Fix controlnet guess mode euler (#3571)

* Fix guess mode controlnet for euler-like schedulers

* make style

* Co-authored-by: Chanchana Sornsoontorn <off.chanchana@gmail.com>

* Add co author Co-authored-by: Chanchana Sornsoontorn <off.chanchana@gmail.com>

* 2nd try
Co-authored-by: Chanchana Sornsoontorn <off.chanchana@gmail.com>

src/diffusers/pipelines/controlnet/pipeline_controlnet.py

@@ -956,14 +956,15 @@ class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoade
                 # controlnet(s) inference
                 if guess_mode and do_classifier_free_guidance:
                     # Infer ControlNet only for the conditional batch.
-                    controlnet_latent_model_input = latents
+                    control_model_input = latents
+                    control_model_input = self.scheduler.scale_model_input(control_model_input, t)
                     controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
                 else:
-                    controlnet_latent_model_input = latent_model_input
+                    control_model_input = latent_model_input
                     controlnet_prompt_embeds = prompt_embeds
 
                 down_block_res_samples, mid_block_res_sample = self.controlnet(
-                    controlnet_latent_model_input,
+                    control_model_input,
                     t,
                     encoder_hidden_states=controlnet_prompt_embeds,
                     controlnet_cond=image,

src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py

@@ -1034,14 +1034,15 @@ class StableDiffusionControlNetImg2ImgPipeline(DiffusionPipeline, TextualInversi
                 # controlnet(s) inference
                 if guess_mode and do_classifier_free_guidance:
                     # Infer ControlNet only for the conditional batch.
-                    controlnet_latent_model_input = latents
+                    control_model_input = latents
+                    control_model_input = self.scheduler.scale_model_input(control_model_input, t)
                     controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
                 else:
-                    controlnet_latent_model_input = latent_model_input
+                    control_model_input = latent_model_input
                     controlnet_prompt_embeds = prompt_embeds
 
                 down_block_res_samples, mid_block_res_sample = self.controlnet(
-                    controlnet_latent_model_input,
+                    control_model_input,
                     t,
                     encoder_hidden_states=controlnet_prompt_embeds,
                     controlnet_cond=control_image,

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py

@@ -1248,16 +1248,18 @@ class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversi
                 latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
 
+                # controlnet(s) inference
                 if guess_mode and do_classifier_free_guidance:
                     # Infer ControlNet only for the conditional batch.
-                    controlnet_latent_model_input = latents
+                    control_model_input = latents
+                    control_model_input = self.scheduler.scale_model_input(control_model_input, t)
                     controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
                 else:
-                    controlnet_latent_model_input = latent_model_input
+                    control_model_input = latent_model_input
                     controlnet_prompt_embeds = prompt_embeds
 
                 down_block_res_samples, mid_block_res_sample = self.controlnet(
-                    controlnet_latent_model_input,
+                    control_model_input,
                     t,
                     encoder_hidden_states=controlnet_prompt_embeds,
                     controlnet_cond=control_image,

tests/pipelines/controlnet/test_controlnet.py

@@ -26,6 +26,7 @@ from diffusers import (
     AutoencoderKL,
     ControlNetModel,
     DDIMScheduler,
+    EulerDiscreteScheduler,
     StableDiffusionControlNetPipeline,
     UNet2DConditionModel,
 )
@@ -644,6 +645,39 @@ class ControlNetPipelineSlowTests(unittest.TestCase):
         expected_slice = np.array([0.2724, 0.2846, 0.2724, 0.3843, 0.3682, 0.2736, 0.4675, 0.3862, 0.2887])
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
+    def test_canny_guess_mode_euler(self):
+        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
+
+        pipe = StableDiffusionControlNetPipeline.from_pretrained(
+            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+        )
+        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+        pipe.enable_model_cpu_offload()
+        pipe.set_progress_bar_config(disable=None)
+
+        generator = torch.Generator(device="cpu").manual_seed(0)
+        prompt = ""
+        image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
+        )
+
+        output = pipe(
+            prompt,
+            image,
+            generator=generator,
+            output_type="np",
+            num_inference_steps=3,
+            guidance_scale=3.0,
+            guess_mode=True,
+        )
+
+        image = output.images[0]
+        assert image.shape == (768, 512, 3)
+
+        image_slice = image[-3:, -3:, -1]
+        expected_slice = np.array([0.1655, 0.1721, 0.1623, 0.1685, 0.1711, 0.1646, 0.1651, 0.1631, 0.1494])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
     @require_torch_2
     def test_stable_diffusion_compile(self):
         run_test_in_subprocess(test_case=self, target_func=_test_stable_diffusion_compile, inputs=None)