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branching setup and autosetup as part of latendblending

This commit is contained in:
lugo 2022-11-28 12:41:15 +01:00
parent fbf90d7f5f
commit bbe8269146
3 changed files with 175 additions and 177 deletions
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71
cherry_picknick.py
View File

@ -21,83 +21,58 @@ warnings.filterwarnings('ignore')
import warnings
import torch
from tqdm.auto import tqdm
from diffusers import StableDiffusionPipeline
from diffusers.schedulers import DDIMScheduler
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 diffusers pipe using DDIMScheduler
#%% First let us spawn a stable diffusion holder
device = "cuda:0"
model_path = "../stable_diffusion_models/stable-diffusion-v1-5"
num_inference_steps = 20 # Number of diffusion interations
fp_ckpt = "../stable_diffusion_models/ckpt/768-v-ema.ckpt"
fp_config = '../stablediffusion/configs/stable-diffusion/v2-inference-v.yaml'
scheduler = DDIMScheduler(beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False)
pipe = StableDiffusionPipeline.from_pretrained(
model_path,
revision="fp16",
torch_dtype=torch.float16,
scheduler=scheduler,
use_auth_token=True
)
pipe = pipe.to(device)
sdh = StableDiffusionHolder(fp_ckpt, fp_config, device, num_inference_steps=num_inference_steps)
#%% Next let's set up all parameters
num_inference_steps = 30 # Number of diffusion interations
list_nmb_branches = [2, 3, 10, 24]#, 50] # Branching structure: how many branches
list_injection_strength = [0.0, 0.6, 0.8, 0.9]#, 0.95] # Branching structure: how deep is the blending
width = 512
height = 512
guidance_scale = 5
fps = 30
duration_target = 10
width = 512
height = 512
lb = LatentBlending(pipe, device, height, width, num_inference_steps, guidance_scale)
lb = LatentBlending(sdh, num_inference_steps, guidance_scale)
list_prompts = []
list_prompts.append("surrealistic statue made of glitter and dirt, standing in a lake, atmospheric light, strange glow")
list_prompts.append("weird statue of a frog monkey, many colors, standing next to the ruins of an ancient city")
list_prompts.append("statue of a mix between a tree and human, made of marble, incredibly detailed")
list_prompts.append("statue made of hot metal, bizzarre, dark clouds in the sky")
list_prompts.append("statue of a spider that looked like a human")
list_prompts.append("statue of a bird that looked like a scorpion")
list_prompts.append("statue of an ancient cybernetic messenger annoucing good news, golden, futuristic")
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")
k = 6
prompt = list_prompts[k]
for i in range(4):
lb.set_prompt1(prompt)
for k, prompt in enumerate(list_prompts):
# k = 6
seed = np.random.randint(999999999)
lb.set_seed(seed)
plt.imshow(lb.run_diffusion(lb.text_embedding1, return_image=True))
plt.title(f"{i} seed {seed}")
plt.show()
print(f"prompt {k} seed {seed} trial {i}")
# 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}")
#%%
"""
prompt 3 seed 28652396 trial 2
prompt 4 seed 783279867 trial 3
prompt 5 seed 831049796 trial 3
prompt 6 seed 798876383 trial 2
prompt 6 seed 750494819 trial 2
prompt 6 seed 416472011 trial 1
69731932, 504430820
"""

25
example1_standard.py
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@ -32,32 +32,25 @@ torch.set_grad_enabled(False)
#%% First let us spawn a stable diffusion holder
device = "cuda:0"
num_inference_steps = 20 # Number of diffusion interations
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, num_inference_steps=num_inference_steps)
sdh = StableDiffusionHolder(fp_ckpt, fp_config, device)
#%% Next let's set up all parameters
# FIXME below fix numbers
# We want 20 diffusion steps in total, begin with 2 branches, have 3 branches at step 12 (=0.6*20)
# 10 branches at step 16 (=0.8*20) and 24 branches at step 18 (=0.9*20)
# Furthermore we want seed 993621550 for keyframeA and seed 54878562 for keyframeB ()
list_nmb_branches = [2, 3, 10, 24] # Branching structure: how many branches
list_injection_strength = [0.0, 0.6, 0.8, 0.9] # Branching structure: how deep is the blending
width = 768
height = 768
guidance_scale = 5
fixed_seeds = [993621550, 280335986]
quality = 'high'
fixed_seeds = [69731932, 504430820]
lb = LatentBlending(sdh, num_inference_steps, guidance_scale)
lb = LatentBlending(sdh, guidance_scale)
prompt1 = "photo of a beautiful forest covered in white flowers, ambient light, very detailed, magic"
prompt2 = "photo of an golden statue with a funny hat, surrounded by ferns and vines, grainy analog photograph,, mystical ambience, incredible detail"
prompt2 = "photo of an golden statue with a funny hat, surrounded by ferns and vines, grainy analog photograph, mystical ambience, incredible detail"
lb.set_prompt1(prompt1)
lb.set_prompt2(prompt2)
lb.autosetup_branching(quality=quality)
imgs_transition = lb.run_transition(list_nmb_branches, list_injection_strength, fixed_seeds=fixed_seeds)
imgs_transition = lb.run_transition(fixed_seeds=fixed_seeds)
# let's get more cheap frames via linear interpolation
duration_transition = 12
@ -65,10 +58,10 @@ fps = 60
imgs_transition_ext = add_frames_linear_interp(imgs_transition, duration_transition, fps)
# movie saving
fp_movie = "movie_example1.mp4"
fp_movie = f"movie_example1_{quality}.mp4"
if os.path.isfile(fp_movie):
os.remove(fp_movie)
ms = MovieSaver(fp_movie, fps=fps)
ms = MovieSaver(fp_movie, fps=fps, shape_hw=[sdh.height, sdh.width])
for img in tqdm(imgs_transition_ext):
ms.write_frame(img)
ms.finalize()

252
latent_blending.py
View File

@ -47,9 +47,7 @@ class LatentBlending():
def __init__(
self,
sdh: None,
num_inference_steps: int = 30,
guidance_scale: float = 7.5,
seed: int = 420,
):
r"""
Initializes the latent blending class.
