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import os
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import sys
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import torch
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..',
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'src', 'ghibli_stable_diffusion_synthesis',
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'inference')))
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from full_finetuning import inference_process as full_finetuning_inference
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from lora import inference_process as lora_inference
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def generate_image(prompt, height, width, num_inference_steps, guidance_scale, seed,
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random_seed, use_lora, finetune_model_id, lora_model_id, base_model_id,
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lora_scale, config_path, device, dtype):
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batch_size = 1
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if random_seed:
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seed = torch.randint(0, 4294967295, (1,)).item()
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try:
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model_id = finetune_model_id
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if not use_lora:
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pil_image = full_finetuning_inference(
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prompt=prompt,
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height=height,
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width=width,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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batch_size=batch_size,
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seed=seed,
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config_path=config_path,
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model_id=model_id,
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device=device,
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dtype=dtype
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)
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else:
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model_id = lora_model_id
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pil_image = lora_inference(
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prompt=prompt,
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height=height,
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width=width,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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batch_size=batch_size,
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seed=seed,
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lora_scale=lora_scale,
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config_path=config_path,
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model_id=model_id,
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device=device,
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dtype=dtype
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)
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return pil_image, f"Generated image successfully! Seed used: {seed}"
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except Exception as e:
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return None, f"Failed to generate image: {e}"
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