Hi JX,
Do you have an approximation of absorption / transmission % for your samples? Even with relatively low absorption (80-90% transmission) I've found a background can often be scaled well to data at low angles, but as angle increases the background is not representative. As John and Matthew said, this will necessarily depend on your sample and the wavelength.
An approach I've found useful is fitting the background data with a peaks phase (assuming a structural model is not available), then fixing the peak positions. Depending how absorbing your samples are, you may be able to fix the individual intensities and breadths then refine a common scale factor for the intensities. If the intensities are not modeled well at higher angles, you can refine the individual peak intensities while noting the relative intensity changes and fit quality, or even treat the background pattern in multiple sections (e.g., low, mid, high angles) with separate scale factors. Those last 2 comments come with a warning that you can easily start fitting sample contributions as background, and this will be especially problematic if you have broad peaks and / or amorphous content.
I'm unfamiliar with the beamline you collected on, but am assuming transmission geometry and an area detector for the comments below:
If you plan to subtract backgrounds rather than modeling them, I recommend collecting with longer exposure times on your background and averaging many background patterns together. You'll limit the noise propagated to your background-subtracted data.
If the detector was not a photon counting detector, you may see some differences between the perceived background in your sample data and background in your empty capillary data due to differences in the dark current or accumulated charge on the detector. If you did collect data using a photon counting detector and are seeing large differences in the baseline, you may also want to rule out fluorescence depending on your incident beam energy and the elements in your sample.
Hope that's helpful - best of luck with your data!