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* Use HF Papers * Apply style fixes --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
1203 lines
61 KiB
Python
1203 lines
61 KiB
Python
# Based on stable_diffusion_reference.py
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import inspect
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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import numpy as np
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import PIL.Image
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import torch
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from diffusers import StableDiffusionXLPipeline
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from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
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from diffusers.image_processor import PipelineImageInput
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from diffusers.models.attention import BasicTransformerBlock
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from diffusers.models.unets.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
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from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
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from diffusers.utils import PIL_INTERPOLATION, deprecate, is_torch_xla_available, logging, replace_example_docstring
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from diffusers.utils.torch_utils import randn_tensor
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if is_torch_xla_available():
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import torch_xla.core.xla_model as xm # type: ignore
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XLA_AVAILABLE = True
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else:
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XLA_AVAILABLE = False
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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EXAMPLE_DOC_STRING = """
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Examples:
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```py
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>>> import torch
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>>> from diffusers.schedulers import UniPCMultistepScheduler
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>>> from diffusers.utils import load_image
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>>> input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_reference_input_cat.jpg")
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>>> pipe = StableDiffusionXLReferencePipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0",
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torch_dtype=torch.float16,
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use_safetensors=True,
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variant="fp16").to('cuda:0')
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>>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
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>>> result_img = pipe(ref_image=input_image,
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prompt="a dog",
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num_inference_steps=20,
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reference_attn=True,
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reference_adain=True).images[0]
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>>> result_img.show()
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```
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"""
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def torch_dfs(model: torch.nn.Module):
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result = [model]
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for child in model.children():
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result += torch_dfs(child)
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return result
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
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def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
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"""
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Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
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Sample Steps are Flawed](https://huggingface.co/papers/2305.08891). See Section 3.4
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"""
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std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
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std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
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# rescale the results from guidance (fixes overexposure)
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noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
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# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
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noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
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return noise_cfg
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
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def retrieve_timesteps(
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scheduler,
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num_inference_steps: Optional[int] = None,
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device: Optional[Union[str, torch.device]] = None,
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timesteps: Optional[List[int]] = None,
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sigmas: Optional[List[float]] = None,
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**kwargs,
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):
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r"""
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Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
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custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
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Args:
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scheduler (`SchedulerMixin`):
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The scheduler to get timesteps from.
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num_inference_steps (`int`):
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The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
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must be `None`.
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device (`str` or `torch.device`, *optional*):
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The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
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timesteps (`List[int]`, *optional*):
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Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
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`num_inference_steps` and `sigmas` must be `None`.
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sigmas (`List[float]`, *optional*):
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Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
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`num_inference_steps` and `timesteps` must be `None`.
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Returns:
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`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
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second element is the number of inference steps.
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"""
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if timesteps is not None and sigmas is not None:
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raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
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if timesteps is not None:
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accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
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if not accepts_timesteps:
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raise ValueError(
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f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
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f" timestep schedules. Please check whether you are using the correct scheduler."
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)
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scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
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timesteps = scheduler.timesteps
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num_inference_steps = len(timesteps)
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elif sigmas is not None:
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accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
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if not accept_sigmas:
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raise ValueError(
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f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
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f" sigmas schedules. Please check whether you are using the correct scheduler."
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)
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scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
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timesteps = scheduler.timesteps
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num_inference_steps = len(timesteps)
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else:
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scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
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timesteps = scheduler.timesteps
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return timesteps, num_inference_steps
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class StableDiffusionXLReferencePipeline(StableDiffusionXLPipeline):
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def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do_classifier_free_guidance):
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refimage = refimage.to(device=device)
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needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
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if needs_upcasting:
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self.upcast_vae()
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refimage = refimage.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
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if refimage.dtype != self.vae.dtype:
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refimage = refimage.to(dtype=self.vae.dtype)
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# encode the mask image into latents space so we can concatenate it to the latents
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if isinstance(generator, list):
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ref_image_latents = [
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self.vae.encode(refimage[i : i + 1]).latent_dist.sample(generator=generator[i])
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for i in range(batch_size)
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]
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ref_image_latents = torch.cat(ref_image_latents, dim=0)
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else:
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ref_image_latents = self.vae.encode(refimage).latent_dist.sample(generator=generator)
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ref_image_latents = self.vae.config.scaling_factor * ref_image_latents
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# duplicate mask and ref_image_latents for each generation per prompt, using mps friendly method
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if ref_image_latents.shape[0] < batch_size:
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if not batch_size % ref_image_latents.shape[0] == 0:
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raise ValueError(
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"The passed images and the required batch size don't match. Images are supposed to be duplicated"
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f" to a total batch size of {batch_size}, but {ref_image_latents.shape[0]} images were passed."
