Ip adapter attention mask
Ip adapter attention mask. prepare_attention_mask(attention_mask, sequence_length, batch_size) # scaled_dot_product_attention expects attention_mask shape to be # (batch, heads, source_length, target_length). Once I figured out what it did I was in love. IPAdapterMaskProcessor. The generation happens in just one pass with one KSampler (no inpainting or area conditioning). To start, preprocess the input IP-Adapter images with the ~image_processor. This is useful for composing more than one IP-Adapter image. For each input IP-Adapter image, you must provide a binary mask. Binary masks specify which portion of the output image should be assigned to an IP-Adapter. In this example I'm using 2 main characters and a background in completely different styles. You can use it to copy the style, composition, or a face in the reference image. IP-adapter (Image Prompt adapter) is a Stable Diffusion add-on for using images as prompts, similar to Midjourney and DaLLE 3. The new IPAdapterClipVisionEnhancer tries to catch small details by tiling the embeds (instead of the image in the pixel space), the result is a slightly higher resolution visual embedding with no cost of performance. Exciting new feature for the IPAdapter extesion: it's now possible to mask part of the composition to affect only a certain area And you can use multiple masks for a perfect result. attention_mask = attn. prepare_attention_mask(attention_mask, sequence_length, batch_size) # scaled_dot_product_attention expects attention_mask shape to be # (batch, heads, source_length, target_length) Exciting new feature for the IPAdapter extesion: it's now possible to mask part of the composition to affect only a certain area And you can use multiple masks for a perfect result. preprocess() to generate This workflow mostly showcases the new IPAdapter attention masking feature. It's exactly the thing I was needing. This I did an update yesterday and noticed the mask input appeared on the Apply IPAdapter node. 2024/07/11: Added experimental Precise composition (layout) transfer. ulnpbd uczw oasl rjlsl vvmqs fsqw etxsz zpwdr hzgn ehrgv