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"""
Self-contained DiffusionSat ControlNet pipeline that can be loaded directly from
the checkpoint folder without importing the project package.
"""
from __future__ import annotations
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import einops
import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
from torch import nn
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers.loaders import TextualInversionLoaderMixin
from diffusers.models import AutoencoderKL
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import (
PIL_INTERPOLATION,
logging,
randn_tensor,
replace_example_docstring,
is_accelerate_available,
is_accelerate_version,
)
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import (
StableDiffusionPipeline as DiffusersStableDiffusionPipeline,
)
from diffusers.pipelines.controlnet.pipeline_controlnet import (
StableDiffusionControlNetPipeline as DiffusersControlNetPipeline,
)
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> # !pip install opencv-python transformers accelerate
>>> from diffusers import DiffusionPipeline
>>> from diffusers.utils import load_image
>>> import numpy as np
>>> import torch
>>> import cv2
>>> from PIL import Image
>>>
>>> image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
>>> image = np.array(image)
>>> image = cv2.Canny(image, 100, 200)
>>> image = image[:, :, None]
>>> image = np.concatenate([image, image, image], axis=2)
>>> canny_image = Image.fromarray(image)
>>>
>>> pipe = DiffusionPipeline.from_pretrained("path/to/ckpt/diffusionsat", torch_dtype=torch.float16)
>>> pipe = pipe.to("cuda")
>>> pipe.enable_xformers_memory_efficient_attention()
>>> generator = torch.manual_seed(0)
>>> image = pipe(
... "futuristic-looking woman", num_inference_steps=20, generator=generator, image=canny_image
... ).images[0]
```
"""
class DiffusionSatControlNetPipeline(DiffusionPipeline, TextualInversionLoaderMixin):
"""
ControlNet-aware pipeline for DiffusionSat. This is a mostly direct copy of
the project pipeline to avoid importing the `diffusionsat` package when
loading from the checkpoint folder. Minimal tweaks:
- auto-fills metadata/cond_metadata with zeros when the model expects them.
"""
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: Any,
controlnet: Any,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__()
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results."
)
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
)
# Support MultiControlNetModel-like objects without importing the project module.
if isinstance(controlnet, (list, tuple)):
# defer to diffusers' MultiControlNetModel if available
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
controlnet = MultiControlNetModel(controlnet)
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
controlnet=controlnet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.register_to_config(requires_safety_checker=requires_safety_checker)
# Reuse helpers from diffusers baseline pipelines.
enable_vae_slicing = DiffusersStableDiffusionPipeline.enable_vae_slicing
disable_vae_slicing = DiffusersStableDiffusionPipeline.disable_vae_slicing
enable_vae_tiling = DiffusersStableDiffusionPipeline.enable_vae_tiling
disable_vae_tiling = DiffusersStableDiffusionPipeline.disable_vae_tiling
enable_sequential_cpu_offload = DiffusersControlNetPipeline.enable_sequential_cpu_offload
enable_model_cpu_offload = DiffusersControlNetPipeline.enable_model_cpu_offload
_execution_device = DiffusersStableDiffusionPipeline._execution_device
_encode_prompt = DiffusersStableDiffusionPipeline._encode_prompt
run_safety_checker = DiffusersStableDiffusionPipeline.run_safety_checker
decode_latents = DiffusersStableDiffusionPipeline.decode_latents
prepare_extra_step_kwargs = DiffusersStableDiffusionPipeline.prepare_extra_step_kwargs
check_inputs = DiffusersControlNetPipeline.check_inputs
check_image = DiffusersControlNetPipeline.check_image
prepare_image = DiffusersControlNetPipeline.prepare_image
prepare_latents = DiffusersStableDiffusionPipeline.prepare_latents
def prepare_metadata(self, batch_size, metadata, ndims, do_classifier_free_guidance, device, dtype):
has_metadata = getattr(self.unet.config, "use_metadata", False)
num_metadata = getattr(self.unet.config, "num_metadata", 0)
if metadata is None and has_metadata and num_metadata > 0:
shape = (batch_size, num_metadata) if ndims == 2 else (batch_size, num_metadata, 1)
metadata = torch.zeros(shape, device=device, dtype=dtype)
if metadata is None:
return None
md = torch.as_tensor(metadata)
if ndims == 2:
assert (len(md.shape) == 1 and batch_size == 1) or (len(md.shape) == 2 and batch_size > 1)
if len(md.shape) == 1:
md = md.unsqueeze(0).expand(batch_size, -1)
elif ndims == 3:
assert (len(md.shape) == 2 and batch_size == 1) or (len(md.shape) == 3 and batch_size > 1)
if len(md.shape) == 2:
md = md.unsqueeze(0).expand(batch_size, -1, -1)
if do_classifier_free_guidance:
md = torch.cat([torch.zeros_like(md), md])
md = md.to(device=device, dtype=dtype)
return md
def _default_height_width(self, height, width, image):
while isinstance(image, list):
image = image[0]
if height is None:
if isinstance(image, PIL.Image.Image):
height = image.height
elif isinstance(image, torch.Tensor):
height = image.shape[2]
height = (height // 8) * 8
if width is None:
if isinstance(image, PIL.Image.Image):
width = image.width
elif isinstance(image, torch.Tensor):
width = image.shape[3]
width = (width // 8) * 8
return height, width
# override DiffusionPipeline
def save_pretrained(
self,
save_directory: Union[str, os.PathLike],
safe_serialization: bool = False,
variant: Optional[str] = None,
):