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" Make sure the number of images that you pass is divisible by the total requested batch size."
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)
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ref_image_latents = ref_image_latents.repeat(batch_size // ref_image_latents.shape[0], 1, 1, 1)
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ref_image_latents = torch.cat([ref_image_latents] * 2) if do_classifier_free_guidance else ref_image_latents
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# aligning device to prevent device errors when concating it with the latent model input
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ref_image_latents = ref_image_latents.to(device=device, dtype=dtype)
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# cast back to fp16 if needed
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if needs_upcasting:
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self.vae.to(dtype=torch.float16)
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return ref_image_latents
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def prepare_ref_image(
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self,
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image,
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width,
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height,
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batch_size,
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num_images_per_prompt,
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device,
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dtype,
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do_classifier_free_guidance=False,
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guess_mode=False,
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):
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if not isinstance(image, torch.Tensor):
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if isinstance(image, PIL.Image.Image):
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image = [image]
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if isinstance(image[0], PIL.Image.Image):
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images = []
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for image_ in image:
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image_ = image_.convert("RGB")
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image_ = image_.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
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image_ = np.array(image_)
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image_ = image_[None, :]
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images.append(image_)
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image = images
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image = np.concatenate(image, axis=0)
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image = np.array(image).astype(np.float32) / 255.0
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image = (image - 0.5) / 0.5
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image = image.transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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elif isinstance(image[0], torch.Tensor):
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image = torch.stack(image, dim=0)
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image_batch_size = image.shape[0]
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if image_batch_size == 1:
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repeat_by = batch_size
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else:
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repeat_by = num_images_per_prompt
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image = image.repeat_interleave(repeat_by, dim=0)
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image = image.to(device=device, dtype=dtype)
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if do_classifier_free_guidance and not guess_mode:
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image = torch.cat([image] * 2)
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return image
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def check_ref_inputs(
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self,
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ref_image,
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reference_guidance_start,
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reference_guidance_end,
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style_fidelity,
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reference_attn,
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reference_adain,
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):
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ref_image_is_pil = isinstance(ref_image, PIL.Image.Image)
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ref_image_is_tensor = isinstance(ref_image, torch.Tensor)
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if not ref_image_is_pil and not ref_image_is_tensor:
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raise TypeError(
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f"ref image must be passed and be one of PIL image or torch tensor, but is {type(ref_image)}"
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)
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if not reference_attn and not reference_adain:
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raise ValueError("`reference_attn` or `reference_adain` must be True.")
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if style_fidelity < 0.0:
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raise ValueError(f"style fidelity: {style_fidelity} can't be smaller than 0.")
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if style_fidelity > 1.0:
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raise ValueError(f"style fidelity: {style_fidelity} can't be larger than 1.0.")
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if reference_guidance_start >= reference_guidance_end:
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raise ValueError(
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f"reference guidance start: {reference_guidance_start} cannot be larger or equal to reference guidance end: {reference_guidance_end}."
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)
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if reference_guidance_start < 0.0:
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raise ValueError(f"reference guidance start: {reference_guidance_start} can't be smaller than 0.")
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if reference_guidance_end > 1.0:
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raise ValueError(f"reference guidance end: {reference_guidance_end} can't be larger than 1.0.")
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@torch.no_grad()
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@replace_example_docstring(EXAMPLE_DOC_STRING)
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def __call__(
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self,
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prompt: Union[str, List[str]] = None,
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prompt_2: Optional[Union[str, List[str]]] = None,
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ref_image: Union[torch.Tensor, PIL.Image.Image] = None,
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height: Optional[int] = None,
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width: Optional[int] = None,
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num_inference_steps: int = 50,
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timesteps: List[int] = None,
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sigmas: List[float] = None,
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denoising_end: Optional[float] = None,
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guidance_scale: float = 5.0,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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negative_prompt_2: Optional[Union[str, List[str]]] = None,
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num_images_per_prompt: Optional[int] = 1,
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eta: float = 0.0,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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latents: Optional[torch.Tensor] = None,
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prompt_embeds: Optional[torch.Tensor] = None,
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negative_prompt_embeds: Optional[torch.Tensor] = None,
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pooled_prompt_embeds: Optional[torch.Tensor] = None,
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negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
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ip_adapter_image: Optional[PipelineImageInput] = None,
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ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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cross_attention_kwargs: Optional[Dict[str, Any]] = None,
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guidance_rescale: float = 0.0,
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original_size: Optional[Tuple[int, int]] = None,
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crops_coords_top_left: Tuple[int, int] = (0, 0),
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target_size: Optional[Tuple[int, int]] = None,
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negative_original_size: Optional[Tuple[int, int]] = None,
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negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
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negative_target_size: Optional[Tuple[int, int]] = None,
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clip_skip: Optional[int] = None,
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callback_on_step_end: Optional[
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Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
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] = None,
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callback_on_step_end_tensor_inputs: List[str] = ["latents"],
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attention_auto_machine_weight: float = 1.0,
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gn_auto_machine_weight: float = 1.0,
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reference_guidance_start: float = 0.0,
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reference_guidance_end: float = 1.0,
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style_fidelity: float = 0.5,
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reference_attn: bool = True,
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reference_adain: bool = True,
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**kwargs,
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):
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r"""
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Function invoked when calling the pipeline for generation.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
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instead.
