Delete test_inference.py

test_inference.py

@@ -1,96 +0,0 @@
-#!/usr/bin/env python3
-"""
-Standalone inference script for the NiT-XL Diffusers checkpoint.
-
-This script only uses code vendored in this model repository:
-`custom_pipeline/` for NiT pipeline, transformer, and scheduler classes.
-"""
-
-from __future__ import annotations
-
-import argparse
-from pathlib import Path
-
-import torch
-from diffusers import DiffusionPipeline
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Run class-conditional NiT-XL inference.")
-    parser.add_argument(
-        "--model-dir",
-        type=Path,
-        default=Path(__file__).resolve().parent,
-        help="Path to model repository root.",
-    )
-    parser.add_argument("--class-label", type=int, default=207, help="ImageNet class label to sample.")
-    parser.add_argument("--height", type=int, default=512, help="Output image height.")
-    parser.add_argument("--width", type=int, default=512, help="Output image width.")
-    parser.add_argument("--steps", type=int, default=250, help="Number of inference steps.")
-    parser.add_argument("--mode", choices=["ode", "sde"], default="sde", help="Sampling mode.")
-    parser.add_argument("--guidance-scale", type=float, default=2.05, help="Classifier-free guidance scale.")
-    parser.add_argument("--guidance-low", type=float, default=0.0, help="Guidance start timestep fraction.")
-    parser.add_argument("--guidance-high", type=float, default=0.7, help="Guidance end timestep fraction.")
-    parser.add_argument("--heun", action="store_true", help="Enable Heun correction for ODE mode.")
-    parser.add_argument("--seed", type=int, default=42, help="Random seed.")
-    parser.add_argument(
-        "--output",
-        type=Path,
-        default=Path("demo_images/demo_sde250_class207_seed42.png"),
-        help="Output image path relative to model dir, or absolute path.",
-    )
-    return parser.parse_args()
-
-
-def resolve_output_path(model_dir: Path, output: Path) -> Path:
-    if output.is_absolute():
-        return output
-    return model_dir / output
-
-
-def main() -> None:
-    args = parse_args()
-    model_dir = args.model_dir.resolve()
-    custom_dir = model_dir / "custom_pipeline"
-    if not custom_dir.exists():
-        raise FileNotFoundError(f"Missing custom pipeline dir: {custom_dir}")
-    if not (model_dir / "pipeline.py").exists():
-        raise FileNotFoundError(f"Missing custom entrypoint: {model_dir / 'pipeline.py'}")
-
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    torch_dtype = torch.bfloat16 if device.type == "cuda" and torch.cuda.is_bf16_supported() else torch.float32
-    generator_device = device.type if device.type != "cpu" else "cpu"
-    generator = torch.Generator(device=generator_device).manual_seed(args.seed)
-
-    pipe = DiffusionPipeline.from_pretrained(
-        model_dir,
-        custom_pipeline=str(model_dir / "pipeline.py"),
-        local_files_only=True,
-    ).to(device=device)
-    if device.type == "cuda":
-        pipe.transformer.to(dtype=torch_dtype)
-        pipe.vae.to(dtype=torch_dtype)
-
-    output = pipe(
-        class_labels=[args.class_label],
-        height=args.height,
-        width=args.width,
-        num_inference_steps=args.steps,
-        mode=args.mode,
-        guidance_scale=args.guidance_scale,
-        guidance_interval=(args.guidance_low, args.guidance_high),
-        heun=args.heun,
-        generator=generator,
-        output_type="pil",
-    )
-
-    output_path = resolve_output_path(model_dir, args.output)
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    output.images[0].save(output_path)
-
-    print(f"Saved image to: {output_path}")
-    print(f"Device: {device} | dtype: {torch_dtype}")
-
-
-if __name__ == "__main__":
-    main()