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latentblending
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more efficient similarity computation

This commit is contained in:
Johannes Stelzer 2024-01-09 17:04:53 +01:00
parent 57e4de2d6c
commit 0083971802
1 changed files with 21 additions and 9 deletions
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latent_blending.py
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@ -341,6 +341,7 @@ class LatentBlending():
self.tree_fracts = [0.0, 1.0]
self.tree_final_imgs = [self.dh.latent2image((self.tree_latents[0][-1])), self.dh.latent2image((self.tree_latents[-1][-1]))]
self.tree_idx_injection = [0, 0]
self.tree_similarities = [self.get_tree_similarities]
# Run iteratively, starting with the longest trajectory.
@ -532,7 +533,8 @@ class LatentBlending():
the index in terms of diffusion steps, where the next insertion will start.
"""
# get_lpips_similarity
similarities = self.get_tree_similarities()
similarities = self.tree_similarities
# similarities = self.get_tree_similarities()
b_closest1 = np.argmax(similarities)
b_closest2 = b_closest1 + 1
fract_closest1 = self.tree_fracts[b_closest1]
@ -565,12 +567,21 @@ class LatentBlending():
list_latents: list
list of the latents to be inserted
"""
img_insert = self.dh.latent2image(list_latents[-1])
b_parent1, b_parent2 = self.get_closest_idx(fract_mixing)
idx_tree = b_parent1 + 1
self.tree_latents.insert(idx_tree, list_latents)
self.tree_final_imgs.insert(idx_tree, self.dh.latent2image(list_latents[-1]))
self.tree_fracts.insert(idx_tree, fract_mixing)
self.tree_idx_injection.insert(idx_tree, idx_injection)
left_sim = self.get_lpips_similarity(img_insert, self.tree_final_imgs[b_parent1])
right_sim = self.get_lpips_similarity(img_insert, self.tree_final_imgs[b_parent2])
idx_insert = b_parent1 + 1
self.tree_latents.insert(idx_insert, list_latents)
self.tree_final_imgs.insert(idx_insert, img_insert)
self.tree_fracts.insert(idx_insert, fract_mixing)
self.tree_idx_injection.insert(idx_insert, idx_injection)
# update similarities
self.tree_similarities[b_parent1] = left_sim
self.tree_similarities.insert(idx_insert, right_sim)
def get_noise(self, seed):
r"""
@ -821,8 +832,8 @@ if __name__ == "__main__":
pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16")
pipe.to("cuda")
# pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16)
# pipe.vae = pipe.vae.cuda()
pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16)
pipe.vae = pipe.vae.cuda()
dh = DiffusersHolder(pipe)
# %% Next let's set up all parameters
@ -830,7 +841,6 @@ if __name__ == "__main__":
prompt2 = "rendering of an alien planet, strange plants, strange creatures, surreal"
negative_prompt = "blurry, ugly, pale" # Optional
duration_transition = 12 # In seconds
# Spawn latent blending
@ -840,7 +850,9 @@ if __name__ == "__main__":
lb.set_negative_prompt(negative_prompt)
# Run latent blending
t0 = time.time()
lb.run_transition(fixed_seeds=[420, 421])
dt = time.time() - t0
# Save movie
fp_movie = f'test.mp4'