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upscaling x4 model support

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Johannes Stelzer 2023-01-08 10:32:58 +01:00
parent ca0f818317
commit cd45b2e585
3 changed files with 681 additions and 221 deletions
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105
cherry_picknick.py
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@ -1,105 +0,0 @@
# 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
torch.set_grad_enabled(False)
#%% First let us spawn a stable diffusion holder
use_inpaint = True
device = "cuda"
fp_ckpt= "../stable_diffusion_models/ckpt/512-inpainting-ema.ckpt"
fp_config = '../stablediffusion/configs//stable-diffusion/v2-inpainting-inference.yaml'
# 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)
#%% 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)
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")
for k, prompt in enumerate(list_prompts):
# k = 6
# 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}")
#%%
#%% 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)
lb.autosetup_branching(quality='medium', depth_strength=0.65)
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)
#%%
"""
69731932, 504430820
"""

404
latent_blending.py
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@ -27,7 +27,7 @@ import warnings
import torch import torch
from tqdm.auto import tqdm from tqdm.auto import tqdm
from PIL import Image from PIL import Image
import matplotlib.pyplot as plt # import matplotlib.pyplot as plt
import torch import torch
from movie_util import MovieSaver from movie_util import MovieSaver
import datetime import datetime
@ -41,7 +41,10 @@ from contextlib import nullcontext
from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import instantiate_from_config from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddpm import LatentUpscaleDiffusion, LatentInpaintDiffusion
from stable_diffusion_holder import StableDiffusionHolder from stable_diffusion_holder import StableDiffusionHolder
import yaml
#%% #%%
class LatentBlending(): class LatentBlending():
def __init__( def __init__(
@ -49,7 +52,7 @@ class LatentBlending():
sdh: None, sdh: None,
guidance_scale: float = 4, guidance_scale: float = 4,
guidance_scale_mid_damper: float = 0.5, guidance_scale_mid_damper: float = 0.5,
mid_compression_scaler: float = 2.0, mid_compression_scaler: float = 1.2,
): ):
r""" r"""
Initializes the latent blending class. Initializes the latent blending class.
@ -77,7 +80,8 @@ class LatentBlending():
self.height = self.sdh.height self.height = self.sdh.height
self.guidance_scale_mid_damper = guidance_scale_mid_damper self.guidance_scale_mid_damper = guidance_scale_mid_damper
self.mid_compression_scaler = mid_compression_scaler self.mid_compression_scaler = mid_compression_scaler
self.seed = 420 # Run self.set_seed or fixed_seeds argument in run_transition self.seed1 = 0
self.seed2 = 0
# Initialize vars # Initialize vars
self.prompt1 = "" self.prompt1 = ""
@ -90,20 +94,25 @@ class LatentBlending():
self.list_injection_idx_prev = [] self.list_injection_idx_prev = []
self.text_embedding1 = None self.text_embedding1 = None
self.text_embedding2 = None self.text_embedding2 = None
self.image1_lowres = None
self.image2_lowres = None
self.stop_diffusion = False self.stop_diffusion = False
self.negative_prompt = None self.negative_prompt = None
self.num_inference_steps = -1 self.num_inference_steps = self.sdh.num_inference_steps
self.noise_level_upscaling = 20
self.list_injection_idx = None self.list_injection_idx = None
self.list_nmb_branches = None self.list_nmb_branches = None
self.set_guidance_scale(guidance_scale) self.set_guidance_scale(guidance_scale)
self.init_mode() self.init_mode()
def init_mode(self, mode='standard'): def init_mode(self):
r""" r"""
Sets the mode of this class, either inpaint of standard. Sets the operational mode. Currently supported are standard, inpainting and x4 upscaling.
""" """
if mode == 'inpaint': if isinstance(self.sdh.model, LatentUpscaleDiffusion):
self.mode = 'upscale'
elif isinstance(self.sdh.model, LatentInpaintDiffusion):
self.sdh.image_source = None self.sdh.image_source = None
self.sdh.mask_image = None self.sdh.mask_image = None
self.mode = 'inpaint' self.mode = 'inpaint'
@ -152,10 +161,26 @@ class LatentBlending():
self.prompt2 = prompt self.prompt2 = prompt
self.text_embedding2 = self.get_text_embeddings(self.prompt2) self.text_embedding2 = self.get_text_embeddings(self.prompt2)
def autosetup_branching( def set_image1(self, image: Image):
r"""
Sets the first image (keyframe), relevant for the upscaling model transitions.
Args:
image: Image
"""
self.image1_lowres = image
def set_image2(self, image: Image):
r"""
Sets the second image (keyframe), relevant for the upscaling model transitions.
Args:
image: Image
"""
self.image2_lowres = image
def load_branching_profile(
self, self,
quality: str = 'medium', quality: str = 'medium',
deepth_strength: float = 0.65, depth_strength: float = 0.65,
nmb_frames: int = 360, nmb_frames: int = 360,
nmb_mindist: int = 3, nmb_mindist: int = 3,
): ):
@ -167,7 +192,7 @@ class LatentBlending():
Determines how many diffusion steps are being made + how many branches in total. Determines how many diffusion steps are being made + how many branches in total.
Tradeoff between quality and speed of computation. Tradeoff between quality and speed of computation.
Choose: lowest, low, medium, high, ultra Choose: lowest, low, medium, high, ultra
deepth_strength: float = 0.65, depth_strength: float = 0.65,
Determines how deep the first injection will happen. Determines how deep the first injection will happen.
Deeper injections will cause (unwanted) formation of new structures, Deeper injections will cause (unwanted) formation of new structures,
more shallow values will go into alpha-blendy land. more shallow values will go into alpha-blendy land.
