Delete pixeldit/text_encoder_qwen.py with huggingface_hub

pixeldit/text_encoder_qwen.py

@@ -1,72 +0,0 @@
-"""
-Qwen3-2B text encoder for PixelDiT.
-Requires a trained projection (train_qwen_proj.py) to map 2048→2304.
-
-Usage:
-    from pixeldit.text_encoder_qwen import QwenEncoder
-    enc  = QwenEncoder(proj_path="pixeldit/qwen_proj.pt")
-    cond = enc.encode(["a dragon at sunset"])  # [1, 300, 2304]
-    null = enc.encode_null(1)                  # [1, 300, 2304]
-"""
-
-import torch
-import torch.nn as nn
-from transformers import AutoTokenizer, AutoModel
-
-_QWEN_ID   = "Qwen/Qwen3-2B"
-_QWEN_DIM  = 2048
-_GEMMA_DIM = 2304
-_TXT_MAX   = 300
-
-
-class QwenEncoder:
-    def __init__(
-        self,
-        model_id=_QWEN_ID,
-        proj_path=None,           # path to trained qwen_proj.pt
-        output_device="cuda",
-        output_dtype=torch.bfloat16,
-    ):
-        self.output_device = torch.device(output_device)
-        self.output_dtype  = output_dtype
-
-        print(f"[QwenEncoder] loading {model_id} (CPU)")
-        self.tokenizer = AutoTokenizer.from_pretrained(model_id)
-        self.tokenizer.padding_side = "right"
-        self._model = AutoModel.from_pretrained(model_id, torch_dtype=torch.float32).eval()
-
-        self.proj = nn.Linear(_QWEN_DIM, _GEMMA_DIM, bias=False)
-        if proj_path:
-            sd = torch.load(proj_path, map_location="cpu", weights_only=True)
-            self.proj.load_state_dict(sd)
-            print(f"[QwenEncoder] loaded projection: {proj_path}")
-        else:
-            with torch.no_grad():
-                w = torch.zeros(_GEMMA_DIM, _QWEN_DIM)
-                w[:_QWEN_DIM] = torch.eye(_QWEN_DIM)
-                self.proj.weight.copy_(w)
-            print("[QwenEncoder] projection: identity init — run train_qwen_proj.py for real quality")
-        self.proj = self.proj.to(self.output_device).to(output_dtype)
-        print("[QwenEncoder] ready")
-
-    @torch.no_grad()
-    def encode(self, texts: list[str]) -> torch.Tensor:
-        """Returns [B, 300, 2304]."""
-        tok = self.tokenizer(
-            texts, max_length=_TXT_MAX,
-            padding="max_length", truncation=True, return_tensors="pt",
-        )
-        emb = self._model(**tok).last_hidden_state
-        emb = emb.to(self.output_device).to(self.output_dtype)
-        return self.proj(emb)
-
-    @torch.no_grad()
-    def encode_null(self, batch_size: int) -> torch.Tensor:
-        """Returns [B, 300, 2304] for empty string (CFG unconditional)."""
-        tok = self.tokenizer(
-            [""] * batch_size, max_length=_TXT_MAX,
-            padding="max_length", truncation=True, return_tensors="pt",
-        )
-        emb = self._model(**tok).last_hidden_state
-        emb = emb.to(self.output_device).to(self.output_dtype)
-        return self.proj(emb)