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import os
import imageio
from PIL import Image
import torch
import torch.nn.functional as F
from diffusers import IFSuperResolutionPipeline, VideoToVideoSDPipeline
from diffusers.utils.torch_utils import randn_tensor
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
# 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.
pretrained_model_path = "showlab/show-1-base"
pipe_base = TextToVideoIFPipeline.from_pretrained(
pretrained_model_path,
torch_dtype=torch.float16,
variant="fp16"
)
pipe_base.enable_model_cpu_offload()
# Interpolation Model
pretrained_model_path = "showlab/show-1-interpolation"
pipe_interp_1 = TextToVideoIFInterpPipeline.from_pretrained(
pretrained_model_path,
torch_dtype=torch.float16,
variant="fp16"
)
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 = "DeepFloyd/IF-II-L-v1.0"
pipe_sr_1_image = IFSuperResolutionPipeline.from_pretrained(
pretrained_model_path,
text_encoder=None,
torch_dtype=torch.float16,
variant="fp16"
)
pipe_sr_1_image.enable_model_cpu_offload()
pretrained_model_path = "showlab/show-1-sr1"
pipe_sr_1_cond = TextToVideoIFSuperResolutionPipeline_Cond.from_pretrained(
pretrained_model_path,
torch_dtype=torch.float16
)
pipe_sr_1_cond.enable_model_cpu_offload()
# Super-Resolution Model 2
pretrained_model_path = "showlab/show-1-sr2"
pipe_sr_2 = VideoToVideoSDPipeline.from_pretrained(
pretrained_model_path,
torch_dtype=torch.float16
)
pipe_sr_2.enable_model_cpu_offload()
pipe_sr_2.enable_vae_slicing()
# Inference
prompt = "A burning lamborghini driving on rainbow."
output_dir = "./outputs/example"
negative_prompt = "low resolution, blur"
seed = 345
os.makedirs(output_dir, exist_ok=True)
# Text embeds
prompt_embeds, negative_embeds = pipe_base.encode_prompt(prompt)
# Keyframes generation (8x64x40, 2fps)
video_frames = pipe_base(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_embeds,
num_frames=8,
height=40,
width=64,
num_inference_steps=75,
guidance_scale=9.0,
generator=torch.manual_seed(seed),
output_type="pt"
).frames
imageio.mimsave(f"{output_dir}/{prompt}_base.gif", tensor2vid(video_frames.clone()), fps=2)
# Frame interpolation (8x64x40, 2fps -> 29x64x40, 7.5fps)
bsz, channel, num_frames, height, width = video_frames.shape
new_num_frames = 3 * (num_frames - 1) + num_frames
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, 4), ...] = video_frames
init_noise = randn_tensor((bsz, channel, 5, height, width), dtype=video_frames.dtype,
device=video_frames.device, generator=torch.manual_seed(seed))
for i in range(num_frames - 1):
batch_i = torch.zeros((bsz, channel, 5, 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 = 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 * 4:i * 4 + 5, ...] = batch_i
video_frames = new_video_frames
imageio.mimsave(f"{output_dir}/{prompt}_interp.gif", tensor2vid(video_frames.clone()), fps=8)
# Super-resolution 1 (29x64x40 -> 29x256x160)
bsz, channel, num_frames, height, width = video_frames.shape
window_size, stride = 8, 7
new_video_frames = torch.zeros(
(bsz, channel, num_frames, height * 4, width * 4),
dtype=video_frames.dtype,
device=video_frames.device)
for i in range(0, num_frames - window_size + 1, stride):
batch_i = video_frames[:, :, i:i + window_size, ...]
all_frame_cond = None
if i == 0:
first_frame_cond = 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 = 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
imageio.mimsave(f"{output_dir}/{prompt}_sr1.gif", tensor2vid(video_frames.clone()), fps=8)
# Super-resolution 2 (29x256x160 -> 29x576x320)
video_frames = [Image.fromarray(frame).resize((576, 320)) for frame in tensor2vid(video_frames.clone())]
video_frames = 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
imageio.mimsave(f"{output_dir}/{prompt}.gif", tensor2vid(video_frames.clone()), fps=8)
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