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[Text-to-Video] Clean up pipeline (#6213)
* make style * make style * make style * make style
This commit is contained in:
Patrick von Platen
committed by
GitHub
GitHub
parent
d816bcb5e8
commit
cce1fe2d41
@@ -1,4 +1,5 @@
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import copy
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import inspect
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from dataclasses import dataclass
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from typing import Callable, List, Optional, Union
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@@ -9,11 +10,18 @@ import torch.nn.functional as F
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from torch.nn.functional import grid_sample
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from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
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from diffusers.models import AutoencoderKL, UNet2DConditionModel
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from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline, StableDiffusionSafetyChecker
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from diffusers.schedulers import KarrasDiffusionSchedulers
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from diffusers.utils import BaseOutput
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from diffusers.utils.torch_utils import randn_tensor
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from ...image_processor import VaeImageProcessor
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from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
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from ...models import AutoencoderKL, UNet2DConditionModel
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from ...models.lora import adjust_lora_scale_text_encoder
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from ...schedulers import KarrasDiffusionSchedulers
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from ...utils import USE_PEFT_BACKEND, BaseOutput, logging, scale_lora_layers, unscale_lora_layers
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from ...utils.torch_utils import randn_tensor
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from ..pipeline_utils import DiffusionPipeline
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from ..stable_diffusion import StableDiffusionSafetyChecker
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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def rearrange_0(tensor, f):
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@@ -273,7 +281,7 @@ def create_motion_field_and_warp_latents(motion_field_strength_x, motion_field_s
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return warped_latents
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class TextToVideoZeroPipeline(StableDiffusionPipeline):
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class TextToVideoZeroPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
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r"""
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Pipeline for zero-shot text-to-video generation using Stable Diffusion.
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@@ -311,8 +319,15 @@ class TextToVideoZeroPipeline(StableDiffusionPipeline):
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feature_extractor: CLIPImageProcessor,
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requires_safety_checker: bool = True,
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):
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super().__init__(
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vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, requires_safety_checker
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super().__init__()
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self.register_modules(
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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unet=unet,
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scheduler=scheduler,
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safety_checker=safety_checker,
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feature_extractor=feature_extractor,
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)
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processor = (
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CrossFrameAttnProcessor2_0(batch_size=2)
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@@ -321,6 +336,18 @@ class TextToVideoZeroPipeline(StableDiffusionPipeline):
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)
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self.unet.set_attn_processor(processor)
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if safety_checker is None and requires_safety_checker:
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logger.warning(
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f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
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" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
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" results in services or applications open to the public. Both the diffusers team and Hugging Face"
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" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
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" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
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" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
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)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
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def forward_loop(self, x_t0, t0, t1, generator):
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"""
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Perform DDPM forward process from time t0 to t1. This is the same as adding noise with corresponding variance.
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@@ -420,6 +447,77 @@ class TextToVideoZeroPipeline(StableDiffusionPipeline):
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callback(step_idx, t, latents)
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return latents.clone().detach()
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
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def check_inputs(
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self,
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prompt,
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height,
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width,
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callback_steps,
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negative_prompt=None,
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prompt_embeds=None,
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negative_prompt_embeds=None,
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callback_on_step_end_tensor_inputs=None,
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):
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if height % 8 != 0 or width % 8 != 0:
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raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
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if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
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raise ValueError(
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f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
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f" {type(callback_steps)}."
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)
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if callback_on_step_end_tensor_inputs is not None and not all(
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k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
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):
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raise ValueError(
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f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
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)
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if prompt is not None and prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
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" only forward one of the two."
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)
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elif prompt is None and prompt_embeds is None:
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raise ValueError(
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"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
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)
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elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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if negative_prompt is not None and negative_prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
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f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
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)
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if prompt_embeds is not None and negative_prompt_embeds is not None:
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if prompt_embeds.shape != negative_prompt_embeds.shape:
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raise ValueError(
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"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
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f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
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f" {negative_prompt_embeds.shape}."
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)
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
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def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
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shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
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if isinstance(generator, list) and len(generator) != batch_size:
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raise ValueError(
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f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
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f" size of {batch_size}. Make sure the batch size matches the length of the generators."
