Fix upcast attention dtype error.
Without this fix, enabling the "Upcast cross attention layer to float32" option while also using `--opt-sdp-attention` breaks generation with an error: ``` File "/ext3/automatic1111/stable-diffusion-webui/modules/sd_hijack_optimizations.py", line 612, in sdp_attnblock_forward out = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=0.0, is_causal=False) RuntimeError: Expected query, key, and value to have the same dtype, but got query.dtype: float key.dtype: float and value.dtype: c10::Half instead. ``` The fix is to make sure to upcast the value tensor too.
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@ -605,7 +605,7 @@ def sdp_attnblock_forward(self, x):
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q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
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q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
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dtype = q.dtype
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dtype = q.dtype
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if shared.opts.upcast_attn:
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if shared.opts.upcast_attn:
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q, k = q.float(), k.float()
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q, k, v = q.float(), k.float(), v.float()
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q = q.contiguous()
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q = q.contiguous()
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k = k.contiguous()
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k = k.contiguous()
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v = v.contiguous()
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v = v.contiguous()
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