Delete run_jit_diffusers_inference.py

run_jit_diffusers_inference.py

@@ -1,131 +0,0 @@
-import argparse
-from pathlib import Path
-import sys
-
-import torch
-
-SCRIPT_DIR = Path(__file__).resolve().parent
-if str(SCRIPT_DIR) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_DIR))
-
-from jit_diffusers import JiTPipeline
-
-
-RECOMMENDED_CFG_BY_MODEL = {
-    "JiT-B/16": 3.0,
-    "JiT-L/16": 2.4,
-    "JiT-H/16": 2.2,
-    "JiT-B/32": 3.0,
-    "JiT-L/32": 2.5,
-    "JiT-H/32": 2.3,
-}
-
-RECOMMENDED_NOISE_BY_RESOLUTION = {
-    256: 1.0,
-    512: 2.0,
-}
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Run single-image JiT diffusers inference.")
-    parser.add_argument("--model_path", type=str, required=True, help="Path to converted diffusers model directory.")
-    parser.add_argument("--output_path", type=str, required=True, help="Path to save output PNG image.")
-    parser.add_argument("--class_label", type=int, default=207, help="ImageNet class id for conditional generation.")
-    parser.add_argument("--seed", type=int, default=42, help="Random seed.")
-    parser.add_argument("--steps", type=int, default=50, help="Number of ODE sampling steps.")
-    parser.add_argument(
-        "--cfg",
-        type=float,
-        default=None,
-        help="Classifier-free guidance scale. Defaults to paper recommendation for the loaded model.",
-    )
-    parser.add_argument("--interval_min", type=float, default=0.1, help="CFG interval min.")
-    parser.add_argument("--interval_max", type=float, default=1.0, help="CFG interval max.")
-    parser.add_argument(
-        "--noise_scale",
-        type=float,
-        default=None,
-        help="Initial Gaussian noise scale. Defaults to paper recommendation for the loaded resolution.",
-    )
-    parser.add_argument("--t_eps", type=float, default=5e-2, help="Small epsilon for timestep denominator.")
-    parser.add_argument(
-        "--device",
-        type=str,
-        default="auto",
-        choices=["auto", "cuda", "cpu"],
-        help="Inference device.",
-    )
-    parser.add_argument(
-        "--dtype",
-        type=str,
-        default="bf16",
-        choices=["bf16", "fp32"],
-        help="Inference dtype. Defaults to bf16 on CUDA.",
-    )
-    parser.add_argument(
-        "--solver",
-        type=str,
-        default="scheduler",
-        choices=["scheduler", "heun", "euler"],
-        help="Sampling solver. Use scheduler to keep pipeline default.",
-    )
-    return parser.parse_args()
-
-
-def resolve_device(name: str) -> torch.device:
-    if name == "auto":
-        return torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    return torch.device(name)
-
-
-def resolve_dtype(name: str, device: torch.device) -> torch.dtype:
-    if name == "bf16":
-        return torch.bfloat16 if device.type == "cuda" else torch.float32
-    return torch.float32
-
-
-def resolve_generation_defaults(pipe: JiTPipeline, cfg: float | None, noise_scale: float | None) -> tuple[float, float]:
-    model_type = str(getattr(pipe.transformer.config, "model_type", ""))
-    sample_size = int(getattr(pipe.transformer.config, "sample_size", 256))
-    resolved_cfg = cfg if cfg is not None else RECOMMENDED_CFG_BY_MODEL.get(model_type, 2.9)
-    resolved_noise_scale = noise_scale if noise_scale is not None else RECOMMENDED_NOISE_BY_RESOLUTION.get(sample_size, 1.0)
-    return resolved_cfg, resolved_noise_scale
-
-
-def main() -> None:
-    args = parse_args()
-    device = resolve_device(args.device)
-    dtype = resolve_dtype(args.dtype, device)
-    if device.type == "cuda":
-        torch.set_float32_matmul_precision("high")
-
-    pipe = JiTPipeline.from_pretrained(args.model_path).to(device)
-    pipe.transformer = pipe.transformer.to(device=device, dtype=dtype)
-    pipe.transformer.eval()
-    sampling_method = None if args.solver == "scheduler" else args.solver
-    cfg, noise_scale = resolve_generation_defaults(pipe, args.cfg, args.noise_scale)
-
-    generator = torch.Generator(device=device).manual_seed(args.seed)
-    output = pipe(
-        class_labels=[args.class_label],
-        num_inference_steps=args.steps,
-        guidance_scale=cfg,
-        guidance_interval_min=args.interval_min,
-        guidance_interval_max=args.interval_max,
-        noise_scale=noise_scale,
-        t_eps=args.t_eps,
-        sampling_method=sampling_method,
-        generator=generator,
-        output_type="pil",
-    )
-    image = output.images[0]
-
-    output_path = Path(args.output_path)
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    image.save(output_path)
-    print(f"Used sampling hyperparameters: cfg={cfg}, noise_scale={noise_scale}")
-    print(f"Saved image to: {output_path}")
-
-
-if __name__ == "__main__":
-    main()