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)
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if latents is None:
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latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
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else:
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latents = latents.to(device)
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# scale the initial noise by the standard deviation required by the scheduler
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latents = latents * self.scheduler.init_noise_sigma
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return latents
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@torch.no_grad()
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def __call__(
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self,
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@@ -539,9 +637,10 @@ class TextToVideoZeroPipeline(StableDiffusionPipeline):
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do_classifier_free_guidance = guidance_scale > 1.0
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# Encode input prompt
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prompt_embeds = self._encode_prompt(
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prompt_embeds_tuple = self.encode_prompt(
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prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
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)
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prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
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# Prepare timesteps
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self.scheduler.set_timesteps(num_inference_steps, device=device)
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@@ -645,3 +744,226 @@ class TextToVideoZeroPipeline(StableDiffusionPipeline):
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return (image, has_nsfw_concept)
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return TextToVideoPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
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def run_safety_checker(self, image, device, dtype):
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if self.safety_checker is None:
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has_nsfw_concept = None
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else:
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if torch.is_tensor(image):
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feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
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else:
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feature_extractor_input = self.image_processor.numpy_to_pil(image)
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safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
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image, has_nsfw_concept = self.safety_checker(
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images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
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)
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return image, has_nsfw_concept
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
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def prepare_extra_step_kwargs(self, generator, eta):
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# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
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# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
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# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
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# and should be between [0, 1]
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accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
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extra_step_kwargs = {}
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if accepts_eta:
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extra_step_kwargs["eta"] = eta
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# check if the scheduler accepts generator
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accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
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if accepts_generator:
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extra_step_kwargs["generator"] = generator
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return extra_step_kwargs
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
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def encode_prompt(
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self,
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prompt,
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device,
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num_images_per_prompt,
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do_classifier_free_guidance,
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negative_prompt=None,
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prompt_embeds: Optional[torch.FloatTensor] = None,
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negative_prompt_embeds: Optional[torch.FloatTensor] = None,
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lora_scale: Optional[float] = None,
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clip_skip: Optional[int] = None,
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):
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r"""
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Encodes the prompt into text encoder hidden states.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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prompt to be encoded
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device: (`torch.device`):
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torch device
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num_images_per_prompt (`int`):
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number of images that should be generated per prompt
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do_classifier_free_guidance (`bool`):
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whether to use classifier free guidance or not
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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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prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *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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lora_scale (`float`, *optional*):
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A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
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clip_skip (`int`, *optional*):
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Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
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the output of the pre-final layer will be used for computing the prompt embeddings.
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"""
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# set lora scale so that monkey patched LoRA
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# function of text encoder can correctly access it
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if lora_scale is not None and isinstance(self, LoraLoaderMixin):
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self._lora_scale = lora_scale
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# dynamically adjust the LoRA scale
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if not USE_PEFT_BACKEND:
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adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
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else:
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scale_lora_layers(self.text_encoder, lora_scale)
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if prompt is not None and isinstance(prompt, str):
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batch_size = 1
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elif prompt is not None and isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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batch_size = prompt_embeds.shape[0]
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if prompt_embeds is None:
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# textual inversion: procecss multi-vector tokens if necessary
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if isinstance(self, TextualInversionLoaderMixin):
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prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
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text_inputs = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
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if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
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text_input_ids, untruncated_ids
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):
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removed_text = self.tokenizer.batch_decode(
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untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
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)
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
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attention_mask = text_inputs.attention_mask.to(device)
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else:
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attention_mask = None
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if clip_skip is None:
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prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
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prompt_embeds = prompt_embeds[0]
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else:
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prompt_embeds = self.text_encoder(
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text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
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)
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# Access the `hidden_states` first, that contains a tuple of
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# all the hidden states from the encoder layers. Then index into
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# the tuple to access the hidden states from the desired layer.
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prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
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# We also need to apply the final LayerNorm here to not mess with the
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# representations. The `last_hidden_states` that we typically use for
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# obtaining the final prompt representations passes through the LayerNorm
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# layer.
