206 lines
8.1 KiB
Python
206 lines
8.1 KiB
Python
import os
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import folder_paths
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import numpy as np
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from PIL import Image
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import torch
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diffusers_path = folder_paths.get_folder_paths("diffusers")[0]
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MuseVCheckPointDir = os.path.join(
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diffusers_path, "TMElyralab/MuseV"
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)
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current_dir = os.path.dirname(__file__)
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import sys
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sys.path.insert(0, current_dir)
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sys.path.insert(0, os.path.join(current_dir, "MMCM"))
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sys.path.insert(0, os.path.join(current_dir, "diffusers/src"))
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sys.path.insert(0, os.path.join(current_dir, "controlnet_aux/src"))
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from einops import repeat
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from .MMCM.mmcm.utils.seed_util import set_all_seed
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from .MMCM.mmcm.utils.task_util import fiss_tasks, generate_tasks as generate_tasks_from_table
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from musev.pipelines.pipeline_controlnet_predictor import (
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DiffusersPipelinePredictor,
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)
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from musev.models.unet_loader import load_unet_by_name
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from musev import logger
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logger.setLevel("INFO")
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file_dir = os.path.dirname(__file__)
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PROJECT_DIR = file_dir
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DATA_DIR = os.path.join(PROJECT_DIR, "data")
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class MuseVPredictorV1:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {}
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}
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RETURN_TYPES = ("MUSEV_PREDICTOR",)
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FUNCTION = "main"
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CATEGORY = "MuseV Evolved"
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def main(self):
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sd_model_path = os.path.join(folder_paths.models_dir, 'diffusers/TMElyralab/MuseV/t2i/sd1.5/majicmixRealv6Fp16')
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sd_unet_model = os.path.join(folder_paths.models_dir, 'diffusers/TMElyralab/MuseV/motion/musev_referencenet')
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vae_path = os.path.join(folder_paths.models_dir, 'diffusers/TMElyralab/MuseV/vae/sd-vae-ft-mse')
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negative_embedding = [
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[
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os.path.join(folder_paths.models_dir, "diffusers/TMElyralab/MuseV/embedding/badhandv4.pt"),
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"badhandv4"
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],
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[
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os.path.join(folder_paths.models_dir, "diffusers/TMElyralab/MuseV/embedding/ng_deepnegative_v1_75t.pt"),
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"ng_deepnegative_v1_75t"
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],
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[
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os.path.join(folder_paths.models_dir, "diffusers/TMElyralab/MuseV/embedding/EasyNegativeV2.safetensors"),
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"EasyNegativeV2"
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],
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[
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os.path.join(folder_paths.models_dir, "diffusers/TMElyralab/MuseV/embedding/bad_prompt_version2-neg.pt"),
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"bad_prompt_version2-neg"
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]
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]
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unet = load_unet_by_name(
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model_name='musev_referencenet',
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sd_unet_model=sd_unet_model,
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sd_model=sd_model_path,
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cross_attention_dim=768,
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need_t2i_facein=False,
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strict=True,
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need_t2i_ip_adapter_face=False,
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)
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sd_predictor = DiffusersPipelinePredictor(
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sd_model_path=sd_model_path,
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unet=unet,
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lora_dict=None,
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lcm_lora_dct=None,
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device='cuda',
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dtype=torch.float16,
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negative_embedding=negative_embedding,
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referencenet=None,
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ip_adapter_image_proj=None,
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vision_clip_extractor=None,
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facein_image_proj=None,
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face_emb_extractor=None,
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vae_model=vae_path,
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ip_adapter_face_emb_extractor=None,
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ip_adapter_face_image_proj=None,
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)
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return (sd_predictor, )
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class MuseVImg2VidV1:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE", ),
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"musev_predictor": ("MUSEV_PREDICTOR", ),
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"time_size": ("INT", {"default":12}),
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"seed": ("INT", {"default":1234}),
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"video_num_inference_steps": ("INT", {"default":10}),
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"video_guidance_scale": ("FLOAT", {"default":3.5, "round": False, "step":0.01}),
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"w_ind_noise": ("FLOAT", {"default":0.5, "round": False, "step":0.01}),
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"image_weight": ("FLOAT", {"default":0.001, "round": False, "step":0.001}),
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"motion_speed": ("FLOAT", {"default":8.0, "round": False, "step":0.01}),
