63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
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import torch
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import warnings
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from diffusers import AutoPipelineForText2Image
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from lunar_tools import concatenate_movies
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from latentblending.blending_engine import BlendingEngine
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import numpy as np
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torch.set_grad_enabled(False)
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torch.backends.cudnn.benchmark = False
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warnings.filterwarnings('ignore')
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# %% First let us spawn a stable diffusion holder. Uncomment your version of choice.
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pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
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# pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
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pipe = AutoPipelineForText2Image.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16")
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pipe.to('cuda')
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be = BlendingEngine(pipe, do_compile=True)
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be.set_negative_prompt("blurry, pale, low-res, lofi")
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# %% Let's setup the multi transition
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fps = 30
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duration_single_trans = 10
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be.set_dimensions((1024, 1024))
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nmb_prompts = 20
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# Specify a list of prompts below
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#%%
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list_prompts = []
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list_prompts.append("high resolution ultra 8K image with lake and forest")
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list_prompts.append("strange and alien desolate lanscapes 8K")
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list_prompts.append("ultra high res psychedelic skyscraper city landscape 8K unreal engine")
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#%%
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fp_movie = f'surreal_nmb{len(list_prompts)}.mp4'
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# Specify the seeds
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list_seeds = np.random.randint(0, np.iinfo(np.int32).max, len(list_prompts))
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list_movie_parts = []
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for i in range(len(list_prompts) - 1):
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# For a multi transition we can save some computation time and recycle the latents
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if i == 0:
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be.set_prompt1(list_prompts[i])
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be.set_prompt2(list_prompts[i + 1])
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recycle_img1 = False
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else:
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be.swap_forward()
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be.set_prompt2(list_prompts[i + 1])
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recycle_img1 = True
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fp_movie_part = f"tmp_part_{str(i).zfill(3)}.mp4"
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fixed_seeds = list_seeds[i:i + 2]
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# Run latent blending
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be.run_transition(
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recycle_img1=recycle_img1,
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fixed_seeds=fixed_seeds)
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# Save movie
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be.write_movie_transition(fp_movie_part, duration_single_trans)
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list_movie_parts.append(fp_movie_part)
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# Finally, concatente the result
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concatenate_movies(fp_movie, list_movie_parts)
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