105 lines
3.2 KiB
Python
105 lines
3.2 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 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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from stable_diffusion_holder import StableDiffusionHolder
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torch.set_grad_enabled(False)
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#%% First let us spawn a stable diffusion holder
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use_inpaint = True
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device = "cuda"
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fp_ckpt= "../stable_diffusion_models/ckpt/512-inpainting-ema.ckpt"
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fp_config = '../stablediffusion/configs//stable-diffusion/v2-inpainting-inference.yaml'
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# fp_ckpt = "../stable_diffusion_models/ckpt/768-v-ema.ckpt"
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# fp_config = '../stablediffusion/configs/stable-diffusion/v2-inference-v.yaml'
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sdh = StableDiffusionHolder(fp_ckpt, fp_config, 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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guidance_scale = 5
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lb = LatentBlending(sdh, num_inference_steps, guidance_scale)
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list_prompts = []
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list_prompts.append("photo of a beautiful forest covered in white flowers, ambient light, very detailed, magic")
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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):
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# k = 6
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# prompt = list_prompts[k]
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for i in range(10):
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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"prompt {k}, seed {i} {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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#%% Let's make a source image and mask.
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k=0
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for i in range(10):
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seed = 190791709# np.random.randint(999999999)
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# seed0 = 629575320
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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"
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lb.set_prompt1(prompt1)
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lb.init_inpainting(init_empty=True)
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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"prompt1 {k}, seed {i} {seed}")
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plt.show()
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print(f"prompt1 {k} seed {seed} trial {i}")
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xx
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#%%
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mask_image = 255*np.ones([512,512], dtype=np.uint8)
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mask_image[340:420, 170:280, ] = 0
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mask_image = Image.fromarray(mask_image)
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#%%
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"""
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69731932, 504430820
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""" |