# For single or multi controlnet, rely on default save logic.
super().save_pretrained(save_directory, safe_serialization=safe_serialization, variant=variant)
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
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,
metadata: Optional[List[float]] = None,
cond_metadata: Optional[List[float]] = None,
is_temporal: bool = False,
conditioning_downsample: bool = True,
):
# 0. Default height and width to unet
height, width = self._default_height_width(height, width, image)
cond_height, cond_width = height, width
if not conditioning_downsample:
cond_height, cond_width = height // 8, width // 8
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
image,
height,
width,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
controlnet_conditioning_scale,
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
do_classifier_free_guidance = guidance_scale > 1.0
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
if isinstance(self.controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(self.controlnet.nets)
# 3. Encode input prompt
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
)
# 4. Prepare image
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
self.controlnet, torch._dynamo.eval_frame.OptimizedModule
)
is_multi_cond = isinstance(image, list)
if (
hasattr(self.controlnet, "controlnet_cond_embedding")
or is_compiled
and hasattr(self.controlnet._orig_mod, "controlnet_cond_embedding")
):
image = self.prepare_image(
image=image,
width=cond_width,
height=cond_height,
batch_size=batch_size * num_images_per_prompt,
num_images_per_prompt=num_images_per_prompt,
device=device,
dtype=self.controlnet.dtype,
do_classifier_free_guidance=do_classifier_free_guidance,
guess_mode=guess_mode,
)
# 5. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 6. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 7. Prepare extra step kwargs.
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# CUSTOM metadata handling (auto-zero filled)
input_metadata = self.prepare_metadata(batch_size, metadata, 2, do_classifier_free_guidance, device, prompt_embeds.dtype)
ndims_cond = 3 if is_multi_cond else 2
cond_metadata = self.prepare_metadata(
batch_size, cond_metadata, ndims_cond, do_classifier_free_guidance, device, prompt_embeds.dtype
)
if input_metadata is not None:
assert len(input_metadata.shape) == 2 and input_metadata.shape[-1] == getattr(self.unet.config, "num_metadata", input_metadata.shape[-1])
if cond_metadata is not None:
assert len(cond_metadata.shape) == ndims_cond and cond_metadata.shape[1] == getattr(self.unet.config, "num_metadata", cond_metadata.shape[1])
if is_multi_cond and not is_temporal and not isinstance(self.controlnet, MultiControlNetModel):
assert cond_metadata.shape[2] == self.controlnet.controlnet_cond_embedding.conv_in.in_channels / 3
if input_metadata is not None:
assert input_metadata.shape[0] == prompt_embeds.shape[0]
if is_temporal:
num_cond = cond_metadata.shape[-1] if cond_metadata is not None else image.shape[1] // self.controlnet.config.conditioning_in_channels
image = einops.rearrange(image, 'b (t c) h w -> b c t h w', t=num_cond)
elif isinstance(self.controlnet, MultiControlNetModel) and cond_metadata is not None:
num_cond = cond_metadata.shape[-1] if cond_metadata is not None else image.shape[1] // self.controlnet.config.conditioning_in_channels
image = einops.rearrange(image, 'b (t c) h w -> t b c h w', t=num_cond)
image = [im for im in image]
cond_metadata = einops.rearrange(cond_metadata, 'b m t -> t b m')
cond_metadata = [cond_md for cond_md in cond_metadata]
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
if guess_mode and do_classifier_free_guidance:
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(
controlnet_latent_model_input,
t,
encoder_hidden_states=controlnet_prompt_embeds,
controlnet_cond=image,
metadata=input_metadata,
cond_metadata=cond_metadata,
conditioning_scale=controlnet_conditioning_scale,
guess_mode=guess_mode,
return_dict=False,
)
if guess_mode and do_classifier_free_guidance:
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])
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
metadata=input_metadata,
cross_attention_kwargs=cross_attention_kwargs,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
return_dict=False,
)[0]
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.unet.to("cpu")
self.controlnet.to("cpu")
torch.cuda.empty_cache()
if output_type == "latent":
image = latents
has_nsfw_concept = None
elif output_type == "pil":
image = self.decode_latents(latents)
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
image = self.numpy_to_pil(image)
else:
image = self.decode_latents(latents)
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
__all__ = ["DiffusionSatControlNetPipeline"]
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