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prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
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if self.text_encoder is not None:
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prompt_embeds_dtype = self.text_encoder.dtype
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elif self.unet is not None:
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prompt_embeds_dtype = self.unet.dtype
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else:
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prompt_embeds_dtype = prompt_embeds.dtype
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prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
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bs_embed, seq_len, _ = prompt_embeds.shape
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
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prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance and negative_prompt_embeds is None:
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uncond_tokens: List[str]
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if negative_prompt is None:
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uncond_tokens = [""] * batch_size
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elif prompt is not None and type(prompt) is not type(negative_prompt):
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raise TypeError(
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f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
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f" {type(prompt)}."
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)
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elif isinstance(negative_prompt, str):
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uncond_tokens = [negative_prompt]
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elif batch_size != len(negative_prompt):
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raise ValueError(
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f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
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f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
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" the batch size of `prompt`."
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)
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else:
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uncond_tokens = negative_prompt
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# textual inversion: procecss multi-vector tokens if necessary
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if isinstance(self, TextualInversionLoaderMixin):
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uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
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max_length = prompt_embeds.shape[1]
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uncond_input = self.tokenizer(
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uncond_tokens,
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padding="max_length",
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max_length=max_length,
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truncation=True,
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return_tensors="pt",
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)
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if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
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attention_mask = uncond_input.attention_mask.to(device)
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else:
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attention_mask = None
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negative_prompt_embeds = self.text_encoder(
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uncond_input.input_ids.to(device),
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attention_mask=attention_mask,
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)
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negative_prompt_embeds = negative_prompt_embeds[0]
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if do_classifier_free_guidance:
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# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
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seq_len = negative_prompt_embeds.shape[1]
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negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
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negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
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negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
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if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
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# Retrieve the original scale by scaling back the LoRA layers
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unscale_lora_layers(self.text_encoder, lora_scale)
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return prompt_embeds, negative_prompt_embeds
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def decode_latents(self, latents):
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latents = 1 / self.vae.config.scaling_factor * latents
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image = self.vae.decode(latents, return_dict=False)[0]
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image = (image / 2 + 0.5).clamp(0, 1)
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# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
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image = image.cpu().permute(0, 2, 3, 1).float().numpy()
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return image
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@@ -1,4 +1,5 @@
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import copy
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||||
import inspect
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
|
||||
|
||||
@@ -15,11 +16,35 @@ from transformers import (
|
||||
CLIPVisionModelWithProjection,
|
||||
)
|
||||
|
||||
from diffusers.models import AutoencoderKL, UNet2DConditionModel
|
||||
from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipeline
|
||||
from diffusers.schedulers import KarrasDiffusionSchedulers
|
||||
from diffusers.utils import BaseOutput
|
||||
from diffusers.utils.torch_utils import randn_tensor
|
||||
from ...image_processor import VaeImageProcessor
|
||||
from ...loaders import StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
|
||||
from ...models import AutoencoderKL, UNet2DConditionModel
|
||||
from ...models.attention_processor import (
|
||||
AttnProcessor2_0,
|
||||
FusedAttnProcessor2_0,
|
||||
LoRAAttnProcessor2_0,
|
||||
LoRAXFormersAttnProcessor,
|
||||
XFormersAttnProcessor,
|
||||
)
|
||||
from ...models.lora import adjust_lora_scale_text_encoder
|
||||
from ...schedulers import KarrasDiffusionSchedulers
|
||||
from ...utils import (
|
||||
USE_PEFT_BACKEND,
|
||||
BaseOutput,
|
||||
is_invisible_watermark_available,
|
||||
logging,
|
||||
scale_lora_layers,
|
||||
unscale_lora_layers,
|
||||
)
|
||||
from ...utils.torch_utils import randn_tensor
|
||||
from ..pipeline_utils import DiffusionPipeline
|
||||
|
||||
|
||||
if is_invisible_watermark_available():
|
||||
from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
||||
|
||||
|
||||
# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.rearrange_0
|
||||
@@ -300,7 +325,11 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
|
||||
return noise_cfg
|
||||
|
||||
|
||||
class TextToVideoZeroSDXLPipeline(StableDiffusionXLPipeline):
|
||||
class TextToVideoZeroSDXLPipeline(
|
||||
DiffusionPipeline,
|
||||
StableDiffusionXLLoraLoaderMixin,
|
||||
TextualInversionLoaderMixin,
|
||||
):
|
||||
r"""
|
||||
Pipeline for zero-shot text-to-video generation using Stable Diffusion XL.
