flux series vram management

diffsynth/models/flux_infiniteyou.py

@@ -104,6 +104,7 @@ class InfiniteYouImageProjector(nn.Module):
     def forward(self, x):
 
         latents = self.latents.repeat(x.size(0), 1, 1)
+        latents = latents.to(dtype=x.dtype, device=x.device)
 
         x = self.proj_in(x)
 

diffsynth/models/flux_value_control.py

@@ -40,7 +40,8 @@ class SingleValueEncoder(torch.nn.Module):
         emb = self.prefer_proj(value).to(dtype)
         emb = self.prefer_value_embedder(emb).squeeze(0)
         base_embeddings = emb.expand(self.prefer_len, -1)
-        learned_embeddings = base_embeddings + self.positional_embedding
+        positional_embedding = self.positional_embedding.to(dtype=base_embeddings.dtype, device=base_embeddings.device)
+        learned_embeddings = base_embeddings + positional_embedding
         return learned_embeddings
 
     @staticmethod

diffsynth/models/step1x_connector.py

@@ -162,7 +162,7 @@ class TimestepEmbedder(nn.Module):
     def forward(self, t):
         t_freq = self.timestep_embedding(
             t, self.frequency_embedding_size, self.max_period
-        ).type(self.mlp[0].weight.dtype)  # type: ignore
+        ).type(t.dtype)  # type: ignore
         t_emb = self.mlp(t_freq)
         return t_emb
     
@@ -656,7 +656,7 @@ class Qwen2Connector(torch.nn.Module):
         mask_float = mask.unsqueeze(-1)  # [b, s1, 1]
         x_mean = (x * mask_float).sum(
                 dim=1
-            ) / mask_float.sum(dim=1) * (1 + self.scale_factor)
+            ) / mask_float.sum(dim=1) * (1 + self.scale_factor.to(dtype=x.dtype, device=x.device))
 
         global_out=self.global_proj_out(x_mean)
         encoder_hidden_states = self.S(x,t,mask)