better branch handling
This commit is contained in:
Johannes Stelzer
parent
a69b86e2cd
commit
e889c2a0cc
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@ -415,7 +415,7 @@ if __name__ == "__main__":
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# img_refx = self.pipe(prompt=prompt1, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale)[0]
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img_refx = self.run_diffusion_sd_xl(text_embeddings=text_embeddings, latents_start=latents_start, return_image=True)
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img_refx = self.run_diffusion_sd_xl(text_embeddings=text_embeddings, latents_start=latents_start, return_image=False)
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dt_ref = time.time() - t0
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img_refx.save(f"x_{prefix}_{i}.jpg")
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@ -76,17 +76,11 @@ class LatentBlending():
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self.tree_status = None
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self.tree_final_imgs = []
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self.list_nmb_branches_prev = []
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self.list_injection_idx_prev = []
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self.text_embedding1 = None
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self.text_embedding2 = None
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self.image1_lowres = None
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self.image2_lowres = None
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self.negative_prompt = None
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self.num_inference_steps = self.dh.num_inference_steps
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self.noise_level_upscaling = 20
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self.list_injection_idx = None
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self.list_nmb_branches = None
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# Mixing parameters
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self.branch1_crossfeed_power = 0.0
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@ -100,12 +94,35 @@ class LatentBlending():
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self.set_guidance_scale(guidance_scale)
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self.multi_transition_img_first = None
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self.multi_transition_img_last = None
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self.dt_per_diff = 0
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self.dt_unet_step = 0
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self.lpips = lpips.LPIPS(net='alex').cuda(self.device)
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self.set_prompt1("")
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self.set_prompt2("")
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self.set_num_inference_steps()
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self.benchmark_speed()
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self.set_branching()
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def benchmark_speed(self):
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"""
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Measures the time per diffusion step and for the vae decoding
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"""
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text_embeddings = self.dh.get_text_embedding("test")
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latents_start = self.dh.get_noise(np.random.randint(111111))
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# warmup
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list_latents = self.dh.run_diffusion_sd_xl(text_embeddings=text_embeddings, latents_start=latents_start, return_image=False, idx_start=self.num_inference_steps-1)
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# bench unet
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t0 = time.time()
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list_latents = self.dh.run_diffusion_sd_xl(text_embeddings=text_embeddings, latents_start=latents_start, return_image=False, idx_start=self.num_inference_steps-1)
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self.dt_unet_step = time.time() - t0
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# bench vae
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t0 = time.time()
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img = self.dh.latent2image(list_latents[-1])
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self.dt_vae = time.time() - t0
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def set_dimensions(self, size_output=None):
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r"""
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sets the size of the output video.
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@ -209,27 +226,20 @@ class LatentBlending():
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"""
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self.image2_lowres = image
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def run_transition(
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self,
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recycle_img1: Optional[bool] = False,
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recycle_img2: Optional[bool] = False,
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num_inference_steps: Optional[int] = 30,
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list_idx_injection: Optional[int] = None,
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list_nmb_stems: Optional[int] = None,
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depth_strength: Optional[float] = 0.3,
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t_compute_max_allowed: Optional[float] = None,
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nmb_max_branches: Optional[int] = None,
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fixed_seeds: Optional[List[int]] = None):
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r"""
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Function for computing transitions.
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Returns a list of transition images using spherical latent blending.
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Args:
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recycle_img1: Optional[bool]:
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Don't recompute the latents for the first keyframe (purely prompt1). Saves compute.
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recycle_img2: Optional[bool]:
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Don't recompute the latents for the second keyframe (purely prompt2). Saves compute.
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num_inference_steps:
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Number of diffusion steps. Higher values will take more compute time.
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def set_num_inference_steps(self, num_inference_steps=None):
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if self.dh.is_sdxl_turbo:
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if num_inference_steps is None:
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num_inference_steps = 4
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else:
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if num_inference_steps is None:
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num_inference_steps = 30
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self.num_inference_steps = num_inference_steps
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self.dh.set_num_inference_steps(num_inference_steps)
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def set_branching(self, depth_strength=None, t_compute_max_allowed=None, nmb_max_branches=None):
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"""
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Sets the branching structure of the blending tree. Default arguments depend on pipe!
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depth_strength:
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Determines how deep the first injection will happen.
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Deeper injections will cause (unwanted) formation of new structures,
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@ -241,6 +251,45 @@ class LatentBlending():
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Either provide t_compute_max_allowed or nmb_max_branches. The maximum number of branches to be computed. Higher values give better
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results. Use this if you want to have controllable results independent
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of your computer.
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"""
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if self.dh.is_sdxl_turbo:
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assert t_compute_max_allowed is None, "time-based branching not supported for SDXL Turbo"
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if depth_strength is not None:
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idx_inject = int(round(self.num_inference_steps*depth_strength))
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else:
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idx_inject = 2
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if nmb_max_branches is None:
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nmb_max_branches = 10
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self.list_idx_injection = [idx_inject]
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self.list_nmb_stems = [nmb_max_branches]
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else:
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if depth_strength is None:
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depth_strength = 0.5
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if t_compute_max_allowed is None and nmb_max_branches is None:
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t_compute_max_allowed = 20
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elif t_compute_max_allowed is not None and nmb_max_branches is not None:
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raise ValueErorr("Either specify t_compute_max_allowed or nmb_max_branches")
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self.list_idx_injection, self.list_nmb_stems = self.get_time_based_branching(depth_strength, t_compute_max_allowed, nmb_max_branches)
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def run_transition(
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self,
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recycle_img1: Optional[bool] = False,
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recycle_img2: Optional[bool] = False,
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fixed_seeds: Optional[List[int]] = None):
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r"""
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Function for computing transitions.
