0.8.4

Note: Advanced MSW-MSA Attention node parameters changed. May break workflows.

Note: This update may slightly change seeds.

• MSW-MSA attention can now work with all images sizes. When the size is incompatible it will scale the latent which may affect quality. Contributed by @pamparamm. Thanks!
• Scaling now tries to make the output size a multiple of 8 so it's compatible with MSW-MSA attention. May change seeds, set ca_latent_pixel_increment: 1 in YAML parameters for the old behavior. Note: Does not apply if you use avg_pool2d for downscaling.
• CA downscaling now uses adaptive_avg_pool2d as the default method which supports fractional downscale sizes. As far as I know, it's the same as avg_pool2d with integer sizes but it's possible this will change seeds.
• Simple nodes now support an "auto" model type parameter that will try to guess the model from the latent type.
• Added a yaml_parameters input to the advanced nodes which allows specifying advanced/uncommon parameters. See main README for possible settings.
• You can now use a different scale factor for width and height in RAUNet CA scaling. See ca_downscale_factor_w in YAML parameters.
• You can now fade out the CA scaling effect in RAUNet node. See ca_fadeout_start_time and ca_fadeout_cap in YAML parameters.
• Simple nodes default parameters for SDXL models adjusted to match the official HiDiffusion ones more closely.

Check the expandable "YAML Parameters" sections in the main README for more information about advanced parameters added in this update.

Full Changelog: 0.8.3...0.8.4