#!/usr/bin/env python3 """ BC-250: Test CLIP with Gemma2 key fix — verify correct model detection. """ import os, sys, time, signal os.environ["HSA_OVERRIDE_GFX_VERSION"] = "10.1.0" os.environ["HSA_ENABLE_SDMA"] = "0" os.environ["HIP_VISIBLE_DEVICES"] = "0" os.environ["BC250_SOFTMAX_THRESHOLD"] = "512" sys.path.insert(0, "/home/fabian/ComfyUI") def timeout_handler(sig, frame): print("\n[T] === TIMEOUT HIT ===", flush=True) os._exit(1) signal.signal(signal.SIGALRM, timeout_handler) print("[T] Importing...", flush=True) import bc250_softmax_patch import torch import comfy.sd import comfy.model_management # Force CLIP to CPU to avoid GPU kernel compilation delays comfy.model_management.text_encoder_device = lambda: torch.device("cpu") comfy.model_management.text_encoder_offload_device = lambda: torch.device("cpu") clip_path = "/home/fabian/ComfyUI/models/text_encoders/gemma2_2b_lumina2.safetensors" # Load CLIP print(f"[T] Loading CLIP...", flush=True) t0 = time.time() clip = comfy.sd.load_clip( ckpt_paths=[clip_path], embedding_directory=None, clip_type=comfy.sd.CLIPType.LUMINA2, ) dt = time.time() - t0 print(f"[T] CLIP loaded in {dt:.1f}s", flush=True) print(f"[T] CLIP type: {type(clip)}", flush=True) print(f"[T] cond_stage_model type: {type(clip.cond_stage_model)}", flush=True) # Check if it's Gemma2 now csm = clip.cond_stage_model print(f"[T] Has gemma2_2b attr: {hasattr(csm, 'gemma2_2b')}", flush=True) # List attributes attrs = [a for a in dir(csm) if not a.startswith('_') and not callable(getattr(csm, a, None))] print(f"[T] CSM attrs (non-callable): {attrs[:15]}", flush=True) # Tokenize text = "a photo of a cat sitting on a windowsill" print(f"\n[T] Tokenizing: '{text}'", flush=True) t1 = time.time() tokens = clip.tokenize(text) dt = time.time() - t1 print(f"[T] Tokenized in {dt:.3f}s", flush=True) print(f"[T] Token keys: {list(tokens.keys()) if isinstance(tokens, dict) else type(tokens)}", flush=True) for k, v in tokens.items(): if isinstance(v, list): for j, item in enumerate(v[:2]): if isinstance(item, list): print(f"[T] {k}[{j}]: list len={len(item)}", flush=True) elif hasattr(item, 'shape'): print(f"[T] {k}[{j}]: shape={item.shape}", flush=True) else: print(f"[T] {k}[{j}]: {type(item)}", flush=True) elif hasattr(v, 'shape'): print(f"[T] {k}: shape={v.shape}", flush=True) else: print(f"[T] {k}: {type(v)}", flush=True) # Encode with 180s timeout (Gemma-2 2B on CPU = slow!) print(f"\n[T] Encoding (180s timeout)...", flush=True) signal.alarm(180) t2 = time.time() try: output = clip.encode_from_tokens_scheduled(tokens) dt = time.time() - t2 signal.alarm(0) print(f"[T] Encoded in {dt:.1f}s", flush=True) if isinstance(output, (list, tuple)): for i, item in enumerate(output): if isinstance(item, (list, tuple)): print(f"[T] [{i}]: list/tuple len={len(item)}", flush=True) if len(item) > 0 and isinstance(item[0], dict): for k, v in item[0].items(): if hasattr(v, 'shape'): print(f"[T] [{i}][0]['{k}']: shape={v.shape} dtype={v.dtype}", flush=True) else: print(f"[T] [{i}][0]['{k}']: {type(v)} = {v}", flush=True) elif len(item) > 0 and hasattr(item[0], 'shape'): print(f"[T] [{i}][0]: shape={item[0].shape} dtype={item[0].dtype}", flush=True) elif hasattr(item, 'shape'): print(f"[T] [{i}]: shape={item.shape} dtype={item.dtype}", flush=True) else: print(f"[T] [{i}]: {type(item)}", flush=True) elif isinstance(output, dict): for k, v in output.items(): if hasattr(v, 'shape'): print(f"[T] {k}: shape={v.shape} dtype={v.dtype}", flush=True) else: print(f"[T] {k}: {type(v)}", flush=True) print(f"\n[T] === CLIP ENCODE SUCCESS ===", flush=True) except Exception as e: signal.alarm(0) print(f"[T] ERROR: {e}", flush=True) import traceback traceback.print_exc() print(f"[T] Total: {time.time()-t0:.1f}s", flush=True) os._exit(0)