About "reversed_latents_w"
qilong-zhang opened this issue · 2 comments
qilong-zhang commented
Hi Yuxin,
Sorry for bothering your again. In run_tree_ring_watermark.py
, the reversed_latents_w
is obtained by
reversed_latents_w = pipe.forward_diffusion(
latents=image_latents_w,
text_embeddings=text_embeddings, # why provides the information about prompt?
guidance_scale=1,
num_inference_steps=args.test_num_inference_steps,
)
However, I notice you write "While it may not be surprising that inversion is accurate for unconditional diffusion models, inversion also succeeds well-enough for conditional diffusion models, even when the conditioning c is not provided. This property of inversion will be exploited heavily by our watermark below."
Does the conditioning c mean text_embeddings
?
YuxinWenRick commented
Hi Qilong, thanks for asking. text_embeddings
is actually a dummy embedding of an empty string as we defined here. Therefore, we don't provide any prompt information during the inversion.
qilong-zhang commented
Thanks for your reply! It's right