@ -175,7 +200,6 @@ class LatentBlending():
total number of frames total number of frames
nmb_mindist: int = 3 nmb_mindist: int = 3
minimum distance in terms of diffusion iteratinos between subsequent injections minimum distance in terms of diffusion iteratinos between subsequent injections
""" """
if quality == 'lowest': if quality == 'lowest':
@ -193,10 +217,42 @@ class LatentBlending():
elif quality == 'ultra': elif quality == 'ultra':
num_inference_steps = 100 num_inference_steps = 100
nmb_branches_final = nmb_frames//2 nmb_branches_final = nmb_frames//2
elif quality == 'upscaling_step1':
num_inference_steps = 40
nmb_branches_final = 12
elif quality == 'upscaling_step2':
num_inference_steps = 100
nmb_branches_final = 4
else: else:
raise ValueError("quality = '{quality}' not supported") raise ValueError(f"quality = '{quality}' not supported")
idx_injection_first = int(np.round(num_inference_steps*deepth_strength)) self.autosetup_branching(depth_strength, num_inference_steps, nmb_branches_final)
def autosetup_branching(
self,
depth_strength: float = 0.65,
num_inference_steps: int = 30,
nmb_branches_final: int = 20,
nmb_mindist: int = 3,
):
r"""
Automatically sets up the branching schedule.
Args:
depth_strength: float = 0.65,
Determines how deep the first injection will happen.
Deeper injections will cause (unwanted) formation of new structures,
more shallow values will go into alpha-blendy land.
num_inference_steps: int
Number of diffusion steps. Larger values will take more compute time.
nmb_branches_final (int): The number of diffusion-generated images
at the end of the inference.
nmb_mindist (int): The minimum number of diffusion steps
between two injections.
"""
idx_injection_first = int(np.round(num_inference_steps*depth_strength))
idx_injection_last = num_inference_steps - 3 idx_injection_last = num_inference_steps - 3
nmb_injections = int(np.floor(num_inference_steps/5)) - 1 nmb_injections = int(np.floor(num_inference_steps/5)) - 1
@ -219,10 +275,6 @@ class LatentBlending():
list_injection_idx = list_injection_idx_clean list_injection_idx = list_injection_idx_clean
list_nmb_branches = list_nmb_branches_clean list_nmb_branches = list_nmb_branches_clean
# print(f"num_inference_steps: {num_inference_steps}")
# print(f"list_injection_idx: {list_injection_idx}")
# print(f"list_nmb_branches: {list_nmb_branches}")
list_nmb_branches = list_nmb_branches list_nmb_branches = list_nmb_branches
list_injection_idx = list_injection_idx list_injection_idx = list_injection_idx
self.setup_branching(num_inference_steps, list_nmb_branches=list_nmb_branches, list_injection_idx=list_injection_idx) self.setup_branching(num_inference_steps, list_nmb_branches=list_nmb_branches, list_injection_idx=list_injection_idx)
@ -313,6 +365,7 @@ class LatentBlending():
recycle_img1: Optional[bool] = False, recycle_img1: Optional[bool] = False,
recycle_img2: Optional[bool] = False, recycle_img2: Optional[bool] = False,
fixed_seeds: Optional[List[int]] = None, fixed_seeds: Optional[List[int]] = None,
premature_stop: Optional[int] = np.inf,
): ):
r""" r"""
Returns a list of transition images using spherical latent blending. Returns a list of transition images using spherical latent blending.
@ -324,6 +377,8 @@ class LatentBlending():
fixed_seeds: Optional[List[int)]: fixed_seeds: Optional[List[int)]:
You can supply two seeds that are used for the first and second keyframe (prompt1 and prompt2). You can supply two seeds that are used for the first and second keyframe (prompt1 and prompt2).
Otherwise random seeds will be taken. Otherwise random seeds will be taken.
premature_stop: Optional[int]:
Stop the computation after premature_stop frames have been computed in the transition
""" """
# Sanity checks first # Sanity checks first
@ -337,27 +392,15 @@ class LatentBlending():
else: else:
assert len(fixed_seeds)==2, "Supply a list with len = 2" assert len(fixed_seeds)==2, "Supply a list with len = 2"
self.seed1 = fixed_seeds[0]
self.seed2 = fixed_seeds[1]
# Process interruption variable # Process interruption variable
self.stop_diffusion = False self.stop_diffusion = False
# Ensure correct num_inference_steps in holder # Ensure correct num_inference_steps in holder
self.sdh.num_inference_steps = self.num_inference_steps self.sdh.num_inference_steps = self.num_inference_steps
