debug

app.py

@@ -345,7 +345,7 @@ def evaluate_v1(inputs, model, quantizer, tokenizer, width, height, device, do_s
     inputs = tokenizer(
         input_intension, return_tensors="pt"
     ).to(model.lm.device)
-    print(inputs.device)
+    # print(inputs.device)
     print("tokenizer2")
     
     stopping_criteria = StoppingCriteriaList()
@@ -401,6 +401,8 @@ def inference(generate_method, intention, model, quantizer, tokenizer, width, he
 
 # @spaces.GPU(enable_queue=True, duration=60)
 def construction():
+    global pipeline
+    global transp_vae
     from custom_model_mmdit import CustomFluxTransformer2DModel
     from custom_model_transp_vae import AutoencoderKLTransformerTraining as CustomVAE
     from custom_pipeline import CustomFluxPipelineCfg
@@ -429,7 +431,7 @@ def construction():
     ).to("cuda")
     pipeline.enable_model_cpu_offload(gpu_id=0) # Save GPU memory
     
-    return pipeline, transp_vae
+    # return pipeline, transp_vae
 
 @spaces.GPU(duration=60)
 def test_one_sample(validation_box, validation_prompt, true_gs, inference_steps, pipeline, generator, transp_vae):
@@ -461,6 +463,7 @@ def test_one_sample(validation_box, validation_prompt, true_gs, inference_steps,
     return output_gradio
 
 def svg_test_one_sample(validation_prompt, validation_box_str, seed, true_gs, inference_steps, pipeline, transp_vae):
+    print("svg_test_one_sample")
     generator = torch.Generator().manual_seed(seed)
     try:
         validation_box = ast.literal_eval(validation_box_str)
@@ -471,8 +474,9 @@ def svg_test_one_sample(validation_prompt, validation_box_str, seed, true_gs, in
 
     validation_box = adjust_validation_box(validation_box)
     
+    print("result_images = test_one_sample")
     result_images = test_one_sample(validation_box, validation_prompt, true_gs, inference_steps, pipeline, generator, transp_vae)
-    
+    print("after result_images = test_one_sample")
     svg_img = pngs_to_svg(result_images[1:])
     
     svg_file_path = './image.svg'
@@ -491,6 +495,31 @@ def svg_test_one_sample(validation_prompt, validation_box_str, seed, true_gs, in
         raise ValueError(f"文件 {svg_file_path} 内容为空")
     
     return result_images, svg_file_path
+
+def process_svg(text_input, tuple_input, seed, true_gs, inference_steps):
+    print("precess_svg")
+    result_images = []
+    result_images, svg_file_path = svg_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)
+
+    url, unique_filename = upload_to_github(file_path=svg_file_path)
+    unique_filename = f'{unique_filename}'
+    
+    if url != None:
+        print(f"File uploaded to: {url}")
+        svg_editor = f"""
+            <iframe src="https://svgedit.netlify.app/editor/index.html?\
+            storagePrompt=false&url={url}" \
+            width="100%", height="800px"></iframe>
+        """
+    else:
+        print('upload_to_github FAILED!')
+        svg_editor = f"""
+            <iframe src="https://svgedit.netlify.app/editor/index.html" \
+            width="100%", height="800px"></iframe>
+        """
+    
+    return result_images, svg_file_path, svg_editor
     
 def main():
     model, quantizer, tokenizer, width, height, device = construction_layout()
@@ -550,36 +579,39 @@ def main():
     #     json_file = "/home/wyb/openseg_blob/v-yanbin/GradioDemo/LLM-For-Layout-Planning/inference_test.json"
     #     return wholecaption, str(list_box), json_file
 
-    pipeline, transp_vae = construction()
+    # pipeline, transp_vae = construction()
+    construction()
 
-    gradio_test_one_sample_partial = partial(
-        svg_test_one_sample,
-        pipeline=pipeline,
-        transp_vae=transp_vae,
-    )
+    # gradio_test_one_sample_partial = partial(
+    #     svg_test_one_sample,
+    #     pipeline=pipeline,
+    #     transp_vae=transp_vae,
+    # )
     
-    def process_svg(text_input, tuple_input, seed, true_gs, inference_steps):
-        result_images = []
-        result_images, svg_file_path = gradio_test_one_sample_partial(text_input, tuple_input, seed, true_gs, inference_steps)
+    # def process_svg(text_input, tuple_input, seed, true_gs, inference_steps):
+    #     print("precess_svg")
+    #     result_images = []
+    #     result_images, svg_file_path = svg_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)
 
-        url, unique_filename = upload_to_github(file_path=svg_file_path)
-        unique_filename = f'{unique_filename}'
+    #     url, unique_filename = upload_to_github(file_path=svg_file_path)
+    #     unique_filename = f'{unique_filename}'
         
-        if url != None:
-            print(f"File uploaded to: {url}")
-            svg_editor = f"""
-                <iframe src="https://svgedit.netlify.app/editor/index.html?\
-                storagePrompt=false&url={url}" \
-                width="100%", height="800px"></iframe>
-            """
-        else:
-            print('upload_to_github FAILED!')
-            svg_editor = f"""
-                <iframe src="https://svgedit.netlify.app/editor/index.html" \
-                width="100%", height="800px"></iframe>
-            """
+    #     if url != None:
+    #         print(f"File uploaded to: {url}")
+    #         svg_editor = f"""
+    #             <iframe src="https://svgedit.netlify.app/editor/index.html?\
+    #             storagePrompt=false&url={url}" \
+    #             width="100%", height="800px"></iframe>
+    #         """
+    #     else:
+    #         print('upload_to_github FAILED!')
+    #         svg_editor = f"""
+    #             <iframe src="https://svgedit.netlify.app/editor/index.html" \
+    #             width="100%", height="800px"></iframe>
+    #         """
         
-        return result_images, svg_file_path, svg_editor
+    #     return result_images, svg_file_path, svg_editor
     
     def one_click_generate(intention_input, temperature, top_p, seed, true_gs, inference_steps):
         # 首先调用process_preddate