| | Here is a workflow to fix most of the Qwen-Image-Edit-2509 zooming problems, and allows any resolution to work as intended. Here is an example of pixel-perfect match between an edit and its source. First image is with the fixed workflow, second image with a default workflow, third image is the source. You can switch back between the 1st and 3rd images and see that they match perfectly, rendered at a native 1852×1440 size. The prompt was : “The blonde girl from image 1 in a dark forest under a thunderstorm, a tornado in the distance, heavy rain in front. Change the overall lighting to dark blue tint. Bright backlight.” Technical context, skip ahead if you want : when working on the Qwen-Image & Edit support for krita-ai-diffusion (coming soon©) I was looking at the code from the TextEncodeQwenImageEditPlus node and saw that the forced 1Mp resolution scale can be skipped if the VAE is input if not filled, and that the reference latent part is exactly the same as in the ReferenceLatent node. So like with TextEncodeQwenImageEdit normal node, you should be able to give your own reference latents to improve coherency, even with multiple sources. The resulting workflow is pretty simple : Qwen Edit Plus Fixed v1.json Note that the VAE input is not connected to the Text Encode node (there is a regexp in the Anything Everywhere VAE node), instead the input pictures are manually encoded and passed through reference latents nodes. Just bypass the nodes not needed if you have fewer than 3 pictures. Here are some interesting results with the pose input : using the standard workflow the poses are automatically scaled to 1024×1024 and don’t match the output size. The fixed workflow has the correct size and a sharper render. Once again, fixed then standard, and the poses for the prompt “The blonde girl from image 1 using the poses from image 2. White background.” : And finally a result at lower resolution. The problem is less visible, but still the fix gives a better match (switch quickly between pictures to see the difference) : Enjoy ! submitted by /u/danamir_ |
Embeddings — vector-based numerical representations of typically unstructured data like text — have been primarily…
Search-augmented large language models (LLMs) excel at knowledge-intensive tasks by integrating external retrieval. However, they…
This post is co-written with Sunaina Kavi, AI/ML Product Manager at Omada Health. Omada Health,…
Anthropic released Cowork on Monday, a new AI agent capability that extends the power of…
New York governor Kathy Hochul says she will propose a new law allowing limited autonomous…
Artificial intelligence (AI) is increasingly used to analyze medical images, materials data and scientific measurements,…