diff --git a/docs/source/en/api/pipelines/text_to_video_zero.md b/docs/source/en/api/pipelines/text_to_video_zero.md index 626e75f94936..f58a151c2a51 100644 --- a/docs/source/en/api/pipelines/text_to_video_zero.md +++ b/docs/source/en/api/pipelines/text_to_video_zero.md @@ -92,6 +92,19 @@ imageio.mimsave("video.mp4", result, fps=4) ``` +- #### SDXL Support +In order to use the SDXL model when generating a video from prompt, use the `TextToVideoZeroSDXLPipeline` pipeline: + +```python +import torch +from diffusers import TextToVideoZeroSDXLPipeline + +model_id = "stabilityai/stable-diffusion-xl-base-1.0" +pipe = TextToVideoZeroSDXLPipeline.from_pretrained( + model_id, torch_dtype=torch.float16, variant="fp16", use_safetensors=True +).to("cuda") +``` + ### Text-To-Video with Pose Control To generate a video from prompt with additional pose control @@ -141,7 +154,33 @@ To generate a video from prompt with additional pose control result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images imageio.mimsave("video.mp4", result, fps=4) ``` - +- #### SDXL Support + + Since our attention processor also works with SDXL, it can be utilized to generate a video from prompt using ControlNet models powered by SDXL: + ```python + import torch + from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel + from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero import CrossFrameAttnProcessor + + controlnet_model_id = 'thibaud/controlnet-openpose-sdxl-1.0' + model_id = 'stabilityai/stable-diffusion-xl-base-1.0' + + controlnet = ControlNetModel.from_pretrained(controlnet_model_id, torch_dtype=torch.float16) + pipe = StableDiffusionControlNetPipeline.from_pretrained( + model_id, controlnet=controlnet, torch_dtype=torch.float16 + ).to('cuda') + + # Set the attention processor + pipe.unet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2)) + pipe.controlnet.set_attn_processor(CrossFrameAttnProcessor(batch_size=2)) + + # fix latents for all frames + latents = torch.randn((1, 4, 128, 128), device="cuda", dtype=torch.float16).repeat(len(pose_images), 1, 1, 1) + + prompt = "Darth Vader dancing in a desert" + result = pipe(prompt=[prompt] * len(pose_images), image=pose_images, latents=latents).images + imageio.mimsave("video.mp4", result, fps=4) + ``` ### Text-To-Video with Edge Control @@ -253,5 +292,10 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) - all - __call__ +## TextToVideoZeroSDXLPipeline +[[autodoc]] TextToVideoZeroSDXLPipeline + - all + - __call__ + ## TextToVideoPipelineOutput [[autodoc]] pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoPipelineOutput diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 8a0dc2b923d3..9df462dbd250 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -279,6 +279,7 @@ "StableUnCLIPPipeline", "TextToVideoSDPipeline", "TextToVideoZeroPipeline", + "TextToVideoZeroSDXLPipeline", "UnCLIPImageVariationPipeline", "UnCLIPPipeline", "UniDiffuserModel", @@ -628,6 +629,7 @@ StableUnCLIPPipeline, TextToVideoSDPipeline, TextToVideoZeroPipeline, + TextToVideoZeroSDXLPipeline, UnCLIPImageVariationPipeline, UnCLIPPipeline, UniDiffuserModel, diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index 78c1b7c6285d..fb93b940cea7 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -162,6 +162,7 @@ _import_structure["text_to_video_synthesis"] = [ "TextToVideoSDPipeline", "TextToVideoZeroPipeline", + "TextToVideoZeroSDXLPipeline", "VideoToVideoSDPipeline", ] _import_structure["unclip"] = ["UnCLIPImageVariationPipeline", "UnCLIPPipeline"] @@ -386,6 +387,7 @@ from .text_to_video_synthesis import ( TextToVideoSDPipeline, TextToVideoZeroPipeline, + TextToVideoZeroSDXLPipeline, VideoToVideoSDPipeline, ) from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline diff --git a/src/diffusers/pipelines/text_to_video_synthesis/__init__.py b/src/diffusers/pipelines/text_to_video_synthesis/__init__.py index 9304d5c7d818..8d8fdb92769b 100644 --- a/src/diffusers/pipelines/text_to_video_synthesis/__init__.py +++ b/src/diffusers/pipelines/text_to_video_synthesis/__init__.py @@ -25,6 +25,7 @@ _import_structure["pipeline_text_to_video_synth"] = ["TextToVideoSDPipeline"] _import_structure["pipeline_text_to_video_synth_img2img"] = ["VideoToVideoSDPipeline"] _import_structure["pipeline_text_to_video_zero"] = ["TextToVideoZeroPipeline"] + _import_structure["pipeline_text_to_video_zero_sdxl"] = ["TextToVideoZeroSDXLPipeline"] if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: @@ -38,6 +39,7 @@ from .pipeline_text_to_video_synth import TextToVideoSDPipeline from .pipeline_text_to_video_synth_img2img import VideoToVideoSDPipeline from .pipeline_text_to_video_zero import TextToVideoZeroPipeline + from .pipeline_text_to_video_zero_sdxl import TextToVideoZeroSDXLPipeline else: import sys diff --git a/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py b/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py index 9751abec2c98..0f9ffbebdcf6 100644 --- a/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py +++ b/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py @@ -13,6 +13,7 @@ from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline, StableDiffusionSafetyChecker from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import BaseOutput +from diffusers.utils.torch_utils import randn_tensor def rearrange_0(tensor, f): @@ -135,7 +136,7 @@ def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_ma # Cross Frame Attention if not is_cross_attention: - video_length = key.size()[0] // self.batch_size + video_length = max(1, key.size()[0] // self.batch_size) first_frame_index = [0] * video_length # rearrange keys to have batch and frames in the 1st and 2nd dims respectively @@ -339,7 +340,7 @@ def forward_loop(self, x_t0, t0, t1, generator): x_t1: Forward process applied to x_t0 from time t0 to t1. """ - eps = torch.randn(x_t0.size(), generator=generator, dtype=x_t0.dtype, device=x_t0.device) + eps = randn_tensor(x_t0.size(), generator=generator, dtype=x_t0.dtype, device=x_t0.device) alpha_vec = torch.prod(self.scheduler.alphas[t0:t1]) x_t1 = torch.sqrt(alpha_vec) * x_t0 + torch.sqrt(1 - alpha_vec) * eps return x_t1 diff --git a/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py b/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py new file mode 100644 index 000000000000..fd020841494c --- /dev/null +++ b/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py @@ -0,0 +1,872 @@ +import copy +from dataclasses import dataclass +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import numpy as np +import PIL +import torch +import torch.nn.functional as F +from torch.nn.functional import grid_sample +from transformers import ( + CLIPImageProcessor, + CLIPTextModel, + CLIPTextModelWithProjection, + CLIPTokenizer, + 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 + + +# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.rearrange_0 +def rearrange_0(tensor, f): + F, C, H, W = tensor.size() + tensor = torch.permute(torch.reshape(tensor, (F // f, f, C, H, W)), (0, 2, 1, 3, 4)) + return tensor + + +# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.rearrange_1 +def rearrange_1(tensor): + B, C, F, H, W = tensor.size() + return torch.reshape(torch.permute(tensor, (0, 2, 1, 3, 4)), (B * F, C, H, W)) + + +# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.rearrange_3 +def rearrange_3(tensor, f): + F, D, C = tensor.size() + return torch.reshape(tensor, (F // f, f, D, C)) + + +# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.rearrange_4 +def rearrange_4(tensor): + B, F, D, C = tensor.size() + return torch.reshape(tensor, (B * F, D, C)) + + +# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor +class CrossFrameAttnProcessor: + """ + Cross frame attention processor. Each frame attends the first frame. + + Args: + batch_size: The number that represents actual batch size, other than the frames. + For example, calling unet with a single prompt and num_images_per_prompt=1, batch_size should be equal to + 2, due to classifier-free guidance. + """ + + def __init__(self, batch_size=2): + self.batch_size = batch_size + + def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None): + batch_size, sequence_length, _ = hidden_states.shape + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + query = attn.to_q(hidden_states) + + is_cross_attention = encoder_hidden_states is not None + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + # Cross Frame Attention + if not is_cross_attention: + video_length = key.size()[0] // self.batch_size + first_frame_index = [0] * video_length + + # rearrange keys to have batch and frames in the 1st and 2nd dims respectively + key = rearrange_3(key, video_length) + key = key[:, first_frame_index] + # rearrange values to have batch and frames in the 1st and 2nd dims respectively + value = rearrange_3(value, video_length) + value = value[:, first_frame_index] + + # rearrange back to original shape + key = rearrange_4(key) + value = rearrange_4(value) + + query = attn.head_to_batch_dim(query) + key = attn.head_to_batch_dim(key) + value = attn.head_to_batch_dim(value) + + attention_probs = attn.get_attention_scores(query, key, attention_mask) + hidden_states = torch.bmm(attention_probs, value) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + return hidden_states + + +# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.CrossFrameAttnProcessor2_0 +class CrossFrameAttnProcessor2_0: + """ + Cross frame attention processor with scaled_dot_product attention of Pytorch 2.0. + + Args: + batch_size: The number that represents actual batch size, other than the frames. + For example, calling unet with a single prompt and num_images_per_prompt=1, batch_size should be equal to + 2, due to classifier-free guidance. + """ + + def __init__(self, batch_size=2): + if not hasattr(F, "scaled_dot_product_attention"): + raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") + self.batch_size = batch_size + + def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None): + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + inner_dim = hidden_states.shape[-1] + + if attention_mask is not None: + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + # scaled_dot_product_attention expects attention_mask shape to be + # (batch, heads, source_length, target_length) + attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) + + query = attn.to_q(hidden_states) + + is_cross_attention = encoder_hidden_states is not None + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + # Cross Frame Attention + if not is_cross_attention: + video_length = max(1, key.size()[0] // self.batch_size) + first_frame_index = [0] * video_length + + # rearrange keys to have batch and frames in the 1st and 2nd dims respectively + key = rearrange_3(key, video_length) + key = key[:, first_frame_index] + # rearrange