@ -59,8 +57,6 @@ class LatentBlending():
Height of the desired output image. The model was trained on 512.
width: int
Width of the desired output image. The model was trained on 512.
num_inference_steps: int
Number of diffusion steps. Larger values will take more compute time.
guidance_scale: float
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
@ -72,13 +68,11 @@ class LatentBlending():
"""
self.sdh = sdh
self.num_inference_steps = num_inference_steps
self.sdh.num_inference_steps = num_inference_steps
self.device = self.sdh.device
self.guidance_scale = guidance_scale
self.width = self.sdh.width
self.height = self.sdh.height
self.seed = seed
self.seed = 420 #use self.set_seed or fixed_seeds argument in run_transition
# Initialize vars
self.prompt1 = ""
@ -93,6 +87,9 @@ class LatentBlending():
self.text_embedding2 = None
self.stop_diffusion = False
self.negative_prompt = None
self.num_inference_steps = -1
self.list_injection_idx = None
self.list_nmb_branches = None
self.init_mode()
@ -133,19 +130,92 @@ class LatentBlending():
self.prompt2 = prompt
self.text_embedding2 = self.get_text_embeddings(self.prompt2)
def run_transition(
def autosetup_branching(
self,
list_nmb_branches: List[int],
list_injection_strength: List[float] = None,
list_injection_idx: List[int] = None,
recycle_img1: Optional[bool] = False,
recycle_img2: Optional[bool] = False,
fixed_seeds: Optional[List[int]] = None,
quality: str = 'medium',
deepth_strength: float = 0.65,
nmb_frames: int = 360,
nmb_mindist: int = 3,
):
r"""
Returns a list of transition images using spherical latent blending.
Helper function to set up the branching structure automatically.
Args:
quality: str
Determines how many diffusion steps are being made + how many branches in total.
Tradeoff between quality and speed of computation.
Choose: lowest, low, medium, high, ultra
deepth_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.
nmb_frames: int = 360,
total number of frames
nmb_mindist: int = 3
minimum distance in terms of diffusion iteratinos between subsequent injections
"""
if quality == 'lowest':
num_inference_steps = 12
nmb_branches_final = 5
elif quality == 'low':
num_inference_steps = 15
nmb_branches_final = nmb_frames//16
elif quality == 'medium':
num_inference_steps = 30
nmb_branches_final = nmb_frames//8
elif quality == 'high':
num_inference_steps = 60
nmb_branches_final = nmb_frames//4
elif quality == 'ultra':
num_inference_steps = 100
nmb_branches_final = nmb_frames//2
else:
raise ValueError("quality = '{quality}' not supported")
idx_injection_first = int(np.round(num_inference_steps*deepth_strength))
idx_injection_last = num_inference_steps - 3
nmb_injections = int(np.floor(num_inference_steps/5)) - 1
list_injection_idx = [0]
list_injection_idx.extend(np.linspace(idx_injection_first, idx_injection_last, nmb_injections).astype(int))
list_nmb_branches = np.round(np.logspace(np.log10(2), np.log10(nmb_branches_final), nmb_injections+1)).astype(int)
# Cleanup. There should be at least 3 diffusion steps between each injection
list_injection_idx_clean = [list_injection_idx[0]]
list_nmb_branches_clean = [list_nmb_branches[0]]
idx_last_check = 0
for i in range(len(list_injection_idx)-1):
if list_injection_idx[i+1] - list_injection_idx_clean[idx_last_check] >= nmb_mindist:
list_injection_idx_clean.append(list_injection_idx[i+1])
list_nmb_branches_clean.append(list_nmb_branches[i+1])
idx_last_check +=1
list_injection_idx_clean = [int(l) for l in list_injection_idx_clean]
list_nmb_branches_clean = [int(l) for l in list_nmb_branches_clean]
list_injection_idx = list_injection_idx_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}")
self.num_inference_steps = num_inference_steps
self.list_injection_idx = list_injection_idx
self.list_nmb_branches = list_nmb_branches
def setup_branching(self,
num_inference_steps: int =30,
list_nmb_branches: List[int] = None,
list_injection_strength: List[float] = None,
list_injection_idx: List[int] = None,
guidance_downscale: float = 1.0,
):
r"""
Sets the branching structure for making transitions.
