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