Qwen-Image-2.1 on gfx1151: cold vs warm, MIOpen kernel-db finding

gen.py

@@ -0,0 +1,78 @@
+#!/usr/bin/env python3
+"""Qwen/Qwen-Image-2.1 text-to-image on the halo box (gfx1151 / ROCm 7.13).
+
+Hard requirement: runs on GPU or not at all. Prints machine-readable receipts.
+"""
+import argparse, os, sys, time
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--model", default="/home/kingjones/models/qwen-image-2.1")
+    ap.add_argument("--out", required=True)
+    ap.add_argument("--prompt", required=True)
+    ap.add_argument("--negative", default=None)
+    ap.add_argument("--steps", type=int, default=40)
+    ap.add_argument("--width", type=int, default=2048)
+    ap.add_argument("--height", type=int, default=2048)
+    ap.add_argument("--seed", type=int, default=42)
+    ap.add_argument("--cfg", type=float, default=None, help="true_cfg_scale, if the pipeline takes it")
+    a = ap.parse_args()
+
+    import torch
+    print(f"python {sys.version.split()[0]}", flush=True)
+    print(f"torch {torch.__version__} hip {torch.version.hip}", flush=True)
+    if not torch.cuda.is_available():
+        print("FATAL: CUDA/HIP not available — refusing to run on CPU", flush=True)
+        sys.exit(2)
+    print(f"device {torch.cuda.get_device_name(0)}", flush=True)
+
+    import diffusers, transformers
+    print(f"diffusers {diffusers.__version__} transformers {transformers.__version__}", flush=True)
+    from diffusers import QwenImage21Pipeline
+
+    t0 = time.time()
+    pipe = QwenImage21Pipeline.from_pretrained(a.model, torch_dtype=torch.bfloat16)
+    pipe.to("cuda")
+    print(f"LOAD_TIME_S {time.time() - t0:.1f}", flush=True)
+    print(f"GPU_MEM_AFTER_LOAD_GB {torch.cuda.memory_allocated() / 2**30:.2f}", flush=True)
+
+    state = {"t_first": None}
+
+    def cb(_pipe, step_index, _timestep, cb_kwargs):
+        now = time.time()
+        if state["t_first"] is None:
+            state["t_first"] = now
+        print(f"step {step_index + 1}/{a.steps} @ {now - state['t_first']:.1f}s", flush=True)
+        return cb_kwargs
+
+    kwargs = dict(
+        prompt=a.prompt,
+        num_inference_steps=a.steps,
+        width=a.width,
+        height=a.height,
+        generator=torch.Generator("cuda").manual_seed(a.seed),
+        callback_on_step_end=cb,
+    )
+    if a.negative is not None:
+        kwargs["negative_prompt"] = a.negative
+    if a.cfg is not None:
+        kwargs["true_cfg_scale"] = a.cfg
+
+    t1 = time.time()
+    img = pipe(**kwargs).images[0]
+    t_gen = time.time() - t1
+
+    os.makedirs(os.path.dirname(a.out), exist_ok=True)
+    img.save(a.out)
+    print(f"GEN_TIME_S {t_gen:.1f}", flush=True)
+    print(f"SAVED {a.out}", flush=True)
+    print(f"BYTES {os.path.getsize(a.out)}", flush=True)
+    print(f"DIMS {img.size[0]}x{img.size[1]}", flush=True)
+    print(f"PEAK_GPU_ALLOC_GB {torch.cuda.max_memory_allocated() / 2**30:.2f}", flush=True)
+    print(f"PEAK_GPU_RESERVED_GB {torch.cuda.max_memory_reserved() / 2**30:.2f}", flush=True)
+    print("GEN-DONE", flush=True)
+
+
+if __name__ == "__main__":
+    main()