flux controlnet fix (control_modes batch & others) (#9507)

* flux controlnet mode to take into account batch size

* incorporate yiyixuxu's suggestions (cleaner logic) as well as clean up control mode handling for multi case

* fix

* fix use_guidance when controlnet is a multi and does not have config

---------
Co-authored-by: Christopher Beckham <christopher.j.beckham@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

src/diffusers/models/controlnet_flux.py

@@ -502,11 +502,12 @@ class FluxMultiControlNetModel(ModelMixin):
                     control_block_samples = block_samples
                     control_single_block_samples = single_block_samples
                 else:
+                    if block_samples is not None and control_block_samples is not None:
                         control_block_samples = [
                             control_block_sample + block_sample
                             for control_block_sample, block_sample in zip(control_block_samples, block_samples)
                         ]
-
+                    if single_block_samples is not None and control_single_block_samples is not None:
                         control_single_block_samples = [
                             control_single_block_sample + block_sample
                             for control_single_block_sample, block_sample in zip(

src/diffusers/pipelines/flux/pipeline_flux_controlnet.py

@@ -747,10 +747,12 @@ class FluxControlNetPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleF
                 width_control_image,
             )
 
-            # set control mode
+            # Here we ensure that `control_mode` has the same length as the control_image.
             if control_mode is not None:
+                if not isinstance(control_mode, int):
+                    raise ValueError(" For `FluxControlNet`, `control_mode` should be an `int` or `None`")
                 control_mode = torch.tensor(control_mode).to(device, dtype=torch.long)
-                control_mode = control_mode.reshape([-1, 1])
+                control_mode = control_mode.view(-1, 1).expand(control_image.shape[0], 1)
 
         elif isinstance(self.controlnet, FluxMultiControlNetModel):
             control_images = []
@@ -785,16 +787,22 @@ class FluxControlNetPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleF
 
             control_image = control_images
 
+            # Here we ensure that `control_mode` has the same length as the control_image.
+            if isinstance(control_mode, list) and len(control_mode) != len(control_image):
+                raise ValueError(
+                    "For Multi-ControlNet, `control_mode` must be a list of the same "
+                    + " length as the number of controlnets (control images) specified"
+                )
+            if not isinstance(control_mode, list):
+                control_mode = [control_mode] * len(control_image)
             # set control mode
-            control_mode_ = []
-            if isinstance(control_mode, list):
+            control_modes = []
             for cmode in control_mode:
                 if cmode is None:
-                        control_mode_.append(-1)
-                    else:
-                        control_mode_.append(cmode)
-            control_mode = torch.tensor(control_mode_).to(device, dtype=torch.long)
-            control_mode = control_mode.reshape([-1, 1])
+                    cmode = -1
+                control_mode = torch.tensor(cmode).expand(control_images[0].shape[0]).to(device, dtype=torch.long)
+                control_modes.append(control_mode)
+            control_mode = control_modes
 
         # 4. Prepare latent variables
         num_channels_latents = self.transformer.config.in_channels // 4
@@ -840,9 +848,12 @@ class FluxControlNetPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleF
                 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                 timestep = t.expand(latents.shape[0]).to(latents.dtype)
 
-                guidance = (
-                    torch.tensor([guidance_scale], device=device) if self.controlnet.config.guidance_embeds else None
-                )
+                if isinstance(self.controlnet, FluxMultiControlNetModel):
+                    use_guidance = self.controlnet.nets[0].config.guidance_embeds
+                else:
+                    use_guidance = self.controlnet.config.guidance_embeds
+
+                guidance = torch.tensor([guidance_scale], device=device) if use_guidance else None
                 guidance = guidance.expand(latents.shape[0]) if guidance is not None else None
 
                 # controlnet