![]() It includes a rank-256 lora and an attention sharing module to share attention between multiple diffusion processes on par. layer_sd15_fg2bg.safetensors This model file allows for generating background from foreground with SD1.5. ![]() Generating blended images together with fg and bg is helpful for structural understanding in our very recent tests. ![]() Note that different from paper, this model file includes an additional "blended lora", and it actually can generate three images together (fg, bg, and blended image). It includes two rank-256 loras (foreground lora and background lora), and an attention sharing module to share attention between multiple diffusion processes on par. layer_sd15_joint.safetensors This model file allows for generating all layers together with SD1.5.It will change the latent distribution of the model to a "transparent latent space" that can be decoded by the special VAE pipeline. layer_sd15_transparent_attn.safetensors This is a rank-256 LoRA to turn a SD1.5 into a transparent image generator.layer_sd15_vae_transparent_decoder.safetensors Same as above VAE decoder, but fine-tuned for SD1.5.layer_sd15_vae_transparent_encoder.safetensors Same as above VAE encoder, but fine-tuned for SD1.5.I have made sure that the reduced parameters does not influence result quality. The model architecture is also more lightweight than the paper version to reduce VRAM requirement. vae_transparent_decoder.safetensors This is an image decoder that takes SD VAE outputs and latent image as inputs, and outputs a real PNG image.The released model is more light weighted, requires much less vram, and does not influence result quality in my tests. Note that in the paper we used a relatively heavy model with exactly same amount of parameters as the SD VAE. The offset can be added to latent images to help the diffusion of transparency. vae_transparent_encoder.safetensors This is an image encoder to extract a latent offset from pixel space. ![]()
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