Quickstart

Fit a toy flow-matching model and draw samples in a few lines.

import torch
import torch.nn as nn
from deltaflow.core import BaseVelocityField
from deltaflow.losses import FlowMatchingLoss
from deltaflow.samplers import FlowSampler


class MLPVelocityField(BaseVelocityField):
    def __init__(self, dim=2, hidden=64):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(dim + 1, hidden), nn.SiLU(), nn.Linear(hidden, dim)
        )

    def forward(self, x, t, **cond):
        return self.net(torch.cat([x, t.view(-1, 1)], dim=-1))


field = MLPVelocityField()
loss_fn = FlowMatchingLoss()
opt = torch.optim.Adam(field.parameters(), lr=1e-3)

data = torch.randn(2000, 2)
for _ in range(300):
    x1 = data[torch.randint(len(data), (128,))]
    loss = loss_fn(field, x1)          # (1)!
    opt.zero_grad()
    loss.backward()
    opt.step()

samples = FlowSampler(field).sample(torch.randn(1000, 2), n_steps=50)  # (2)!

1. FlowMatchingLoss samples a time t, builds x_t from the interpolant, and regresses field(x_t, t) onto the conditional target velocity.
2. FlowSampler integrates dx/dt = field(x, t) with Euler from t=0 (noise) to t=1 (data).

Time convention

Throughout DeltaFlow, t=0 is noise and t=1 is data.

Where to next

• Flow Matching, the objective in depth, plus interpolant choices.
• Delta Alignment, the guidance-representation pretraining loss.
• Examples, a runnable, tiered curriculum in the repo.