import requests
response = requests.post(
"https://api.downloader.org/api/v1/submit/",
headers={"Authorization": "API_KEY"},
json={"url": "URL"},
)
for item in response.json()["items"]:
print(item["type"], item["url"])
Tumblr Саволҳо оид ба боргирӣ
Paste any public Tumblr URL into the box at the top of this page and click Download. Your file is ready in a few seconds — no signup, no install.
Tumblr is an image-sharing platform. Posts can be a single image or a gallery; multi-image posts download in their upload order.
No — Downloader doesn't sign in to Tumblr. Anything Tumblr serves publicly can be downloaded without authentication on either side.
Tumblr hosts a mix of content types. Each download comes back in JPG, GIF, and MP4 — the format matches the asset you actually link to.
Yes. We pass through whatever Tumblr serves — no re-encoding, no recompression, no resolution downgrade. What you see playing on Tumblr is exactly what you download.
Tumblr has no platform-specific gotchas worth flagging. The standard paste-and-download flow handles it cleanly.
No. Tumblr sees a normal page-load request; the poster receives no notification. Downloads are anonymous from the platform's perspective.
Yes. Open Downloader in your mobile browser, paste a Tumblr link, and tap Download. The file saves to your Photos / Files / Music app — no separate app required.
Processing on our side is constant — typically under a second. Actual download time after that depends on the file size and your internet connection.
Free accounts have a daily download cap (counted across all platforms, not just Tumblr). Pro accounts remove the cap entirely and add priority processing.
Most users downloading from Tumblr are creators — archiving their own work, repurposing clips for other platforms, or making compilation reels.
Downloading content you have the right to save — your own posts, content released under an open license, public-domain material — is standard fair use in most jurisdictions. For anything else, respect copyright and Tumblr's terms.
[Error: All translation engines failed for batch: MADLAD batch translation failed: CUDA out of memory. Tried to allocate 2.00 MiB. GPU 0 has a total capacity of 23.87 GiB of which 3.62 MiB is free. Process 3280094 has 228.00 MiB memory in use. Process 2050901 has 244.00 MiB memory in use. Process 3310941 has 1.43 GiB memory in use. Process 3310930 has 1.56 GiB memory in use. Process 3310934 has 1.06 GiB memory in use. Process 3310933 has 1.12 GiB memory in use. Process 3310931 has 1.10 GiB memory in use. Process 3310938 has 1.53 GiB memory in use. Process 3310945 has 1.19 GiB memory in use. Process 3310935 has 1.02 GiB memory in use. Process 3310940 has 1.06 GiB memory in use. Process 3310929 has 1.04 GiB memory in use. Process 3310947 has 1000.00 MiB memory in use. Process 3310943 has 1.06 GiB memory in use. Including non-PyTorch memory, this process has 8.95 GiB memory in use. Process 3358747 has 336.00 MiB memory in use. Of the allocated memory 8.76 GiB is allocated by PyTorch, and 14.78 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)]
[Error: All translation engines failed for batch: MADLAD batch translation failed: CUDA out of memory. Tried to allocate 2.00 MiB. GPU 0 has a total capacity of 23.87 GiB of which 3.62 MiB is free. Process 3280094 has 228.00 MiB memory in use. Process 2050901 has 244.00 MiB memory in use. Process 3310941 has 1.43 GiB memory in use. Process 3310930 has 1.56 GiB memory in use. Process 3310934 has 1.06 GiB memory in use. Process 3310933 has 1.12 GiB memory in use. Process 3310931 has 1.10 GiB memory in use. Process 3310938 has 1.53 GiB memory in use. Process 3310945 has 1.19 GiB memory in use. Process 3310935 has 1.02 GiB memory in use. Process 3310940 has 1.06 GiB memory in use. Process 3310929 has 1.04 GiB memory in use. Process 3310947 has 1000.00 MiB memory in use. Process 3310943 has 1.06 GiB memory in use. Including non-PyTorch memory, this process has 8.95 GiB memory in use. Process 3358747 has 336.00 MiB memory in use. Of the allocated memory 8.76 GiB is allocated by PyTorch, and 14.78 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)]
[Error: All translation engines failed for batch: MADLAD batch translation failed: CUDA out of memory. Tried to allocate 2.00 MiB. GPU 0 has a total capacity of 23.87 GiB of which 3.62 MiB is free. Process 3280094 has 228.00 MiB memory in use. Process 2050901 has 244.00 MiB memory in use. Process 3310941 has 1.43 GiB memory in use. Process 3310930 has 1.56 GiB memory in use. Process 3310934 has 1.06 GiB memory in use. Process 3310933 has 1.12 GiB memory in use. Process 3310931 has 1.10 GiB memory in use. Process 3310938 has 1.53 GiB memory in use. Process 3310945 has 1.19 GiB memory in use. Process 3310935 has 1.02 GiB memory in use. Process 3310940 has 1.06 GiB memory in use. Process 3310929 has 1.04 GiB memory in use. Process 3310947 has 1000.00 MiB memory in use. Process 3310943 has 1.06 GiB memory in use. Including non-PyTorch memory, this process has 8.95 GiB memory in use. Process 3358747 has 336.00 MiB memory in use. Of the allocated memory 8.76 GiB is allocated by PyTorch, and 14.77 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)]
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