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prompt_2 (`str` or `List[str]`, *optional*):
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The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
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used in both text-encoders
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ref_image (`torch.Tensor`, `PIL.Image.Image`):
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The Reference Control input condition. Reference Control uses this input condition to generate guidance to Unet. If
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the type is specified as `Torch.Tensor`, it is passed to Reference Control as is. `PIL.Image.Image` can
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also be accepted as an image.
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height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
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The height in pixels of the generated image. This is set to 1024 by default for the best results.
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Anything below 512 pixels won't work well for
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[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
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and checkpoints that are not specifically fine-tuned on low resolutions.
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width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
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The width in pixels of the generated image. This is set to 1024 by default for the best results.
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Anything below 512 pixels won't work well for
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[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
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and checkpoints that are not specifically fine-tuned on low resolutions.
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num_inference_steps (`int`, *optional*, defaults to 50):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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timesteps (`List[int]`, *optional*):
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Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
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in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
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passed will be used. Must be in descending order.
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sigmas (`List[float]`, *optional*):
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Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
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their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
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will be used.
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denoising_end (`float`, *optional*):
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When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
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completed before it is intentionally prematurely terminated. As a result, the returned sample will
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still retain a substantial amount of noise as determined by the discrete timesteps selected by the
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scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
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"Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
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Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
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guidance_scale (`float`, *optional*, defaults to 5.0):
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Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598).
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`guidance_scale` is defined as `w` of equation 2. of [Imagen
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Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `guidance_scale >
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1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
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usually at the expense of lower image quality.
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation. If not defined, one has to pass
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`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
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less than `1`).
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negative_prompt_2 (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
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`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
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num_images_per_prompt (`int`, *optional*, defaults to 1):
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The number of images to generate per prompt.
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eta (`float`, *optional*, defaults to 0.0):
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Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only applies to
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[`schedulers.DDIMScheduler`], will be ignored for others.
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generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
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One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
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to make generation deterministic.
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latents (`torch.Tensor`, *optional*):
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Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
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generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
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tensor will ge generated by sampling using the supplied random `generator`.
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prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
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provided, text embeddings will be generated from `prompt` input argument.
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negative_prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
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weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
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argument.
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pooled_prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
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If not provided, pooled text embeddings will be generated from `prompt` input argument.
|
|
negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
|
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
|
|
input argument.
|
|
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
|
|
ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
|
|
Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
|
|
IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
|
|
contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
|
|
provided, embeddings are computed from the `ip_adapter_image` input argument.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generate image. Choose between
|
|
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
|
|
of a plain tuple.
|
|
cross_attention_kwargs (`dict`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
|
`self.processor` in
|
|
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
|
guidance_rescale (`float`, *optional*, defaults to 0.0):
|
|
Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
|
|
Flawed](https://huggingface.co/papers/2305.08891) `guidance_scale` is defined as `φ` in equation 16. of
|
|
[Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891).
|
|
Guidance rescale factor should fix overexposure when using zero terminal SNR.
|
|
original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
|
If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
|
|
`original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
|
|
explained in section 2.2 of
|
|
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
|
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
|
|
`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
|
|
`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
|
|
`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
|
|
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
|
target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
|
For most cases, `target_size` should be set to the desired height and width of the generated image. If
|
|
not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
|
|
section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
|
negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
|
To negatively condition the generation process based on a specific image resolution. Part of SDXL's
|
|
micro-conditioning as explained in section 2.2 of
|
|
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
|
|
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
|
|
negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
|
|
To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
|
|
micro-conditioning as explained in section 2.2 of
|
|
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
|
|
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
|
|
negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
|
To negatively condition the generation process based on a target image resolution. It should be as same
|
|
as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
|
|
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
|
|
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
|
|
callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
|
|
A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
|
|
each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
|
|
DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
|
|
list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
|
|
callback_on_step_end_tensor_inputs (`List`, *optional*):
|
|
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
|
|
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
|
|
`._callback_tensor_inputs` attribute of your pipeline class.
