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import numpy as np
import torch
from modules import images, shared
from modules.devices import device, dtype_vae, torch_gc
from modules.processing import StableDiffusionProcessingImg2Img
from modules.sd_samplers_common import (approximation_indexes,
images_tensor_to_samples)
from scripts.animatediff_logger import logger_animatediff as logger
from scripts.animatediff_ui import AnimateDiffProcess
class AnimateDiffI2VLatent:
def randomize(
self, p: StableDiffusionProcessingImg2Img, params: AnimateDiffProcess
):
# Get init_alpha
init_alpha = [
1 - pow(i, params.latent_power) / params.latent_scale
for i in range(params.video_length)
]
logger.info(f"Randomizing init_latent according to {init_alpha}.")
init_alpha = torch.tensor(init_alpha, dtype=torch.float32, device=device)[
:, None, None, None
]
init_alpha[init_alpha < 0] = 0
if params.last_frame is not None:
last_frame = params.last_frame
if type(last_frame) == str:
from modules.api.api import decode_base64_to_image
last_frame = decode_base64_to_image(last_frame)
# Get last_alpha
last_alpha = [
1 - pow(i, params.latent_power_last) / params.latent_scale_last
for i in range(params.video_length)
]
last_alpha.reverse()
logger.info(f"Randomizing last_latent according to {last_alpha}.")
last_alpha = torch.tensor(last_alpha, dtype=torch.float32, device=device)[
:, None, None, None
]
last_alpha[last_alpha < 0] = 0
# Normalize alpha
sum_alpha = init_alpha + last_alpha
mask_alpha = sum_alpha > 1
scaling_factor = 1 / sum_alpha[mask_alpha]
init_alpha[mask_alpha] *= scaling_factor
last_alpha[mask_alpha] *= scaling_factor
init_alpha[0] = 1
init_alpha[-1] = 0
last_alpha[0] = 0
last_alpha[-1] = 1
# Calculate last_latent
if p.resize_mode != 3:
last_frame = images.resize_image(
p.resize_mode, last_frame, p.width, p.height
)
last_frame = np.array(last_frame).astype(np.float32) / 255.0
last_frame = np.moveaxis(last_frame, 2, 0)[None, ...]
last_frame = torch.from_numpy(last_frame).to(device).to(dtype_vae)
last_latent = images_tensor_to_samples(
last_frame,
approximation_indexes.get(shared.opts.sd_vae_encode_method),
p.sd_model,
)
torch_gc()
if p.resize_mode == 3:
opt_f = 8
last_latent = torch.nn.functional.interpolate(
last_latent,
size=(p.height // opt_f, p.width // opt_f),
mode="bilinear",
)
# Modify init_latent
p.init_latent = (
p.init_latent * init_alpha
+ last_latent * last_alpha
+ p.rng.next() * (1 - init_alpha - last_alpha)
)
else:
p.init_latent = p.init_latent * init_alpha + p.rng.next() * (1 - init_alpha)
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