123 lines
4.3 KiB
Python
123 lines
4.3 KiB
Python
# Copyright 2022 Lunar Ring. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os, sys
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import torch
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torch.backends.cudnn.benchmark = False
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import numpy as np
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import warnings
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warnings.filterwarnings('ignore')
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import warnings
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import torch
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from tqdm.auto import tqdm
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from diffusers import StableDiffusionPipeline
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from diffusers.schedulers import DDIMScheduler
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from PIL import Image
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import matplotlib.pyplot as plt
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import torch
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from movie_util import MovieSaver
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from typing import Callable, List, Optional, Union
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from latent_blending import LatentBlending, add_frames_linear_interp
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torch.set_grad_enabled(False)
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#%% First let us spawn a diffusers pipe using DDIMScheduler
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device = "cuda:0"
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model_path = "../stable_diffusion_models/stable-diffusion-v1-5"
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scheduler = DDIMScheduler(beta_start=0.00085,
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beta_end=0.012,
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beta_schedule="scaled_linear",
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clip_sample=False,
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set_alpha_to_one=False)
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pipe = StableDiffusionPipeline.from_pretrained(
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model_path,
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revision="fp16",
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torch_dtype=torch.float16,
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scheduler=scheduler,
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use_auth_token=True
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)
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pipe = pipe.to(device)
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#%% MULTITRANS
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# XXX FIXME AssertionError: Need to supply floats for list_injection_strength
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# GO AS DEEP AS POSSIBLE WITHOUT CAUSING MOTION
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num_inference_steps = 100 # Number of diffusion interations
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#list_nmb_branches = [2, 12, 24, 55, 77] # Branching structure: how many branches
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#list_injection_strength = [0.0, 0.35, 0.5, 0.65, 0.95] # Branching structure: how deep is the blending
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list_nmb_branches = list(np.linspace(2, 600, 15).astype(int)) #
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list_injection_strength = list(np.linspace(0.45, 0.97, 14).astype(np.float32)) # Branching structure: how deep is the blending
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list_injection_strength = [float(x) for x in list_injection_strength]
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list_injection_strength.insert(0,0.0)
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width = 512
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height = 512
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guidance_scale = 5
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fps = 30
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duration_target = 20
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width = 512
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height = 512
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lb = LatentBlending(pipe, device, height, width, num_inference_steps, guidance_scale)
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#list_nmb_branches = [2, 3, 10, 24] # Branching structure: how many branches
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#list_injection_strength = [0.0, 0.6, 0.8, 0.9] #
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list_prompts = []
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list_prompts.append("surrealistic statue made of glitter and dirt, standing in a lake, atmospheric light, strange glow")
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list_prompts.append("statue of a mix between a tree and human, made of marble, incredibly detailed")
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list_prompts.append("weird statue of a frog monkey, many colors, standing next to the ruins of an ancient city")
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list_prompts.append("statue made of hot metal, bizzarre, dark clouds in the sky")
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list_prompts.append("statue of a spider that looked like a human")
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list_prompts.append("statue of a bird that looked like a scorpion")
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list_prompts.append("statue of an ancient cybernetic messenger annoucing good news, golden, futuristic")
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list_seeds = [234187386, 422209351, 241845736, 28652396, 783279867, 831049796, 234903931]
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fp_movie = "/home/lugo/tmp/latentblending/bubu.mp4"
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ms = MovieSaver(fp_movie, fps=fps)
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for i in range(len(list_prompts)-1):
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print(f"Starting movie segment {i+1}/{len(list_prompts)-1}")
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if i==0:
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lb.set_prompt1(list_prompts[i])
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lb.set_prompt2(list_prompts[i+1])
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recycle_img1 = False
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else:
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lb.swap_forward()
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lb.set_prompt2(list_prompts[i+1])
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recycle_img1 = True
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local_seeds = [list_seeds[i], list_seeds[i+1]]
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list_imgs = lb.run_transition(list_nmb_branches, list_injection_strength, recycle_img1=recycle_img1, fixed_seeds=local_seeds)
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list_imgs_interp = add_frames_linear_interp(list_imgs, fps, duration_target)
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# Save movie frame
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for img in list_imgs_interp:
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ms.write_frame(img)
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ms.finalize()
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#%%
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#for img in lb.tree_final_imgs:
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# if img is not None:
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# ms.write_frame(img)
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#
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#ms.finalize()
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