diff --git a/modules/models/flux/flux.py b/modules/models/flux/flux.py
index fc1e91e9d..eb17a31c7 100644
--- a/modules/models/flux/flux.py
+++ b/modules/models/flux/flux.py
@@ -80,7 +80,7 @@ class FluxCond(torch.nn.Module):
         with torch.no_grad():
             self.clip_l = SDClipModel(layer="hidden", layer_idx=-2, device="cpu", dtype=devices.dtype, layer_norm_hidden_state=False, return_projected_pooled=False, textmodel_json_config=CLIPL_CONFIG)
 
-            if shared.opts.sd3_enable_t5:
+            if shared.opts.flux_enable_t5:
                 self.t5xxl = T5XXLModel(T5_CONFIG, device="cpu", dtype=devices.dtype)
             else:
                 self.t5xxl = None
@@ -107,7 +107,7 @@ class FluxCond(torch.nn.Module):
             with safetensors.safe_open(clip_l_file, framework="pt") as file:
                 self.clip_l.transformer.load_state_dict(SafetensorsMapping(file), strict=False)
 
-        if self.t5xxl and 'text_encoders.t5xxl.transformer.encoder.embed_tokens.weight' not in state_dict:
+        if self.t5xxl and 'text_encoders.t5xxl.transformer.encoder.block.0.layer.0.SelfAttention.k.weight' not in state_dict:
             t5_file = modelloader.load_file_from_url(T5_URL, model_dir=clip_path, file_name="t5xxl_fp16.safetensors")
             with safetensors.safe_open(t5_file, framework="pt") as file:
                 self.t5xxl.transformer.load_state_dict(SafetensorsMapping(file), strict=False)
@@ -194,7 +194,7 @@ class BaseModel(torch.nn.Module):
             guidance_embed=True,
         )
 
-        self.diffusion_model = Flux(device=device, dtype=devices.dtype, **params)
+        self.diffusion_model = Flux(device=device, dtype=dtype, **params)
         self.model_sampling = ModelSamplingFlux()
         self.depth = params['depth']
         self.depth_single_block = params['depth_single_blocks']
@@ -301,7 +301,10 @@ class FLUX1Inferencer(torch.nn.Module):
 
     def decode_first_stage(self, latent):
         latent = self.latent_format.process_out(latent)
-        return self.first_stage_model.decode(latent)
+        x = self.first_stage_model.decode(latent)
+        if x.dtype == torch.float16:
+            x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
+        return x
 
     def encode_first_stage(self, image):
         latent = self.first_stage_model.encode(image)
diff --git a/modules/shared_options.py b/modules/shared_options.py
index 6b6faf332..0cb10acfd 100644
--- a/modules/shared_options.py
+++ b/modules/shared_options.py
@@ -195,6 +195,9 @@ options_templates.update(options_section(('sdxl', "Stable Diffusion XL", "sd"),
 options_templates.update(options_section(('sd3', "Stable Diffusion 3", "sd"), {
     "sd3_enable_t5": OptionInfo(False, "Enable T5").info("load T5 text encoder; increases VRAM use by a lot, potentially improving quality of generation; requires model reload to apply"),
 }))
+options_templates.update(options_section(('flux', "Stable Diffusion FLUX", "sd"), {
+    "flux_enable_t5": OptionInfo(False, "Enable T5").info("load T5 text encoder; increases VRAM use by a lot, potentially improving quality of generation; requires model reload to apply"),
+}))
 
 options_templates.update(options_section(('vae', "VAE", "sd"), {
     "sd_vae_explanation": OptionHTML("""