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62 lines
2.7 KiB
Python
62 lines
2.7 KiB
Python
# ==============================================================================
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# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
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# PROJECT: SOVEREIGN STACK
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# This artifact is entirely proprietary and cryptographically proven.
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# Open-Source usage requires explicit permission from Brandon Scott Schneider.
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# ==============================================================================
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import argparse
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import os
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import torch
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from diffusers import StableDiffusionUpscalePipeline
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from PIL import Image
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def upscale(input_file, output_file, steps=50):
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print(f"[TSM] Initializing Quantum Super-Resolution Vector Array...")
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# Load the base image
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try:
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init_image = Image.open(input_file).convert("RGB")
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except Exception as e:
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print(f"[ERROR] Failed to load {input_file}: {e}")
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return
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print(f"[TSM] Base Tensor Loaded: {init_image.width}x{init_image.height}")
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# Load heavily optimized 4x Upscaler
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model_id = "stabilityai/stable-diffusion-x4-upscaler"
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print(f"[TSM] Booting pi-MoE Optical Node -> {model_id}")
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pipe = StableDiffusionUpscalePipeline.from_pretrained(
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model_id,
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torch_dtype=torch.float16
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)
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pipe = pipe.to("cuda")
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# Optional: Enable memory efficient attention if xformers is installed
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try:
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pipe.enable_xformers_memory_efficient_attention()
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print("[TSM] Xformers memory efficient attention enabled.")
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except Exception:
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pass
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# The prompt helps guide the upscaler's "hallucination" of new pixels
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prompt = "highly detailed, hyper-realistic, pristine, 8k resolution, photorealistic, sharp focus, masterpiece"
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print(f"[TSM] Injecting physics heuristics and unfolding super-resolution...")
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print(f"[TSM] Factoring {steps} inference iterations. Extrapolating to 4x native resolution.")
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upscaled_image = pipe(prompt=prompt, image=init_image, num_inference_steps=steps).images[0]
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os.makedirs(os.path.dirname(output_file), exist_ok=True)
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upscaled_image.save(output_file)
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print(f"[TSM] Image Matrix Re-compiled. Final Resolution: {upscaled_image.width}x{upscaled_image.height}")
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print(f"[TSM] Vision Solidified at: {os.path.abspath(output_file)}")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="TSM 4x Super-Resolution Upscaler.")
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parser.add_argument("--input", type=str, required=True, help="Input image path.")
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parser.add_argument("--output", type=str, default="5-Applications/out/mind_eye/logic_signal_substrate_upscaled.png", help="Output image path.")
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parser.add_argument("--steps", type=int, default=50, help="Inference steps for diffusion upscaling.")
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args = parser.parse_args()
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upscale(args.input, args.output, args.steps)
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