values to have batch and frames in the 1st and 2nd dims respectively + value = rearrange_3(value, video_length) + value = value[:, first_frame_index] + + # rearrange back to original shape + key = rearrange_4(key) + value = rearrange_4(value) + + head_dim = inner_dim // attn.heads + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + # the output of sdp = (batch, num_heads, seq_len, head_dim) + # TODO: add support for attn.scale when we move to Torch 2.1 + hidden_states = F.scaled_dot_product_attention( + query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False + ) + + hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) + hidden_states = hidden_states.to(query.dtype) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + return hidden_states + + +@dataclass +class TextToVideoSDXLPipelineOutput(BaseOutput): + """ + Output class for zero-shot text-to-video pipeline. + + Args: + images (`List[PIL.Image.Image]` or `np.ndarray`) + List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, + num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. + """ + + images: Union[List[PIL.Image.Image], np.ndarray] + + +# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.coords_grid +def coords_grid(batch, ht, wd, device): + # Adapted from https://github.com/princeton-vl/RAFT/blob/master/core/utils/utils.py + coords = torch.meshgrid(torch.arange(ht, device=device), torch.arange(wd, device=device)) + coords = torch.stack(coords[::-1], dim=0).float() + return coords[None].repeat(batch, 1, 1, 1) + + +# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.warp_single_latent +def warp_single_latent(latent, reference_flow): + """ + Warp latent of a single frame with given flow + + Args: + latent: latent code of a single frame + reference_flow: flow which to warp the latent with + + Returns: + warped: warped latent + """ + _, _, H, W = reference_flow.size() + _, _, h, w = latent.size() + coords0 = coords_grid(1, H, W, device=latent.device).to(latent.dtype) + + coords_t0 = coords0 + reference_flow + coords_t0[:, 0] /= W + coords_t0[:, 1] /= H + + coords_t0 = coords_t0 * 2.0 - 1.0 + coords_t0 = F.interpolate(coords_t0, size=(h, w), mode="bilinear") + coords_t0 = torch.permute(coords_t0, (0, 2, 3, 1)) + + warped = grid_sample(latent, coords_t0, mode="nearest", padding_mode="reflection") + return warped + + +# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.create_motion_field +def create_motion_field(motion_field_strength_x, motion_field_strength_y, frame_ids, device, dtype): + """ + Create translation motion field + + Args: + motion_field_strength_x: motion strength along x-axis + motion_field_strength_y: motion strength along y-axis + frame_ids: indexes of the frames the latents of which are being processed. + This is needed when we perform chunk-by-chunk inference + device: device + dtype: dtype + + Returns: + + """ + seq_length = len(frame_ids) + reference_flow = torch.zeros((seq_length, 2, 512, 512), device=device, dtype=dtype) + for fr_idx in range(seq_length): + reference_flow[fr_idx, 0, :, :] = motion_field_strength_x * (frame_ids[fr_idx]) + reference_flow[fr_idx, 1, :, :] = motion_field_strength_y * (frame_ids[fr_idx]) + return reference_flow + + +# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.create_motion_field_and_warp_latents +def create_motion_field_and_warp_latents(motion_field_strength_x, motion_field_strength_y, frame_ids, latents): + """ + Creates translation motion and warps the latents accordingly + + Args: + motion_field_strength_x: motion strength along x-axis + motion_field_strength_y: motion strength along y-axis + frame_ids: indexes of the frames the latents of which are being processed. + This is needed when we perform chunk-by-chunk inference + latents: latent codes of frames + + Returns: + warped_latents: warped latents + """ + motion_field = create_motion_field( + motion_field_strength_x=motion_field_strength_x, + motion_field_strength_y=motion_field_strength_y, + frame_ids=frame_ids, + device=latents.device, + dtype=latents.dtype, + ) + warped_latents = latents.clone().detach() + for i in range(len(warped_latents)): + warped_latents[i] = warp_single_latent(latents[i][None], motion_field[i][None]) + return warped_latents + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg +def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): + """ + Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 + """ + std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) + std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) + # rescale the results from guidance (fixes overexposure) + noise_pred_rescaled = noise_cfg * (std_text / std_cfg) + # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images + noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg + return noise_cfg + + +class TextToVideoZeroSDXLPipeline(StableDiffusionXLPipeline): + r""" + Pipeline for zero-shot text-to-video generation using Stable Diffusion XL. