Try this to increase resolution w/o finetuning (Instruction) about dust3r HOT 4 OPEN

How to modify the parameter t？

def get_cos_sin(self, D, seq_len, device, dtype): if (D,seq_len,device,dtype) not in self.cache: inv_freq = 1.0 / (self.base ** (torch.arange(0, D, 2).float().to(device) / D)) t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) freqs = torch.einsum("i,j->ij", t, inv_freq).to(dtype) freqs = torch.cat((freqs, freqs), dim=-1) cos = freqs.cos() # (Seq, Dim) sin = freqs.sin() self.cache[D,seq_len,device,dtype] = (cos,sin) return self.cache[D,seq_len,device,dtype]

if you're going from 512 to 1024, you would do:
t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype) * (512/1024)

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Another resolution (1536 x 1152) was tested with multiplication factor of (512/1536) for t with appropriate results. So far this works well for objects that have "good number" of features.

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How to modify the parameter t？

def get_cos_sin(self, D, seq_len, device, dtype):
if (D,seq_len,device,dtype) not in self.cache:
inv_freq = 1.0 / (self.base ** (torch.arange(0, D, 2).float().to(device) / D))
t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype)
freqs = torch.einsum("i,j->ij", t, inv_freq).to(dtype)
freqs = torch.cat((freqs, freqs), dim=-1)
cos = freqs.cos() # (Seq, Dim)
sin = freqs.sin()
self.cache[D,seq_len,device,dtype] = (cos,sin)
return self.cache[D,seq_len,device,dtype]

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@KyunHwan thanks

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