|
|
attention_auto_machine_weight (`float`):
|
|
Weight of using reference query for self attention's context.
|
|
If attention_auto_machine_weight=1.0, use reference query for all self attention's context.
|
|
gn_auto_machine_weight (`float`):
|
|
Weight of using reference adain. If gn_auto_machine_weight=2.0, use all reference adain plugins.
|
|
reference_guidance_start (`float`, *optional*, defaults to 0.0):
|
|
The percentage of total steps at which the reference ControlNet starts applying.
|
|
reference_guidance_end (`float`, *optional*, defaults to 1.0):
|
|
The percentage of total steps at which the reference ControlNet stops applying.
|
|
style_fidelity (`float`):
|
|
style fidelity of ref_uncond_xt. If style_fidelity=1.0, control more important,
|
|
elif style_fidelity=0.0, prompt more important, else balanced.
|
|
reference_attn (`bool`):
|
|
Whether to use reference query for self attention's context.
|
|
reference_adain (`bool`):
|
|
Whether to use reference adain.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
|
|
[`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
|
|
`tuple`. When returning a tuple, the first element is a list with the generated images.
|
|
"""
|
|
|
|
callback = kwargs.pop("callback", None)
|
|
callback_steps = kwargs.pop("callback_steps", None)
|
|
|
|
if callback is not None:
|
|
deprecate(
|
|
"callback",
|
|
"1.0.0",
|
|
"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
|
|
)
|
|
if callback_steps is not None:
|
|
deprecate(
|
|
"callback_steps",
|
|
"1.0.0",
|
|
"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
|
|
)
|
|
|
|
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
|
|
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
|
|
|
|
# 0. Default height and width to unet
|
|
height = height or self.default_sample_size * self.vae_scale_factor
|
|
width = width or self.default_sample_size * self.vae_scale_factor
|
|
|
|
original_size = original_size or (height, width)
|
|
target_size = target_size or (height, width)
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt,
|
|
prompt_2,
|
|
height,
|
|
width,
|
|
callback_steps,
|
|
negative_prompt,
|
|
negative_prompt_2,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds,
|
|
ip_adapter_image,
|
|
ip_adapter_image_embeds,
|
|
callback_on_step_end_tensor_inputs,
|
|
)
|
|
|
|
self.check_ref_inputs(
|
|
ref_image,
|
|
reference_guidance_start,
|
|
reference_guidance_end,
|
|
style_fidelity,
|
|
reference_attn,
|
|
reference_adain,
|
|
)
|
|
|
|
self._guidance_scale = guidance_scale
|
|
self._guidance_rescale = guidance_rescale
|
|
self._clip_skip = clip_skip
|
|
self._cross_attention_kwargs = cross_attention_kwargs
|
|
self._denoising_end = denoising_end
|
|
self._interrupt = False
|
|
|
|
# 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
|
|
|
|
# 3. Encode input prompt
|
|
lora_scale = (
|
|
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
|
|
)
|
|
|
|
(
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds,
|
|
) = self.encode_prompt(
|
|
prompt=prompt,
|
|
prompt_2=prompt_2,
|
|
device=device,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
do_classifier_free_guidance=self.do_classifier_free_guidance,
|
|
negative_prompt=negative_prompt,
|
|
negative_prompt_2=negative_prompt_2,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
pooled_prompt_embeds=pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
|
lora_scale=lora_scale,
|
|
clip_skip=self.clip_skip,
|
|
)
|
|
|
|
# 4. Preprocess reference image
|
|
ref_image = self.prepare_ref_image(
|
|
image=ref_image,
|
|
width=width,
|
|
height=height,
|
|
batch_size=batch_size * num_images_per_prompt,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
device=device,
|
|
dtype=prompt_embeds.dtype,
|
|
)
|
|
|
|
# 5. Prepare timesteps
|
|
timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler, num_inference_steps, device, timesteps, sigmas
|
|
)
|
|
|
|
# 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 reference latent variables
|
|
ref_image_latents = self.prepare_ref_latents(
|
|
ref_image,
|
|
batch_size * num_images_per_prompt,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
generator,
|
|
self.do_classifier_free_guidance,
|
|
)
|
|
|
|
# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
|
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
|
|
|
# 8.1 Create tensor stating which reference controlnets to keep
|
|
reference_keeps = []
|
|
for i in range(len(timesteps)):
|
|
reference_keep = 1.0 - float(
|
|
i / len(timesteps) < reference_guidance_start or (i + 1) / len(timesteps) > reference_guidance_end
|
|
)
|
|
reference_keeps.append(reference_keep)
|
|
|
|
# 8.2 Modify self attention and group norm
|
|
MODE = "write"
|
|
uc_mask = (
|
|