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods + implemented for all pipelines (downloading, saving, running on a particular device, etc.). + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`CLIPTextModel`]): + Frozen text-encoder. Stable Diffusion XL uses the text portion of + [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically + the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. + text_encoder_2 ([` CLIPTextModelWithProjection`]): + Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of + [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), + specifically the + [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) + variant. + tokenizer (`CLIPTokenizer`): + Tokenizer of class + [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). + tokenizer_2 (`CLIPTokenizer`): + Second Tokenizer of class + [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). + unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of + [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. + """ + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + text_encoder_2: CLIPTextModelWithProjection, + tokenizer: CLIPTokenizer, + tokenizer_2: CLIPTokenizer, + unet: UNet2DConditionModel, + scheduler: KarrasDiffusionSchedulers, + image_encoder: CLIPVisionModelWithProjection = None, + feature_extractor: CLIPImageProcessor = None, + force_zeros_for_empty_prompt: bool = True, + add_watermarker: Optional[bool] = None, + ): + super().__init__( + vae=vae, + text_encoder=text_encoder, + text_encoder_2=text_encoder_2, + tokenizer=tokenizer, + tokenizer_2=tokenizer_2, + unet=unet, + scheduler=scheduler, + image_encoder=image_encoder, + feature_extractor=feature_extractor, + force_zeros_for_empty_prompt=force_zeros_for_empty_prompt, + add_watermarker=add_watermarker, + ) + 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.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoZeroPipeline.forward_loop + def forward_loop(self, x_t0, t0, t1, generator): + """ + Perform DDPM forward process from time t0 to t1. This is the same as adding noise with corresponding variance. + + Args: + x_t0: + Latent code at time t0. + t0: + Timestep at t0. + t1: + Timestamp at t1. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make + generation deterministic. + + Returns: + x_t1: + Forward process applied to x_t0 from time t0 to t1. + """ + eps = randn_tensor(x_t0.size(), generator=generator, dtype=x_t0.dtype, device=x_t0.device) + alpha_vec = torch.prod(self.scheduler.alphas[t0:t1]) + x_t1 = torch.sqrt(alpha_vec) * x_t0 + torch.sqrt(1 - alpha_vec) * eps + return x_t1 + + def backward_loop( + self, + latents, + timesteps, + prompt_embeds, + guidance_scale, + callback, + callback_steps, + num_warmup_steps, + extra_step_kwargs, + add_text_embeds, + add_time_ids, + cross_attention_kwargs=None, + guidance_rescale: float = 0.0, + ): + """ + Perform backward process given list of time steps + + Args: + latents: + Latents at time timesteps[0]. + timesteps: + Time steps along which to perform backward process. + prompt_embeds: + Pre-generated text embeddings. + guidance_scale: + A higher guidance scale value encourages the model to generate images closely linked to the text + `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. + callback (`Callable`, *optional*): + A function that calls every `callback_steps` steps during inference. The function is called with the + following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function is called. If not specified, the callback is called at + every step. + extra_step_kwargs: + Extra_step_kwargs. + cross_attention_kwargs: + A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in + [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + num_warmup_steps: + number of warmup steps. + + Returns: + latents: latents of backward process output at time timesteps[-1] + """ + + do_classifier_free_guidance = guidance_scale > 1.0 + num_steps = (len(timesteps) - num_warmup_steps) // self.scheduler.order + + with self.progress_bar(total=num_steps) as progress_bar: + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + # predict the noise residual + added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=prompt_embeds, + cross_attention_kwargs=cross_attention_kwargs, + added_cond_kwargs=added_cond_kwargs, + return_dict=False, + )[0] + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + if do_classifier_free_guidance and guidance_rescale > 0.0: + # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf + noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + callback(i, t, latents) + return latents.clone().detach() + + @torch.no_grad() + def __call__( + self, + prompt: Union[str, List[str]], + prompt_2: Optional[Union[str, List[str]]] = None, + video_length: Optional[int] = 8, + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: int = 50, + denoising_end: Optional[float] = None, + guidance_scale: float = 7.5, + negative_prompt: Optional[Union[str, List[str]]] = None, + negative_prompt_2: Optional[Union[str, List[str]]] = None, + num_videos_per_prompt: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + frame_ids: Optional[List[int]] = 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, + latents: Optional[torch.FloatTensor] = None, + motion_field_strength_x: float = 12, + motion_field_strength_y: float = 12, + output_type: Optional[str] = "tensor", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: int = 1, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + guidance_rescale: float = 0.0, + original_size: Optional[Tuple[int, int]] = None, + crops_coords_top_left: Tuple[int, int] = (0, 0), + target_size: Optional[Tuple[int, int]] = None, + t0: int = 44, + t1: int = 47, + ): + """ + Function invoked when calling the pipeline for generation. + + Args: + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + 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 + video_length (`int`, *optional*, defaults to 8): + The number of generated video frames. + height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The width in pixels of the generated image. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + denoising_end (`float`, *optional*): + When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be + completed before it is intentionally prematurely terminated. As a result, the returned sample will + still retain a substantial amount of noise as determined by the discrete timesteps selected by the + scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a + "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image + Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output) + guidance_scale (`float`, *optional*, defaults to 7.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + 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 + num_videos_per_prompt (`int`, *optional*, defaults to 1): + The number of videos 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. + frame_ids (`List[int]`, *optional*): + Indexes of the frames that are being generated. This is used when generating longer videos + chunk-by-chunk. + 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. + latents (`torch.FloatTensor`, *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`. + motion_field_strength_x (`float`, *optional*, defaults to 12): + Strength of motion in generated video along x-axis. See the [paper](https://arxiv.org/abs/2303.13439), + Sect. 3.3.1. + motion_field_strength_y (`float`, *optional*, defaults to 12): + Strength of motion in generated video along y-axis. See the [paper](https://arxiv.org/abs/2303.13439), + Sect. 3.3.1. + 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. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). + guidance_rescale (`float`, *optional*, defaults to 0.7): + 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 `(width, height)` 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 `(width, height)`. 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). + t0 (`int`, *optional*, defaults to 44): + Timestep t0. Should be in the range [0, num_inference_steps - 1]. See the + [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1. + t1 (`int`, *optional*, defaults to 47): + Timestep t0. Should be in the range [t0 + 1, num_inference_steps - 1]. See the + [paper](https://arxiv.org/abs/2303.13439), Sect. 3.3.1. + + Returns: + [`~pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoSDXLPipelineOutput`] or + `tuple`: [`~pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoSDXLPipelineOutput`] + if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the + generated images. + """ + assert video_length > 0 + if frame_ids is None: + frame_ids = list(range(video_length)) + assert len(frame_ids) == video_length + + assert num_videos_per_prompt == 1 + + if isinstance(prompt, str): + prompt = [prompt] + if isinstance(negative_prompt, str): + negative_prompt = [negative_prompt] + + # 0. Default height and width to unet + height = height or self.default_sample_size * self.vae_scale_factor + width = width or self.default_sample_size * self.vae_scale_factor + + original_size = original_size or (height, width) + target_size = target_size or (height, width) + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + prompt, + prompt_2, + height, + width, + callback_steps, + negative_prompt, + negative_prompt_2, + prompt_embeds, + negative_prompt_embeds, + pooled_prompt_embeds, + negative_pooled_prompt_embeds, + ) + + # 2. Define call parameters + batch_size = ( + 1 if isinstance(prompt, str) else len(prompt) if isinstance(prompt, list) else prompt_embeds.shape[0] + ) + device = self._execution_device + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # 3. Encode input prompt + text_encoder_lora_scale = ( + cross_attention_kwargs.get("scale", None) if 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_videos_per_prompt, + do_classifier_free_guidance=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=text_encoder_lora_scale, + ) + + # 4. Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + # 5. Prepare latent variables + num_channels_latents = self.unet.config.in_channels + + latents = self.prepare_latents( + batch_size * num_videos_per_prompt, + num_channels_latents, + height, + width, + prompt_embeds.dtype, + device, + generator, + latents, + ) + + # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # 7. Prepare added time ids & embeddings + add_text_embeds = pooled_prompt_embeds + if self.text_encoder_2 is None: + text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) + else: + text_encoder_projection_dim = self.text_encoder_2.config.projection_dim + + add_time_ids = self._get_add_time_ids( + original_size, + crops_coords_top_left, + target_size, + dtype=prompt_embeds.dtype, + text_encoder_projection_dim=text_encoder_projection_dim, + ) + + if 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([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_videos_per_prompt, 1) + + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + + # Perform the first backward process up to time T_1 + x_1_t1 = self.backward_loop( + timesteps=timesteps[: -t1 - 1], + prompt_embeds=prompt_embeds, + latents=latents, + guidance_scale=guidance_scale, + callback=callback, + callback_steps=callback_steps, + extra_step_kwargs=extra_step_kwargs, + num_warmup_steps=num_warmup_steps, + add_text_embeds=add_text_embeds, + add_time_ids=add_time_ids, + ) + + scheduler_copy = copy.deepcopy(self.scheduler) + + # Perform the second backward process up to time T_0 + x_1_t0 = self.backward_loop( + timesteps=timesteps[-t1 - 1 : -t0 - 1], + prompt_embeds=prompt_embeds, + latents=x_1_t1, + guidance_scale=guidance_scale, + callback=callback, + callback_steps=callback_steps, + extra_step_kwargs=extra_step_kwargs, + num_warmup_steps=0, + add_text_embeds=add_text_embeds, + add_time_ids=add_time_ids, + ) + + # Propagate first frame latents at time T_0 to remaining frames + x_2k_t0 = x_1_t0.repeat(video_length - 1, 1, 1, 1) + + # Add motion in latents at time T_0 + x_2k_t0 = create_motion_field_and_warp_latents( + motion_field_strength_x=motion_field_strength_x, + motion_field_strength_y=motion_field_strength_y, + latents=x_2k_t0, + frame_ids=frame_ids[1:], + ) + + # Perform forward process up to time T_1 + x_2k_t1 = self.forward_loop( + x_t0=x_2k_t0, + t0=timesteps[-t0 - 1].to(torch.long), + t1=timesteps[-t1 - 1].to(torch.long), + generator=generator, + ) + + # Perform backward process from time T_1 to 0 + latents = torch.cat([x_1_t1, x_2k_t1]) + + self.scheduler = scheduler_copy + timesteps = timesteps[-t1 - 1 :] + + b, l, d = prompt_embeds.size() + prompt_embeds = prompt_embeds[:, None].repeat(1, video_length, 1, 1).reshape(b * video_length, l, d) + + b, k = add_text_embeds.size() + add_text_embeds = add_text_embeds[:, None].repeat(1, video_length, 1).reshape(b * video_length, k) + + b, k = add_time_ids.size() + add_time_ids = add_time_ids[:, None].repeat(1, video_length, 1).reshape(b * video_length, k) + + # 7.1 Apply denoising_end + if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1: + discrete_timestep_cutoff = int( + round( + self.scheduler.config.num_train_timesteps + - (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] + + x_1k_0 = self.backward_loop( + timesteps=timesteps, + prompt_embeds=prompt_embeds, + latents=latents, + guidance_scale=guidance_scale, + callback=callback, + callback_steps=callback_steps, + extra_step_kwargs=extra_step_kwargs, + num_warmup_steps=0, + add_text_embeds=add_text_embeds, + add_time_ids=add_time_ids, + ) + + latents = x_1k_0 + + if not output_type == "latent": + # make sure the VAE is in float32 mode, as it overflows in float16 + needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast + + if needs_upcasting: + self.upcast_vae() + latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) + + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] + + # cast back to fp16 if needed + if needs_upcasting: + self.vae.to(dtype=torch.float16) + else: + image = latents + return TextToVideoSDXLPipelineOutput(images=image) + + # apply watermark if available + if self.watermark is not None: + image = self.watermark.apply_watermark(image) + + image = self.image_processor.postprocess(image, output_type=output_type) + + # Offload last model to CPU manually for max memory savings + if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: + self.final_offload_hook.offload() + + if not return_dict: + return (image,) + + return TextToVideoSDXLPipelineOutput(images=image) diff --git a/src/diffusers/utils/dummy_torch_and_transformers_objects.py b/src/diffusers/utils/dummy_torch_and_transformers_objects.py index 3386a95eb7d4..11ba4eb5b4db 100644 --- a/src/diffusers/utils/dummy_torch_and_transformers_objects.py +++ b/src/diffusers/utils/dummy_torch_and_transformers_objects.py @@ -1202,6 +1202,21 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) +class TextToVideoZeroSDXLPipeline(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + class UnCLIPImageVariationPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] diff --git a/tests/pipelines/text_to_video_synthesis/test_text_to_video_zero_sdxl.py b/tests/pipelines/text_to_video_synthesis/test_text_to_video_zero_sdxl.py new file mode 100644 index 000000000000..54faa9de6d62 --- /dev/null +++ b/tests/pipelines/text_to_video_synthesis/test_text_to_video_zero_sdxl.py @@ -0,0 +1,405 @@ +# coding=utf-8 +# Copyright 2023 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import contextlib +import inspect +import io +import re +import tempfile +import unittest + +import numpy as np +import torch +from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer + +from diffusers import AutoencoderKL, DDIMScheduler, TextToVideoZeroSDXLPipeline, UNet2DConditionModel +from diffusers.utils.import_utils import is_accelerate_available, is_accelerate_version +from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device + +from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS +from ..test_pipelines_common import PipelineTesterMixin + + +enable_full_determinism() + + +def to_np(tensor): + if isinstance(tensor, torch.Tensor): + tensor = tensor.detach().cpu().numpy() + + return tensor + + +class TextToVideoZeroSDXLPipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = TextToVideoZeroSDXLPipeline + params = TEXT_TO_IMAGE_PARAMS + batch_params = TEXT_TO_IMAGE_BATCH_PARAMS + image_params = TEXT_TO_IMAGE_IMAGE_PARAMS + image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS + generator_device = "cpu" + + def get_dummy_components(self, seed=0): + torch.manual_seed(seed) + unet = UNet2DConditionModel( + block_out_channels=(2, 