cleanup
This commit is contained in:
lugo
parent
635cf0445f
commit
ce60791df0
|
|
@ -21,8 +21,6 @@ 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
|
||||
|
|
@ -67,7 +65,7 @@ fps = 60
|
|||
imgs_transition_ext = add_frames_linear_interp(imgs_transition, duration_transition, fps)
|
||||
|
||||
# movie saving
|
||||
fp_movie = "/home/lugo/tmp/latentblending/bobo_incoming.mp4"
|
||||
fp_movie = "movie_example1.mp4"
|
||||
if os.path.isfile(fp_movie):
|
||||
os.remove(fp_movie)
|
||||
ms = MovieSaver(fp_movie, fps=fps)
|
||||
|
|
|
|||
|
|
@ -26,9 +26,6 @@ import subprocess
|
|||
import warnings
|
||||
import torch
|
||||
from tqdm.auto import tqdm
|
||||
from diffusers import StableDiffusionInpaintPipeline
|
||||
from diffusers import StableDiffusionPipeline
|
||||
from diffusers.schedulers import DDIMScheduler
|
||||
from PIL import Image
|
||||
import matplotlib.pyplot as plt
|
||||
import torch
|
||||
|
|
@ -41,6 +38,10 @@ torch.set_grad_enabled(False)
|
|||
from omegaconf import OmegaConf
|
||||
from torch import autocast
|
||||
from contextlib import nullcontext
|
||||
sys.path.append('../stablediffusion/ldm')
|
||||
from ldm.util import instantiate_from_config
|
||||
from ldm.models.diffusion.ddim import DDIMSampler
|
||||
from stable_diffusion_holder import StableDiffusionHolder
|
||||
#%%
|
||||
class LatentBlending():
|
||||
def __init__(
|
||||
|
|
@ -163,8 +164,8 @@ class LatentBlending():
|
|||
|
||||
"""
|
||||
# Sanity checks first
|
||||
assert self.text_embedding1 is not None, 'Set the first text embedding with .set_prompt1(...) first'
|
||||
assert self.text_embedding2 is not None, 'Set the second text embedding with .set_prompt2(...) 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:
|
||||
|
|
@ -366,11 +367,11 @@ class LatentBlending():
|
|||
|
||||
"""
|
||||
|
||||
# Ensure correct
|
||||
assert len(list_prompts) == len(list_seeds), "Supply the same number of prompts and seeds"
|
||||
|
||||
if list_seeds is None:
|
||||
list_seeds = list(np.random.randint(0, 10e10, len(list_prompts)))
|
||||
|
||||
assert len(list_prompts) == len(list_seeds), "Supply the same number of prompts and seeds"
|
||||
|
||||
for i in range(len(list_prompts)-1):
|
||||
print(f"Starting movie segment {i+1}/{len(list_prompts)-1}")
|
||||
|
|
@ -487,7 +488,8 @@ class LatentBlending():
|
|||
|
||||
def swap_forward(self):
|
||||
r"""
|
||||
Moves over keyframe two -> keyframe one. Useful for making a sequence of transitions.
|
||||
Moves over keyframe two -> keyframe one. Useful for making a sequence of transitions
|
||||
as in run_multi_transition()
|
||||
"""
|
||||
# Move over all latents
|
||||
for t_block in range(len(self.tree_latents)):
|
||||
|
|
@ -500,6 +502,7 @@ class LatentBlending():
|
|||
# Final cleanup for extra sanity
|
||||
self.tree_final_imgs = []
|
||||
|
||||
|
||||
# Auxiliary functions
|
||||
def get_closest_idx(
|
||||
fract_mixing: float,
|
||||
|
|
@ -722,7 +725,6 @@ def get_branching(
|
|||
nmb_mindist: int = 3
|
||||
minimum distance in terms of diffusion iteratinos between subsequent injections
|
||||
|
||||
|
||||
"""
|
||||
#%%
|
||||
if quality == 'lowest':
|
||||
|
|
@ -767,144 +769,20 @@ def get_branching(
|
|||
print(f"list_injection_idx: {list_injection_idx_clean}")
|
||||
print(f"list_nmb_branches: {list_nmb_branches_clean}")
|
||||
|
||||
# return num_inference_steps, list_injection_idx_clean, list_nmb_branches_clean
|
||||
return num_inference_steps, list_injection_idx_clean, list_nmb_branches_clean
|
||||
|
||||
|
||||
|
||||
|
||||
#%% le main
|
||||
if __name__ == "__main__":
|
||||
sys.path.append('../stablediffusion/ldm')
|
||||
from ldm.util import instantiate_from_config
|
||||
from ldm.models.diffusion.ddim import DDIMSampler
|
||||
from ldm.models.diffusion.dpm_solver import DPMSolverSampler
|
||||
|
||||
num_inference_steps = 20 # Number of diffusion interations
|
||||
sdh = StableDiffusionHolder(num_inference_steps)
|
||||
# fp_ckpt = "../stable_diffusion_models/ckpt/768-v-ema.ckpt"
|