torch.Tensor([1] * batch_size * num_images_per_prompt + [0] * batch_size * num_images_per_prompt)
|
|
.type_as(ref_image_latents)
|
|
.bool()
|
|
)
|
|
|
|
do_classifier_free_guidance = self.do_classifier_free_guidance
|
|
|
|
def hacked_basic_transformer_inner_forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
timestep: Optional[torch.LongTensor] = None,
|
|
cross_attention_kwargs: Dict[str, Any] = None,
|
|
class_labels: Optional[torch.LongTensor] = None,
|
|
):
|
|
if self.use_ada_layer_norm:
|
|
norm_hidden_states = self.norm1(hidden_states, timestep)
|
|
elif self.use_ada_layer_norm_zero:
|
|
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
|
|
hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
|
|
)
|
|
else:
|
|
norm_hidden_states = self.norm1(hidden_states)
|
|
|
|
# 1. Self-Attention
|
|
cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
|
|
if self.only_cross_attention:
|
|
attn_output = self.attn1(
|
|
norm_hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
|
|
attention_mask=attention_mask,
|
|
**cross_attention_kwargs,
|
|
)
|
|
else:
|
|
if MODE == "write":
|
|
self.bank.append(norm_hidden_states.detach().clone())
|
|
attn_output = self.attn1(
|
|
norm_hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
|
|
attention_mask=attention_mask,
|
|
**cross_attention_kwargs,
|
|
)
|
|
if MODE == "read":
|
|
if attention_auto_machine_weight > self.attn_weight:
|
|
attn_output_uc = self.attn1(
|
|
norm_hidden_states,
|
|
encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),
|
|
# attention_mask=attention_mask,
|
|
**cross_attention_kwargs,
|
|
)
|
|
attn_output_c = attn_output_uc.clone()
|
|
if do_classifier_free_guidance and style_fidelity > 0:
|
|
attn_output_c[uc_mask] = self.attn1(
|
|
norm_hidden_states[uc_mask],
|
|
encoder_hidden_states=norm_hidden_states[uc_mask],
|
|
**cross_attention_kwargs,
|
|
)
|
|
attn_output = style_fidelity * attn_output_c + (1.0 - style_fidelity) * attn_output_uc
|
|
self.bank.clear()
|
|
else:
|
|
attn_output = self.attn1(
|
|
norm_hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
|
|
attention_mask=attention_mask,
|
|
**cross_attention_kwargs,
|
|
)
|
|
if self.use_ada_layer_norm_zero:
|
|
attn_output = gate_msa.unsqueeze(1) * attn_output
|
|
hidden_states = attn_output + hidden_states
|
|
|
|
if self.attn2 is not None:
|
|
norm_hidden_states = (
|
|
self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
|
|
)
|
|
|
|
# 2. Cross-Attention
|
|
attn_output = self.attn2(
|
|
norm_hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
attention_mask=encoder_attention_mask,
|
|
**cross_attention_kwargs,
|
|
)
|
|
hidden_states = attn_output + hidden_states
|
|
|
|
# 3. Feed-forward
|
|
norm_hidden_states = self.norm3(hidden_states)
|
|
|
|
if self.use_ada_layer_norm_zero:
|
|
norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
|
|
|
|
ff_output = self.ff(norm_hidden_states)
|
|
|
|
if self.use_ada_layer_norm_zero:
|
|
ff_output = gate_mlp.unsqueeze(1) * ff_output
|
|
|
|
hidden_states = ff_output + hidden_states
|
|
|
|
return hidden_states
|
|
|
|
def hacked_mid_forward(self, *args, **kwargs):
|
|
eps = 1e-6
|
|
x = self.original_forward(*args, **kwargs)
|
|
if MODE == "write":
|
|
if gn_auto_machine_weight >= self.gn_weight:
|
|
var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
|
|
self.mean_bank.append(mean)
|
|
self.var_bank.append(var)
|
|
if MODE == "read":
|
|
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
|
|
var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
|
|
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
|
|
mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))
|
|
var_acc = sum(self.var_bank) / float(len(self.var_bank))
|
|
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
|
|
x_uc = (((x - mean) / std) * std_acc) + mean_acc
|
|
x_c = x_uc.clone()
|
|
if do_classifier_free_guidance and style_fidelity > 0:
|
|
x_c[uc_mask] = x[uc_mask]
|
|
x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc
|
|
self.mean_bank = []
|
|
self.var_bank = []
|
|
return x
|
|
|
|
def hack_CrossAttnDownBlock2D_forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
temb: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
):
|
|
eps = 1e-6
|
|
|
|
# TODO(Patrick, William) - attention mask is not used
|
|
output_states = ()
|
|
|
|
for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
|
|
hidden_states = resnet(hidden_states, temb)
|
|
hidden_states = attn(
|
|
hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
attention_mask=attention_mask,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
return_dict=False,
|
|
)[0]
|
|
if MODE == "write":
|
|
if gn_auto_machine_weight >= self.gn_weight:
|
|
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
|
self.mean_bank.append([mean])
|
|
