Support ControlNet v1.1 shuffle properly

src/diffusers/models/controlnet.py

@@ -558,7 +558,7 @@ def forward(
         mid_block_res_sample = self.controlnet_mid_block(sample)
 
         # 6. scaling
-        if guess_mode:
+        if guess_mode and not self.config.global_pool_conditions:
             scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device)  # 0.1 to 1.0
 
             scales = scales * conditioning_scale

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py

@@ -918,6 +918,13 @@ def __call__(
         if isinstance(self.controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
             controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(self.controlnet.nets)
 
+        global_pool_conditions = (
+            self.controlnet.config.global_pool_conditions
+            if isinstance(self.controlnet, ControlNetModel)
+            else self.controlnet.nets[0].config.global_pool_conditions
+        )
+        guess_mode = guess_mode or global_pool_conditions
+
         # 3. Encode input prompt
         prompt_embeds = self._encode_prompt(
             prompt,

tests/pipelines/stable_diffusion/test_stable_diffusion_controlnet.py

@@ -622,6 +622,37 @@ def test_stable_diffusion_compile(self):
 
         assert np.abs(expected_image - image).max() < 1e-1
 
+    def test_v11_shuffle_global_pool_conditions(self):
+        controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11e_sd15_shuffle")
+
+        pipe = StableDiffusionControlNetPipeline.from_pretrained(
+            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+        )
+        pipe.enable_model_cpu_offload()
+        pipe.set_progress_bar_config(disable=None)
+
+        generator = torch.Generator(device="cpu").manual_seed(0)
+        prompt = "New York"
+        image = load_image(
+            "https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"
+        )
+
+        output = pipe(
+            prompt,
+            image,
+            generator=generator,
+            output_type="np",
+            num_inference_steps=3,
+            guidance_scale=7.0,
+        )
+
+        image = output.images[0]
+        assert image.shape == (512, 640, 3)
+
+        image_slice = image[-3:, -3:, -1]
+        expected_slice = np.array([0.1338, 0.1597, 0.1202, 0.1687, 0.1377, 0.1017, 0.2070, 0.1574, 0.1348])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+
 
 @slow
 @require_torch_gpu