4), + layers_per_block=2, + sample_size=2, + norm_num_groups=2, + in_channels=4, + out_channels=4, + down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), + up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), + # SD2-specific config below + attention_head_dim=(2, 4), + use_linear_projection=True, + addition_embed_type="text_time", + addition_time_embed_dim=8, + transformer_layers_per_block=(1, 2), + projection_class_embeddings_input_dim=80, # 6 * 8 + 32 + cross_attention_dim=64, + ) + scheduler = DDIMScheduler( + num_train_timesteps=1000, + beta_start=0.0001, + beta_end=0.02, + beta_schedule="linear", + trained_betas=None, + clip_sample=True, + set_alpha_to_one=True, + steps_offset=0, + prediction_type="epsilon", + thresholding=False, + dynamic_thresholding_ratio=0.995, + clip_sample_range=1.0, + sample_max_value=1.0, + timestep_spacing="leading", + rescale_betas_zero_snr=False, + ) + torch.manual_seed(seed) + vae = AutoencoderKL( + block_out_channels=[32, 64], + in_channels=3, + out_channels=3, + down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], + up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], + latent_channels=4, + sample_size=128, + ) + torch.manual_seed(seed) + text_encoder_config = CLIPTextConfig( + bos_token_id=0, + eos_token_id=2, + hidden_size=32, + intermediate_size=37, + layer_norm_eps=1e-05, + num_attention_heads=4, + num_hidden_layers=5, + pad_token_id=1, + vocab_size=1000, + # SD2-specific config below + hidden_act="gelu", + projection_dim=32, + ) + text_encoder = CLIPTextModel(text_encoder_config) + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config) + tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + components = { + "unet": unet, + "scheduler": scheduler, + "vae": vae, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + "text_encoder_2": text_encoder_2, + "tokenizer_2": tokenizer_2, + "image_encoder": None, + "feature_extractor": None, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + inputs = { + "prompt": "A panda dancing in Antarctica", + "generator": generator, + "num_inference_steps": 5, + "t0": 1, + "t1": 3, + "height": 64, + "width": 64, + "video_length": 3, + "output_type": "np", + } + return inputs + + def get_generator(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + return generator + + def test_text_to_video_zero_sdxl(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe = pipe.to(torch_device) + + inputs = self.get_dummy_inputs(self.generator_device) + result = pipe(**inputs).images + + first_frame_slice = result[0, -3:, -3:, -1] + last_frame_slice = result[-1, -3:, -3:, 0] + + expected_slice1 = np.array([0.48, 0.58, 0.53, 0.59, 0.50, 0.44, 0.60, 0.65, 0.52]) + expected_slice2 = np.array([0.66, 0.49, 0.40, 0.70, 0.47, 0.51, 0.73, 0.65, 0.52]) + + assert np.abs(first_frame_slice.flatten() - expected_slice1).max() < 1e-2 + assert np.abs(last_frame_slice.flatten() - expected_slice2).max() < 1e-2 + + @unittest.skip( + reason="Cannot call `set_default_attn_processor` as this pipeline uses a specific attention processor." + ) + def test_attention_slicing_forward_pass(self): + pass + + def test_cfg(self): + sig = inspect.signature(self.pipeline_class.__call__) + if "guidance_scale" not in sig.parameters: + return + components = self.get_dummy_components() + + pipe = self.pipeline_class(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(self.generator_device) + + inputs["guidance_scale"] = 1.0 + out_no_cfg = pipe(**inputs)[0] + + inputs["guidance_scale"] = 7.5 + out_cfg = pipe(**inputs)[0] + + assert out_cfg.shape == out_no_cfg.shape + + def test_dict_tuple_outputs_equivalent(self, expected_max_difference=1e-4): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + output = pipe(**self.get_dummy_inputs(self.generator_device))[0] + output_tuple = pipe(**self.get_dummy_inputs(self.generator_device), return_dict=False)[0] + + max_diff = np.abs(to_np(output) - to_np(output_tuple)).max() + self.assertLess(max_diff, expected_max_difference) + + @unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA") + def test_float16_inference(self, expected_max_diff=5e-2): + components = self.get_dummy_components() + for name, module in components.items(): + if hasattr(module, "half"): + components[name] = module.to(torch_device).half() + pipe = self.pipeline_class(**components) + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + components = self.get_dummy_components() + pipe_fp16 = self.pipeline_class(**components) + pipe_fp16.to(torch_device, torch.float16) + pipe_fp16.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(self.generator_device) + # # Reset generator in case it is used inside dummy inputs + if "generator" in inputs: + inputs["generator"] = self.get_generator(self.generator_device) + + output = pipe(**inputs)[0] + + fp16_inputs = self.get_dummy_inputs(self.generator_device) + # Reset generator in case it is used inside dummy inputs + if "generator" in fp16_inputs: + fp16_inputs["generator"] = self.get_generator(self.generator_device) + + output_fp16 = pipe_fp16(**fp16_inputs)[0] + + max_diff = np.abs(to_np(output) - to_np(output_fp16)).max() + self.assertLess(max_diff, expected_max_diff, "The outputs of the fp16 and fp32 pipelines are too different.") + + @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline.") + def test_inference_batch_consistent(self): + pass + + @unittest.skip( + reason="Cannot call `set_default_attn_processor` as this pipeline uses a specific attention processor." + ) + def