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QwenLM
Qwen-Image
Python2025–2025
QwenLM / Qwen-Image

Qwen-Image

Qwen-Image is a powerful image generation foundation model capable of complex text rendering and precise image editing.

Creative & Media
Explore on GitHubRead Hacker News discussions
3Distinct people
3Mentions
0Explicit recommenders
8.5KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Qwen-Image is a powerful image generation foundation model capable of complex text rendering and precise image editing.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week3 dated mentions

Observed mentions: 2025-08-04 20:01:17 UTC to 2025-12-31 10:52:08 UTC. Weeks start Monday, UTC.

0122025-08-04 00:00:00 UTC to 2025-08-11 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 commentsAug 42025-08-11 00:00:00 UTC to 2025-08-18 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-08-18 00:00:00 UTC to 2025-08-25 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-08-25 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-01 00:00:00 UTC to 2025-09-08 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-08 00:00:00 UTC to 2025-09-15 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-15 00:00:00 UTC to 2025-09-22 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-22 00:00:00 UTC to 2025-09-29 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-29 00:00:00 UTC to 2025-10-06 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-06 00:00:00 UTC to 2025-10-13 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-13 00:00:00 UTC to 2025-10-20 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsOct 132025-10-20 00:00:00 UTC to 2025-10-27 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-27 00:00:00 UTC to 2025-11-03 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-03 00:00:00 UTC to 2025-11-10 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-10 00:00:00 UTC to 2025-11-17 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-17 00:00:00 UTC to 2025-11-24 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-24 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-01 00:00:00 UTC to 2025-12-08 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-08 00:00:00 UTC to 2025-12-15 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-15 00:00:00 UTC to 2025-12-22 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-22 00:00:00 UTC to 2025-12-29 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-12-29 00:00:00 UTC to 2026-01-05 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsDec 29

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Aug 42025-08-04 00:00:00 UTC2025-08-11 00:00:00 UTC202
Aug 112025-08-11 00:00:00 UTC2025-08-18 00:00:00 UTC000
Aug 182025-08-18 00:00:00 UTC2025-08-25 00:00:00 UTC000
Aug 252025-08-25 00:00:00 UTC2025-09-01 00:00:00 UTC000
Sep 12025-09-01 00:00:00 UTC2025-09-08 00:00:00 UTC000
Sep 82025-09-08 00:00:00 UTC2025-09-15 00:00:00 UTC000
Sep 152025-09-15 00:00:00 UTC2025-09-22 00:00:00 UTC000
Sep 222025-09-22 00:00:00 UTC2025-09-29 00:00:00 UTC000
Sep 292025-09-29 00:00:00 UTC2025-10-06 00:00:00 UTC000
Oct 62025-10-06 00:00:00 UTC2025-10-13 00:00:00 UTC000
Oct 132025-10-13 00:00:00 UTC2025-10-20 00:00:00 UTC000
Oct 202025-10-20 00:00:00 UTC2025-10-27 00:00:00 UTC000
Oct 272025-10-27 00:00:00 UTC2025-11-03 00:00:00 UTC000
Nov 32025-11-03 00:00:00 UTC2025-11-10 00:00:00 UTC000
Nov 102025-11-10 00:00:00 UTC2025-11-17 00:00:00 UTC000
Nov 172025-11-17 00:00:00 UTC2025-11-24 00:00:00 UTC000
Nov 242025-11-24 00:00:00 UTC2025-12-01 00:00:00 UTC000
Dec 12025-12-01 00:00:00 UTC2025-12-08 00:00:00 UTC000
Dec 82025-12-08 00:00:00 UTC2025-12-15 00:00:00 UTC000
Dec 152025-12-15 00:00:00 UTC2025-12-22 00:00:00 UTC000
Dec 222025-12-22 00:00:00 UTC2025-12-29 00:00:00 UTC000
Dec 292025-12-29 00:00:00 UTC2026-01-05 00:00:00 UTC110

What Hacker News says

3 sources
pollinations·Aug 4, 2025
# Configure NF4 quantization quant_config = PipelineQuantizationConfig( quant_backend="bitsandbytes_4bit", quant_kwargs={"load_in_4bit": True, "bnb_4bit_quant_type": "nf4", "bnb_4bit_compute_dtype": torch.bfloat16}, components_to_quantize=["transformer", "text_encoder"], ) # Load the pipeline with NF4 quantization pipe = DiffusionPipeline.from_pretrained( model_name, quantization_config=quant_config, torch_dtype=torch.bfloat16, use_safetensors=True, low_cpu_mem_usage=True ).to(device) seems to use 17gb of vram like this update: doesn't work well. this approach seems to be recommended: https://github.com/QwenLM/Qwen-Image/pull/6/files
In Qwen-Image: Crafting with native text rendering

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