cleanup
This commit is contained in:
Johannes Stelzer
parent
7df09e8d0b
commit
4ce995a023
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@ -101,7 +101,6 @@ class LatentBlending():
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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.stop_diffusion = False
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self.negative_prompt = None
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self.num_inference_steps = self.sdh.num_inference_steps
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self.noise_level_upscaling = 20
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@ -117,7 +116,6 @@ class LatentBlending():
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self.parental_crossfeed_range = 0.8
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self.parental_crossfeed_power_decay = 0.8
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self.branch1_insertion_completed = False
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self.set_guidance_scale(guidance_scale)
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self.init_mode()
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self.multi_transition_img_first = None
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@ -355,7 +353,7 @@ class LatentBlending():
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if return_image:
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return self.sdh.latent2image(list_latents2[-1])
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else:
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return list_latents2
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return list_latents2
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def compute_latents_mix(self, fract_mixing, b_parent1, b_parent2, idx_injection):
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@ -454,7 +452,6 @@ class LatentBlending():
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if stop_criterion == "t_compute_max_allowed" and t_compute > t_compute_max_allowed:
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stop_criterion_reached = True
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# FIXME: also undersample here... but how... maybe drop them iteratively?
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elif stop_criterion == "nmb_max_branches" and np.sum(list_nmb_stems) >= nmb_max_branches:
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stop_criterion_reached = True
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if is_first_iteration:
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@ -527,16 +524,21 @@ class LatentBlending():
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self.tree_idx_injection.insert(b_parent1+1, idx_injection)
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def get_spatial_mask_template(self):
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def get_spatial_mask_template(self):
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r"""
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Experimental helper function to get a spatial mask template.
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"""
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shape_latents = [self.sdh.C, self.sdh.height // self.sdh.f, self.sdh.width // self.sdh.f]
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C, H, W = shape_latents
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return np.ones((H, W))
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def set_spatial_mask(self, img_mask):
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r"""
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Helper function to #FIXME
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Experimental helper function to set a spatial mask.
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The mask forces latents to be overwritten.
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Args:
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seed: int
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img_mask:
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mask image [0,1]. You can get a template using get_spatial_mask_template
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"""
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@ -591,7 +593,8 @@ class LatentBlending():
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Depending on the mode, the correct one will be executed.
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Args:
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list_conditionings: List of all conditionings for the diffusion model.
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list_conditionings: list
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List of all conditionings for the diffusion model.
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latents_start: torch.FloatTensor
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Latents that are used for injection
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idx_start: int
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@ -640,10 +643,33 @@ class LatentBlending():
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num_inference_steps: int = 100,
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nmb_max_branches_highres: int = 5,
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nmb_max_branches_lowres: int = 6,
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fixed_seeds: Optional[List[int]] = None,
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duration_single_segment = 3,
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fixed_seeds: Optional[List[int]] = None,
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):
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#FIXME
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r"""
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Runs upscaling with the x4 model. Requires that you run a transition before with a low-res model and save the results using write_imgs_transition.
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Args:
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dp_img: str
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Path to the low-res transition path (as saved in write_imgs_transition)
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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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more shallow values will go into alpha-blendy land.
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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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nmb_max_branches_highres: int
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Number of final branches of the upscaling transition pass. Note this is the number
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of branches between each pair of low-res images.
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nmb_max_branches_lowres: int
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Number of input low-res images, subsampling all transition images written in the low-res pass.
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Setting this number lower (e.g. 6) will decrease the compute time but not affect the results too much.
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duration_single_segment: float
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The duration of each high-res movie segment. You will have nmb_max_branches_lowres-1 segments in total.
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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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"""
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fp_yml = os.path.join(dp_img, "lowres.yaml")
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fp_movie = os.path.join(dp_img, "movie_highres.mp4")
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fps = 24
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@ -265,7 +265,9 @@ class StableDiffusionHolder:
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idx_start: int
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Index of the diffusion process start and where the latents_for_injection are injected
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mixing_coeff:
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# FIXME spatial_mask
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mixing coefficients for latent blending
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spatial_mask:
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experimental feature for enforcing pixels from list_latents_mixing
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return_image: Optional[bool]
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Optionally return image directly
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@ -352,15 +354,6 @@ class StableDiffusionHolder:
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):
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r"""
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Diffusion upscaling version.
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# FIXME
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Args:
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??
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latents_for_injection: torch.FloatTensor
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Latents that are used for injection
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idx_start: int
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Index of the diffusion process start and where the latents_for_injection are injected
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return_image: Optional[bool]
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Optionally return image directly
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"""
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# Asserts
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@ -376,7 +369,6 @@ class StableDiffusionHolder:
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assert len(list_latents_mixing) == self.num_inference_steps
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precision_scope = autocast if self.precision == "autocast" else nullcontext
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generator = torch.Generator(device=self.device).manual_seed(int(self.seed))
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h = uc_full['c_concat'][0].shape[2]
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w = uc_full['c_concat'][0].shape[3]
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