load_t5

app.py

@@ -352,7 +352,8 @@ class FluxEditor:
         with torch.no_grad():
             z, info = denoise_func(self.model, **inp, timesteps=timesteps, guidance=1, inverse=True, info=info)
         
-        
+        print(z)
+        print(info)
         
         
         #-----------------------------     2 history_tensors used to implement dual-LQR guiding editing     -------------------------------------#

src/flux/util.py

@@ -131,7 +131,7 @@ def load_flow_model(name: str, device: str | torch.device = "cuda", hf_download:
 
 def load_t5(device: str | torch.device = "cuda", max_length: int = 77) -> HFEmbedder:
     # max length 64, 128, 256 and 512 should work (if your sequence is short enough)
-    return HFEmbedder("google/t5-v1_1-xxl", max_length=max_length, is_clip=False, torch_dtype=torch.bfloat16).to(device)
+    return HFEmbedder("director432/Flux1-T5Encoder", max_length=max_length, is_clip=False, torch_dtype=torch.bfloat16).to(device)
     #return HFEmbedder("/homedata/HuggingFace/black-forest-labs/FLUX.1-dev/text_encoder_2", max_length=max_length, is_clip=False, torch_dtype=torch.bfloat16).to(device)