||||
# fp_config = '../stablediffusion/configs/stable-diffusion/v2-inference-v.yaml'
|
||||
|
||||
fp_ckpt= "../stable_diffusion_models/ckpt/512-base-ema.ckpt"
|
||||
fp_config = '../stablediffusion/configs//stable-diffusion/v2-inference.yaml'
|
||||
|
||||
sdh.init_model(fp_ckpt, fp_config)
|
||||
|
||||
#%%
|
||||
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 = 512
|
||||
height = 512
|
||||
guidance_scale = 5
|
||||
fixed_seeds = [993621550, 280335986]
|
||||
device = "cuda:0"
|
||||
lb = LatentBlending(sdh, device, height, width, num_inference_steps, guidance_scale)
|
||||
prompt1 = "photo of a forest covered in white flowers, ambient light, very detailed, magic"
|
||||
prompt2 = "photo of an eerie statue surrounded by ferns and vines, analog photograph kodak portra, mystical ambience, incredible detail"
|
||||
lb.set_prompt1(prompt1)
|
||||
lb.set_prompt2(prompt2)
|
||||
|
||||
lx = lb.run_transition(list_nmb_branches, list_injection_strength)
|
||||
|
||||
|
||||
|
||||
|
||||
#%%
|
||||
xxx
|
||||
device = "cuda:0"
|
||||
model_path = "../stable_diffusion_models/stable-diffusion-v1-5"
|
||||
|
||||
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_Union[StableDiffusionInpaintPipeline, StableDiffusionPipeline],pretrained(
|
||||
model_path,
|
||||
revision="fp16",
|
||||
torch_dtype=torch.float16,
|
||||
scheduler=scheduler,
|
||||
use_auth_token=True
|
||||
)
|
||||
pipe = pipe.to(device)
|
||||
|
||||
num_inference_steps = 20 # Number of diffusion interations
|
||||
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 = 512
|
||||
height = 512
|
||||
guidance_scale = 5
|
||||
fixed_seeds = [993621550, 280335986]
|
||||
|
||||
lb = LatentBlending(pipe, device, height, width, num_inference_steps, guidance_scale)
|
||||
lb.negative_prompt = 'text, letters'
|
||||
prompt1 = "photo of a beautiful newspaper covered in white flowers, ambient light, very detailed, magic"
|
||||
prompt2 = "photo of an eerie statue surrounded by ferns and vines, analog photograph kodak portra, mystical ambience, incredible detail"
|
||||
lb.set_prompt1(prompt1)
|
||||
lb.set_prompt2(prompt2)
|
||||
|
||||
imgs_transition = lb.run_transition(list_nmb_branches, list_injection_strength, fixed_seeds=fixed_seeds)
|
||||
|
||||
xxx
|
||||
|
||||
#%%
|
||||
|
||||
num_inference_steps = 30 # Number of diffusion interations
|
||||
list_nmb_branches = [2, 10, 50, 100, 200] #
|
||||
list_injection_strength = list(np.linspace(0.5, 0.95, 4)) # Branching structure: how deep is the blending
|
||||
list_injection_strength.insert(0, 0.0)
|
||||
|
||||
width = 512
|
||||
height = 512
|
||||
guidance_scale = 5
|
||||
fps = 30
|
||||
duration_single_trans = 20
|
||||
width = 512
|
||||
height = 512
|
||||
|
||||
lb = LatentBlending(pipe, device, height, width, 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("statue of a mix between a tree and human, made of marble, incredibly detailed")
|
||||
list_prompts.append("weird statue of a frog monkey, many colors, standing next to the ruins of an ancient city")
|
||||
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_seeds = [234187386, 422209351, 241845736, 28652396, 783279867, 831049796, 234903931]
|
||||
|
||||
fp_movie = "/home/lugo/tmp/latentblending/bubua.mp4"
|
||||
ms = MovieSaver(fp_movie, fps=fps)
|
||||
|
||||
lb.run_multi_transition(
|
||||
list_prompts,
|
||||
list_seeds,
|
||||
list_nmb_branches,
|
||||
list_injection_strength=list_injection_strength,
|
||||
ms=ms,
|
||||
fps=fps,
|
||||
duration_single_trans=duration_single_trans
|
||||
)
|
||||
|
||||
#%% get good branching struct
|
||||
|
||||
|
||||
#%%
|
||||
|
||||
|
||||
|
||||
pass
|
||||
|
||||
#%%
|
||||
"""
|
||||
TODO Coding:
|
||||
RUNNING WITHOUT PROMPT!
|
||||
|
||||
auto mode (quality settings)
|
||||
save value ranges, can it be trashed?
|
||||
set all variables in init! self.img2...
|
||||
|
||||
TODO Other:
|
||||
github
|
||||
|
|
|
|||
Loading…
Reference in New Issue