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from __future__ import annotations |
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import gc |
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import pathlib |
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import sys |
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import tempfile |
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import os |
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import gradio as gr |
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import imageio |
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import PIL.Image |
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import torch |
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from diffusers.utils.import_utils import is_xformers_available |
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from einops import rearrange |
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from huggingface_hub import ModelCard |
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from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection, CLIPTextModelWithProjection |
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from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler, PNDMScheduler, ControlNetModel, PriorTransformer, UnCLIPScheduler |
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from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer |
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from omegaconf import OmegaConf |
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from typing import Any, Callable, Dict, List, Optional, Union, Tuple |
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sys.path.append('Make-A-Protagonist') |
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from makeaprotagonist.models.unet import UNet3DConditionModel |
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from makeaprotagonist.pipelines.pipeline_stable_unclip_controlavideo import MakeAProtagonistStableUnCLIPPipeline, MultiControlNetModel |
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from makeaprotagonist.dataset.dataset import MakeAProtagonistDataset |
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from makeaprotagonist.util import save_videos_grid, ddim_inversion_unclip, ddim_inversion_prior |
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from experts.grounded_sam_mask_out import mask_out_reference_image |
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import ipdb |
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class InferencePipeline: |
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def __init__(self, hf_token: str | None = None): |
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self.hf_token = hf_token |
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self.pipe = None |
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self.device = torch.device( |
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'cuda:0' if torch.cuda.is_available() else 'cpu') |
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self.model_id = None |
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self.conditions = None |
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self.masks = None |
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self.ddim_inv_latent = None |
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self.train_dataset, self.sample_indices = None, None |
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def clear(self) -> None: |
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self.model_id = None |
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del self.pipe |
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self.pipe = None |
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self.conditions = None |
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self.masks = None |
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self.ddim_inv_latent = None |
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self.train_dataset, self.sample_indices = None, None |
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torch.cuda.empty_cache() |
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gc.collect() |
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@staticmethod |
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def check_if_model_is_local(model_id: str) -> bool: |
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return pathlib.Path(model_id).exists() |
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@staticmethod |
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def get_model_card(model_id: str, |
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hf_token: str | None = None) -> ModelCard: |
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if InferencePipeline.check_if_model_is_local(model_id): |
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card_path = (pathlib.Path(model_id) / 'README.md').as_posix() |
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else: |
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card_path = model_id |
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return ModelCard.load(card_path, token=hf_token) |
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@staticmethod |
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def get_base_model_info(model_id: str, hf_token: str | None = None) -> str: |
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card = InferencePipeline.get_model_card(model_id, hf_token) |
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return card.data.base_model |
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@torch.no_grad() |
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def load_pipe(self, model_id: str, n_steps, seed) -> None: |
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if model_id == self.model_id: |
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return self.conditions, self.masks, self.ddim_inv_latent, self.train_dataset, self.sample_indices |
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base_model_id = self.get_base_model_info(model_id, self.hf_token) |
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pretrained_model_path = 'stabilityai/stable-diffusion-2-1-unclip-small' |
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feature_extractor = CLIPImageProcessor.from_pretrained(pretrained_model_path, subfolder="feature_extractor") |
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image_encoder = CLIPVisionModelWithProjection.from_pretrained(pretrained_model_path, subfolder="image_encoder") |
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image_normalizer = StableUnCLIPImageNormalizer.from_pretrained(pretrained_model_path, subfolder="image_normalizer", torch_dtype=torch.float16,) |
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image_noising_scheduler = DDPMScheduler.from_pretrained(pretrained_model_path, subfolder="image_noising_scheduler") |
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tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer") |
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text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder", torch_dtype=torch.float16,) |
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vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae", torch_dtype=torch.float16,) |
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self.ddim_inv_scheduler = DDIMScheduler.from_pretrained(pretrained_model_path, subfolder='scheduler') |
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self.ddim_inv_scheduler.set_timesteps(n_steps) |
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prior_model_id = "kakaobrain/karlo-v1-alpha" |
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data_type = torch.float16 |
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prior = PriorTransformer.from_pretrained(prior_model_id, subfolder="prior", torch_dtype=data_type) |
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prior_text_model_id = "openai/clip-vit-large-patch14" |
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prior_tokenizer = CLIPTokenizer.from_pretrained(prior_text_model_id) |
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prior_text_model = CLIPTextModelWithProjection.from_pretrained(prior_text_model_id, torch_dtype=data_type) |
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prior_scheduler = UnCLIPScheduler.from_pretrained(prior_model_id, subfolder="prior_scheduler") |
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prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config) |
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controlnet_model_id = ['controlnet-2-1-unclip-small-openposefull', 'controlnet-2-1-unclip-small-depth'] |
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controlnet = MultiControlNetModel( [ControlNetModel.from_pretrained('Make-A-Protagonist/controlnet-2-1-unclip-small', subfolder=subfolder_id, torch_dtype=torch.float16) for subfolder_id in controlnet_model_id] ) |
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unet = UNet3DConditionModel.from_pretrained( |
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model_id, |
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subfolder='unet', |
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torch_dtype=torch.float16, |
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use_auth_token=self.hf_token) |
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vae.requires_grad_(False) |
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text_encoder.requires_grad_(False) |
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image_encoder.requires_grad_(False) |
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unet.requires_grad_(False) |
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controlnet.requires_grad_(False) |
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prior.requires_grad_(False) |
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prior_text_model.requires_grad_(False) |
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config_file = os.path.join('Make-A-Protagonist/configs', model_id.split('/')[-1] + '.yaml') |
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self.cfg = OmegaConf.load(config_file) |
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train_dataset = MakeAProtagonistDataset(**self.cfg) |