# # Recycling? There are requirements
# if recycle_img1 or recycle_img2:
# # if self.list_nmb_branches_prev == []:
# # print("Warning. You want to recycle but there is nothing here. Disabling recycling.")
# # recycle_img1 = False
# # recycle_img2 = False
# if self.list_nmb_branches_prev != self.list_nmb_branches:
# print("Warning. Cannot change list_nmb_branches if recycling latent. Disabling recycling.")
# recycle_img1 = False
# recycle_img2 = False
# elif self.list_injection_idx_prev != self.list_injection_idx:
# print("Warning. Cannot change list_nmb_branches if recycling latent. Disabling recycling.")
# recycle_img1 = False
# recycle_img2 = False
# Make a backup for future reference # Make a backup for future reference
self.list_nmb_branches_prev = self.list_nmb_branches[:] self.list_nmb_branches_prev = self.list_nmb_branches[:]
self.list_injection_idx_prev = self.list_injection_idx[:] self.list_injection_idx_prev = self.list_injection_idx[:]
@ -415,15 +458,19 @@ class LatentBlending():
# Diffusion computations start here # Diffusion computations start here
time_start = time.time() time_start = time.time()
for t_block, idx_branch in tqdm(list_compute, desc="computing transition", smoothing=-1): for t_block, idx_branch in tqdm(list_compute, desc="computing transition", smoothing=0.01):
if self.stop_diffusion: if self.stop_diffusion:
print("run_transition: process interrupted") print("run_transition: process interrupted")
return self.tree_final_imgs return self.tree_final_imgs
if idx_branch > premature_stop:
print(f"run_transition: premature_stop criterion reached. returning tree with {premature_stop} branches")
return self.tree_final_imgs
# print(f"computing t_block {t_block} idx_branch {idx_branch}") # print(f"computing t_block {t_block} idx_branch {idx_branch}")
idx_stop = self.list_injection_idx_ext[t_block+1] idx_stop = self.list_injection_idx_ext[t_block+1]
fract_mixing = self.tree_fracts[t_block][idx_branch] fract_mixing = self.tree_fracts[t_block][idx_branch]
text_embeddings_mix = interpolate_linear(self.text_embedding1, self.text_embedding2, fract_mixing)
list_conditionings = self.get_mixed_conditioning(fract_mixing)
self.set_guidance_mid_dampening(fract_mixing) self.set_guidance_mid_dampening(fract_mixing)
# print(f"fract_mixing {fract_mixing} guid {self.sdh.guidance_scale}") # print(f"fract_mixing {fract_mixing} guid {self.sdh.guidance_scale}")
if t_block == 0: if t_block == 0:
@ -432,7 +479,7 @@ class LatentBlending():
self.set_seed(fixed_seeds[0]) self.set_seed(fixed_seeds[0])
elif idx_branch == self.list_nmb_branches[0] -1: elif idx_branch == self.list_nmb_branches[0] -1:
self.set_seed(fixed_seeds[1]) self.set_seed(fixed_seeds[1])
list_latents = self.run_diffusion(text_embeddings_mix, idx_stop=idx_stop) list_latents = self.run_diffusion(list_conditionings, idx_stop=idx_stop)
else: else:
# find parents latents # find parents latents
b_parent1, b_parent2 = get_closest_idx(fract_mixing, self.tree_fracts[t_block-1]) b_parent1, b_parent2 = get_closest_idx(fract_mixing, self.tree_fracts[t_block-1])
@ -444,7 +491,7 @@ class LatentBlending():
idx_start = self.list_injection_idx_ext[t_block] idx_start = self.list_injection_idx_ext[t_block]
fract_mixing_parental = (fract_mixing - self.tree_fracts[t_block-1][b_parent1]) / (self.tree_fracts[t_block-1][b_parent2] - self.tree_fracts[t_block-1][b_parent1]) fract_mixing_parental = (fract_mixing - self.tree_fracts[t_block-1][b_parent1]) / (self.tree_fracts[t_block-1][b_parent2] - self.tree_fracts[t_block-1][b_parent1])
latents_for_injection = interpolate_spherical(latents1, latents2, fract_mixing_parental) latents_for_injection = interpolate_spherical(latents1, latents2, fract_mixing_parental)
list_latents = self.run_diffusion(text_embeddings_mix, latents_for_injection, idx_start=idx_start, idx_stop=idx_stop) list_latents = self.run_diffusion(list_conditionings, latents_for_injection, idx_start=idx_start, idx_stop=idx_stop)
self.tree_latents[t_block][idx_branch] = list_latents self.tree_latents[t_block][idx_branch] = list_latents
self.tree_status[t_block][idx_branch] = 'computed' self.tree_status[t_block][idx_branch] = 'computed'
@ -459,21 +506,20 @@ class LatentBlending():
def run_multi_transition( def run_multi_transition(
self, self,
fp_movie: str,
list_prompts: List[str], list_prompts: List[str],
list_seeds: List[int] = None, list_seeds: List[int] = None,
ms: MovieSaver = None,
fps: float = 24, fps: float = 24,
duration_single_trans: float = 15, duration_single_trans: float = 15,
): ):
r""" r"""
Runs multiple transitions and stitches them together. You can supply the seeds for each prompt. Runs multiple transitions and stitches them together. You can supply the seeds for each prompt.
Args: Args:
fp_movie: file path for movie saving
list_prompts: List[float]: list_prompts: List[float]:
list of the prompts. There will be a transition starting from the first to the last. list of the prompts. There will be a transition starting from the first to the last.
list_seeds: List[int] = None: list_seeds: List[int] = None:
Random Seeds for each prompt. Random Seeds for each prompt.
ms: MovieSaver
You need to spawn a moviesaver instance.
fps: float: fps: float:
frames per second frames per second
duration_single_trans: float: duration_single_trans: float:
@ -487,6 +533,7 @@ class LatentBlending():
if list_seeds is None: if list_seeds is None:
list_seeds = list(np.random.randint(0, 10e10, len(list_prompts))) list_seeds = list(np.random.randint(0, 10e10, len(list_prompts)))
ms = MovieSaver(fp_movie, fps=fps)
for i in range(len(list_prompts)-1): for i in range(len(list_prompts)-1):
print(f"Starting movie segment {i+1}/{len(list_prompts)-1}") print(f"Starting movie segment {i+1}/{len(list_prompts)-1}")
@ -516,7 +563,7 @@ class LatentBlending():
@torch.no_grad() @torch.no_grad()
def run_diffusion( def run_diffusion(
self, self,
text_embeddings: torch.FloatTensor, list_conditionings,
latents_for_injection: torch.FloatTensor = None, latents_for_injection: torch.FloatTensor = None,
idx_start: int = -1, idx_start: int = -1,
idx_stop: int = -1, idx_stop: int = -1,
@ -527,8 +574,7 @@ class LatentBlending():
Depending on the mode, the correct one will be executed. Depending on the mode, the correct one will be executed.
Args: Args:
text_embeddings: torch.FloatTensor list_conditionings: List of all conditionings for the diffusion model.
Text embeddings used for diffusion
latents_for_injection: torch.FloatTensor latents_for_injection: torch.FloatTensor
Latents that are used for injection Latents that are used for injection
idx_start: int idx_start: int
@ -541,15 +587,131 @@ class LatentBlending():
# Ensure correct num_inference_steps in Holder # Ensure correct num_inference_steps in Holder
self.sdh.num_inference_steps = self.num_inference_steps self.sdh.num_inference_steps = self.num_inference_steps
assert type(list_conditionings) is list, "list_conditionings need to be a list"
if self.mode == 'standard': if self.mode == 'standard':
text_embeddings = list_conditionings[0]
return self.sdh.run_diffusion_standard(text_embeddings, latents_for_injection=latents_for_injection, idx_start=idx_start, idx_stop=idx_stop, return_image=return_image) return self.sdh.run_diffusion_standard(text_embeddings, latents_for_injection=latents_for_injection, idx_start=idx_start, idx_stop=idx_stop, return_image=return_image)
elif self.mode == 'inpaint': elif self.mode == 'inpaint':
text_embeddings = list_conditionings[0]
assert self.sdh.image_source is not None, "image_source is None. Please run init_inpainting first." assert self.sdh.image_source is not None, "image_source is None. Please run init_inpainting first."
assert self.sdh.mask_image is not None, "image_source is None. Please run init_inpainting first." assert self.sdh.mask_image is not None, "image_source is None. Please run init_inpainting first."
