Spaces:
Runtime error
Runtime error
| import torch | |
| import warnings | |
| from diffusers import AutoPipelineForText2Image | |
| from latentblending.blending_engine import BlendingEngine | |
| from lunar_tools import concatenate_movies | |
| import numpy as np | |
| torch.set_grad_enabled(False) | |
| torch.backends.cudnn.benchmark = False | |
| warnings.filterwarnings('ignore') | |
| import json | |
| # %% 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=False) | |
| fp_movie = f'test.mp4' | |
| fp_json = "movie_240221_1520.json" | |
| duration_single_trans = 10 | |
| # Load the JSON data from the file | |
| with open(fp_json, 'r') as file: | |
| data = json.load(file) | |
| # Set up width, height, num_inference steps | |
| width = data[0]["width"] | |
| height = data[0]["height"] | |
| num_inference_steps = data[0]["num_inference_steps"] | |
| be.set_dimensions((width, height)) | |
| be.set_num_inference_steps(num_inference_steps) | |
| # Initialize lists for prompts, negative prompts, and seeds | |
| list_prompts = [] | |
| list_negative_prompts = [] | |
| list_seeds = [] | |
| # Extract prompts, negative prompts, and seeds from the data | |
| for item in data[1:]: # Skip the first item as it contains settings | |
| list_prompts.append(item["prompt"]) | |
| list_negative_prompts.append(item["negative_prompt"]) | |
| list_seeds.append(item["seed"]) | |
| 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_negative_prompt(list_negative_prompts[i]) | |
| be.set_prompt2(list_prompts[i + 1]) | |
| recycle_img1 = False | |
| else: | |
| be.swap_forward() | |
| be.set_negative_prompt(list_negative_prompts[i+1]) | |
| 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) | |
| print(f"DONE! MOVIE SAVED IN {fp_movie}") |