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train_dataset.preprocess_img_embedding(feature_extractor, image_encoder) |
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train_dataloader = torch.utils.data.DataLoader( |
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train_dataset, batch_size=1, num_workers=0, |
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) |
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image_encoder.to(dtype=data_type) |
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pipe = MakeAProtagonistStableUnCLIPPipeline( |
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prior_tokenizer=prior_tokenizer, |
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prior_text_encoder=prior_text_model, |
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prior=prior, |
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prior_scheduler=prior_scheduler, |
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feature_extractor=feature_extractor, |
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image_encoder=image_encoder, |
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image_normalizer=image_normalizer, |
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image_noising_scheduler=image_noising_scheduler, |
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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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controlnet=controlnet, |
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scheduler=DDIMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler") |
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) |
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pipe = pipe.to(self.device) |
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if is_xformers_available(): |
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pipe.unet.enable_xformers_memory_efficient_attention() |
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pipe.controlnet.enable_xformers_memory_efficient_attention() |
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self.pipe = pipe |
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self.model_id = model_id |
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self.vae = vae |
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batch = next(iter(train_dataloader)) |
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weight_dtype = torch.float16 |
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pixel_values = batch["pixel_values"].to(weight_dtype).to(self.device) |
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video_length = pixel_values.shape[1] |
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pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w") |
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latents = self.vae.encode(pixel_values).latent_dist.sample() |
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latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length) |
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latents = latents * self.vae.config.scaling_factor |
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conditions = [_condition.to(weight_dtype).to(self.device) for _, _condition in batch["conditions"].items()] |
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masks = batch["masks"].to(weight_dtype).to(self.device) |
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emb_dim = train_dataset.img_embeddings[0].size(0) |
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key_frame_embed = torch.zeros((1, emb_dim)).to(device=latents.device, dtype=latents.dtype) |
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ddim_inv_latent = ddim_inversion_unclip( |
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self.pipe, self.ddim_inv_scheduler, video_latent=latents, |
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num_inv_steps=n_steps, prompt="", image_embed=key_frame_embed, noise_level=0, seed=seed)[-1].to(weight_dtype) |
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self.conditions = conditions |
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self.masks = masks |
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self.ddim_inv_latent = ddim_inv_latent |
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self.train_dataset = train_dataset |
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self.sample_indices = batch["sample_indices"][0] |
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return conditions, masks, ddim_inv_latent, train_dataset, batch["sample_indices"][0] |
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def run( |
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self, |
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model_id: str, |
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prompt: str, |
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video_length: int, |
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fps: int, |
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seed: int, |
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n_steps: int, |
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guidance_scale: float, |
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ref_image: PIL.Image.Image, |
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ref_pro_prompt: str, |
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noise_level: int, |
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start_step: int, |
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control_pose: float, |
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control_depth: float, |
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source_pro: int = 0, |
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source_bg: int = 0, |
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) -> PIL.Image.Image: |
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if not torch.cuda.is_available(): |
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raise gr.Error('CUDA is not available.') |
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torch.cuda.empty_cache() |
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conditions, masks, ddim_inv_latent, _, _ = self.load_pipe(model_id, n_steps, seed) |
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conditions = [_condition[:,:video_length] for _condition in conditions] |
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masks = masks[:, :video_length] |
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ddim_inv_latent = ddim_inv_latent[:,:,:video_length] |
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generator = torch.Generator(device=self.device).manual_seed(seed) |
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ref_image = mask_out_reference_image(ref_image, ref_pro_prompt) |
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controlnet_conditioning_scale = [control_pose, control_depth] |
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prior_denoised_embeds = None |
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image_embed = None |
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if source_bg: |
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prior_denoised_embeds = self.train_dataset.img_embeddings[0][None].to(device=ddim_inv_latent.device, dtype=ddim_inv_latent.dtype) |
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if source_pro: |
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sample_indices = self.sample_indices |
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image_embed = [self.train_dataset.img_embeddings[idx] for idx in sample_indices] |
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image_embed = torch.stack(image_embed, dim=0).to(device=ddim_inv_latent.device, dtype=ddim_inv_latent.dtype) |
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image_embed = image_embed[:video_length] |
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ref_image = None |
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out = self.pipe( |
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image=ref_image, |
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prompt=prompt, |
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control_image=conditions, |
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video_length=video_length, |
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width=768, |
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height=768, |
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num_inference_steps=n_steps, |
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guidance_scale=guidance_scale, |
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generator=generator, |
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latents=ddim_inv_latent, |
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noise_level=noise_level, |
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controlnet_conditioning_scale=controlnet_conditioning_scale, |
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masks=masks, |
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mask_mode='all', |
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mask_latent_fuse_mode = 'all', |
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start_step=start_step, |
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prior_latents=None, |
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image_embeds=image_embed, |
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prior_denoised_embeds=prior_denoised_embeds |
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) |
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frames = rearrange(out.videos[0], 'c t h w -> t h w c') |
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frames = (frames * 255).to(torch.uint8).numpy() |
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out_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) |
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writer = imageio.get_writer(out_file.name, fps=fps) |
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for frame in frames: |
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writer.append_data(frame) |
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writer.close() |
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return out_file.name |
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