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import os
import imageio
from PIL import Image
from typing import List
import torch
import torch.nn.functional as F
from diffusers import IFSuperResolutionPipeline, VideoToVideoSDPipeline
from diffusers.utils.torch_utils import randn_tensor
class ShowOnePipeline():
def __init__(self, base_path, interp_path, deepfloyd_path, sr1_path, sr2_path):
"""
Downloading the necessary models from huggingface and utilize them to load their pipelines,
https://github.com/showlab/Show-1
"""
from .showone.pipelines import TextToVideoIFPipeline, TextToVideoIFInterpPipeline, \
TextToVideoIFSuperResolutionPipeline
from .showone.pipelines.pipeline_t2v_base_pixel import tensor2vid
from .showone.pipelines.pipeline_t2v_sr_pixel_cond import TextToVideoIFSuperResolutionPipeline_Cond
self.tensor2vid = tensor2vid
# Base Model
# When using "showlab/show-1-base-0.0", it's advisable to increase the number of inference steps (e.g., 100)
# and opt for a larger guidance scale (e.g., 12.0) to enhance visual quality.
self.pipe_base = TextToVideoIFPipeline.from_pretrained(
base_path,
torch_dtype=torch.float16,
variant="fp16"
)
self.pipe_base.enable_model_cpu_offload()
# Interpolation Model
self.pipe_interp_1 = TextToVideoIFInterpPipeline.from_pretrained(
interp_path,
torch_dtype=torch.float16,
variant="fp16"
)
self.pipe_interp_1.enable_model_cpu_offload()
# Super-Resolution Model 1
# Image super-resolution model from DeepFloyd https://huggingface.co/DeepFloyd/IF-II-L-v1.0
# pretrained_model_path = "./checkpoints/DeepFloyd/IF-II-L-v1.0"
self.pipe_sr_1_image = IFSuperResolutionPipeline.from_pretrained(
deepfloyd_path,
text_encoder=None,
torch_dtype=torch.float16,
variant="fp16"
)
self.pipe_sr_1_image.enable_model_cpu_offload()
self.pipe_sr_1_cond = TextToVideoIFSuperResolutionPipeline_Cond.from_pretrained(
sr1_path,
torch_dtype=torch.float16
)
self.pipe_sr_1_cond.enable_model_cpu_offload()
# Super-Resolution Model 2
self.pipe_sr_2 = VideoToVideoSDPipeline.from_pretrained(
sr2_path,
torch_dtype=torch.float16
)
self.pipe_sr_2.enable_model_cpu_offload()
self.pipe_sr_2.enable_vae_slicing()
def inference(self, prompt: str = "",
negative_prompt: str = "",
output_size: List[int] = [240, 560],
initial_num_frames: int = 8,
scaling_factor: int = 4,
seed: int = 42):
"""
Generates a single video based on a textual prompt. The output is a tensor representing the video.
The initial_num_frames is set to be 8 as shown in paper.
https://github.com/showlab/Show-1
Args:
prompt (str, optional): The text prompt that guides the video generation. If not specified, the video generation will rely solely on the input image. Defaults to "".
negative_prompt (str, optional): The negative prompt that guided the video generation. Defaults to "".
output_size (list, optional): Specifies the resolution of the output video as [height, width]. Defaults to [240, 560].
initial_num_frames: the number of frames generated using the base model. Defaults to 8 as proposed in the paper.
scaling_factor: The amount of scaling during the interpolation step. Defaults to 4 as proposed in the paper, which interpolates number of frames from 8 to 29.
seed (int, optional): A seed value for random number generation, ensuring reproducibility of the video generation process. Defaults to 42.
Returns:
The generated video as a tensor with shape (num_frames, channels, height, width).
"""
# Inference
# Text embeds
prompt_embeds, negative_embeds = self.pipe_base.encode_prompt(prompt)
# Keyframes generation (8x64x40, 2fps)
video_frames = self.pipe_base(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
num_frames=initial_num_frames,
height=40,
width=64,
num_inference_steps=75,
guidance_scale=9.0,
generator=torch.manual_seed(seed),
output_type="pt"
).frames
# Frame interpolation (8x64x40, 2fps -> 29x64x40, 7.5fps)
bsz, channel, num_frames_1, height, width = video_frames.shape
k = scaling_factor
new_num_frames = (k - 1) * (num_frames_1 - 1) + num_frames_1
new_video_frames = torch.zeros((bsz, channel, new_num_frames, height, width),
dtype=video_frames.dtype, device=video_frames.device)
new_video_frames[:, :, torch.arange(0, new_num_frames, k), ...] = video_frames
init_noise = randn_tensor((bsz, channel, k + 1, height, width), dtype=video_frames.dtype,
device=video_frames.device, generator=torch.manual_seed(seed))
for i in range(num_frames_1 - 1):
batch_i = torch.zeros((bsz, channel, k + 1, height, width), dtype=video_frames.dtype,
device=video_frames.device)
batch_i[:, :, 0, ...] = video_frames[:, :, i, ...]
batch_i[:, :, -1, ...] = video_frames[:, :, i + 1, ...]
batch_i = self.pipe_interp_1(
pixel_values=batch_i,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
num_frames=batch_i.shape[2],
height=40,
width=64,
num_inference_steps=75,
guidance_scale=4.0,
generator=torch.manual_seed(seed),
output_type="pt",
init_noise=init_noise,
cond_interpolation=True,
).frames
new_video_frames[:, :, i * k:i * k + k + 1, ...] = batch_i
video_frames = new_video_frames
# Super-resolution 1 (29x64x40 -> 29x256x160)
bsz, channel, num_frames_2, height, width = video_frames.shape
window_size, stride = 8, 7
new_video_frames = torch.zeros(
(bsz, channel, num_frames_2, height * 4, width * 4),
dtype=video_frames.dtype,
device=video_frames.device)
for i in range(0, num_frames_2 - window_size + 1, stride):
batch_i = video_frames[:, :, i:i + window_size, ...]
all_frame_cond = None
if i == 0:
first_frame_cond = self.pipe_sr_1_image(
image=video_frames[:, :, 0, ...],
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
height=height * 4,
width=width * 4,
num_inference_steps=70,
guidance_scale=4.0,
noise_level=150,
generator=torch.manual_seed(seed),
output_type="pt"
).images
first_frame_cond = first_frame_cond.unsqueeze(2)
else:
first_frame_cond = new_video_frames[:, :, i:i + 1, ...]
batch_i = self.pipe_sr_1_cond(
image=batch_i,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
first_frame_cond=first_frame_cond,
height=height * 4,
width=width * 4,
num_inference_steps=125,
guidance_scale=7.0,
noise_level=250,
generator=torch.manual_seed(seed),
output_type="pt"
).frames
new_video_frames[:, :, i:i + window_size, ...] = batch_i
video_frames = new_video_frames
# Super-resolution 2 (29x256x160 -> 29x576x320)
video_frames = [Image.fromarray(frame).resize((output_size[1], output_size[0])) for frame in
self.tensor2vid(video_frames.clone())]
video_frames = self.pipe_sr_2(
prompt,
negative_prompt=negative_prompt,
video=video_frames,
strength=0.8,
num_inference_steps=50,
generator=torch.manual_seed(seed),
output_type="pt"
).frames
output = video_frames.squeeze()
return output
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