return self.sdh.run_diffusion_inpaint(text_embeddings, latents_for_injection=latents_for_injection, idx_start=idx_start, idx_stop=idx_stop, return_image=return_image) return self.sdh.run_diffusion_inpaint(text_embeddings, latents_for_injection=latents_for_injection, idx_start=idx_start, idx_stop=idx_stop, return_image=return_image)
elif self.mode == 'upscale':
cond = list_conditionings[0]
uc_full = list_conditionings[1]
return self.sdh.run_diffusion_upscaling(cond, uc_full, latents_for_injection=latents_for_injection, idx_start=idx_start, idx_stop=idx_stop, return_image=return_image)
def run_upscaling_step1(
self,
dp_img: str,
quality: str = 'upscaling_step1',
depth_strength: float = 0.65,
fixed_seeds: Optional[List[int]] = None,
overwrite_folder: bool = False,
):
r"""
Initializes inpainting with a source and maks image.
Args:
dp_img:
Path to directory where the low-res images and yaml will be saved to.
This directory cannot exist and will be created here.
quality: str
Determines how many diffusion steps are being made + how many branches in total.
We suggest to leave it with upscaling_step1 which has 10 final branches.
depth_strength: float = 0.65,
Determines how deep the first injection will happen.
Deeper injections will cause (unwanted) formation of new structures,
more shallow values will go into alpha-blendy land.
fixed_seeds: Optional[List[int)]:
You can supply two seeds that are used for the first and second keyframe (prompt1 and prompt2).
Otherwise random seeds will be taken.
"""
assert self.text_embedding1 is not None, 'run set_prompt1(yourprompt1) first'
assert self.text_embedding2 is not None, 'run set_prompt2(yourprompt2) first'
assert not os.path.isdir(dp_img), f"directory already exists: {dp_img}"
if fixed_seeds is None:
fixed_seeds = list(np.random.randint(0, 1000000, 2).astype(np.int32))
# Run latent blending
self.autosetup_branching(quality='upscaling_step1', depth_strength=depth_strength)
imgs_transition = self.run_transition(fixed_seeds=fixed_seeds)
self.write_imgs_transition(dp_img, imgs_transition)
print(f"run_upscaling_step1: completed! {dp_img}")
def run_upscaling_step2(
self,
dp_img: str,
quality: str = 'upscaling_step2',
depth_strength: float = 0.65,
fixed_seeds: Optional[List[int]] = None,
overwrite_folder: bool = False,
):
fp_yml = os.path.join(dp_img, "lowres.yaml")
fp_movie = os.path.join(dp_img, "movie.mp4")
fps = 24
ms = MovieSaver(fp_movie, fps=fps)
assert os.path.isfile(fp_yml), "lowres.yaml does not exist. did you forget run_upscaling_step1?"
dict_stuff = yml_load(fp_yml)
# load lowres images
nmb_images_lowres = dict_stuff['nmb_images']
prompt1 = dict_stuff['prompt1']
prompt2 = dict_stuff['prompt2']
imgs_lowres = []
for i in range(nmb_images_lowres):
fp_img_lowres = os.path.join(dp_img, f"lowres_img_{str(i).zfill(4)}.jpg")
assert os.path.isfile(fp_img_lowres), f"{fp_img_lowres} does not exist. did you forget run_upscaling_step1?"
imgs_lowres.append(Image.open(fp_img_lowres))
# set up upscaling
text_embeddingA = self.sdh.get_text_embedding(prompt1)
text_embeddingB = self.sdh.get_text_embedding(prompt2)
self.autosetup_branching(quality='upscaling_step2', depth_strength=depth_strength)
# list_nmb_branches = [2, 3, 4]
# list_injection_strength = [0.0, 0.6, 0.95]
# num_inference_steps = 100
# self.setup_branching(num_inference_steps, list_nmb_branches, list_injection_strength)
duration_single_trans = 3
list_fract_mixing = np.linspace(0, 1, nmb_images_lowres-1)
for i in range(nmb_images_lowres-1):
print(f"Starting movie segment {i+1}/{nmb_images_lowres-1}")
self.text_embedding1 = interpolate_linear(text_embeddingA, text_embeddingB, list_fract_mixing[i])
self.text_embedding2 = interpolate_linear(text_embeddingA, text_embeddingB, 1-list_fract_mixing[i])
if i==0:
recycle_img1 = False
else:
self.swap_forward()
recycle_img1 = True
self.set_image1(imgs_lowres[i])
self.set_image2(imgs_lowres[i+1])
list_imgs = self.run_transition(recycle_img1=recycle_img1)
list_imgs_interp = add_frames_linear_interp(list_imgs, fps, duration_single_trans)
# Save movie frame
for img in list_imgs_interp:
ms.write_frame(img)
ms.finalize()
def init_inpainting( def init_inpainting(
self, self,
image_source: Union[Image.Image, np.ndarray] = None, image_source: Union[Image.Image, np.ndarray] = None,
@ -567,10 +729,29 @@ class LatentBlending():
Initialize inpainting with an empty image and mask, effectively disabling inpainting, Initialize inpainting with an empty image and mask, effectively disabling inpainting,
useful for generating a first image for transitions using diffusion. useful for generating a first image for transitions using diffusion.
""" """
self.init_mode('inpaint') self.init_mode()
self.sdh.init_inpainting(image_source, mask_image, init_empty) self.sdh.init_inpainting(image_source, mask_image, init_empty)
@torch.no_grad()
def get_mixed_conditioning(self, fract_mixing):
if self.mode == 'standard':
text_embeddings_mix = interpolate_linear(self.text_embedding1, self.text_embedding2, fract_mixing)
list_conditionings = [text_embeddings_mix]
elif self.mode == 'inpaint':
text_embeddings_mix = interpolate_linear(self.text_embedding1, self.text_embedding2, fract_mixing)
list_conditionings = [text_embeddings_mix]
elif self.mode == 'upscale':
text_embeddings_mix = interpolate_linear(self.text_embedding1, self.text_embedding2, fract_mixing)
cond, uc_full = self.sdh.get_cond_upscaling(self.image1_lowres, text_embeddings_mix, self.noise_level_upscaling)
condB, uc_fullB = self.sdh.get_cond_upscaling(self.image2_lowres, text_embeddings_mix, self.noise_level_upscaling)
cond['c_concat'][0] = interpolate_spherical(cond['c_concat'][0], condB['c_concat'][0], fract_mixing)
uc_full['c_concat'][0] = interpolate_spherical(uc_full['c_concat'][0], uc_fullB['c_concat'][0], fract_mixing)
list_conditionings = [cond, uc_full]
else:
raise ValueError(f"mix_conditioning: unknown mode {self.mode}")
return list_conditionings
@torch.no_grad() @torch.no_grad()
def get_text_embeddings( def get_text_embeddings(
self, self,
@ -587,6 +768,27 @@ class LatentBlending():
return self.sdh.get_text_embedding(prompt) return self.sdh.get_text_embedding(prompt)
def write_imgs_transition(self, dp_img, imgs_transition):
r"""
Writes the transition images into the folder dp_img.
