teste checkpoint novo

app.py

@@ -245,23 +245,25 @@ loras = [
 ]
 
 # Initialize the base model
-use_auth_token=True
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
 base_model = "SG161222/Verus_Vision_1.0b"
 
+# Carregar Autoencoder do Verus Vision
+good_vae = AutoencoderKL.from_pretrained(base_model, filename="ae.safetensors", torch_dtype=dtype).to(device)
 
-taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
-good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
-pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
+# Configurar o pipeline principal do Verus Vision
+pipe = DiffusionPipeline.from_pretrained(
+    base_model,
+    filename="VerusVision_1.0b_Transformer_fp16.safetensors",  # Ajuste para FP16 ou FP8
+    torch_dtype=dtype
+).to(device)
+
+# Se precisar do Image-to-Image
 pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
     base_model,
     vae=good_vae,
     transformer=pipe.transformer,
     text_encoder=pipe.text_encoder,
     tokenizer=pipe.tokenizer,
-    text_encoder_2=pipe.text_encoder_2,
-    tokenizer_2=pipe.tokenizer_2,
     torch_dtype=dtype
 )