latentblending/cherry_picknick.py

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2022-11-21 23:07:55 +00:00
# Copyright 2022 Lunar Ring. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os, sys
import torch
torch.backends.cudnn.benchmark = False
import numpy as np
import warnings
warnings.filterwarnings('ignore')
import warnings
import torch
from tqdm.auto import tqdm
from PIL import Image
import matplotlib.pyplot as plt
import torch
from movie_util import MovieSaver
from typing import Callable, List, Optional, Union
from latent_blending import LatentBlending, add_frames_linear_interp
from stable_diffusion_holder import StableDiffusionHolder
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torch.set_grad_enabled(False)
#%% First let us spawn a stable diffusion holder
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use_inpaint = True
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device = "cuda"
fp_ckpt= "../stable_diffusion_models/ckpt/512-inpainting-ema.ckpt"
fp_config = '../stablediffusion/configs//stable-diffusion/v2-inpainting-inference.yaml'
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# fp_ckpt = "../stable_diffusion_models/ckpt/768-v-ema.ckpt"
# fp_config = '../stablediffusion/configs/stable-diffusion/v2-inference-v.yaml'
sdh = StableDiffusionHolder(fp_ckpt, fp_config, device)
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#%% Next let's set up all parameters
num_inference_steps = 30 # Number of diffusion interations
guidance_scale = 5
lb = LatentBlending(sdh, num_inference_steps, guidance_scale)
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list_prompts = []
list_prompts.append("photo of a beautiful forest covered in white flowers, ambient light, very detailed, magic")
list_prompts.append("photo of an golden statue with a funny hat, surrounded by ferns and vines, grainy analog photograph, mystical ambience, incredible detail")
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for k, prompt in enumerate(list_prompts):
# k = 6
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# prompt = list_prompts[k]
for i in range(10):
lb.set_prompt1(prompt)
seed = np.random.randint(999999999)
lb.set_seed(seed)
plt.imshow(lb.run_diffusion(lb.text_embedding1, return_image=True))
plt.title(f"prompt {k}, seed {i} {seed}")
plt.show()
print(f"prompt {k} seed {seed} trial {i}")
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#%%
#%% Let's make a source image and mask.
k=0
for i in range(10):
seed = 190791709# np.random.randint(999999999)
# seed0 = 629575320
lb = LatentBlending(sdh)
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lb.autosetup_branching(quality='medium', depth_strength=0.65)
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prompt1 = "photo of a futuristic alien temple in a desert, mystic, glowing, organic, intricate, sci-fi movie, mesmerizing, scary"
lb.set_prompt1(prompt1)
lb.init_inpainting(init_empty=True)
lb.set_seed(seed)
plt.imshow(lb.run_diffusion(lb.text_embedding1, return_image=True))
plt.title(f"prompt1 {k}, seed {i} {seed}")
plt.show()
print(f"prompt1 {k} seed {seed} trial {i}")
xx
#%%
mask_image = 255*np.ones([512,512], dtype=np.uint8)
mask_image[340:420, 170:280, ] = 0
mask_image = Image.fromarray(mask_image)
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#%%
"""
69731932, 504430820
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"""