56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
import torch
|
|
import warnings
|
|
from diffusers import AutoPipelineForText2Image
|
|
from latentblending.movie_util import concatenate_movies
|
|
from latentblending.blending_engine import BlendingEngine
|
|
torch.set_grad_enabled(False)
|
|
torch.backends.cudnn.benchmark = False
|
|
warnings.filterwarnings('ignore')
|
|
|
|
# %% First let us spawn a stable diffusion holder. Uncomment your version of choice.
|
|
pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
|
|
pipe.to('cuda')
|
|
be = BlendingEngine(pipe)
|
|
|
|
# %% Let's setup the multi transition
|
|
fps = 30
|
|
duration_single_trans = 10
|
|
|
|
# Specify a list of prompts below
|
|
list_prompts = []
|
|
list_prompts.append("Photo of a house, high detail")
|
|
list_prompts.append("Photo of an elephant in african savannah")
|
|
list_prompts.append("photo of a house, high detail")
|
|
|
|
|
|
# Specify the seeds
|
|
list_seeds = np.random.randint(0, 10^9, len(list_prompts))
|
|
fp_movie = 'movie_example2.mp4'
|
|
|
|
|
|
list_movie_parts = []
|
|
for i in range(len(list_prompts) - 1):
|
|
# For a multi transition we can save some computation time and recycle the latents
|
|
if i == 0:
|
|
be.set_prompt1(list_prompts[i])
|
|
be.set_prompt2(list_prompts[i + 1])
|
|
recycle_img1 = False
|
|
else:
|
|
be.swap_forward()
|
|
be.set_prompt2(list_prompts[i + 1])
|
|
recycle_img1 = True
|
|
|
|
fp_movie_part = f"tmp_part_{str(i).zfill(3)}.mp4"
|
|
fixed_seeds = list_seeds[i:i + 2]
|
|
# Run latent blending
|
|
be.run_transition(
|
|
recycle_img1=recycle_img1,
|
|
fixed_seeds=fixed_seeds)
|
|
|
|
# Save movie
|
|
be.write_movie_transition(fp_movie_part, duration_single_trans)
|
|
list_movie_parts.append(fp_movie_part)
|
|
|
|
# Finally, concatente the result
|
|
concatenate_movies(fp_movie, list_movie_parts)
|