Request: official float32 inference path for seedvr2_ema_7b.pth.
The public 7B apply script (projects/inference_seedvr2_7b.py) loads seedvr2_ema_7b.pth then runs torch.autocast("cuda", torch.bfloat16). configs_7b/main.yaml sets VAE dtype to bfloat16.
Will you publish an official lossless float32 inference config and command for the original seedvr2_ema_7b.pth (no dtype cast on weights or activations), including VAE ema_vae.pth in float32?
in simpler terms: I use the original seedvr2_ema_7b.pth.
Your public 7B script loads that file but then runs the calculations in bfloat16, not full 32-bit. The VAE is also set to bfloat16.
Please publish an official way to run seedvr2_ema_7b.pth and ema_vae.pth in full 32-bit, with no conversion of the weights or the math.
Thank you.
Request: official float32 inference path for seedvr2_ema_7b.pth.
The public 7B apply script (projects/inference_seedvr2_7b.py) loads seedvr2_ema_7b.pth then runs torch.autocast("cuda", torch.bfloat16). configs_7b/main.yaml sets VAE dtype to bfloat16.
Will you publish an official lossless float32 inference config and command for the original seedvr2_ema_7b.pth (no dtype cast on weights or activations), including VAE ema_vae.pth in float32?
in simpler terms: I use the original seedvr2_ema_7b.pth.
Your public 7B script loads that file but then runs the calculations in bfloat16, not full 32-bit. The VAE is also set to bfloat16.
Please publish an official way to run seedvr2_ema_7b.pth and ema_vae.pth in full 32-bit, with no conversion of the weights or the math.
Thank you.