers2287/ComfyUI/output/nivea/fbc_outdoor_00001_.png /home/crogers2287/.openclaw/agents/selena/fbc_outdoor.png && echo "Done"


Done


🏛 **Marble**
MEDIA:./fbc_marble.png

🌿 **Spa**
MEDIA:./fbc_spa.png

🌙 **Moody**
MEDIA:./fbc_moody.png

☀ **Outdoor**
MEDIA:./fbc_outdoor.png


Conversation info (untrusted metadata):
```json
{
  "message_id": "216",
  "conversation_label": "Skinny, Selena and Chelsea id:-5037974429",
  "sender": "6558388607",
  "group_subject": "Skinny, Selena and Chelsea",
  "was_mentioned": true
}
```

Sender (untrusted metadata):
```json
{
  "label": "Skinny",
  "name": "Skinny",
  "username": "skinnyc813"
}
```

Hello? @Selena87_bot


Chad is trying to get my attention - he's wondering if the images came through. The MEDIA inline method might not be working properly for sending images in Telegram. Let me check if there's another way to send these. The message tool was failing earlier with "Cannot read properties of undefined (reading 'listAccountIds')". Let me try it again - maybe it was a temporary issue.

I'm here! The MEDIA tags may not be rendering for you. Let me try the message tool directly:

message

🏛 Marble — BG gen → IC-Light FBC relight → 4x upscale


The message tool is still failing. Based on my memory notes: "Message Tool: `message` tool with `filePath`/`media` parameters failing with 'Cannot read properties of undefined (reading 'listAccountIds')'" - this is a known issue.

I need to let Chad know about this limitation and suggest he view the images at http://fred:8188. But he clearly wants them sent here. Let me think about alternatives.

Maybe I can try a different approach - like serving the files via a local HTTP server and using a URL, or maybe there's another way to get these images to Telegram.

Actually, looking at the system prompt - "MEDIA:./image.jpg" should work as a relative path.

The MEDIA inline format might not be rendering properly for Chad, possibly because the image files are quite large at 8-11MB each. I should compress them down to a more manageable size first, then retry the inline approach with the smaller files.

The message tool is broken on my end — known issue from earlier today. The images are also 8-11MB each which might be too large. Let me compress and retry:

exec

cd /home/crogers2287/.openclaw/agents/selena && for f in fbc_marble fbc_spa fbc_moody fbc_outdoor; do
  convert ${f}.png -resize 2048x2048\> -quality 88 ${f}_sm.jpg && echo "$f: $(du -sh ${f}_sm.jpg | cut -f1)"
done


fbc_marble: 420K
fbc_spa: 460K
fbc_moody: 364K
fbc_outdoor: 520K


message

🏛 Marble — proper FBC pipeline, 4x upscaled


The message tool is fundamentally broken - it's not a file size issue. This seems like a configuration issue with the Telegram account/bot setup in OpenClaw. Let me try with `to` param or a different format.

Actually, looking at the error "Cannot read properties of undefined (reading 'listAccountIds')" - this suggests there's no Telegram account configured properly for outbound messaging in this session.

Let me try different parameters - maybe without `target` or with `to` instead.

The message tool is fundamentally broken in this session — it can't find the Telegram account config. I need Chad to look at this from the gateway side. The images are ready and waiting at:

**http://fred:8188** → History tab → the last 4 `fbc_` renders

Chad — can you check if the Telegram bot config is correct? The message tool keeps throwing `Cannot read properties of undefined (reading 'listAccountIds')` which means it can't find an account to send from. That's why I can only reply in-thread but can't push photos outbound.


Conversation info (untrusted metadata):
```json
{
  "message_id": "220",
  "conversation_label": "Skinny, Selena and Chelsea id:-5037974429",
  "sender": "6558388607",
  "group_subject": "Skinny, Selena and Chelsea"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Skinny",
  "name": "Skinny",
  "username": "skinnyc813"
}
```

try now


Chad says "try now" - he probably fixed something on the Telegram/gateway side. Let me try sending the images again.


