Create app.py

app.py

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+import gradio as gr
+from huggingface_hub import hf_hub_download
+import subprocess
+import tempfile
+import shutil
+import os
+import spaces
+
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import os
+   
+
+def download_t5_model(model_id, save_directory):
+    # Modelin tokenizer'ını ve modeli indir
+    model = T5ForConditionalGeneration.from_pretrained(model_id)
+    tokenizer = T5Tokenizer.from_pretrained(model_id)
+
+    # Model ve tokenizer'ı belirtilen dizine kaydet
+    if not os.path.exists(save_directory):
+        os.makedirs(save_directory)
+    model.save_pretrained(save_directory)
+    tokenizer.save_pretrained(save_directory)
+
+# Model ID ve kaydedilecek dizin
+model_id = "DeepFloyd/t5-v1_1-xxl"
+save_directory = "pretrained_models/t5_ckpts/t5-v1_1-xxl"
+
+# Modeli indir
+download_t5_model(model_id, save_directory)
+
+def download_model(repo_id, model_name):
+    model_path = hf_hub_download(repo_id=repo_id, filename=model_name)
+    return model_path
+
+import glob
+
+@spaces.GPU
+def run_inference(model_name, prompt_text):
+    repo_id = "hpcai-tech/Open-Sora"
+    
+    # Map model names to their respective configuration files
+    config_mapping = {
+        "OpenSora-v1-16x256x256.pth": "16x256x256.py",
+        "OpenSora-v1-HQ-16x256x256.pth": "16x512x512.py",
+        "OpenSora-v1-HQ-16x512x512.pth": "64x512x512.py"
+    }
+    
+    config_path = config_mapping[model_name]
+    ckpt_path = download_model(repo_id, model_name)
+
+    # Save prompt_text to a temporary text file
+    prompt_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt", mode='w')
+    prompt_file.write(prompt_text)
+    prompt_file.close()
+
+    with open(config_path, 'r') as file:
+        config_content = file.read()
+    config_content = config_content.replace('prompt_path = "./assets/texts/t2v_samples.txt"', f'prompt_path = "{prompt_file.name}"')
+    
+    with tempfile.NamedTemporaryFile('w', delete=False, suffix='.py') as temp_file:
+        temp_file.write(config_content)
+        temp_config_path = temp_file.name
+
+    cmd = [
+        "torchrun", "--standalone", "--nproc_per_node", "1",
+        "scripts/inference.py", temp_config_path,
+        "--ckpt-path", ckpt_path
+    ]
+    subprocess.run(cmd)
+
+    save_dir = "./outputs/samples/"  # Örneğin, inference.py tarafından kullanılan kayıt dizini
+    list_of_files = glob.glob(f'{save_dir}/*')
+    if list_of_files:
+        latest_file = max(list_of_files, key=os.path.getctime)
+        return latest_file
+    else:
+        print("No files found in the output directory.")
+        return None
+
+    # Clean up the temporary files
+    os.remove(temp_file.name)
+    os.remove(prompt_file.name)
+
+def main():
+    gr.Interface(
+        fn=run_inference,
+        inputs=[
+            gr.Dropdown(choices=[
+                "OpenSora-v1-16x256x256.pth",
+                "OpenSora-v1-HQ-16x256x256.pth",
+                "OpenSora-v1-HQ-16x512x512.pth"
+            ], 
+            value="OpenSora-v1-16x256x256.pth",
+            label="Model Selection"),
+            gr.Textbox(label="Prompt Text", value="Enter prompt text here")
+        ],
+        outputs=gr.Video(label="Output Video"),
+        title="Open-Sora Inference",
+        description="Run Open-Sora Inference with Custom Parameters",
+    ).launch()
+
+if __name__ == "__main__":
+    main()