Update all files for BitDance-ImageNet-diffusers

BitDance_L_1x/transformer/modeling_transformer.py

@@ -0,0 +1,124 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import torch
+from safetensors.torch import load_file as load_safetensors
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.modeling_utils import ModelMixin
+
+# NOTE: Diffusers dynamic module loader only copies directly-referenced relative imports.
+# These guarded imports are intentionally never executed, but they force dependent files
+# (and their siblings) to be copied into the dynamic module cache.
+if False:  # pragma: no cover
+    from .model import BitDance_B as _BD_B_STD
+    from .model import BitDance_H as _BD_H_STD
+    from .model import BitDance_L as _BD_L_STD
+    from .model_parallel import BitDance_B as _BD_B_PAR
+    from .model_parallel import BitDance_H as _BD_H_PAR
+    from .model_parallel import BitDance_L as _BD_L_PAR
+    from .diff_head import DiffHead as _DiffHead
+    from .diff_head_parallel import DiffHead as _DiffHeadParallel
+    from .layers import TransformerBlock as _TB
+    from .layers_parallel import TransformerBlock as _TBP
+    from .qae import VQModel as _VQ
+    from .gfq import GFQ as _GFQ
+    from .sampling import euler_maruyama as _EM
+    from .sampling_parallel import euler_maruyama as _EMP
+    from .utils import patchify_raster as _PR
+
+
+class BitDanceImageNetTransformer(ModelMixin, ConfigMixin):
+    @register_to_config
+    def __init__(
+        self,
+        architecture: str,
+        parallel_num: int,
+        resolution: int,
+        down_size: int,
+        latent_dim: int,
+        num_classes: int,
+        runtime_impl: str,
+        parallel_mode: str = "patch",
+        time_schedule: str = "logit_normal",
+        time_shift: float = 1.0,
+        p_std: float = 1.0,
+        p_mean: float = 0.0,
+    ):
+        super().__init__()
+
+        kwargs = dict(
+            resolution=resolution,
+            down_size=down_size,
+            patch_size=1,
+            latent_dim=latent_dim,
+            diff_batch_mul=4,
+            cls_token_num=64,
+            num_classes=num_classes,
+            grad_checkpointing=False,
+            trained_vae="",
+            drop_rate=0.0,
+            perturb_schedule="constant",
+            perturb_rate=0.0,
+            perturb_rate_max=0.3,
+            time_schedule=time_schedule,
+            time_shift=time_shift,
+            P_std=p_std,
+            P_mean=p_mean,
+        )
+
+        if runtime_impl == "model_parallel.py" or parallel_num > 1:
+            from .model_parallel import BitDance_B, BitDance_H, BitDance_L
+
+            ctors = {"BitDance-B": BitDance_B, "BitDance-L": BitDance_L, "BitDance-H": BitDance_H}
+            kwargs.update(parallel_num=parallel_num, parallel_mode=parallel_mode)
+        else:
+            from .model import BitDance_B, BitDance_H, BitDance_L
+
+            ctors = {"BitDance-B": BitDance_B, "BitDance-L": BitDance_L, "BitDance-H": BitDance_H}
+
+        self.runtime_model = ctors[architecture](**kwargs)
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: str, *args, **kwargs):
+        del args, kwargs
+        model_dir = Path(pretrained_model_name_or_path)
+        config = json.loads((model_dir / "config.json").read_text(encoding="utf-8"))
+        model = cls(
+            architecture=config["architecture"],
+            parallel_num=int(config["parallel_num"]),
+            resolution=int(config["resolution"]),
+            down_size=int(config["down_size"]),
+            latent_dim=int(config["latent_dim"]),
+            num_classes=int(config["num_classes"]),
+            runtime_impl=config["runtime_impl"],
+            parallel_mode=config.get("parallel_mode", "patch"),
+            time_schedule=config.get("time_schedule", "logit_normal"),
+            time_shift=float(config.get("time_shift", 1.0)),
+            p_std=float(config.get("p_std", 1.0)),
+            p_mean=float(config.get("p_mean", 0.0)),
+        )
+        state = load_safetensors(model_dir / "diffusion_pytorch_model.safetensors")
+        model.runtime_model.load_state_dict(state, strict=True)
+        model.eval()
+        return model
+
+    @torch.no_grad()
+    def sample(
+        self,
+        class_ids: torch.Tensor,
+        sample_steps: int = 100,
+        cfg_scale: float = 4.6,
+        chunk_size: int = 0,
+    ) -> torch.Tensor:
+        return self.runtime_model.sample(
+            cond=class_ids,
+            sample_steps=sample_steps,
+            cfg_scale=cfg_scale,
+            chunk_size=chunk_size,
+        )
+
+    def forward(self, *args, **kwargs):
+        return self.runtime_model(*args, **kwargs)