|
||||
|
||||
@@ -332,6 +361,16 @@ class TextToVideoZeroSDXLPipeline(StableDiffusionXLPipeline):
|
||||
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
|
||||
"""
|
||||
|
||||
model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
|
||||
_optional_components = [
|
||||
"tokenizer",
|
||||
"tokenizer_2",
|
||||
"text_encoder",
|
||||
"text_encoder_2",
|
||||
"image_encoder",
|
||||
"feature_extractor",
|
||||
]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vae: AutoencoderKL,
|
||||
@@ -346,7 +385,8 @@ class TextToVideoZeroSDXLPipeline(StableDiffusionXLPipeline):
|
||||
force_zeros_for_empty_prompt: bool = True,
|
||||
add_watermarker: Optional[bool] = None,
|
||||
):
|
||||
super().__init__(
|
||||
super().__init__()
|
||||
self.register_modules(
|
||||
vae=vae,
|
||||
text_encoder=text_encoder,
|
||||
text_encoder_2=text_encoder_2,
|
||||
@@ -356,16 +396,435 @@ class TextToVideoZeroSDXLPipeline(StableDiffusionXLPipeline):
|
||||
scheduler=scheduler,
|
||||
image_encoder=image_encoder,
|
||||
feature_extractor=feature_extractor,
|
||||
force_zeros_for_empty_prompt=force_zeros_for_empty_prompt,
|
||||
add_watermarker=add_watermarker,
|
||||
)
|
||||
self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
|
||||
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
|
||||
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
|
||||
|
||||
self.default_sample_size = self.unet.config.sample_size
|
||||
|
||||
add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
|
||||
|
||||
if add_watermarker:
|
||||
self.watermark = StableDiffusionXLWatermarker()
|
||||
else:
|
||||
self.watermark = None
|
||||
|
||||
processor = (
|
||||
CrossFrameAttnProcessor2_0(batch_size=2)
|
||||
if hasattr(F, "scaled_dot_product_attention")
|
||||
else CrossFrameAttnProcessor(batch_size=2)
|
||||
)
|
||||
|
||||
self.unet.set_attn_processor(processor)
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
|
||||
def prepare_extra_step_kwargs(self, generator, eta):
|
||||
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
||||
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
||||
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
||||
# and should be between [0, 1]
|
||||
|
||||
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
||||
extra_step_kwargs = {}
|
||||
if accepts_eta:
|
||||
extra_step_kwargs["eta"] = eta
|
||||
|
||||
# check if the scheduler accepts generator
|
||||
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
||||
if accepts_generator:
|
||||
extra_step_kwargs["generator"] = generator
|
||||
return extra_step_kwargs
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
|
||||
def enable_vae_slicing(self):
|
||||
r"""
|
||||
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
|
||||
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
|
||||
"""
|
||||
self.vae.enable_slicing()
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
|
||||
def disable_vae_slicing(self):
|
||||
r"""
|
||||
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
|
||||
computing decoding in one step.
