Delete pixeldit/modeling_pixeldit_hf.py with huggingface_hub

pixeldit/modeling_pixeldit_hf.py

@@ -1,75 +0,0 @@
-"""
-HF-compatible PixelDiT wrapper.
-
-Allows save_pretrained / from_pretrained and peft LoRA targeting.
-
-Usage:
-    # Convert from original .pth
-    model = PixelDiTModel.from_pth("pixeldit_t2i_v1.pth")
-    model.save_pretrained("pixeldit-diffusers/")
-
-    # Load back
-    model = PixelDiTModel.from_pretrained("pixeldit-diffusers/")
-
-    # LoRA
-    from peft import get_peft_model, LoraConfig
-    lora_cfg = LoraConfig(target_modules=["qkv_x", "qkv_y", "proj_x", "proj_y"])
-    model = get_peft_model(model, lora_cfg)
-"""
-
-import sys
-import torch
-from transformers import PreTrainedModel
-
-sys.path.insert(0, "/home/nobus/Raid0/PixelDiT")
-from pixdit_core.pixeldit_t2i import PixDiT_T2I
-
-from .configuration_pixeldit import PixelDiTConfig
-
-
-class PixelDiTModel(PreTrainedModel):
-    config_class = PixelDiTConfig
-    _tied_weights_keys = []
-
-    @property
-    def all_tied_weights_keys(self):
-        return {}
-
-    def __init__(self, config: PixelDiTConfig):
-        super().__init__(config)
-        self.model = PixDiT_T2I(
-            in_channels            = config.in_channels,
-            num_groups             = config.num_groups,
-            hidden_size            = config.hidden_size,
-            pixel_hidden_size      = config.pixel_hidden_size,
-            pixel_attn_hidden_size = config.pixel_attn_hidden_size,
-            pixel_num_groups       = config.pixel_num_groups,
-            patch_depth            = config.patch_depth,
-            pixel_depth            = config.pixel_depth,
-            num_text_blocks        = config.num_text_blocks,
-            patch_size             = config.patch_size,
-            txt_embed_dim          = config.txt_embed_dim,
-            txt_max_length         = config.txt_max_length,
-            use_text_rope          = config.use_text_rope,
-            text_rope_theta        = config.text_rope_theta,
-            repa_encoder_index     = config.repa_encoder_index,
-            use_pixel_abs_pos      = config.use_pixel_abs_pos,
-        )
-
-    def forward(self, x, t, y, s=None, mask=None):
-        return self.model(x, t, y, s=s, mask=mask)
-
-    @classmethod
-    def from_pth(cls, pth_path: str, config: PixelDiTConfig = None):
-        """Load from original nvidia .pth checkpoint, handles core. prefix."""
-        if config is None:
-            config = PixelDiTConfig()
-        model = cls(config)
-        state = torch.load(pth_path, map_location="cpu", weights_only=False)
-        sd = state.get("state_dict", state)
-        # strip trainer wrapper prefix, then add HF model. prefix
-        sd = {(k[5:] if k.startswith("core.") else k): v for k, v in sd.items()}
-        sd = {"model." + k: v for k, v in sd.items()}
-        missing, unexpected = model.load_state_dict(sd, strict=False)
-        print(f"[PixelDiTModel.from_pth] loaded — {len(missing)} missing, {len(unexpected)} unexpected")
-        return model