self.var_bank.append([var])
|
|
if MODE == "read":
|
|
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
|
|
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
|
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
|
|
mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
|
|
var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
|
|
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
|
|
hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
|
|
hidden_states_c = hidden_states_uc.clone()
|
|
if do_classifier_free_guidance and style_fidelity > 0:
|
|
hidden_states_c[uc_mask] = hidden_states[uc_mask]
|
|
hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
|
|
|
|
output_states = output_states + (hidden_states,)
|
|
|
|
if MODE == "read":
|
|
self.mean_bank = []
|
|
self.var_bank = []
|
|
|
|
if self.downsamplers is not None:
|
|
for downsampler in self.downsamplers:
|
|
hidden_states = downsampler(hidden_states)
|
|
|
|
output_states = output_states + (hidden_states,)
|
|
|
|
return hidden_states, output_states
|
|
|
|
def hacked_DownBlock2D_forward(self, hidden_states, temb=None, *args, **kwargs):
|
|
eps = 1e-6
|
|
|
|
output_states = ()
|
|
|
|
for i, resnet in enumerate(self.resnets):
|
|
hidden_states = resnet(hidden_states, temb)
|
|
|
|
if MODE == "write":
|
|
if gn_auto_machine_weight >= self.gn_weight:
|
|
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
|
self.mean_bank.append([mean])
|
|
self.var_bank.append([var])
|
|
if MODE == "read":
|
|
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
|
|
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
|
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
|
|
mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
|
|
var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
|
|
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
|
|
hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
|
|
hidden_states_c = hidden_states_uc.clone()
|
|
if do_classifier_free_guidance and style_fidelity > 0:
|
|
hidden_states_c[uc_mask] = hidden_states[uc_mask]
|
|
hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
|
|
|
|
output_states = output_states + (hidden_states,)
|
|
|
|
if MODE == "read":
|
|
self.mean_bank = []
|
|
self.var_bank = []
|
|
|
|
if self.downsamplers is not None:
|
|
for downsampler in self.downsamplers:
|
|
hidden_states = downsampler(hidden_states)
|
|
|
|
output_states = output_states + (hidden_states,)
|
|
|
|
return hidden_states, output_states
|
|
|
|
def hacked_CrossAttnUpBlock2D_forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
res_hidden_states_tuple: Tuple[torch.Tensor, ...],
|
|
temb: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
upsample_size: Optional[int] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
):
|
|
eps = 1e-6
|
|
# TODO(Patrick, William) - attention mask is not used
|
|
for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
|
|
# pop res hidden states
|
|
res_hidden_states = res_hidden_states_tuple[-1]
|
|
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
|
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
|
hidden_states = resnet(hidden_states, temb)
|
|
hidden_states = attn(
|
|
hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
attention_mask=attention_mask,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
if MODE == "write":
|
|
if gn_auto_machine_weight >= self.gn_weight:
|
|
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
|
self.mean_bank.append([mean])
|
|
self.var_bank.append([var])
|
|
if MODE == "read":
|
|
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
|
|
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
|
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
|
|
mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
|
|
var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
|
|
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
|
|
hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
|
|
hidden_states_c = hidden_states_uc.clone()
|
|
if do_classifier_free_guidance and style_fidelity > 0:
|
|
hidden_states_c[uc_mask] = hidden_states[uc_mask]
|
|
hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
|
|
|
|
if MODE == "read":
|
|
self.mean_bank = []
|
|
self.var_bank = []
|
|
|
|
if self.upsamplers is not None:
|
|
for upsampler in self.upsamplers:
|
|
hidden_states = upsampler(hidden_states, upsample_size)
|
|
|
|
return hidden_states
|
|
|
|
def hacked_UpBlock2D_forward(
|
|
self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None, *args, **kwargs
|
|
):
|
|
eps = 1e-6
|
|
for i, resnet in enumerate(self.resnets):
|
|
# pop res hidden states
|
|
res_hidden_states = res_hidden_states_tuple[-1]
|
|
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
|
hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
