Spaces:
Running on Zero
Running on Zero
| """Decide how the encoder is loaded and released. | |
| The Qwen3 text encoder is ~8 GB in bfloat16 - the single biggest allocation of a | |
| CPU render, and it is only needed once, before the denoising loop. If | |
| `Flux2KleinPipeline.encode_prompt` can produce the embeds the loop consumes, the | |
| encoder can be unloaded before the transformer runs and the peak resident set is | |
| max(8 GB, 3 GB) instead of the sum. | |
| Run: python scripts/_probe_encode.py | |
| """ | |
| import inspect | |
| import re | |
| from diffusers import Flux2KleinPipeline | |
| cls = Flux2KleinPipeline | |
| print("has encode_prompt:", hasattr(cls, "encode_prompt")) | |
| if hasattr(cls, "encode_prompt"): | |
| print("signature:", inspect.signature(cls.encode_prompt)) | |
| src = inspect.getsource(cls.__call__) | |
| for marker in ("prompt_embeds", "self.text_encoder", "self.tokenizer", "negative_prompt_embeds"): | |
| hits = len(re.findall(re.escape(marker), src)) | |
| print(" __call__ references %-22s %d time(s)" % (marker, hits)) | |
| lines = inspect.getsource(cls).splitlines() | |
| for i, line in enumerate(lines): | |
| if "def encode_prompt" in line: | |
| print("encode_prompt source starts at relative line", i) | |
| for offset in range(i, min(i + 40, len(lines))): | |
| print(" " + lines[offset]) | |
| break | |