Merge branch 'huggingface:main' into main

Author: leisuzz

src/diffusers/pipelines/flux/pipeline_flux_controlnet.py

@@ -72,7 +72,9 @@
         >>> image = pipe(
         ...     prompt,
         ...     control_image=control_image,
-        ...     controlnet_conditioning_scale=0.6,
+        ...     control_guidance_start=0.2,
+        ...     control_guidance_end=0.8,
+        ...     controlnet_conditioning_scale=1.0,
         ...     num_inference_steps=28,
         ...     guidance_scale=3.5,
         ... ).images[0]
@@ -572,6 +574,8 @@ def __call__(
         num_inference_steps: int = 28,
         timesteps: List[int] = None,
         guidance_scale: float = 7.0,
+        control_guidance_start: Union[float, List[float]] = 0.0,
+        control_guidance_end: Union[float, List[float]] = 1.0,
         control_image: PipelineImageInput = None,
         control_mode: Optional[Union[int, List[int]]] = None,
         controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
@@ -614,6 +618,10 @@ def __call__(
                 Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                 usually at the expense of lower image quality.
+            control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
+                The percentage of total steps at which the ControlNet starts applying.
+            control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
+                The percentage of total steps at which the ControlNet stops applying.
             control_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
                     `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
                 The ControlNet input condition to provide guidance to the `unet` for generation. If the type is
@@ -674,6 +682,17 @@ def __call__(
         height = height or self.default_sample_size * self.vae_scale_factor
         width = width or self.default_sample_size * self.vae_scale_factor
 
+        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
+            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
+        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
+            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
+        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
+            mult = len(self.controlnet.nets) if isinstance(self.controlnet, FluxMultiControlNetModel) else 1
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
+
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(
             prompt,
@@ -839,7 +858,16 @@ def __call__(
         num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
         self._num_timesteps = len(timesteps)
 
-        # 6. Denoising loop
+        # 6. Create tensor stating which controlnets to keep
+        controlnet_keep = []
+        for i in range(len(timesteps)):
+            keeps = [
+                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
+                for s, e in zip(control_guidance_start, control_guidance_end)
+            ]
+            controlnet_keep.append(keeps[0] if isinstance(self.controlnet, FluxControlNetModel) else keeps)
+
+        # 7. Denoising loop
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
                 if self.interrupt:
@@ -856,12 +884,20 @@ def __call__(
                 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
 
+                if isinstance(controlnet_keep[i], list):
+                    cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
+                else:
+                    controlnet_cond_scale = controlnet_conditioning_scale
+                    if isinstance(controlnet_cond_scale, list):
+                        controlnet_cond_scale = controlnet_cond_scale[0]
+                    cond_scale = controlnet_cond_scale * controlnet_keep[i]
+
                 # controlnet
                 controlnet_block_samples, controlnet_single_block_samples = self.controlnet(
                     hidden_states=latents,
                     controlnet_cond=control_image,
                     controlnet_mode=control_mode,
-                    conditioning_scale=controlnet_conditioning_scale,
+                    conditioning_scale=cond_scale,
                     timestep=timestep / 1000,
                     guidance=guidance,
                     pooled_projections=pooled_prompt_embeds,

src/diffusers/pipelines/flux/pipeline_flux_controlnet_image_to_image.py

@@ -69,7 +69,9 @@
         ...     prompt,
         ...     image=init_image,
         ...     control_image=control_image,
-        ...     controlnet_conditioning_scale=0.6,
+        ...     control_guidance_start=0.2,
+        ...     control_guidance_end=0.8,
+        ...     controlnet_conditioning_scale=1.0,
         ...     strength=0.7,
         ...     num_inference_steps=2,
         ...     guidance_scale=3.5,
@@ -631,6 +633,8 @@ def __call__(
         num_inference_steps: int = 28,
         timesteps: List[int] = None,
         guidance_scale: float = 7.0,
+        control_guidance_start: Union[float, List[float]] = 0.0,
+        control_guidance_end: Union[float, List[float]] = 1.0,
         control_mode: Optional[Union[int, List[int]]] = None,
         controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
         num_images_per_prompt: Optional[int] = 1,
@@ -710,6 +714,17 @@ def __call__(
         height = height or self.default_sample_size * self.vae_scale_factor
         width = width or self.default_sample_size * self.vae_scale_factor
 
