#3487 Fix inpainting strength for various samplers

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py

@@ -863,12 +863,13 @@ def prepare_latents(
 
         if latents is None:
             noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            # if strength is 1. then initialise the latents to noise, else initial to image + noise
             latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
+            # if pure noise then scale the initial latents by the  Scheduler's init sigma
+            latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
         else:
             latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
+            latents = latents * self.scheduler.init_noise_sigma
 
         outputs = (latents,)
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -648,12 +648,13 @@ def prepare_latents(
 
         if latents is None:
             noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            # if strength is 1. then initialise the latents to noise, else initial to image + noise
             latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
+            # if pure noise then scale the initial latents by the  Scheduler's init sigma
+            latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
         else:
             latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
+            latents = latents * self.scheduler.init_noise_sigma
 
         outputs = (latents,)
 
@@ -912,6 +913,12 @@ def __call__(
         timesteps, num_inference_steps = self.get_timesteps(
             num_inference_steps=num_inference_steps, strength=strength, device=device
         )
+        # check that number of inference steps is not < 1 - as this doesn't make sense
+        if num_inference_steps < 1:
+            raise ValueError(
+                f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline"
+                f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
+            )
         # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
         # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -231,6 +231,20 @@ def test_stable_diffusion_inpaint_lora(self):
     def test_inference_batch_single_identical(self):
         super().test_inference_batch_single_identical(expected_max_diff=3e-3)
 
+    def test_stable_diffusion_inpaint_strength_zero_test(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        components = self.get_dummy_components()
+        sd_pipe = StableDiffusionInpaintPipeline(**components)
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+
+        # check that the pipeline raises value error when num_inference_steps is < 1
+        inputs["strength"] = 0.01
+        with self.assertRaises(ValueError):
+            sd_pipe(**inputs).images
+
 
 class StableDiffusionSimpleInpaintPipelineFastTests(StableDiffusionInpaintPipelineFastTests):
     pipeline_class = StableDiffusionInpaintPipeline