Bing Image Grabber

The name is a promise and a warning at the same time. BingImageGrabber really does reach out to Bing Image Search, pull a random real photograph matching your search term, and drop it into your ComfyUI graph as an IMAGE tensor. No API key, no Bing Image Creator account, no paid service. It's the node the whole ConCarneNode pack exists for - the author posted it to r/StableDiffusion back in December 2023 as "a Bing image grabber node for ComfyUI," and the chat nodes that later joined the pack were built to feed it search terms.

Why would you want random web photos in a diffusion graph? Because they're free reference material. Wire the output into a VAE Encode and you've got a fresh img2img starting point every queue. Wire it into IP-Adapter and you've got image prompting on the fly - the README's own example batches a list of "woman drinking orange juice" photos and drives them with a "selena gomez" prompt, which is exactly the trick the KB describes as "using an image as a prompt." Feed it a person's name and use the multi-image output as face-reference embeds. It turns a workflow into a slot machine, and sometimes that's what you want.

How it works

Under the hood it's not the Bing API at all - it's a scraper. The node hits https://www.bing.com/images/async?q=<term>&count=35 with a browser User-Agent, regexes the image URLs (murl) out of the returned HTML, and keeps paging until the results loop or it hits 20 pages. Then it shuffles the links, downloads each one with a 3-second timeout, verifies it's actually an image, and retries up to ten times per slot. It also caches aggressively: the search-result list and every downloaded jpg land in ComfyUI/custom_nodes/ConCarneNode/searchcache/. IS_CHANGED is set to always-change, so the node re-fires on every queue - you genuinely get a different image each run.

The inputs that matter

Five inputs, and only a few you'll touch:

• search_term - the query (default dog). Case and weird characters are mostly ignored.
• num_of_images - how many images to return (default 1). This is the one that changes behavior.
• cache_search / cache_images - both default true. Leave them on until you want fresh results, then flip one or nuke the cache folder.
• use_number_of_links - how many scraped links to consider before shuffling; -1 means use them all. An odd knob, and the default is right.

The output and a size gotcha

You get a single IMAGE output. With one image it stays at native resolution; with more than one, each image is center-cropped to a square and resized to 1024×1024 so the batch is uniform for IP-Adapter or batch img2img. One honest mismatch: the README claims the resize is 512×512, but the current code actually resizes to 1024. Trust the code. A 4000px photo will not come out of the single-image path pre-scaled, so if your VAE starts groaning, run it through a resize node first.

Installing it

In ComfyUI Manager, search for ConCarneNode (or concarne000/ConCarneNode) and install; otherwise:

cd ComfyUI/custom_nodes
git clone https://github.com/concarne000/ConCarneNode
# restart ComfyUI

Here's the pack-level catch: the pack's requirements.txt only installs auto-gptq (needed by the Hermes node, not this one), but the whole file imports transformers and imghdr at the top. No transformers installed means all three nodes vanish from your node list, and Python 3.13 removed imghdr entirely, which breaks the import on newer setups. If the node doesn't show up, that's your first suspect.

Where people get burned

• Stale cache. The cache never cleans itself. If you're seeing the same handful of images, delete searchcache/ or set both cache flags false.
• Few images per term. The author's own admission in the original thread: some search terms just don't return many usable links. Broaden the query.
• Scrapers rot. Bing's HTML changes, the regex finds nothing, and the node silently comes up short. It's a hack, not infrastructure.
• Randomness. Workflows using it aren't reproducible. Fine for exploration, infuriating when you're chasing a specific output.
• The legal gray zone. You're scraping Bing's image results and using other people's photos as reference. Great for personal experimentation; don't ship a product on it.

It's a genuinely fun little node - my pick of the pack - but treat it as a toy for iterating workflows, not a production image source. For real work, curate a folder.