num_inference_steps: int
Number of diffusion steps. Larger values will take more compute time.
list_nmb_branches: List[int]:
list of the number of branches for each injection.
list_injection_strength: List[float]:
@ -154,6 +224,51 @@ class LatentBlending():
list_injection_idx: List[int]:
list of injection strengths within interval [0, 1), values need to be increasing.
Alternatively you can specify the list_injection_strength.
guidance_downscale: float = 1.0
reduces the guidance scale towards the middle of the transition
"""
# Assert
assert guidance_downscale>0 and guidance_downscale<=1.0, "guidance_downscale neees to be in interval (0,1]"
assert not((list_injection_strength is not None) and (list_injection_idx is not None)), "suppyl either list_injection_strength or list_injection_idx"
if list_injection_strength is None:
assert list_injection_idx is not None, "Supply either list_injection_idx or list_injection_strength"
assert isinstance(list_injection_idx[0], int) or isinstance(list_injection_idx[0], np.int) , "Need to supply integers for list_injection_idx"
if list_injection_idx is None:
assert list_injection_strength is not None, "Supply either list_injection_idx or list_injection_strength"
# Create the injection indexes
list_injection_idx = [int(round(x*num_inference_steps)) for x in list_injection_strength]
assert min(np.diff(list_injection_idx)) > 0, 'Injection idx needs to be increasing'
if min(np.diff(list_injection_idx)) < 2:
print("Warning: your injection spacing is very tight. consider increasing the distances")
assert isinstance(list_injection_strength[1], np.floating) or isinstance(list_injection_strength[1], float), "Need to supply floats for list_injection_strength"
# we are checking element 1 in list_injection_strength because "0" is an int... [0, 0.5]
assert max(list_injection_idx) < num_inference_steps, "Decrease the injection index or strength"
assert len(list_injection_idx) == len(list_nmb_branches), "Need to have same length"
assert max(list_injection_idx) < num_inference_steps,"Injection index cannot happen after last diffusion step! Decrease list_injection_idx or list_injection_strength[-1]"
# Set attributes
self.num_inference_steps = num_inference_steps
self.sdh.num_inference_steps = num_inference_steps
self.list_nmb_branches = list_nmb_branches
self.list_injection_idx = list_injection_idx
def run_transition(
self,
recycle_img1: Optional[bool] = False,
recycle_img2: Optional[bool] = False,
fixed_seeds: Optional[List[int]] = None,
):
r"""
Returns a list of transition images using spherical latent blending.
Args:
recycle_img1: Optional[bool]:
Don't recompute the latents for the first keyframe (purely prompt1). Saves compute.
recycle_img2: Optional[bool]:
@ -166,25 +281,7 @@ class LatentBlending():
# Sanity checks first
assert self.text_embedding1 is not None, 'Set the first text embedding with .set_prompt1(...) before'
assert self.text_embedding2 is not None, 'Set the second text embedding with .set_prompt2(...) before'
assert not((list_injection_strength is not None) and (list_injection_idx is not None)), "suppyl either list_injection_strength or list_injection_idx"
if list_injection_strength is None:
assert list_injection_idx is not None, "Supply either list_injection_idx or list_injection_strength"
assert isinstance(list_injection_idx[0], int) or isinstance(list_injection_idx[0], np.int) , "Need to supply integers for list_injection_idx"
if list_injection_idx is None:
assert list_injection_strength is not None, "Supply either list_injection_idx or list_injection_strength"
# Create the injection indexes
list_injection_idx = [int(round(x*self.num_inference_steps)) for x in list_injection_strength]
assert min(np.diff(list_injection_idx)) > 0, 'Injection idx needs to be increasing'
if min(np.diff(list_injection_idx)) < 2:
print("Warning: your injection spacing is very tight. consider increasing the distances")
assert isinstance(list_injection_strength[1], np.floating) or isinstance(list_injection_strength[1], float), "Need to supply floats for list_injection_strength"
# we are checking element 1 in list_injection_strength because "0" is an int... [0, 0.5]
assert max(list_injection_idx) < self.num_inference_steps, "Decrease the injection index or strength"
assert len(list_injection_idx) == len(list_nmb_branches), "Need to have same length"
assert max(list_injection_idx) < self.num_inference_steps,"Injection index cannot happen after last diffusion step! Decrease list_injection_idx or list_injection_strength[-1]"
assert self.list_injection_idx is not None, 'Set the branching structure before, by calling autosetup_branching or setup_branching'
if fixed_seeds is not None:
if fixed_seeds == 'randomize':
@ -204,21 +301,22 @@ class LatentBlending():
print("Warning. You want to recycle but there is nothing here. Disabling recycling.")