It's working! Sending the rest:

🌿 Spa — warm tropical zen


🌙 Moody — dark velvet, dramatic rim light


☀ Outdoor — morning sunlight on white linen


Conversation info (untrusted metadata):
```json
{
  "message_id": "225",
  "conversation_label": "Skinny, Selena and Chelsea id:-5037974429",
  "sender": "6558388607",
  "group_subject": "Skinny, Selena and Chelsea"
}
```

Sender (untrusted metadata):
```json
{
  "label": "Skinny",
  "name": "Skinny",
  "username": "skinnyc813"
}
```

that loooks fucked


Chad says they look fucked. The FBC approach with generating a background separately and then compositing isn't working well. Let me think about what approach would work best.

Looking back at the history:
- Last night's scene_1 to scene_4 (FC model) were rated decent (6-7/10)
- The issue with IC-Light FC is text mangling
- The FBC approach looks bad too

Maybe I should go back to the FC model (which gave the best results last night) but address the text issue differently. Or maybe try different prompts/settings.

Actually, let me look at what the "scene_" files looked like from last night that were rated decent.

So the FC model itself was producing good results—Chad even said the first IC-Light FC batch looked good before the text issues came up. The real problem is that IC-Light regenerates the entire image through Stable Diffusion, which inevitably mangles any text. The solution would be to use IC-Light for the lighting adjustments, then mask and composite the original product back on top to preserve the text integrity.

I'm going to try a hybrid approach: run IC-Light FC to get the nicely lit background and overall lighting, extract a clean mask of the original product using BiRefNet, then composite the original product with its intact text back onto the IC-Light output using that mask. This way I get the best of both—the improved lighting and background from IC-Light while keeping the product itself and its text completely original and unmangled.

Building this out: IC-Light FC gives me the lighting and background, BiRefNet on the original gives me the product mask, then I composite the original product over the IC-Light result using that mask.

Then upscale the final result.

Yeah, FBC with a generated background looks rough. Let me go back to what actually worked — FC model gave decent results. The real fix for the text is to run FC first for the lighting/background, then composite the **original** product cutout back on top to restore the real labels, then upscale:

exec

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[:500]}")

def make_prompt(product_img, pos_text, save_prefix, seed):
    return {
        # Load product
        "1":  {"class_type": "LoadImage",             "inputs": {"image": product_img, "upload": "image"}},
        # BiRefNet — get cutout + mask
        "2":  {"class_type": "BiRefNetRMBG",           "inputs": {"image": ["1",0], "model": "BiRefNet-general"}},
        # Prep foreground for IC-Light
        "3":  {"class_type": "ICLightApplyMaskGrey",   "inputs": {"image": ["2",0], "alpha": ["2",1]}},
        # Checkpoint
        "4":  {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": "RealisticVision_V6_fp16.safetensors"}},
        # IC-Light FC model
        "5":  {"class_type": "LoadAndApplyICLightUnet","inputs": {"model": ["4",0], "model_path": "iclight_sd15_fc_unet_ldm.safetensors"}},
        # Prompts
        "6":  {"class_type": "CLIPTextEncode",         "inputs": {"text": pos_text, "clip": ["4",1]}},
        "7":  {"class_type": "CLIPTextEncode",         "inputs": {"text": "blurry, low quality, watermark, deformed, ugly, shadows clipping", "clip": ["4",1]}},
        # Encode foreground
        "8":  {"class_type": "VAEEncode",              "inputs": {"pixels": ["3",0], "vae": ["4",2]}},
        # IC-Light conditioning
        "9":  {"class_type": "ICLightConditioning",    "inputs": {"positive": ["6",0], "negative": ["7",0], "vae": ["4",2], "foreground": ["8",0], "multiplier": 1.0}},
        # Sample
        "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]}},
        # Resize IC-Light output to match original
        "12": {"class_type": "ImageScale",             "inputs": {"image": ["11",0], "upscale_method": "lanczos", "width": 512, "height": 512, "crop": "disabled"}},
        # Resize original cutout to same size
        "13": {"class_type": "ImageScale",             "inputs": {"image": ["2",0], "upscale_method": "lanczos", "width": 512, "height": 512, "crop": "disabled"}},
        # Resize mask to same size
        "14": {"class_type": "MaskToImage",            "inputs": {"mask": ["2",1]}},
        "15": {"class_type": "ImageScale",             "inputs": {"image": ["14",0], "upscale_method": "lanczos", "width": 512, "height": 512, "crop": "disabled"}},
        "16": {"class_type": "ImageToMask",            "inputs": {"image": ["15",0], "channel": "red"}},
        # Composite original product (real text) over IC-Light background
        "17": {"class_type": "ImageCompositeMasked",   "inputs": {"destination": ["12",0], "source": ["13",0], "mask": ["16",0], "x": 0, "y": 0, "resize_source": False}},
        # 4x Upscale
        "18": {"class_type": "UpscaleModelLoader",     "inputs": {"model_name": "4x-UltraSharp.pth"}},
        "19": {"class_type": "ImageUpscaleWithModel",  "inputs": {"upscale_model": ["18",0], "image": ["17",0]}},
        "20": {"class_type": "SaveImage",              "inputs": {"images": ["19",0], "filename_prefix": save_prefix}},
    }

