Skip to content

flux controlnet control_guidance_start and control_guidance_end implement #9571

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 9 commits into from
Oct 10, 2024
Merged

flux controlnet control_guidance_start and control_guidance_end implement #9571

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
9 commits
Select commit Hold shift + click to select a range
a388daf
flux controlnet control_guidance_start and control_guidance_end imple…
ighoshsubho Oct 2, 2024
5f1060c
Merge branch 'main' into feature/flux_control_guidance
ighoshsubho Oct 3, 2024
6694af1
Merge branch 'main' into feature/flux_control_guidance
ighoshsubho Oct 4, 2024
6a2e544
Merge branch 'main' into feature/flux_control_guidance
ighoshsubho Oct 8, 2024
98bc26c
Merge branch 'main' into feature/flux_control_guidance
ighoshsubho Oct 9, 2024
0317c0d
Merge branch 'main' into feature/flux_control_guidance
ighoshsubho Oct 9, 2024
2de9c00
Merge branch 'main' into feature/flux_control_guidance
ighoshsubho Oct 10, 2024
0e84b23
minor fix - added docstrings, consistent controlnet scale flux and SD3
ighoshsubho Oct 10, 2024
44aef10
Merge branch 'main' into feature/flux_control_guidance
ighoshsubho Oct 10, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
42 changes: 39 additions & 3 deletions src/diffusers/pipelines/flux/pipeline_flux_controlnet.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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]
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -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:
Expand All @@ -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,
Expand Down
35 changes: 33 additions & 2 deletions src/diffusers/pipelines/flux/pipeline_flux_controlnet_image_to_image.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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,
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -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)

Expand All @@ -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,
Expand Down
37 changes: 36 additions & 1 deletion src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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,
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -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):
Expand Down Expand Up @@ -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,
Expand Down Expand Up @@ -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)
Expand All @@ -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,
Expand Down
Loading