103 lines
3.3 KiB
Python
103 lines
3.3 KiB
Python
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# 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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#%% Next let's set up all parameters
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num_inference_steps = 30 # Number of diffusion interations
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list_nmb_branches = [2, 3, 10, 24]#, 50] # Branching structure: how many branches
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list_injection_strength = [0.0, 0.6, 0.8, 0.9]#, 0.95] # Branching structure: how deep is the blending
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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 = 10
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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_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("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 of a mix between a tree and human, made of marble, incredibly detailed")
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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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k = 6
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prompt = list_prompts[k]
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for i in range(4):
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lb.set_prompt1(prompt)
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seed = np.random.randint(999999999)
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lb.set_seed(seed)
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plt.imshow(lb.run_diffusion(lb.text_embedding1, return_image=True))
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plt.title(f"{i} seed {seed}")
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plt.show()
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print(f"prompt {k} seed {seed} trial {i}")
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#%%
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"""
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prompt 3 seed 28652396 trial 2
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prompt 4 seed 783279867 trial 3
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prompt 5 seed 831049796 trial 3
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prompt 6 seed 798876383 trial 2
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prompt 6 seed 750494819 trial 2
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prompt 6 seed 416472011 trial 1
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"""
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