|
||||
"""
|
||||
self.vae.disable_slicing()
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae
|
||||
def upcast_vae(self):
|
||||
dtype = self.vae.dtype
|
||||
self.vae.to(dtype=torch.float32)
|
||||
use_torch_2_0_or_xformers = isinstance(
|
||||
self.vae.decoder.mid_block.attentions[0].processor,
|
||||
(
|
||||
AttnProcessor2_0,
|
||||
XFormersAttnProcessor,
|
||||
LoRAXFormersAttnProcessor,
|
||||
LoRAAttnProcessor2_0,
|
||||
FusedAttnProcessor2_0,
|
||||
),
|
||||
)
|
||||
# if xformers or torch_2_0 is used attention block does not need
|
||||
# to be in float32 which can save lots of memory
|
||||
if use_torch_2_0_or_xformers:
|
||||
self.vae.post_quant_conv.to(dtype)
|
||||
self.vae.decoder.conv_in.to(dtype)
|
||||
self.vae.decoder.mid_block.to(dtype)
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids
|
||||
def _get_add_time_ids(
|
||||
self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None
|
||||
):
|
||||
add_time_ids = list(original_size + crops_coords_top_left + target_size)
|
||||
|
||||
passed_add_embed_dim = (
|
||||
self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
|
||||
)
|
||||
expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
|
||||
|
||||
if expected_add_embed_dim != passed_add_embed_dim:
|
||||
raise ValueError(
|
||||
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
|
||||
)
|
||||
|
||||
add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
|
||||
return add_time_ids
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
|
||||
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
|
||||
shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
|
||||
if isinstance(generator, list) and len(generator) != batch_size:
|
||||
raise ValueError(
|
||||
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
||||
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
||||
)
|
||||
|
||||
if latents is None:
|
||||
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
||||
else:
|
||||
latents = latents.to(device)
|
||||
|
||||
# scale the initial noise by the standard deviation required by the scheduler
|
||||
latents = latents * self.scheduler.init_noise_sigma
|
||||
return latents
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.check_inputs
|
||||
def check_inputs(
|
||||
self,
|
||||
prompt,
|
||||
prompt_2,
|
||||
height,
|
||||
width,
|
||||
callback_steps,
|
||||
negative_prompt=None,
|
||||
negative_prompt_2=None,
|
||||
prompt_embeds=None,
|
||||
negative_prompt_embeds=None,
|
||||
pooled_prompt_embeds=None,
|
||||
negative_pooled_prompt_embeds=None,
|
||||
callback_on_step_end_tensor_inputs=None,
|
||||
):
|
||||
if height % 8 != 0 or width % 8 != 0:
|
||||
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
||||
|
||||
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
|
||||
raise ValueError(
|
||||
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
|
||||
f" {type(callback_steps)}."
|
||||
)
|
||||
|
||||
if callback_on_step_end_tensor_inputs is not None and not all(
|
||||
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
|
||||
):
|
||||
raise ValueError(
|
||||
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
|
||||
)
|
||||
|
||||
if prompt is not None and prompt_embeds is not None:
|
||||
raise ValueError(
|
||||
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
||||
" only forward one of the two."
|
||||
)
|
||||
elif prompt_2 is not None and prompt_embeds is not None:
|
||||
raise ValueError(
|
||||
f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
||||
" only forward one of the two."
|
||||
)
|
||||
elif prompt is None and prompt_embeds is None:
|
||||
raise ValueError(
|
||||
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
||||
)
|
||||
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
|
||||
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
||||
elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
|
||||
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
|
||||
|
||||
if negative_prompt is not None and negative_prompt_embeds is not None:
|
||||
raise ValueError(
|
||||
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
||||
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
||||
)
|
||||
elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
|
||||
raise ValueError(
|
||||
f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
|
||||
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
||||
)
|
||||
|
||||
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
||||
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
||||
raise ValueError(
|
||||
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
||||
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
||||
f" {negative_prompt_embeds.shape}."
|
||||
)
|
||||
|
||||
if prompt_embeds is not None and pooled_prompt_embeds is None:
|
||||
raise ValueError(
|
||||
"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
|
||||
)
|
||||
|
||||
if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
|
||||
raise ValueError(
|
||||
"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
|
||||
)
|
||||
|
||||
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
|
||||
def encode_prompt(
|
||||
self,
|
||||
prompt: str,
|
||||
prompt_2: Optional[str] = None,
|
||||
device: Optional[torch.device] = None,
|
||||
num_images_per_prompt: int = 1,
|
||||
do_classifier_free_guidance: bool = True,
|
||||
negative_prompt: Optional[str] = None,
|
||||
negative_prompt_2: Optional[str] = None,
|
||||
prompt_embeds: Optional[torch.FloatTensor] = None,
|
||||
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
||||
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
|
||||
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
|
||||
lora_scale: Optional[float] = None,
|
||||
clip_skip: Optional[int] = None,
|
||||
):
|
||||
r"""
|
||||
Encodes the prompt into text encoder hidden states.