|
|
hidden_states = resnet(hidden_states, temb)
|
|
|
|
if MODE == "write":
|
|
if gn_auto_machine_weight >= self.gn_weight:
|
|
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
|
self.mean_bank.append([mean])
|
|
self.var_bank.append([var])
|
|
if MODE == "read":
|
|
if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
|
|
var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
|
|
std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
|
|
mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
|
|
var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
|
|
std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
|
|
hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
|
|
hidden_states_c = hidden_states_uc.clone()
|
|
if do_classifier_free_guidance and style_fidelity > 0:
|
|
hidden_states_c[uc_mask] = hidden_states[uc_mask]
|
|
hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
|
|
|
|
if MODE == "read":
|
|
self.mean_bank = []
|
|
self.var_bank = []
|
|
|
|
if self.upsamplers is not None:
|
|
for upsampler in self.upsamplers:
|
|
hidden_states = upsampler(hidden_states, upsample_size)
|
|
|
|
return hidden_states
|
|
|
|
if reference_attn:
|
|
attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock)]
|
|
attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
|
|
|
|
for i, module in enumerate(attn_modules):
|
|
module._original_inner_forward = module.forward
|
|
module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)
|
|
module.bank = []
|
|
module.attn_weight = float(i) / float(len(attn_modules))
|
|
|
|
if reference_adain:
|
|
gn_modules = [self.unet.mid_block]
|
|
self.unet.mid_block.gn_weight = 0
|
|
|
|
down_blocks = self.unet.down_blocks
|
|
for w, module in enumerate(down_blocks):
|
|
module.gn_weight = 1.0 - float(w) / float(len(down_blocks))
|
|
gn_modules.append(module)
|
|
|
|
up_blocks = self.unet.up_blocks
|
|
for w, module in enumerate(up_blocks):
|
|
module.gn_weight = float(w) / float(len(up_blocks))
|
|
gn_modules.append(module)
|
|
|
|
for i, module in enumerate(gn_modules):
|
|
if getattr(module, "original_forward", None) is None:
|
|
module.original_forward = module.forward
|
|
if i == 0:
|
|
# mid_block
|
|
module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)
|
|
elif isinstance(module, CrossAttnDownBlock2D):
|
|
module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)
|
|
elif isinstance(module, DownBlock2D):
|
|
module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)
|
|
elif isinstance(module, CrossAttnUpBlock2D):
|
|
module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)
|
|
elif isinstance(module, UpBlock2D):
|
|
module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)
|
|
module.mean_bank = []
|
|
module.var_bank = []
|
|
module.gn_weight *= 2
|
|
|
|
# 9. Prepare added time ids & embeddings
|
|
add_text_embeds = pooled_prompt_embeds
|
|
if self.text_encoder_2 is None:
|
|
text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
|
|
else:
|
|
text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
|
|
|
|
add_time_ids = self._get_add_time_ids(
|
|
original_size,
|
|
crops_coords_top_left,
|
|
target_size,
|
|
dtype=prompt_embeds.dtype,
|
|
text_encoder_projection_dim=text_encoder_projection_dim,
|
|
)
|
|
if negative_original_size is not None and negative_target_size is not None:
|
|
negative_add_time_ids = self._get_add_time_ids(
|
|
negative_original_size,
|
|
negative_crops_coords_top_left,
|
|
negative_target_size,
|
|
dtype=prompt_embeds.dtype,
|
|
text_encoder_projection_dim=text_encoder_projection_dim,
|
|
)
|
|
else:
|
|
negative_add_time_ids = add_time_ids
|
|
|
|
if self.do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
|
|
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
|
|
add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
|
|
|
|
prompt_embeds = prompt_embeds.to(device)
|
|
add_text_embeds = add_text_embeds.to(device)
|
|
add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
|
|
|
|
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
|
|
image_embeds = self.prepare_ip_adapter_image_embeds(
|
|
ip_adapter_image,
|
|
ip_adapter_image_embeds,
|
|
device,
|
|
batch_size * num_images_per_prompt,
|
|
self.do_classifier_free_guidance,
|
|
)
|
|
|
|
# 10. Denoising loop
|
|
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
|
|
|
|
# 10.1 Apply denoising_end
|
|
if (
|
|
self.denoising_end is not None
|
|
and isinstance(self.denoising_end, float)
|
|
and self.denoising_end > 0
|
|
and self.denoising_end < 1
|
|
):
|
|
discrete_timestep_cutoff = int(
|
|
round(
|
|
self.scheduler.config.num_train_timesteps
|
|
- (self.denoising_end * self.scheduler.config.num_train_timesteps)
|
|
)
|
|
)
|
|
num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
