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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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|
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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 |
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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__) |
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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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|
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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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|
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>>> result_img.show() |
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``` |
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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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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://arxiv.org/pdf/2305.08891.pdf). 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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noise_pred_rescaled = noise_cfg * (std_text / std_cfg) |
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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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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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|
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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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|
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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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if self.vae.dtype == torch.float16 and self.vae.config.force_upcast: |
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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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|
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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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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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ref_image_latents = ref_image_latents.to(device=device, dtype=dtype) |
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return ref_image_latents |
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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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 |
|
[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 |
|
[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://arxiv.org/abs/2207.12598). |
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`guidance_scale` is defined as `w` of equation 2. of [Imagen |
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Paper](https://arxiv.org/pdf/2205.11487.pdf). 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 |
|
`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*): |
|
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and |
|
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders |
|
num_images_per_prompt (`int`, *optional*, defaults to 1): |
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The number of images to generate per prompt. |
|
eta (`float`, *optional*, defaults to 0.0): |
|
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to |
|
[`schedulers.DDIMScheduler`], will be ignored for others. |
|
generator (`torch.Generator` or `List[torch.Generator]`, *optional*): |
|
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) |
|
to make generation deterministic. |
|
latents (`torch.Tensor`, *optional*): |
|
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image |
|
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents |
|
tensor will ge generated by sampling using the supplied random `generator`. |
|
prompt_embeds (`torch.Tensor`, *optional*): |
|
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not |
|
provided, text embeddings will be generated from `prompt` input argument. |
|
negative_prompt_embeds (`torch.Tensor`, *optional*): |
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt |
|
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input |
|
argument. |
|
pooled_prompt_embeds (`torch.Tensor`, *optional*): |
|
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. |
|
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://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of |
|
[Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). |
|
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 |
|
|
|
|
|
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) |
|
|
|
|
|
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 |
|
|
|
|
|
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 |
|
|
|
|
|
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, |
|
) |
|
|
|
|
|
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, |
|
) |
|
|
|
|
|
timesteps, num_inference_steps = retrieve_timesteps( |
|
self.scheduler, num_inference_steps, device, timesteps, sigmas |
|
) |
|
|
|
|
|
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, |
|
) |
|
|
|
|
|
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, |
|
) |
|
|
|
|
|
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) |
|
|
|
|
|
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) |
|
|
|
|
|
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) |
|
|
|
|
|
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), |
|
|
|
**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) |
|
) |
|
|
|
|
|
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 |
|
|
|
|
|
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 |
|
|
|
|
|
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 |
|
|
|
for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)): |
|
|
|
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): |
|
|
|
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: |
|
|
|
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 |
|
|
|
|
|
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, |
|
) |
|
|
|
|
|
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) |
|
|
|
|
|
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] |
|
|
|
|
|
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 |
|
|
|
|
|
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) |
|
|
|
|
|
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 |
|
|
|
|
|
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, |
|
) |
|
|
|
|
|
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] |
|
|
|
|
|
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: |
|
|
|
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale) |
|
|
|
|
|
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(): |
|
|
|
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) |
|
|
|
|
|
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": |
|
|
|
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(): |
|
|
|
self.vae = self.vae.to(latents.dtype) |
|
|
|
|
|
|
|
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] |
|
|
|
|
|
if needs_upcasting: |
|
self.vae.to(dtype=torch.float16) |
|
else: |
|
image = latents |
|
|
|
if not output_type == "latent": |
|
|
|
if self.watermark is not None: |
|
image = self.watermark.apply_watermark(image) |
|
|
|
image = self.image_processor.postprocess(image, output_type=output_type) |
|
|
|
|
|
self.maybe_free_model_hooks() |
|
|
|
if not return_dict: |
|
return (image,) |
|
|
|
return StableDiffusionXLPipelineOutput(images=image) |
|
|