|
||||
|
||||
Args:
|
||||
prompt (`str` or `List[str]`, *optional*):
|
||||
prompt to be encoded
|
||||
prompt_2 (`str` or `List[str]`, *optional*):
|
||||
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
|
||||
used in both text-encoders
|
||||
device: (`torch.device`):
|
||||
torch device
|
||||
num_images_per_prompt (`int`):
|
||||
number of images that should be generated per prompt
|
||||
do_classifier_free_guidance (`bool`):
|
||||
whether to use classifier free guidance or not
|
||||
negative_prompt (`str` or `List[str]`, *optional*):
|
||||
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
|
||||
less than `1`).
|
||||
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
|
||||
prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *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.FloatTensor`, *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.FloatTensor`, *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.
|
||||
lora_scale (`float`, *optional*):
|
||||
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
|
||||
clip_skip (`int`, *optional*):
|
||||
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
|
||||
the output of the pre-final layer will be used for computing the prompt embeddings.
|
||||
"""
|
||||
device = device or self._execution_device
|
||||
|
||||
# set lora scale so that monkey patched LoRA
|
||||
# function of text encoder can correctly access it
|
||||
if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
|
||||
self._lora_scale = lora_scale
|
||||
|
||||
# dynamically adjust the LoRA scale
|
||||
if self.text_encoder is not None:
|
||||
if not USE_PEFT_BACKEND:
|
||||
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
|
||||
else:
|
||||
scale_lora_layers(self.text_encoder, lora_scale)
|
||||
|
||||
if self.text_encoder_2 is not None:
|
||||
if not USE_PEFT_BACKEND:
|
||||
adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
|
||||
else:
|
||||
scale_lora_layers(self.text_encoder_2, lora_scale)
|
||||
|
||||
prompt = [prompt] if isinstance(prompt, str) else prompt
|
||||
|
||||
if prompt is not None:
|
||||
batch_size = len(prompt)
|
||||
else:
|
||||
batch_size = prompt_embeds.shape[0]
|
||||
|
||||
# Define tokenizers and text encoders
|
||||
tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
|
||||
text_encoders = (
|
||||
[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
|
||||
)
|
||||
|
||||
if prompt_embeds is None:
|
||||
prompt_2 = prompt_2 or prompt
|
||||
prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
|
||||
|
||||
# textual inversion: procecss multi-vector tokens if necessary
|
||||
prompt_embeds_list = []
|
||||
prompts = [prompt, prompt_2]
|
||||
for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
|
||||
if isinstance(self, TextualInversionLoaderMixin):
|
||||
prompt = self.maybe_convert_prompt(prompt, tokenizer)
|
||||
|
||||
text_inputs = tokenizer(
|
||||
prompt,
|
||||
padding="max_length",
|
||||
max_length=tokenizer.model_max_length,
|
||||
truncation=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
|
||||
text_input_ids = text_inputs.input_ids
|
||||
untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
|
||||
|
||||
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
|
||||
text_input_ids, untruncated_ids
|
||||
):
|
||||
removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
|
||||
logger.warning(
|
||||
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
||||
f" {tokenizer.model_max_length} tokens: {removed_text}"
|
||||
)
|
||||
|
||||
prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
|
||||
|
||||
# We are only ALWAYS interested in the pooled output of the final text encoder
|
||||
pooled_prompt_embeds = prompt_embeds[0]
|
||||
if clip_skip is None:
|
||||
prompt_embeds = prompt_embeds.hidden_states[-2]
|
||||
else:
|
||||
# "2" because SDXL always indexes from the penultimate layer.
|
||||
prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
|
||||
|
||||
prompt_embeds_list.append(prompt_embeds)
|
||||
|
||||
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
|
||||
|
||||
# get unconditional embeddings for classifier free guidance
|
||||
zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
|
||||
if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
|
||||
negative_prompt_embeds = torch.zeros_like(prompt_embeds)
|
||||
negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
|
||||
elif do_classifier_free_guidance and negative_prompt_embeds is None:
|
||||
negative_prompt = negative_prompt or ""
|
||||
negative_prompt_2 = negative_prompt_2 or negative_prompt
|
||||
|
||||
# normalize str to list
|
||||
negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
|
||||
negative_prompt_2 = (
|
||||
batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
|
||||
)
|
||||
|
||||
uncond_tokens: List[str]
|
||||
if prompt is not None and type(prompt) is not type(negative_prompt):
|
||||
raise TypeError(
|
||||
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
||||
f" {type(prompt)}."
|
||||
)
|
||||
elif batch_size != len(negative_prompt):
|
||||
raise ValueError(
|
||||
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
||||
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
||||
" the batch size of `prompt`."
|
||||
)
|
||||
else:
|
||||
uncond_tokens = [negative_prompt, negative_prompt_2]
|
||||
|
||||
negative_prompt_embeds_list = []
|
||||
for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
|
||||
if isinstance(self, TextualInversionLoaderMixin):
|
||||
negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
|
||||
|
||||
max_length = prompt_embeds.shape[1]
|
||||
uncond_input = tokenizer(
|
||||
negative_prompt,
|
||||
padding="max_length",
|
||||
max_length=max_length,
|
||||
truncation=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
|
||||
negative_prompt_embeds = text_encoder(
|
||||
uncond_input.input_ids.to(device),
|
||||
output_hidden_states=True,
|
||||
)
|
||||
# We are only ALWAYS interested in the pooled output of the final text encoder
|
||||
negative_pooled_prompt_embeds = negative_prompt_embeds[0]
|
||||
negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
|
||||
|
||||
negative_prompt_embeds_list.append(negative_prompt_embeds)
|
||||
|
||||
negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
|
||||
|
||||
if self.text_encoder_2 is not None:
|
||||
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
|
||||
else:
|
||||
prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)
|
||||
|
||||
bs_embed, seq_len, _ = prompt_embeds.shape
|
||||
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
||||
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
||||
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
||||
|
||||
if do_classifier_free_guidance:
|
||||
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
||||
seq_len = negative_prompt_embeds.shape[1]
|
||||
|
||||
if self.text_encoder_2 is not None:
|
||||
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
|
||||
else:
|
||||
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device)
|
||||
|
||||
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
||||
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
|
||||
|
||||
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
|
||||
bs_embed * num_images_per_prompt, -1
|
||||
)
|
||||
if do_classifier_free_guidance:
|
||||
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
|
||||
bs_embed * num_images_per_prompt, -1
|
||||
)
|
||||
|
||||
if self.text_encoder is not None:
|
||||
if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
|
||||
# Retrieve the original scale by scaling back the LoRA layers
|
||||
unscale_lora_layers(self.text_encoder, lora_scale)
|
||||
|
||||
if self.text_encoder_2 is not None:
|
||||
if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
|
||||
# Retrieve the original scale by scaling back the LoRA layers
|
||||
unscale_lora_layers(self.text_encoder_2, lora_scale)
|
||||
|
||||
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
|
||||
|
||||
# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoZeroPipeline.forward_loop
|
||||
def forward_loop(self, x_t0, t0, t1, generator):
|
||||
"""
|
||||
|
||||
@@ -383,7 +383,7 @@ class TextToVideoZeroSDXLPipelineFastTests(PipelineTesterMixin, unittest.TestCas
|
||||
class TextToVideoZeroSDXLPipelineSlowTests(unittest.TestCase):
|
||||
def test_full_model(self):
|
||||
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
|
||||
pipe = self.pipeline_class.from_pretrained(
|
||||
pipe = TextToVideoZeroSDXLPipeline.from_pretrained(
|
||||
model_id, torch_dtype=torch.float16, variant="fp16", use_safetensors=True
|
||||
)
|
||||
pipe.enable_model_cpu_offload()
|
||||
|
||||
Reference in New Issue
Block a user