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Returns a list of transition images using spherical latent blending.
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Args:
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recycle_img1: Optional[bool]:
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Don't recompute the latents for the first keyframe (purely prompt1). Saves compute.
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recycle_img2: Optional[bool]:
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Don't recompute the latents for the second keyframe (purely prompt2). Saves compute.
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num_inference_steps:
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Number of diffusion steps. Higher values will take more compute time.
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fixed_seeds: Optional[List[int)]:
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You can supply two seeds that are used for the first and second keyframe (prompt1 and prompt2).
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Otherwise random seeds will be taken.
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@ -261,11 +310,6 @@ class LatentBlending():
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self.seed1 = fixed_seeds[0]
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self.seed2 = fixed_seeds[1]
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# Ensure correct num_inference_steps in holder
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if self.dh.is_sdxl_turbo:
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num_inference_steps = 4 #ideal results
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self.num_inference_steps = num_inference_steps
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self.dh.set_num_inference_steps(num_inference_steps)
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# Compute / Recycle first image
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if not recycle_img1 or len(self.tree_latents[0]) != self.num_inference_steps:
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@ -291,16 +335,13 @@ class LatentBlending():
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self.parental_crossfeed_power = 1.0
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self.parental_crossfeed_power_decay = 1.0
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self.parental_crossfeed_range = 1.0
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list_idx_injection = [2]
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list_nmb_stems = [10]
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else:
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list_idx_injection, list_nmb_stems = self.get_time_based_branching(depth_strength, t_compute_max_allowed, nmb_max_branches)
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# Run iteratively, starting with the longest trajectory.
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# Always inserting new branches where they are needed most according to image similarity
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for s_idx in tqdm(range(len(list_idx_injection))):
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nmb_stems = list_nmb_stems[s_idx]
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idx_injection = list_idx_injection[s_idx]
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for s_idx in tqdm(range(len(self.list_idx_injection))):
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nmb_stems = self.list_nmb_stems[s_idx]
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idx_injection = self.list_idx_injection[s_idx]
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for i in range(nmb_stems):
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fract_mixing, b_parent1, b_parent2 = self.get_mixing_parameters(idx_injection)
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@ -311,6 +352,9 @@ class LatentBlending():
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return self.tree_final_imgs
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def compute_latents1(self, return_image=False):
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r"""
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Runs a diffusion trajectory for the first image
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@ -327,7 +371,7 @@ class LatentBlending():
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latents_start=latents_start,
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idx_start=0)
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t1 = time.time()
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self.dt_per_diff = (t1 - t0) / self.num_inference_steps
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self.dt_unet_step = (t1 - t0) / self.num_inference_steps
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self.tree_latents[0] = list_latents1
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if return_image:
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return self.dh.latent2image(list_latents1[-1])
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@ -447,8 +491,8 @@ class LatentBlending():
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while not stop_criterion_reached:
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list_compute_steps = self.num_inference_steps - list_idx_injection
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list_compute_steps *= list_nmb_stems
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t_compute = np.sum(list_compute_steps) * self.dt_per_diff + 0.15 * np.sum(list_nmb_stems)
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t_compute += 2 * self.num_inference_steps * self.dt_per_diff # outer branches
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t_compute = np.sum(list_compute_steps) * self.dt_unet_step + self.dt_vae * np.sum(list_nmb_stems)
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t_compute += 2 * (self.num_inference_steps * self.dt_unet_step + self.dt_vae) # outer branches
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increase_done = False
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for s_idx in range(len(list_nmb_stems) - 1):
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if list_nmb_stems[s_idx + 1] / list_nmb_stems[s_idx] >= 1:
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@ -765,8 +809,8 @@ if __name__ == "__main__":
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from diffusers_holder import DiffusersHolder
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from diffusers import DiffusionPipeline
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from diffusers import AutoencoderTiny
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# pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
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pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
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pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
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# pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16")
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@ -776,8 +820,8 @@ if __name__ == "__main__":
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dh = DiffusersHolder(pipe)
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# %% Next let's set up all parameters
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size_output = (512, 512)
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# size_output = (1024, 1024)
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# size_output = (512, 512)
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size_output = (1024, 1024)
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prompt1 = "photo of underwater landscape, fish, und the sea, incredible detail, high resolution"
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prompt2 = "rendering of an alien planet, strange plants, strange creatures, surreal"
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negative_prompt = "blurry, ugly, pale" # Optional
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@ -794,9 +838,8 @@ if __name__ == "__main__":
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lb.set_dimensions(size_output)
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lb.set_negative_prompt(negative_prompt)
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# Run latent blending
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lb.run_transition(fixed_seeds=[420, 421], t_compute_max_allowed=15)
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lb.run_transition(fixed_seeds=[420, 421])
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# Save movie
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fp_movie = f'test.mp4'
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@ -804,4 +847,4 @@ if __name__ == "__main__":
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#%%
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