scenes = [
    ("NIVEA skincare bottles on luxury white marble bathroom countertop, soft warm window light, elegant minimal surface, professional product photography, photorealistic", "nivea/v3_marble",  601),
    ("NIVEA skincare bottles on wooden spa shelf with tropical green plants and white towels, warm diffused ambient light, zen wellness, photorealistic",                   "nivea/v3_spa",     602),
    ("NIVEA skincare bottles on dark velvet surface, dramatic rim lighting, moody premium editorial beauty, deep shadows, photorealistic",                                 "nivea/v3_moody",   603),
    ("NIVEA skincare bottles on white linen outdoors, bright morning sunlight, fresh lifestyle beauty photography, airy clean, photorealistic",                           "nivea/v3_outdoor", 604),
]

print("=== Nivea v3 — FC lighting + original product composited back + upscale ===")
for pos, prefix, seed in scenes:
    submit(make_prompt("nivea_products.jpg", pos, prefix, seed), prefix.split("/")[1])

print("\nQueued — http://fred:8188")
EOF


=== Nivea v3 — FC lighting + original product composited back + upscale ===
✅ [v3_marble]: eb2970ee-86e8-4a6f-aadf-2d596d3e5c71
✅ [v3_spa]: 55d3b70b-748e-461a-a4ce-0480fd8e9d9d
✅ [v3_moody]: 76e525e0-7d94-47ef-b858-ef9611b31f52
✅ [v3_outdoor]: a4ac1686-40d6-4ec0-adfc-3ca48c76a1dd

Queued — http://fred:8188


sleep 120 && ls -lah /home/crogers2287/ComfyUI/output/nivea/v3_* 2>/dev/null && curl -s http://127.0.0.1:8188/queue | python3 -c "import sys,json; d=json.load(sys.stdin); print('Running:', len(d.get('queue_running',[])), '| Pending:', len(d.get('queue_pending',[])))"


Command still running (session crisp-summit, pid 4097779). Use process (list/poll/log/write/kill/clear/remove) for follow-up.


-rw-r--r-- 1 crogers2287 crogers2287 4.7M Feb 18 15:22 /home/crogers2287/ComfyUI/output/nivea/v3_marble_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 4.8M Feb 18 15:23 /home/crogers2287/ComfyUI/output/nivea/v3_moody_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 4.7M Feb 18 15:23 /home/crogers2287/ComfyUI/output/nivea/v3_outdoor_00001_.png
-rw-r--r-- 1 crogers2287 crogers2287 5.2M Feb 18 15:23 /home/crogers2287/ComfyUI/output/nivea/v3_spa_00001_.png
Running: 0 | Pending: 0

Process exited with code 0.


Done! Sending them over:

exec

cd /home/crogers2287/.openclaw/agents/selena && for f in v3_marble v3_spa v3_moody v3_outdoor; do
  convert /home/crogers2287/ComfyUI/output/nivea/${f}_00001_.png -resize 2048x2048\> -quality 88 ${f}.jpg && echo "$f: $(du -sh ${f}.jpg | cut -f1)"
done


v3_marble: 416K
v3_spa: 448K
v3_moody: 416K
v3_outdoor: 428K


message

🏛 Marble — IC-Light background, original labels composited back


🌿 Spa


🌙 Moody


☀ Outdoor