test_inference_batch_single_identical(self): + pass + + @unittest.skipIf( + torch_device != "cuda" or not is_accelerate_available() or is_accelerate_version("<", "0.17.0"), + reason="CPU offload is only available with CUDA and `accelerate v0.17.0` or higher", + ) + def test_model_cpu_offload_forward_pass(self, expected_max_diff=2e-4): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(self.generator_device) + output_without_offload = pipe(**inputs)[0] + + pipe.enable_model_cpu_offload() + inputs = self.get_dummy_inputs(self.generator_device) + output_with_offload = pipe(**inputs)[0] + + max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max() + self.assertLess(max_diff, expected_max_diff, "CPU offloading should not affect the inference results") + + @unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline.") + def test_pipeline_call_signature(self): + pass + + def test_progress_bar(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(torch_device) + + inputs = self.get_dummy_inputs(self.generator_device) + with io.StringIO() as stderr, contextlib.redirect_stderr(stderr): + _ = pipe(**inputs) + stderr = stderr.getvalue() + # we can't calculate the number of progress steps beforehand e.g. for strength-dependent img2img, + # so we just match "5" in "#####| 1/5 [00:01<00:00]" + max_steps = re.search("/(.*?) ", stderr).group(1) + self.assertTrue(max_steps is not None and len(max_steps) > 0) + self.assertTrue( + f"{max_steps}/{max_steps}" in stderr, "Progress bar should be enabled and stopped at the max step" + ) + + pipe.set_progress_bar_config(disable=True) + with io.StringIO() as stderr, contextlib.redirect_stderr(stderr): + _ = pipe(**inputs) + self.assertTrue(stderr.getvalue() == "", "Progress bar should be disabled") + + @unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA") + def test_save_load_float16(self, expected_max_diff=1e-2): + components = self.get_dummy_components() + for name, module in components.items(): + if hasattr(module, "half"): + components[name] = module.to(torch_device).half() + + pipe = self.pipeline_class(**components) + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(self.generator_device) + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16) + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + for name, component in pipe_loaded.components.items(): + if hasattr(component, "dtype"): + self.assertTrue( + component.dtype == torch.float16, + f"`{name}.dtype` switched from `float16` to {component.dtype} after loading.", + ) + + inputs = self.get_dummy_inputs(self.generator_device) + output_loaded = pipe_loaded(**inputs)[0] + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess( + max_diff, expected_max_diff, "The output of the fp16 pipeline changed after saving and loading." + ) + + @unittest.skip( + reason="Cannot call `set_default_attn_processor` as this pipeline uses a specific attention processor." + ) + def test_save_load_local(self): + pass + + @unittest.skip( + reason="Cannot call `set_default_attn_processor` as this pipeline uses a specific attention processor." + ) + def test_save_load_optional_components(self): + pass + + @unittest.skip( + reason="Cannot call `set_default_attn_processor` as this pipeline uses a specific attention processor." + ) + def test_sequential_cpu_offload_forward_pass(self): + pass + + @unittest.skipIf(torch_device != "cuda", reason="CUDA and CPU are required to switch devices") + def test_to_device(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.set_progress_bar_config(disable=None) + + pipe.to("cpu") + model_devices = [component.device.type for component in components.values() if hasattr(component, "device")] + self.assertTrue(all(device == "cpu" for device in model_devices)) + + output_cpu = pipe(**self.get_dummy_inputs("cpu"))[0] # generator set to cpu + self.assertTrue(np.isnan(output_cpu).sum() == 0) + + pipe.to("cuda") + model_devices = [component.device.type for component in components.values() if hasattr(component, "device")] + self.assertTrue(all(device == "cuda" for device in model_devices)) + + output_cuda = pipe(**self.get_dummy_inputs("cpu"))[0] # generator set to cpu + self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0) + + @unittest.skip( + reason="Cannot call `set_default_attn_processor` as this pipeline uses a specific attention processor." + ) + def test_xformers_attention_forwardGenerator_pass(self): + pass + + +@nightly +@require_torch_gpu +class TextToVideoZeroSDXLPipelineSlowTests(unittest.TestCase): + def test_full_model(self): + model_id = "stabilityai/stable-diffusion-xl-base-1.0" + pipe = self.pipeline_class.from_pretrained( + model_id, torch_dtype=torch.float16, variant="fp16", use_safetensors=True + ) + pipe.enable_model_cpu_offload() + pipe.enable_vae_slicing() + + pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) + generator = torch.Generator(device="cpu").manual_seed(0) + + prompt = "A panda dancing in Antarctica" + result = pipe(prompt=prompt, generator=generator).images + + first_frame_slice = result[0, -3:, -3:, -1] + last_frame_slice = result[-1, -3:, -3:, 0] + + expected_slice1 = np.array([0.57, 0.57, 0.57, 0.57, 0.57, 0.56, 0.55, 0.56, 0.56]) + expected_slice2 = np.array([0.54, 0.53, 0.53, 0.53, 0.53, 0.52, 0.53, 0.53, 0.53]) + + assert np.abs(first_frame_slice.flatten() - expected_slice1).max() < 1e-2 + assert np.abs(last_frame_slice.flatten() - expected_slice2).max() < 1e-2