"""
os.makedirs(dp_img)
for i, img in enumerate(imgs_transition):
img_leaf = Image.fromarray(img)
img_leaf.save(os.path.join(dp_img, f"lowres_img_{str(i).zfill(4)}.jpg"))
# Dump everything relevant into yaml
dict_stuff = {}
dict_stuff['prompt1'] = self.prompt1
dict_stuff['prompt2'] = self.prompt2
dict_stuff['seed1'] = int(self.seed1)
dict_stuff['seed2'] = int(self.seed2)
dict_stuff['num_inference_steps'] = self.num_inference_steps
dict_stuff['height'] = self.sdh.height
dict_stuff['width'] = self.sdh.width
dict_stuff['nmb_images'] = len(imgs_transition)
yml_save(os.path.join(dp_img, "lowres.yaml"), dict_stuff)
def randomize_seed(self): def randomize_seed(self):
r""" r"""
Set a random seed for a fresh start. Set a random seed for a fresh start.
@ -834,9 +1036,7 @@ def get_spacing(nmb_points:int, scaling: float):
else: else:
left_side = np.abs(np.linspace(1, 0, nmb_points_per_side)**scaling / 2 - 0.5)[0:-1] left_side = np.abs(np.linspace(1, 0, nmb_points_per_side)**scaling / 2 - 0.5)[0:-1]
right_side = 1-left_side[::-1] right_side = 1-left_side[::-1]
all_fracts = np.hstack([left_side, right_side]) all_fracts = np.hstack([left_side, right_side])
return all_fracts return all_fracts
@ -861,16 +1061,126 @@ def get_time(resolution=None):
return t return t
def yml_load(fp_yml, print_fields=False):
"""
Helper function for loading yaml files
"""
with open(fp_yml) as f:
data = yaml.load(f, Loader=yaml.loader.SafeLoader)
dict_data = dict(data)
print("load: loaded {}".format(fp_yml))
return dict_data
def yml_save(fp_yml, dict_stuff):
"""
Helper function for saving yaml files
"""
with open(fp_yml, 'w') as f:
data = yaml.dump(dict_stuff, f, sort_keys=False, default_flow_style=False)
print("yml_save: saved {}".format(fp_yml))
#%% le main #%% le main
if __name__ == "__main__": if __name__ == "__main__":
pass # xxxx
# #%% First let us spawn a stable diffusion holder
# device = "cuda:0"
# fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt"
# fp_config = 'configs/v2-inference.yaml'
# sdh = StableDiffusionHolder(fp_ckpt, fp_config, device, height=384, width=512)
# #%%
# # Spawn latent blending
# self = LatentBlending(sdh)
# dp_img = '/home/lugo/latentblending/test5'
# fn1 = '230105_211545_photo_of_a_pyroclastic_ash_cloud_racing_down_mount_etna.txt'
# fn2 = '230105_211815_a_breathtaking_drone_photo_of_a_bizarre_cliff_structure,_lava_streams_flowing_down_into_the_ocean.txt'
# dp_cherries ='/home/lugo/latentblending/cherries/'
# dict1 = yml_load(os.path.join(dp_cherries, fn1))
# dict2 = yml_load(os.path.join(dp_cherries, fn2))
# # prompt1 = "painting of a big pine tree"
# # prompt2 = "painting of the full moon shining, mountains in the background, rocks, eery"
# prompt1 = dict1['prompt']
# prompt2 = dict2['prompt']
# self.set_prompt1(prompt1)
# self.set_prompt2(prompt2)
# fixed_seeds = [dict1['seed'], dict2['seed']]
# self.run_upscaling_step1(dp_img, fixed_seeds=fixed_seeds, depth_strength=0.6)
# # FIXME: depth_strength=0.6 CAN cause trouble. why?!
#%% RUN UPSCALING_STEP2 (highres)
fp_ckpt= "../stable_diffusion_models/ckpt/x4-upscaler-ema.ckpt"
fp_config = 'configs/x4-upscaling.yaml'
sdh = StableDiffusionHolder(fp_ckpt, fp_config)
# self.run_upscaling_step2(dp_img)
#%% /home/lugo/latentblending/230106_210812 /
self = LatentBlending(sdh)
dp_img = '/home/lugo/latentblending/230107_144533'
fp_yml = os.path.join(dp_img, "lowres.yaml")
fp_movie = os.path.join(dp_img, "movie.mp4")
fps = 24
ms = MovieSaver(fp_movie, fps=fps)
assert os.path.isfile(fp_yml), "lowres.yaml does not exist. did you forget run_upscaling_step1?"
dict_stuff = yml_load(fp_yml)
# load lowres images
nmb_images_lowres = dict_stuff['nmb_images']
prompt1 = dict_stuff['prompt1']
prompt2 = dict_stuff['prompt2']
imgs_lowres = []
for i in range(nmb_images_lowres):
fp_img_lowres = os.path.join(dp_img, f"lowres_img_{str(i).zfill(4)}.jpg")
assert os.path.isfile(fp_img_lowres), f"{fp_img_lowres} does not exist. did you forget run_upscaling_step1?"
