1

app.py

@@ -365,6 +365,7 @@ def evaluate_v1(inputs, model, quantizer, tokenizer, width, height, do_sample=Fa
     print(f"evaluate_v1 {model.device} {model.lm.device} {pipeline.device}")
     model = model.to("cuda")
     print(f"after evaluate_v1 {model.device} {model.lm.device} {pipeline.device}")
+    
     json_example = inputs
     input_intension = '{"wholecaption":"' + json_example["wholecaption"] + '","layout":[{"layer":'
     
@@ -471,7 +472,7 @@ def process_preddate(intention, temperature, top_p, generate_method='v1'):
 
 @spaces.GPU(duration=120)
 def test_one_sample(validation_box, validation_prompt, true_gs, inference_steps, pipeline, generator, transp_vae):
-    print(validation_box)
+    print(f"test_one_sample: {validation_box}")
     output, rgba_output, _, _ = pipeline(
         prompt=validation_prompt,
         validation_box=validation_box,
@@ -500,7 +501,8 @@ def test_one_sample(validation_box, validation_prompt, true_gs, inference_steps,
     return output_gradio
 
 def gradio_test_one_sample(validation_prompt, validation_box_str, seed, true_gs, inference_steps, pipeline, transp_vae):
-    print(f"svg_test_one_sample {model.device} {model.lm.device} {pipeline.device}")
+    print(f"svg_test_one_sample")
+    # print(f"svg_test_one_sample {model.device} {model.lm.device} {pipeline.device}")
     # generator = torch.Generator().manual_seed(seed)
     generator = torch.Generator(device=torch.device("cuda", index=0)).manual_seed(seed)
     try:
@@ -538,7 +540,8 @@ def gradio_test_one_sample(validation_prompt, validation_box_str, seed, true_gs,
     return result_images, svg_file_path
 
 def process_svg(text_input, tuple_input, seed, true_gs, inference_steps):
-    print(f"precess_svg {model.device} {model.lm.device} {pipeline.device}")
+    print(f"precess_svg")
+    # print(f"precess_svg {model.device} {model.lm.device} {pipeline.device}")
     result_images = []
     result_images, svg_file_path = gradio_test_one_sample(text_input, tuple_input, seed, true_gs, inference_steps, pipeline=pipeline, transp_vae=transp_vae)
     # result_images, svg_file_path = gradio_test_one_sample_partial(text_input, tuple_input, seed, true_gs, inference_steps)