Several fixes to Flux ControlNet pipelines

src/diffusers/pipelines/auto_pipeline.py

@@ -29,7 +29,14 @@
     StableDiffusionXLControlNetPipeline,
 )
 from .deepfloyd_if import IFImg2ImgPipeline, IFInpaintingPipeline, IFPipeline
-from .flux import FluxControlNetPipeline, FluxImg2ImgPipeline, FluxInpaintPipeline, FluxPipeline
+from .flux import (
+    FluxControlNetPipeline,
+    FluxControlNetImg2ImgPipeline,
+    FluxControlNetInpaintPipeline,
+    FluxImg2ImgPipeline,
+    FluxInpaintPipeline,
+    FluxPipeline
+)
 from .hunyuandit import HunyuanDiTPipeline
 from .kandinsky import (
     KandinskyCombinedPipeline,
@@ -128,6 +135,7 @@
         ("stable-diffusion-xl-controlnet-pag", StableDiffusionXLControlNetPAGImg2ImgPipeline),
         ("lcm", LatentConsistencyModelImg2ImgPipeline),
         ("flux", FluxImg2ImgPipeline),
+        ("flux-controlnet", FluxControlNetImg2ImgPipeline),
     ]
 )
 
@@ -143,6 +151,7 @@
         ("stable-diffusion-xl-controlnet", StableDiffusionXLControlNetInpaintPipeline),
         ("stable-diffusion-xl-pag", StableDiffusionXLPAGInpaintPipeline),
         ("flux", FluxInpaintPipeline),
+        ("flux-controlnet", FluxControlNetInpaintPipeline),
     ]
 )
 

src/diffusers/pipelines/flux/pipeline_flux_controlnet.py

@@ -729,7 +729,7 @@ def __call__(
                 batch_size=batch_size * num_images_per_prompt,
                 num_images_per_prompt=num_images_per_prompt,
                 device=device,
-                dtype=dtype,
+                dtype=self.vae.dtype,
             )
             height, width = control_image.shape[-2:]
 
@@ -763,7 +763,7 @@ def __call__(
                     batch_size=batch_size * num_images_per_prompt,
                     num_images_per_prompt=num_images_per_prompt,
                     device=device,
-                    dtype=dtype,
+                    dtype=self.vae.dtype,
                 )
                 height, width = control_image_.shape[-2:]
 
@@ -840,12 +840,10 @@ def __call__(
                 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                 timestep = t.expand(latents.shape[0]).to(latents.dtype)
 
-                # handle guidance
-                if self.transformer.config.guidance_embeds:
-                    guidance = torch.tensor([guidance_scale], device=device)
-                    guidance = guidance.expand(latents.shape[0])
-                else:
-                    guidance = None
+                guidance = (
+                    torch.tensor([guidance_scale], device=device) if self.controlnet.config.guidance_embeds else None
+                )
+                guidance = guidance.expand(latents.shape[0]) if guidance is not None else None
 
                 # controlnet
                 controlnet_block_samples, controlnet_single_block_samples = self.controlnet(
@@ -863,6 +861,11 @@ def __call__(
                     return_dict=False,
                 )
 
+                guidance = (
+                    torch.tensor([guidance_scale], device=device) if self.transformer.config.guidance_embeds else None
+                )
+                guidance = guidance.expand(latents.shape[0]) if guidance is not None else None
+
                 noise_pred = self.transformer(
                     hidden_states=latents,
                     timestep=timestep / 1000,

src/diffusers/pipelines/flux/pipeline_flux_controlnet_image_to_image.py

@@ -767,7 +767,7 @@ def __call__(
                 batch_size=batch_size * num_images_per_prompt,
                 num_images_per_prompt=num_images_per_prompt,
                 device=device,
-                dtype=dtype,
+                dtype=self.vae.dtype,
             )
             height, width = control_image.shape[-2:]
 
@@ -798,7 +798,7 @@ def __call__(
                     batch_size=batch_size * num_images_per_prompt,
                     num_images_per_prompt=num_images_per_prompt,
                     device=device,
-                    dtype=dtype,
+                    dtype=self.vae.dtype,
                 )
                 height, width = control_image_.shape[-2:]
 

src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py

@@ -899,7 +899,7 @@ def __call__(
                 batch_size=batch_size * num_images_per_prompt,
                 num_images_per_prompt=num_images_per_prompt,
                 device=device,
-                dtype=dtype,
+                dtype=self.vae.dtype,
             )
             height, width = control_image.shape[-2:]
 
@@ -933,7 +933,7 @@ def __call__(
                     batch_size=batch_size * num_images_per_prompt,
                     num_images_per_prompt=num_images_per_prompt,
                     device=device,
-                    dtype=dtype,
+                    dtype=self.vae.dtype,
                 )
                 height, width = control_image_.shape[-2:]