imgs_lowres.append(Image.open(fp_img_lowres))
# set up upscaling
text_embeddingA = self.sdh.get_text_embedding(prompt1)
text_embeddingB = self.sdh.get_text_embedding(prompt2)
list_nmb_branches = [2, 3, 6]
list_injection_strength = [0.0, 0.6, 0.95]
num_inference_steps = 100
duration_single_trans = 3
self.setup_branching(num_inference_steps, list_nmb_branches, list_injection_strength)
list_fract_mixing = np.linspace(0, 1, nmb_images_lowres-1)
for i in range(nmb_images_lowres-1):
print(f"Starting movie segment {i+1}/{nmb_images_lowres-1}")
self.text_embedding1 = interpolate_linear(text_embeddingA, text_embeddingB, list_fract_mixing[i])
self.text_embedding2 = interpolate_linear(text_embeddingA, text_embeddingB, 1-list_fract_mixing[i])
if i==0:
recycle_img1 = False
else:
self.swap_forward()
recycle_img1 = True
self.set_image1(imgs_lowres[i])
self.set_image2(imgs_lowres[i+1])
list_imgs = self.run_transition(recycle_img1=recycle_img1)
self.write_imgs_transition(os.path.join(dp_img, f"highres_{str(i).zfill(4)}"), list_imgs)
list_imgs_interp = add_frames_linear_interp(list_imgs, fps, duration_single_trans)
# Save movie frame
for img in list_imgs_interp:
ms.write_frame(img)
ms.finalize()
#%% #%%
""" """
TODO Coding: TODO Coding:
CHECK IF ALL STUFF WORKS STILL: STANDARD MODEL, INPAINTING
RUNNING WITHOUT PROMPT! RUNNING WITHOUT PROMPT!
save value ranges, can it be trashed? save value ranges, can it be trashed?
in the middle: have more branches + lower guidance scale in the middle: have more branches + lower guidance scale
@ -878,8 +1188,6 @@ TODO Coding:
TODO Other: TODO Other:
github github
write text write text
requirements
make graphic explaining
make colab make colab
license license
twitter et al twitter et al

329
stable_diffusion_holder.py
View File

@ -27,7 +27,7 @@ import warnings
import torch import torch
from tqdm.auto import tqdm from tqdm.auto import tqdm
from PIL import Image from PIL import Image
import matplotlib.pyplot as plt # import matplotlib.pyplot as plt
import torch import torch
from movie_util import MovieSaver from movie_util import MovieSaver
import datetime import datetime
@ -40,29 +40,21 @@ from torch import autocast
from contextlib import nullcontext from contextlib import nullcontext
from ldm.util import instantiate_from_config from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler from ldm.models.diffusion.ddim import DDIMSampler
from einops import repeat from einops import repeat, rearrange
#%%
def load_model_from_config(config, ckpt, verbose=False): def pad_image(input_image):
print(f"Loading model from {ckpt}") pad_w, pad_h = np.max(((2, 2), np.ceil(
pl_sd = torch.load(ckpt, map_location="cpu") np.array(input_image.size) / 64).astype(int)), axis=0) * 64 - input_image.size
if "global_step" in pl_sd: im_padded = Image.fromarray(
print(f"Global Step: {pl_sd['global_step']}") np.pad(np.array(input_image), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
sd = pl_sd["state_dict"] return im_padded
model = instantiate_from_config(config.model)
m, u = model.load_state_dict(sd, strict=False)
if len(m) > 0 and verbose:
print("missing keys:")
print(m)
if len(u) > 0 and verbose:
print("unexpected keys:")
print(u)
model.cuda()
model.eval()
return model
def make_batch_sd(
def make_batch_inpaint(
image, image,
mask, mask,
txt, txt,
@ -89,16 +81,42 @@ def make_batch_sd(
} }
return batch return batch
def make_batch_superres(
image,
txt,
device,
num_samples=1,
):
image = np.array(image.convert("RGB"))
image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
batch = {
"lr": rearrange(image, 'h w c -> 1 c h w'),
"txt": num_samples * [txt],
}
batch["lr"] = repeat(batch["lr"].to(device=device),
"1 ... -> n ...", n=num_samples)
return batch
def make_noise_augmentation(model, batch, noise_level=None):
x_low = batch[model.low_scale_key]
x_low = x_low.to(memory_format=torch.contiguous_format).float()
x_aug, noise_level = model.low_scale_model(x_low, noise_level)
return x_aug, noise_level
class StableDiffusionHolder: class StableDiffusionHolder:
def __init__(self, def __init__(self,
fp_ckpt: str = None, fp_ckpt: str = None,
fp_config: str = None, fp_config: str = None,
device: str = None, num_inference_steps: int = 30,
height: Optional[int] = None, height: Optional[int] = None,
width: Optional[int] = None, width: Optional[int] = None,
num_inference_steps: int = 30, device: str = None,
precision: str='autocast', precision: str='autocast',
): ):
self.seed = 42 self.seed = 42
self.guidance_scale = 5.0 self.guidance_scale = 5.0
@ -130,13 +148,15 @@ class StableDiffusionHolder:
def init_model(self, fp_ckpt, fp_config): def init_model(self, fp_ckpt, fp_config):
assert os.path.isfile(fp_ckpt), f"Your model checkpoint file does not exist: {fp_ckpt}" assert os.path.isfile(fp_ckpt), f"Your model checkpoint file does not exist: {fp_ckpt}"
assert os.path.isfile(fp_config), f"Your config file does not exist: {fp_config}" assert os.path.isfile(fp_config), f"Your config file does not exist: {fp_config}"
config = OmegaConf.load(fp_config) self.fp_ckpt = fp_ckpt
self.model = load_model_from_config(config, fp_ckpt)
config = OmegaConf.load(fp_config)
self.model = instantiate_from_config(config.model)
self.model.load_state_dict(torch.load(fp_ckpt)["state_dict"], strict=False)
self.model = self.model.to(self.device) self.model = self.model.to(self.device)
self.sampler = DDIMSampler(self.model) self.sampler = DDIMSampler(self.model)
self.fp_ckpt = fp_ckpt
@ -187,6 +207,26 @@ class StableDiffusionHolder:
c = self.model.get_learned_conditioning(prompt) c = self.model.get_learned_conditioning(prompt)
return c return c
@torch.no_grad()
def get_cond_upscaling(self, image, text_embedding, noise_level):
r"""
Initializes the conditioning for the x4 upscaling model.
