Delete pixeldit/scheduling_flow.py with huggingface_hub

pixeldit/scheduling_flow.py

@@ -1,65 +0,0 @@
-"""
-Flow-matching DPM-Solver++ sampler for PixelDiT.
-
-Wraps the original DPMS from the PixelDiT repo.
-Order=2 multistep gets quality at 20 steps that Euler needs 100+ for.
-
-Usage:
-    from scheduling_flow import FlowScheduler
-
-    scheduler = FlowScheduler(model_fn, cfg=3.5, flow_shift=4.0)
-    image = scheduler.sample(noise, cond, uncond, steps=20)
-"""
-
-import sys
-import torch
-from tqdm import tqdm
-
-sys.path.insert(0, "/home/nobus/Raid0/PixelDiT/t2i")
-from diffusion.model.flow_dpm import DPMS
-
-_FLOW_SHIFT = 4.0   # 1024px stage-3 config
-
-
-class FlowScheduler:
-    def __init__(self, model_fn, cfg=3.5, flow_shift=_FLOW_SHIFT):
-        """
-        model_fn: callable(x, t, y) -> velocity  [B,3,H,W]
-        cfg:      classifier-free guidance scale
-        """
-        # DPMS passes y as [B,1,L,D] but PixDiT_T2I expects [B,L,D] — squeeze here
-        self.model_fn   = lambda x, t, y: model_fn(x, t, y.squeeze(1) if y.dim() == 4 else y)
-        self.cfg        = cfg
-        self.flow_shift = flow_shift
-
-    @torch.no_grad()
-    def sample(
-        self,
-        noise: torch.Tensor,       # [B, 3, H, W] Gaussian noise
-        cond:  torch.Tensor,       # [B, 300, 2304]
-        uncond: torch.Tensor,      # [B, 300, 2304]
-        steps: int = 20,
-    ) -> torch.Tensor:
-        """Returns denoised image tensor [B, 3, H, W] in [-1, 1]."""
-        # DPMS expects [B, 1, L, D]
-        cond_4d   = cond.unsqueeze(1)
-        uncond_4d = uncond.unsqueeze(1)
-
-        dpm = DPMS(
-            self.model_fn,
-            condition=cond_4d,
-            uncondition=uncond_4d,
-            cfg_scale=self.cfg,
-            model_type="flow",
-            schedule="FLOW",
-            guidance_type="classifier-free",
-            interval_guidance=[0, 1],
-        )
-        return dpm.sample(
-            noise,
-            steps=steps,
-            order=2,
-            skip_type="time_uniform_flow",
-            method="multistep",
-            flow_shift=self.flow_shift,
-        )