|
|
timesteps = timesteps[:num_inference_steps]
|
|
|
|
# 11. Optionally get Guidance Scale Embedding
|
|
timestep_cond = None
|
|
if self.unet.config.time_cond_proj_dim is not None:
|
|
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
|
|
timestep_cond = self.get_guidance_scale_embedding(
|
|
guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
|
|
).to(device=device, dtype=latents.dtype)
|
|
|
|
self._num_timesteps = len(timesteps)
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
if self.interrupt:
|
|
continue
|
|
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
|
|
|
|
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
|
|
|
# predict the noise residual
|
|
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
|
|
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
|
|
added_cond_kwargs["image_embeds"] = image_embeds
|
|
|
|
# ref only part
|
|
if reference_keeps[i] > 0:
|
|
noise = randn_tensor(
|
|
ref_image_latents.shape, generator=generator, device=device, dtype=ref_image_latents.dtype
|
|
)
|
|
ref_xt = self.scheduler.add_noise(
|
|
ref_image_latents,
|
|
noise,
|
|
t.reshape(
|
|
1,
|
|
),
|
|
)
|
|
ref_xt = self.scheduler.scale_model_input(ref_xt, t)
|
|
|
|
MODE = "write"
|
|
self.unet(
|
|
ref_xt,
|
|
t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
added_cond_kwargs=added_cond_kwargs,
|
|
return_dict=False,
|
|
)
|
|
|
|
# predict the noise residual
|
|
MODE = "read"
|
|
noise_pred = self.unet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
timestep_cond=timestep_cond,
|
|
cross_attention_kwargs=self.cross_attention_kwargs,
|
|
added_cond_kwargs=added_cond_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# perform guidance
|
|
if self.do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
|
|
if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
|
|
# Based on 3.4. in https://huggingface.co/papers/2305.08891
|
|
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents_dtype = latents.dtype
|
|
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
|
if latents.dtype != latents_dtype:
|
|
if torch.backends.mps.is_available():
|
|
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
|
|
latents = latents.to(latents_dtype)
|
|
|
|
if callback_on_step_end is not None:
|
|
callback_kwargs = {}
|
|
for k in callback_on_step_end_tensor_inputs:
|
|
callback_kwargs[k] = locals()[k]
|
|
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
|
|
|
|
latents = callback_outputs.pop("latents", latents)
|
|
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
|
|
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
|
|
add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
|
|
negative_pooled_prompt_embeds = callback_outputs.pop(
|
|
"negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
|
|
)
|
|
add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
|
|
negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids)
|
|
|
|
# call the callback, if provided
|
|
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:
|
|
step_idx = i // getattr(self.scheduler, "order", 1)
|
|
callback(step_idx, t, latents)
|
|
|
|
if XLA_AVAILABLE:
|
|
xm.mark_step()
|
|
|
|
if not output_type == "latent":
|
|
# make sure the VAE is in float32 mode, as it overflows in float16
|
|
needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
|
|
|
|
if needs_upcasting:
|
|
self.upcast_vae()
|
|
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
|
|
elif latents.dtype != self.vae.dtype:
|
|
if torch.backends.mps.is_available():
|
|
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
|
|
self.vae = self.vae.to(latents.dtype)
|
|
|
|
# unscale/denormalize the latents
|
|
# denormalize with the mean and std if available and not None
|
|
has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
|
|
has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
|
|
if has_latents_mean and has_latents_std:
|
|
latents_mean = (
|
|
torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
|
|
)
|
|
latents_std = (
|
|
torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
|
|
)
|
|
latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
|
|
else:
|
|
latents = latents / self.vae.config.scaling_factor
|
|
|
|
image = self.vae.decode(latents, return_dict=False)[0]
|
|
|
|
# cast back to fp16 if needed
|
|
if needs_upcasting:
|
|
self.vae.to(dtype=torch.float16)
|
|
else:
|
|
image = latents
|
|
|
|
if not output_type == "latent":
|
|
# apply watermark if available
|
|
if self.watermark is not None:
|
|
image = self.watermark.apply_watermark(image)
|
|
|
|
image = self.image_processor.postprocess(image, output_type=output_type)
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (image,)
|
|
|
|
return StableDiffusionXLPipelineOutput(images=image)
|