Add to support Guess Mode for StableDiffusionControlnetPipleline

docs/source/en/api/pipelines/stable_diffusion/controlnet.mdx

@@ -242,6 +242,42 @@ image.save("./multi_controlnet_output.png")
 
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/multi_controlnet_output.png" width=600/>
 
+### Guess Mode
+
+Guess Mode is [a ControlNet feature that was implemented](https://github.com/lllyasviel/ControlNet#guess-mode--non-prompt-mode) after the publication of [the paper](https://arxiv.org/abs/2302.05543). The description states:
+
+>In this mode, the ControlNet encoder will try best to recognize the content of the input control map, like depth map, edge map, scribbles, etc, even if you remove all prompts.
+
+#### The core implementation:
+
+It adjusts the scale of the output residuals from ControlNet by a fixed ratio depending on the block depth. The shallowest DownBlock corresponds to `0.1`. As the blocks get deeper, the scale increases exponentially, and the scale for the output of the MidBlock becomes `1.0`. 
+
+Since the core implementation is just this, **it does not have any impact on prompt conditioning**. While it is common to use it without specifying any prompts, it is also possible to provide prompts if desired.
+
+#### Usage:
+
+Just specify `guess_mode=True` in the pipe() function. A `guidance_scale` between 3.0 and 5.0 is [recommended](https://github.com/lllyasviel/ControlNet#guess-mode--non-prompt-mode).
+```py
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+import torch
+
+controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
+pipe = StableDiffusionControlNetPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", controlnet=controlnet).to(
+    "cuda"
+)
+image = pipe("", image=canny_image, guess_mode=True, guidance_scale=3.0).images[0]
+image.save("guess_mode_generated.png")
+```
+
+#### Output image comparison:
+Canny Control Example
+
+|no guess_mode with prompt|guess_mode without prompt|
+|---|---|
+|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0.png"><img width="128" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0.png"/></a>|<a href="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0_gm.png"><img width="128" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0_gm.png"/></a>|
+
+
+
 ## Available checkpoints
 
 ControlNet requires a *control image* in addition to the text-to-image *prompt*. 

src/diffusers/models/controlnet.py

@@ -456,6 +456,7 @@ def forward(
         timestep_cond: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guess_mode: bool = False,
         return_dict: bool = True,
     ) -> Union[ControlNetOutput, Tuple]:
         # check channel order
@@ -556,8 +557,14 @@ def forward(
         mid_block_res_sample = self.controlnet_mid_block(sample)
 
         # 6. scaling
-        down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
-        mid_block_res_sample *= conditioning_scale
+        if guess_mode:
+            scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1)  # 0.1 to 1.0
+            scales *= conditioning_scale
+            down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
+            mid_block_res_sample *= scales[-1]  # last one
+        else:
+            down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
+            mid_block_res_sample *= conditioning_scale
 
         if not return_dict:
             return (down_block_res_samples, mid_block_res_sample)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py

@@ -118,6 +118,7 @@ def forward(
         timestep_cond: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guess_mode: bool = False,
         return_dict: bool = True,
     ) -> Union[ControlNetOutput, Tuple]:
         for i, (image, scale, controlnet) in enumerate(zip(controlnet_cond, conditioning_scale, self.nets)):
@@ -131,6 +132,7 @@ def forward(
                 timestep_cond,
                 attention_mask,
                 cross_attention_kwargs,
+                guess_mode,
                 return_dict,
             )
 
@@ -627,7 +629,16 @@ def check_image(self, image, prompt, prompt_embeds):
             )
 
     def prepare_image(
-        self, image, width, height, batch_size, num_images_per_prompt, device, dtype, do_classifier_free_guidance
+        self,
+        image,
+        width,
+        height,
+        batch_size,
+        num_images_per_prompt,
+        device,
+        dtype,
+        do_classifier_free_guidance,
+        guess_mode,
     ):
         if not isinstance(image, torch.Tensor):
             if isinstance(image, PIL.Image.Image):
@@ -664,7 +675,7 @@ def prepare_image(
 
         image = image.to(device=device, dtype=dtype)
 
-        if do_classifier_free_guidance:
+        if do_classifier_free_guidance and not guess_mode:
             image = torch.cat([image] * 2)
 
         return image
@@ -747,6 +758,7 @@ def __call__(
         callback_steps: int = 1,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
         controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        guess_mode: bool = False,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -819,6 +831,10 @@ def __call__(
                 The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
                 to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
                 corresponding scale as a list.
+            guess_mode (`bool`, *optional*, defaults to `False`):
+                In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
+                you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
+
         Examples:
 
         Returns:
@@ -883,6 +899,7 @@ def __call__(
                 device=device,
                 dtype=self.controlnet.dtype,
                 do_classifier_free_guidance=do_classifier_free_guidance,
+                guess_mode=guess_mode,
             )
         elif isinstance(self.controlnet, MultiControlNetModel):
             images = []
@@ -897,6 +914,7 @@ def __call__(
                     device=device,
                     dtype=self.controlnet.dtype,
                     do_classifier_free_guidance=do_classifier_free_guidance,
+                    guess_mode=guess_mode,
                 )
 
                 images.append(image_)
@@ -934,15 +952,31 @@ def __call__(
                 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
+                    controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
+                else:
+                    controlnet_latent_model_input = latent_model_input
+                    controlnet_prompt_embeds = prompt_embeds
+
                 down_block_res_samples, mid_block_res_sample = self.controlnet(
-                    latent_model_input,
+                    controlnet_latent_model_input,
                     t,
-                    encoder_hidden_states=prompt_embeds,
+                    encoder_hidden_states=controlnet_prompt_embeds,
                     controlnet_cond=image,
                     conditioning_scale=controlnet_conditioning_scale,
+                    guess_mode=guess_mode,
                     return_dict=False,
                 )
 
+                if guess_mode and do_classifier_free_guidance:
+                    # Infered ControlNet only for the conditional batch.
+                    # To apply the output of ControlNet to both the unconditional and conditional batches,
+                    # add 0 to the unconditional batch to keep it unchanged.
+                    down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
+                    mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
+
                 # predict the noise residual
                 noise_pred = self.unet(
                     latent_model_input,

tests/pipelines/stable_diffusion/test_stable_diffusion_controlnet.py

@@ -553,6 +553,38 @@ def test_sequential_cpu_offloading(self):
         # make sure that less than 7 GB is allocated
         assert mem_bytes < 4 * 10**9
 
+    def test_canny_guess_mode(self):
+        controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
+
+        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 = ""
+        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([0.2724, 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
+
 
 @slow
 @require_torch_gpu