"""
image = pad_image(image) # resize to integer multiple of 32
w, h = image.size
noise_level = torch.Tensor(1 * [noise_level]).to(self.sampler.model.device).long()
batch = make_batch_superres(image, txt="placeholder", device=self.device, num_samples=1)
x_augment, noise_level = make_noise_augmentation(self.model, batch, noise_level)
cond = {"c_concat": [x_augment], "c_crossattn": [text_embedding], "c_adm": noise_level}
# uncond cond
uc_cross = self.model.get_unconditional_conditioning(1, "")
uc_full = {"c_concat": [x_augment], "c_crossattn": [uc_cross], "c_adm": noise_level}
return cond, uc_full
@torch.no_grad() @torch.no_grad()
def run_diffusion_standard( def run_diffusion_standard(
self, self,
@ -317,7 +357,7 @@ class StableDiffusionHolder:
with precision_scope("cuda"): with precision_scope("cuda"):
with self.model.ema_scope(): with self.model.ema_scope():
batch = make_batch_sd(self.image_source, self.mask_image, txt="willbereplaced", device=self.device, num_samples=1) batch = make_batch_inpaint(self.image_source, self.mask_image, txt="willbereplaced", device=self.device, num_samples=1)
c = text_embeddings c = text_embeddings
c_cat = list() c_cat = list()
for ck in self.model.concat_keys: for ck in self.model.concat_keys:
@ -384,6 +424,92 @@ class StableDiffusionHolder:
else: else:
return list_latents_out return list_latents_out
@torch.no_grad()
def run_diffusion_upscaling(
self,
cond,
uc_full,
latents_for_injection: torch.FloatTensor = None,
idx_start: int = -1,
idx_stop: int = -1,
return_image: Optional[bool] = False
):
r"""
Wrapper function for run_diffusion_standard and run_diffusion_inpaint.
Depending on the mode, the correct one will be executed.
Args:
??
latents_for_injection: torch.FloatTensor
Latents that are used for injection
idx_start: int
Index of the diffusion process start and where the latents_for_injection are injected
idx_stop: int
Index of the diffusion process end.
return_image: Optional[bool]
Optionally return image directly
"""
if latents_for_injection is None:
do_inject_latents = False
else:
do_inject_latents = True
precision_scope = autocast if self.precision == "autocast" else nullcontext
generator = torch.Generator(device=self.device).manual_seed(int(self.seed))
h = uc_full['c_concat'][0].shape[2]
w = uc_full['c_concat'][0].shape[3]
with precision_scope("cuda"):
with self.model.ema_scope():
shape_latents = [self.model.channels, h, w]
self.sampler.make_schedule(ddim_num_steps=self.num_inference_steps-1, ddim_eta=self.ddim_eta, verbose=False)
C, H, W = shape_latents
size = (1, C, H, W)
b = size[0]
latents = torch.randn(size, generator=generator, device=self.device)
timesteps = self.sampler.ddim_timesteps
time_range = np.flip(timesteps)
total_steps = timesteps.shape[0]
# collect latents
list_latents_out = []
for i, step in enumerate(time_range):
if do_inject_latents:
# Inject latent at right place
if i < idx_start:
continue
elif i == idx_start:
latents = latents_for_injection.clone()
if i == idx_stop:
return list_latents_out
# print(f"diffusion iter {i}")
index = total_steps - i - 1
ts = torch.full((b,), step, device=self.device, dtype=torch.long)
outs = self.sampler.p_sample_ddim(latents, cond, ts, index=index, use_original_steps=False,
quantize_denoised=False, temperature=1.0,
noise_dropout=0.0, score_corrector=None,
corrector_kwargs=None,
unconditional_guidance_scale=self.guidance_scale,
unconditional_conditioning=uc_full,
dynamic_threshold=None)
latents, pred_x0 = outs
list_latents_out.append(latents.clone())
if return_image:
return self.latent2image(latents)
else:
return list_latents_out
@torch.no_grad() @torch.no_grad()
def latent2image( def latent2image(
@ -405,6 +531,147 @@ class StableDiffusionHolder:
if __name__ == "__main__": if __name__ == "__main__":
fp_ckpt= "../stable_diffusion_models/ckpt/x4-upscaler-ema.ckpt"
fp_config = 'configs/x4-upscaling.yaml'
num_inference_steps = 100
self = StableDiffusionHolder(fp_ckpt, fp_config, num_inference_steps=num_inference_steps)
xxx
#%% image A
image = Image.open('/home/lugo/latentblending/test1/img_0007.jpg')
image = image.resize((32*20, 32*12))
promptA = "photo of a an ancient castle surrounded by a forest"
noise_level = 20 #gradio min=0, max=350, value=20
text_embeddingA = self.get_text_embedding(promptA)
cond, uc_full = self.get_cond_upscaling(image, text_embeddingA, noise_level)
list_samplesA = self.run_diffusion_upscaling(cond, uc_full)
image_result = Image.fromarray(self.latent2image(list_samplesA[-1]))
image_result.save('/home/lugo/latentblending/test1/high/imgA.jpg')
#%% image B
from latent_blending import interpolate_linear, interpolate_spherical
image = Image.open('/home/lugo/latentblending/test1/img_0006.jpg')
image = image.resize((32*20, 32*12))
promptA = "photo of a an ancient castle surrounded by a forest"
promptB = "photo of a beautiful island on the horizon, blue sea with waves"
noise_level = 20 #gradio min=0, max=350, value=20
text_embeddingA = self.get_text_embedding(promptA)
text_embeddingB = self.get_text_embedding(promptB)
text_embedding = interpolate_linear(text_embeddingA, text_embeddingB, 1/8)
cond, uc_full = self.get_cond_upscaling(image, text_embedding, noise_level)
list_samplesB = self.run_diffusion_upscaling(cond, uc_full)
image_result = Image.fromarray(self.latent2image(list_samplesB[-1]))
image_result.save('/home/lugo/latentblending/test1/high/imgB.jpg')
#%% reality check: run only for 50 iter.
image = Image.open('/home/lugo/latentblending/test1/img_0007.jpg')
image = image.resize((32*20, 32*12))
promptA = "photo of a an ancient castle surrounded by a forest"
noise_level = 20 #gradio min=0, max=350, value=20
text_embeddingA = self.get_text_embedding(promptA)
cond, uc_full = self.get_cond_upscaling(image, text_embeddingA, noise_level)
latents_inject = list_samplesA[50]
list_samplesAx = self.run_diffusion_upscaling(cond, uc_full, latents_inject, idx_start=50)
image_result = Image.fromarray(self.latent2image(list_samplesAx[-1]))
image_result.save('/home/lugo/latentblending/test1/high/imgA_restart.jpg')
# RESULTS ARE NOT EXACTLY IDENTICAL! INVESTIGATE WHY
#%% mix in the middle! which uc_full should be taken?
