diff --git a/webui.py b/webui.py
index 03675032..4ff9d005 100644
--- a/webui.py
+++ b/webui.py
@@ -64,7 +64,7 @@ css_hide_progressbar = """
 
 SamplerData = namedtuple('SamplerData', ['name', 'constructor'])
 samplers = [
-    *[SamplerData(x[0], lambda model: KDiffusionSampler(model, x[1])) for x in [
+    *[SamplerData(x[0], lambda m, funcname=x[1]: KDiffusionSampler(m, funcname)) for x in [
         ('LMS', 'sample_lms'),
         ('Heun', 'sample_heun'),
         ('Euler', 'sample_euler'),
@@ -72,9 +72,10 @@ samplers = [
         ('DPM 2', 'sample_dpm_2'),
         ('DPM 2 Ancestral', 'sample_dpm_2_ancestral'),
     ] if hasattr(k_diffusion.sampling, x[1])],
-    SamplerData('DDIM', lambda model: DDIMSampler(model)),
-    SamplerData('PLMS', lambda model: PLMSSampler(model)),
+    SamplerData('DDIM', lambda m: DDIMSampler(model)),
+    SamplerData('PLMS', lambda m: PLMSSampler(model)),
 ]
+samplers_for_img2img = [x for x in samplers if x.name != 'DDIM' and x.name != 'PLMS']
 
 RealesrganModelInfo = namedtuple("RealesrganModelInfo", ["name", "location", "model", "netscale"])
 
@@ -197,14 +198,14 @@ class KDiffusionSampler:
         self.model = m
         self.model_wrap = k_diffusion.external.CompVisDenoiser(m)
         self.funcname = funcname
+        self.func = getattr(k_diffusion.sampling, self.funcname)
 
     def sample(self, S, conditioning, batch_size, shape, verbose, unconditional_guidance_scale, unconditional_conditioning, eta, x_T):
         sigmas = self.model_wrap.get_sigmas(S)
         x = x_T * sigmas[0]
         model_wrap_cfg = CFGDenoiser(self.model_wrap)
 
-        fun = getattr(k_diffusion.sampling, self.funcname)
-        samples_ddim = fun(model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': unconditional_guidance_scale}, disable=False)
+        samples_ddim = self.func(model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': unconditional_guidance_scale}, disable=False)
 
         return samples_ddim, None
 
@@ -810,10 +811,10 @@ txt2img_interface = gr.Interface(
 )
 
 
-def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_matrix, loopback: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int):
+def img2img(prompt: str, init_img, ddim_steps: int, sampler_index: int, use_GFPGAN: bool, prompt_matrix, loopback: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int):
     outpath = opts.outdir or "outputs/img2img-samples"
 
-    sampler = KDiffusionSampler(model, 'sample_lms')
+    sampler = samplers_for_img2img[sampler_index].constructor(model)
 
     assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
 
@@ -842,7 +843,7 @@ def img2img(prompt: str, init_img, ddim_steps: int, use_GFPGAN: bool, prompt_mat
         xi = x0 + noise
         sigma_sched = sigmas[ddim_steps - t_enc - 1:]
         model_wrap_cfg = CFGDenoiser(sampler.model_wrap)
-        samples_ddim = k_diffusion.sampling.sample_lms(model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': cfg_scale}, disable=False)
+        samples_ddim = sampler.func(model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': cfg_scale}, disable=False)
         return samples_ddim
 
     if loopback:
@@ -919,6 +920,7 @@ img2img_interface = gr.Interface(
         gr.Textbox(placeholder="A fantasy landscape, trending on artstation.", lines=1),
         gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil"),
         gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=50),
+        gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index"),
         gr.Checkbox(label='Fix faces using GFPGAN', value=False, visible=GFPGAN is not None),
         gr.Checkbox(label='Create prompt matrix (separate multiple prompts using |, and get all combinations of them)', value=False),
         gr.Checkbox(label='Loopback (use images from previous batch when creating next batch)', value=False),