recycle_img1 = False
recycle_img2 = False
elif self.list_nmb_branches_prev != list_nmb_branches:
elif 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 != list_injection_idx:
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
self.list_nmb_branches_prev = list_nmb_branches
self.list_injection_idx_prev = list_injection_idx
self.list_nmb_branches_prev = self.list_nmb_branches[:]
self.list_injection_idx_prev = self.list_injection_idx[:]
# Auto inits
list_injection_idx_ext = list_injection_idx[:]
list_injection_idx_ext = self.list_injection_idx[:]
list_nmb_branches = self.list_nmb_branches[:]
list_injection_idx_ext.append(self.num_inference_steps)
# If injection at depth 0 not specified, we will start out with 2 branches
@ -291,7 +389,7 @@ class LatentBlending():
# Diffusion computations start here
time_start = time.time()
for t_block, idx_branch in tqdm(list_compute, desc="computing transition"):
for t_block, idx_branch in tqdm(list_compute, desc="computing transition", smoothing=-1):
if self.stop_diffusion:
print("run_transition: process interrupted")
return self.tree_final_imgs
@ -484,6 +582,7 @@ class LatentBlending():
Set a the seed for a fresh start.
"""
self.seed = seed
self.sdh.seed = seed
def swap_forward(self):
@ -703,76 +802,6 @@ def get_time(resolution=None):
raise ValueError("bad resolution provided: %s" %resolution)
return t
def get_branching(
quality: str = 'medium',
deepth_strength: float = 0.65,
nmb_frames: int = 360,
nmb_mindist: int = 3,
):
r"""
Helper function to set up the branching structure automatically.
Args:
quality: str
Determines how many diffusion steps are being made + how many branches in total.
Choose: fast, medium, high, ultra
deepth_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.
nmb_frames: int = 360,
total number of frames
nmb_mindist: int = 3
minimum distance in terms of diffusion iteratinos between subsequent injections
"""
#%%
if quality == 'lowest':
num_inference_steps = 12
nmb_branches_final = 5
elif quality == 'low':
num_inference_steps = 15
nmb_branches_final = nmb_frames//16
elif quality == 'medium':
num_inference_steps = 30
nmb_branches_final = nmb_frames//8
elif quality == 'high':
num_inference_steps = 60
nmb_branches_final = nmb_frames//4
elif quality == 'ultra':
num_inference_steps = 100
nmb_branches_final = nmb_frames//2
else:
raise ValueError("quality = '{quality}' not supported")
idx_injection_first = int(np.round(num_inference_steps*deepth_strength))
idx_injection_last = num_inference_steps - 3
nmb_injections = int(np.floor(num_inference_steps/5)) - 1
list_injection_idx = [0]
list_injection_idx.extend(np.linspace(idx_injection_first, idx_injection_last, nmb_injections).astype(int))
list_nmb_branches = np.round(np.logspace(np.log10(2), np.log10(nmb_branches_final), nmb_injections+1)).astype(int)
# Cleanup. There should be at least 3 diffusion steps between each injection
list_injection_idx_clean = [list_injection_idx[0]]
list_nmb_branches_clean = [list_nmb_branches[0]]
idx_last_check = 0
for i in range(len(list_injection_idx)-1):
if list_injection_idx[i+1] - list_injection_idx_clean[idx_last_check] >= nmb_mindist:
list_injection_idx_clean.append(list_injection_idx[i+1])
list_nmb_branches_clean.append(list_nmb_branches[i+1])
idx_last_check +=1
list_injection_idx_clean = [int(l) for l in list_injection_idx_clean]
list_nmb_branches_clean = [int(l) for l in list_nmb_branches_clean]
list_injection_idx = list_injection_idx_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}")
return num_inference_steps, list_injection_idx, list_nmb_branches
@ -786,6 +815,7 @@ if __name__ == "__main__":
TODO Coding:
RUNNING WITHOUT PROMPT!
save value ranges, can it be trashed?
in the middle: have more branches + lower guidance scale
TODO Other:
github