import torch import warnings from diffusers import AutoPipelineForText2Image from latentblending.movie_util import concatenate_movies from latentblending.blending_engine import BlendingEngine import numpy as np 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. pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0" # pretrained_model_name_or_path = "stabilityai/sdxl-turbo" pipe = AutoPipelineForText2Image.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16") pipe.to('cuda') be = BlendingEngine(pipe, do_compile=True) be.set_negative_prompt("blurry, pale, low-res, lofi") # %% Let's setup the multi transition fps = 30 duration_single_trans = 10 be.set_dimensions((1024, 1024)) nmb_prompts = 20 # Specify a list of prompts below #%% list_prompts = [] list_prompts.append("high resolution ultra 8K image with lake and forest") # list_prompts.append("strange and alien desolate lanscapes 8K") # list_prompts.append("ultra high res psychedelic skyscraper city landscape 8K unreal engine") list_prompts = list_prompts*10 be.set_prompt1(list_prompts[0]) be.compute_latents1(True) #img = pipe(list_prompts[0]).images[0] #%% # import os # import random # # Directory containing the text files # dir_prompts = "/raid/data/diffusion/flamengalo/prompts_surreal" # # List to store the contents of selected text files # list_prompts = [] # # List all files in the directory # file_list = os.listdir(dir_prompts) # # Shuffle the file list to get random files # random.shuffle(file_list) # # Loop through the first nmb_prompts files and read their contents # for filename in file_list[:nmb_prompts]: # file_path = os.path.join(dir_prompts, filename) # try: # with open(file_path, 'r') as file: # content = file.read() # list_prompts.append(content) # except Exception as e: # print(f"except {e}") #%% fp_movie = f'surreal_nmb{len(list_prompts)}.mp4' # Specify the seeds list_seeds = np.random.randint(0, 1111111111111, len(list_prompts)) 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)