# expA: take the one from A
idx_start = 90
latentsA = list_samplesA[idx_start]
latentsB = list_samplesB[idx_start]
latents_inject = interpolate_spherical(latentsA, latentsB, 0.5)
image = Image.open('/home/lugo/latentblending/test1/img_0007.jpg')
image = image.resize((32*20, 32*12))
promptA = "photo of a an ancient castle surrounded by a forest"
noise_level = 20 #gradio min=0, max=350, value=20
text_embeddingA = self.get_text_embedding(promptA)
cond, uc_full = self.get_cond_upscaling(image, text_embeddingA, noise_level)
list_samples = self.run_diffusion_upscaling(cond, uc_full, latents_inject, idx_start=idx_start)
image_result = Image.fromarray(self.latent2image(list_samples[-1]))
image_result.save('/home/lugo/latentblending/test1/high/img_mix_expA_late.jpg')
#%% mix in the middle! which uc_full should be taken?
# expA: take the one from B
idx_start = 90
latentsA = list_samplesA[idx_start]
latentsB = list_samplesB[idx_start]
latents_inject = interpolate_spherical(latentsA, latentsB, 0.5)
image = Image.open('/home/lugo/latentblending/test1/img_0006.jpg').resize((32*20, 32*12))
promptA = "photo of a an ancient castle surrounded by a forest"
promptB = "photo of a beautiful island on the horizon, blue sea with waves"
noise_level = 20 #gradio min=0, max=350, value=20
text_embeddingA = self.get_text_embedding(promptA)
text_embeddingB = self.get_text_embedding(promptB)
text_embedding = interpolate_linear(text_embeddingA, text_embeddingB, 1/8)
cond, uc_full = self.get_cond_upscaling(image, text_embedding, noise_level)
list_samples = self.run_diffusion_upscaling(cond, uc_full, latents_inject, idx_start=idx_start)
image_result = Image.fromarray(self.latent2image(list_samples[-1]))
image_result.save('/home/lugo/latentblending/test1/high/img_mix_expB_late.jpg')
#%% lets blend the uc_full too!
# expC
idx_start = 50
list_mix = np.linspace(0, 1, 20)
for fract_mix in list_mix:
# fract_mix = 0.75
latentsA = list_samplesA[idx_start]
latentsB = list_samplesB[idx_start]
latents_inject = interpolate_spherical(latentsA, latentsB, fract_mix)
text_embeddingA = self.get_text_embedding(promptA)
text_embeddingB = self.get_text_embedding(promptB)
text_embedding = interpolate_linear(text_embeddingA, text_embeddingB, 1/8)
imageA = Image.open('/home/lugo/latentblending/test1/img_0007.jpg').resize((32*20, 32*12))
condA, uc_fullA = self.get_cond_upscaling(imageA, text_embedding, noise_level)
imageB = Image.open('/home/lugo/latentblending/test1/img_0006.jpg').resize((32*20, 32*12))
condB, uc_fullB = self.get_cond_upscaling(imageB, text_embedding, noise_level)
condA['c_concat'][0] = interpolate_spherical(condA['c_concat'][0], condB['c_concat'][0], fract_mix)
uc_fullA['c_concat'][0] = interpolate_spherical(uc_fullA['c_concat'][0], uc_fullB['c_concat'][0], fract_mix)
list_samples = self.run_diffusion_upscaling(condA, uc_fullA, latents_inject, idx_start=idx_start)
image_result = Image.fromarray(self.latent2image(list_samples[-1]))
image_result.save(f'/home/lugo/latentblending/test1/high/img_mix_expC_{fract_mix}_start{idx_start}.jpg')
#%%
list_imgs = os.listdir('/home/lugo/latentblending/test1/high/')
list_imgs = [l for l in list_imgs if "expC" in l]
list_imgs.pop(0)
lx = []
for fn in list_imgs:
Image.open
#%%
if False:
num_inference_steps = 20 # Number of diffusion interations num_inference_steps = 20 # Number of diffusion interations
# fp_ckpt = "../stable_diffusion_models/ckpt/768-v-ema.ckpt" # fp_ckpt = "../stable_diffusion_models/ckpt/768-v-ema.ckpt"
@ -419,7 +686,6 @@ if __name__ == "__main__":
#%% INPAINT PREPS
image_source = Image.fromarray((255*np.random.rand(512,512,3)).astype(np.uint8)) image_source = Image.fromarray((255*np.random.rand(512,512,3)).astype(np.uint8))
mask = 255*np.ones([512,512], dtype=np.uint8) mask = 255*np.ones([512,512], dtype=np.uint8)
mask[0:50, 0:50] = 0 mask[0:50, 0:50] = 0
@ -429,7 +695,6 @@ if __name__ == "__main__":
text_embedding = sdh.get_text_embedding("photo of a strange house, surreal painting") text_embedding = sdh.get_text_embedding("photo of a strange house, surreal painting")
list_latents = sdh.run_diffusion_inpaint(text_embedding) list_latents = sdh.run_diffusion_inpaint(text_embedding)
#%%
idx_inject = 3 idx_inject = 3
img_orig = sdh.latent2image(list_latents[-1]) img_orig = sdh.latent2image(list_latents[-1])
list_inject = sdh.run_diffusion_inpaint(text_embedding, list_latents[idx_inject], idx_start=idx_inject+1) list_inject = sdh.run_diffusion_inpaint(text_embedding, list_latents[idx_inject], idx_start=idx_inject+1)
@ -441,11 +706,3 @@ if __name__ == "__main__":
#%%
"""
next steps:
incorporate into lb
incorporate into outpaint
"""