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"context_frames": ("INT", {"default":12}),
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"context_stride": ("INT", {"default":1}),
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"context_overlap": ("INT", {"default":4}),
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"output_shift_first_frame": ("BOOLEAN", {"default":True}),
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"positive_prompt": ("STRING", {"multiline": True, "default": "(masterpiece, best quality, highres:1),(1girl, solo:1),(beautiful face, soft skin, costume:1),(eye blinks:1.8),(head wave:1.3)"}),
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"negative_prompt": ("STRING", {"multiline": True, "default": "badhandv4, ng_deepnegative_v1_75t, (((multiple heads))), (((bad body))), (((two people))), ((extra arms)), ((deformed body)), (((sexy))), paintings,(((two heads))), ((big head)),sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, glans, (((nsfw))), nipples, extra fingers, (extra legs), (long neck), mutated hands, (fused fingers), (too many fingers)"}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "main"
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CATEGORY = "MuseV Evolved"
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def main(
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self,
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image,
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musev_predictor,
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time_size,
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seed,
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w_ind_noise,
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context_frames,
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context_stride,
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context_overlap,
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video_num_inference_steps,
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video_guidance_scale,
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output_shift_first_frame,
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positive_prompt,
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negative_prompt,
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image_weight,
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motion_speed,
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):
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cpu_generator, gpu_generator = set_all_seed(seed)
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condition_image = 255.0 * image[0].cpu().numpy()
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condition_image = np.clip(condition_image, 0, 255).astype(np.uint8)
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condition_image = repeat(condition_image, "h w c-> b c t h w", b=1, t=1)
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width = condition_image.shape[4]
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height = condition_image.shape[3]
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out_videos = musev_predictor.run_pipe_text2video(
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video_length=time_size,
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prompt=positive_prompt,
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width=width,
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height=height,
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generator=gpu_generator,
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noise_type='video_fusion',
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negative_prompt=negative_prompt,
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video_negative_prompt=negative_prompt,
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max_batch_num=1,
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strength=0.8,
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need_img_based_video_noise=True,
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video_num_inference_steps=video_num_inference_steps,
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condition_images=condition_image,
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fix_condition_images=False,
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video_guidance_scale=video_guidance_scale,
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guidance_scale=7.5,
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num_inference_steps=30,
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redraw_condition_image=False,
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img_weight=image_weight,
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w_ind_noise=w_ind_noise,
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n_vision_condition=1,
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motion_speed=motion_speed,
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need_hist_match=False,
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video_guidance_scale_end=None,
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video_guidance_scale_method='linear',
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vision_condition_latent_index=None,
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refer_image=None,
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fixed_refer_image=True,
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redraw_condition_image_with_referencenet=True,
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ip_adapter_image=None,
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refer_face_image=None,
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fixed_refer_face_image=True,
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facein_scale=1.0,
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redraw_condition_image_with_facein=True,
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ip_adapter_face_scale=1.0,
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redraw_condition_image_with_ip_adapter_face=True,
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fixed_ip_adapter_image=True,
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ip_adapter_scale=1.0,
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redraw_condition_image_with_ipdapter=True,
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prompt_only_use_image_prompt=False,
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# serial_denoise parameter start
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record_mid_video_noises=False,
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record_mid_video_latents=False,
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video_overlap=1,
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# serial_denoise parameter end
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# parallel_denoise parameter start
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context_schedule='uniform_v2',
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context_frames=context_frames,
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context_stride=context_stride,
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context_overlap=context_overlap,
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context_batch_size=1,
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interpolation_factor=1,
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# parallel_denoise parameter end
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)
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video = torch.from_numpy(out_videos).permute(0,2,3,4,1)
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if output_shift_first_frame:
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video = video[:, 1:, :, :, :]
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return video
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