diff --git a/bpd/pipelines/image_samples.py b/bpd/pipelines/image_samples.py
index f0750f0..0b6ad32 100644
--- a/bpd/pipelines/image_samples.py
+++ b/bpd/pipelines/image_samples.py
@@ -10,7 +10,34 @@
 from bpd.chains import run_inference_nuts
 from bpd.draw import draw_gaussian_galsim
 from bpd.noise import add_noise
-from bpd.prior import ellip_mag_prior, scalar_shear_transformation
+from bpd.prior import ellip_mag_prior, sample_ellip_prior, scalar_shear_transformation
+
+
+def sample_target_galaxy_params_simple(
+    rng_key: PRNGKeyArray,
+    *,
+    shape_noise: float,
+    g1: float = 0.02,
+    g2: float = 0.0,
+):
+    """Fix parameters except position and ellipticity, which come from a prior.
+
+    * The position is drawn uniformly within a pixel (dither).
+    * The ellipticity is drawn from Gary's prior given the shape noise.
+
+    """
+    dkey, ekey = random.split(rng_key, 2)
+
+    x, y = random.uniform(dkey, shape=(2,), minval=-0.5, maxval=0.5)
+    e = sample_ellip_prior(ekey, sigma=shape_noise, n=1)
+    return {
+        "e1": e[0, 0],
+        "e2": e[0, 1],
+        "x": x,
+        "y": y,
+        "g1": g1,
+        "g2": g2,
+    }
 
 
 # interim prior
@@ -21,23 +48,25 @@ def logprior(
     sigma_x: float = 0.5,  # pixels
     flux_bds: tuple = (-1.0, 9.0),
     hlr_bds: tuple = (0.01, 5.0),
+    all_free: bool = True,
 ) -> Array:
     prior = jnp.array(0.0)
 
-    f1, f2 = flux_bds
-    prior += stats.uniform.logpdf(params["lf"], f1, f2 - f1)
+    if all_free:
+        f1, f2 = flux_bds
+        prior += stats.uniform.logpdf(params["lf"], f1, f2 - f1)
 
-    h1, h2 = hlr_bds
-    prior += stats.uniform.logpdf(params["hlr"], h1, h2 - h1)
+        h1, h2 = hlr_bds
+        prior += stats.uniform.logpdf(params["hlr"], h1, h2 - h1)
+
+        # NOTE: hard-coded assumption that galaxy is in center-pixel within odd-size image.
+        # sigma_x in units of pixels.
+        prior += stats.norm.logpdf(params["x"], loc=0.0, scale=sigma_x)
+        prior += stats.norm.logpdf(params["y"], loc=0.0, scale=sigma_x)
 
     e_mag = jnp.sqrt(params["e1"] ** 2 + params["e2"] ** 2)
     prior += jnp.log(ellip_mag_prior(e_mag, sigma=sigma_e))
 
-    # NOTE: hard-coded assumption that galaxy is in center-pixel within odd-size image.
-    # sigma_x in units of pixels.
-    prior += stats.norm.logpdf(params["x"], loc=0.0, scale=sigma_x)
-    prior += stats.norm.logpdf(params["y"], loc=0.0, scale=sigma_x)
-
     return prior
 
 
@@ -47,12 +76,12 @@ def loglikelihood(
     *,
     draw_fnc: Callable,
     background: float,
-    free_f: bool = True,
+    free_flux: bool = True,
 ):
     # NOTE: draw_fnc should already contain `f` and `hlr` as constant arguments.
     _draw_params = {**{"g1": 0.0, "g2": 0.0}, **params}  # function is more general
 
-    if free_f:
+    if free_flux:
         _draw_params["f"] = 10 ** _draw_params.pop("lf")
     model = draw_fnc(**_draw_params)
 
diff --git a/bpd/pipelines/image_samples_fixed_flux.py b/bpd/pipelines/image_samples_fixed_flux.py
deleted file mode 100644
index 4b3bf9a..0000000
--- a/bpd/pipelines/image_samples_fixed_flux.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import jax.numpy as jnp
-from jax import Array, random
-from jax._src.prng import PRNGKeyArray
-from jax.scipy import stats
-
-from bpd.prior import ellip_mag_prior, sample_ellip_prior
-
-
-def get_target_galaxy_params_simple(
-    rng_key: PRNGKeyArray,
-    *,
-    shape_noise: float,
-    g1: float = 0.02,
-    g2: float = 0.0,
-):
-    """Fix parameters except position and ellipticity, which come from a prior.
-
-    * The position is drawn uniformly within a pixel (dither).
-    * The ellipticity is drawn from Gary's prior given the shape noise.
-
-    """
-    dkey, ekey = random.split(rng_key, 2)
-
-    x, y = random.uniform(dkey, shape=(2,), minval=-0.5, maxval=0.5)
-    e = sample_ellip_prior(ekey, sigma=shape_noise, n=1)
-    return {
-        "e1": e[0, 0],
-        "e2": e[0, 1],
-        "x": x,
-        "y": y,
-        "g1": g1,
-        "g2": g2,
-    }
-
-
-def logprior(
-    params: dict[str, Array], *, sigma_e: float, sigma_x: float = 0.5
-) -> Array:
-    prior = jnp.array(0.0)
-
-    e_mag = jnp.sqrt(params["e1"] ** 2 + params["e2"] ** 2)
-    prior += jnp.log(ellip_mag_prior(e_mag, sigma=sigma_e))
-
-    # NOTE: hard-coded assumption that galaxy is in center-pixel within odd-size image.
-    # sigma_x in units of pixels.
-    prior += stats.norm.logpdf(params["x"], loc=0.0, scale=sigma_x)
-    prior += stats.norm.logpdf(params["y"], loc=0.0, scale=sigma_x)
-
-    return prior