+        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
+            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
+        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
+            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
+        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
+            mult = len(self.controlnet.nets) if isinstance(self.controlnet, FluxMultiControlNetModel) else 1
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
+
         self.check_inputs(
             prompt,
             prompt_2,
@@ -862,6 +877,14 @@ def __call__(
             latents,
         )
 
+        controlnet_keep = []
+        for i in range(len(timesteps)):
+            keeps = [
+                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
+                for s, e in zip(control_guidance_start, control_guidance_end)
+            ]
+            controlnet_keep.append(keeps[0] if isinstance(self.controlnet, FluxControlNetModel) else keeps)
+
         num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
         self._num_timesteps = len(timesteps)
 
@@ -877,11 +900,19 @@ def __call__(
                 )
                 guidance = guidance.expand(latents.shape[0]) if guidance is not None else None
 
+                if isinstance(controlnet_keep[i], list):
+                    cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
+                else:
+                    controlnet_cond_scale = controlnet_conditioning_scale
+                    if isinstance(controlnet_cond_scale, list):
+                        controlnet_cond_scale = controlnet_cond_scale[0]
+                    cond_scale = controlnet_cond_scale * controlnet_keep[i]
+
                 controlnet_block_samples, controlnet_single_block_samples = self.controlnet(
                     hidden_states=latents,
                     controlnet_cond=control_image,
                     controlnet_mode=control_mode,
-                    conditioning_scale=controlnet_conditioning_scale,
+                    conditioning_scale=cond_scale,
                     timestep=timestep / 1000,
                     guidance=guidance,
                     pooled_projections=pooled_prompt_embeds,

src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py

@@ -71,6 +71,8 @@
         ...     image=init_image,
         ...     mask_image=mask_image,
         ...     control_image=control_image,
+        ...     control_guidance_start=0.2,
+        ...     control_guidance_end=0.8,
         ...     controlnet_conditioning_scale=0.7,
         ...     strength=0.7,
         ...     num_inference_steps=28,
@@ -737,6 +739,8 @@ def __call__(
         timesteps: List[int] = None,
         num_inference_steps: int = 28,
         guidance_scale: float = 7.0,
+        control_guidance_start: Union[float, List[float]] = 0.0,
+        control_guidance_end: Union[float, List[float]] = 1.0,
         control_mode: Optional[Union[int, List[int]]] = None,
         controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
         num_images_per_prompt: Optional[int] = 1,
@@ -783,6 +787,10 @@ def __call__(
                 Custom timesteps to use for the denoising process.
             guidance_scale (`float`, *optional*, defaults to 7.0):
                 Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+            control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
+                The percentage of total steps at which the ControlNet starts applying.
+            control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
+                The percentage of total steps at which the ControlNet stops applying.
             control_mode (`int` or `List[int]`, *optional*):
                 The mode for the ControlNet. If multiple ControlNets are used, this should be a list.
             controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
@@ -826,6 +834,17 @@ def __call__(
         global_height = height
         global_width = width
 
+        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
+            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
+        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
+            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
+        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
+            mult = len(self.controlnet.nets) if isinstance(self.controlnet, FluxMultiControlNetModel) else 1
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
+
         # 1. Check inputs
         self.check_inputs(
             prompt,
@@ -1031,6 +1050,14 @@ def __call__(
             generator,
         )
 
+        controlnet_keep = []
+        for i in range(len(timesteps)):
+            keeps = [
+                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
+                for s, e in zip(control_guidance_start, control_guidance_end)
+            ]
+            controlnet_keep.append(keeps[0] if isinstance(self.controlnet, FluxControlNetModel) else keeps)
+
         # 9. Denoising loop
         num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
         self._num_timesteps = len(timesteps)
@@ -1049,11 +1076,19 @@ def __call__(
                 else:
                     guidance = None
 
+                if isinstance(controlnet_keep[i], list):
+                    cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
+                else:
+                    controlnet_cond_scale = controlnet_conditioning_scale
+                    if isinstance(controlnet_cond_scale, list):
+                        controlnet_cond_scale = controlnet_cond_scale[0]
+                    cond_scale = controlnet_cond_scale * controlnet_keep[i]
+
                 controlnet_block_samples, controlnet_single_block_samples = self.controlnet(
                     hidden_states=latents,
                     controlnet_cond=control_image,
                     controlnet_mode=control_mode,
-                    conditioning_scale=controlnet_conditioning_scale,
+                    conditioning_scale=cond_scale,
                     timestep=timestep / 1000,
                     guidance=guidance,
                     pooled_projections=pooled_prompt_embeds,