better branch handling

diffusers_holder.py

@@ -415,7 +415,7 @@ if __name__ == "__main__":
     
         # img_refx = self.pipe(prompt=prompt1, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale)[0]
         
-        img_refx = self.run_diffusion_sd_xl(text_embeddings=text_embeddings, latents_start=latents_start, return_image=True)
+        img_refx = self.run_diffusion_sd_xl(text_embeddings=text_embeddings, latents_start=latents_start, return_image=False)
         
         dt_ref = time.time() - t0
         img_refx.save(f"x_{prefix}_{i}.jpg")

latent_blending.py

@@ -76,17 +76,11 @@ class LatentBlending():
         self.tree_status = None
         self.tree_final_imgs = []
 
-        self.list_nmb_branches_prev = []
-        self.list_injection_idx_prev = []
         self.text_embedding1 = None
         self.text_embedding2 = None
         self.image1_lowres = None
         self.image2_lowres = None
         self.negative_prompt = None
-        self.num_inference_steps = self.dh.num_inference_steps
-        self.noise_level_upscaling = 20
-        self.list_injection_idx = None
-        self.list_nmb_branches = None
 
         # Mixing parameters
         self.branch1_crossfeed_power = 0.0
@@ -100,11 +94,34 @@ class LatentBlending():
         self.set_guidance_scale(guidance_scale)
         self.multi_transition_img_first = None
         self.multi_transition_img_last = None
-        self.dt_per_diff = 0
+        self.dt_unet_step = 0
         self.lpips = lpips.LPIPS(net='alex').cuda(self.device)
 
         self.set_prompt1("")
         self.set_prompt2("")
+        
+        self.set_num_inference_steps()
+        self.benchmark_speed()
+        self.set_branching()
+        
+    def benchmark_speed(self):
+        """
+        Measures the time per diffusion step and for the vae decoding
+        """
+        
+        text_embeddings = self.dh.get_text_embedding("test")
+        latents_start = self.dh.get_noise(np.random.randint(111111))
+        # warmup
+        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)
+        # bench unet
+        t0 = time.time()
+        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)
+        self.dt_unet_step = time.time() - t0
+        
+        # bench vae
+        t0 = time.time()
+        img = self.dh.latent2image(list_latents[-1])
+        self.dt_vae = time.time() - t0
 
     def set_dimensions(self, size_output=None):
         r"""
@@ -208,28 +225,21 @@ class LatentBlending():
             image: Image
         """
         self.image2_lowres = image
-
+        
-    def run_transition(
+    def set_num_inference_steps(self, num_inference_steps=None):
-            self,
+        if self.dh.is_sdxl_turbo:
-            recycle_img1: Optional[bool] = False,
+            if num_inference_steps is None:
-            recycle_img2: Optional[bool] = False,
+                num_inference_steps = 4
-            num_inference_steps: Optional[int] = 30,
+        else:
-            list_idx_injection: Optional[int] = None, 
+            if num_inference_steps is None:
-            list_nmb_stems: Optional[int] = None,
+                num_inference_steps = 30
-            depth_strength: Optional[float] = 0.3,
+            
-            t_compute_max_allowed: Optional[float] = None,
+        self.num_inference_steps = num_inference_steps
-            nmb_max_branches: Optional[int] = None,
+        self.dh.set_num_inference_steps(num_inference_steps)
-            fixed_seeds: Optional[List[int]] = None):
+        
-        r"""
+    def set_branching(self, depth_strength=None, t_compute_max_allowed=None, nmb_max_branches=None):
-        Function for computing transitions.
+        """
-        Returns a list of transition images using spherical latent blending.
+        Sets the branching structure of the blending tree. Default arguments depend on pipe!
-        Args:
-            recycle_img1: Optional[bool]:
-                Don't recompute the latents for the first keyframe (purely prompt1). Saves compute.
-            recycle_img2: Optional[bool]:
-                Don't recompute the latents for the second keyframe (purely prompt2). Saves compute.
-            num_inference_steps:
-                Number of diffusion steps. Higher values will take more compute time.
             depth_strength:
                 Determines how deep the first injection will happen.
                 Deeper injections will cause (unwanted) formation of new structures,
@@ -241,6 +251,45 @@ class LatentBlending():
                 Either provide t_compute_max_allowed or nmb_max_branches. The maximum number of branches to be computed. Higher values give better
                 results. Use this if you want to have controllable results independent
                 of your computer.
+        """
+        if self.dh.is_sdxl_turbo:
+            assert t_compute_max_allowed is None, "time-based branching not supported for SDXL Turbo"
+            if depth_strength is not None:
+                idx_inject = int(round(self.num_inference_steps*depth_strength))
+            else:
+                idx_inject = 2
+            if nmb_max_branches is None:
+                nmb_max_branches = 10
+                
+            self.list_idx_injection = [idx_inject]
+            self.list_nmb_stems = [nmb_max_branches]
+            
+        else:
+            if depth_strength is None:
+                depth_strength = 0.5
+            if t_compute_max_allowed is None and nmb_max_branches is None:
+                t_compute_max_allowed = 20
+            elif t_compute_max_allowed is not None and nmb_max_branches is not None:
+                raise ValueErorr("Either specify t_compute_max_allowed or nmb_max_branches")
+            
+            self.list_idx_injection, self.list_nmb_stems = self.get_time_based_branching(depth_strength, t_compute_max_allowed, nmb_max_branches)    
+
+    def run_transition(
+            self,
+            recycle_img1: Optional[bool] = False,
+            recycle_img2: Optional[bool] = False,
+            fixed_seeds: Optional[List[int]] = None):
+        r"""
+        Function for computing transitions.
+        Returns a list of transition images using spherical latent blending.
+        Args:
+            recycle_img1: Optional[bool]:
+                Don't recompute the latents for the first keyframe (purely prompt1). Saves compute.
+            recycle_img2: Optional[bool]:
+                Don't recompute the latents for the second keyframe (purely prompt2). Saves compute.
+            num_inference_steps:
+                Number of diffusion steps. Higher values will take more compute time.
+
             fixed_seeds: Optional[List[int)]:
                 You can supply two seeds that are used for the first and second keyframe (prompt1 and prompt2).
                 Otherwise random seeds will be taken.
@@ -261,12 +310,7 @@ class LatentBlending():
             self.seed1 = fixed_seeds[0]
             self.seed2 = fixed_seeds[1]
 
-        # Ensure correct num_inference_steps in holder
+        
-        if self.dh.is_sdxl_turbo:
-            num_inference_steps = 4 #ideal results
-        self.num_inference_steps = num_inference_steps
-        self.dh.set_num_inference_steps(num_inference_steps)
-
         # Compute / Recycle first image
         if not recycle_img1 or len(self.tree_latents[0]) != self.num_inference_steps:
             list_latents1 = self.compute_latents1()
@@ -291,16 +335,13 @@ class LatentBlending():
             self.parental_crossfeed_power = 1.0
             self.parental_crossfeed_power_decay = 1.0
             self.parental_crossfeed_range = 1.0
-            list_idx_injection = [2]
+            
-            list_nmb_stems = [10]
-        else:
-            list_idx_injection, list_nmb_stems = self.get_time_based_branching(depth_strength, t_compute_max_allowed, nmb_max_branches)
 
         # Run iteratively, starting with the longest trajectory.
         # Always inserting new branches where they are needed most according to image similarity
-        for s_idx in tqdm(range(len(list_idx_injection))):
+        for s_idx in tqdm(range(len(self.list_idx_injection))):
-            nmb_stems = list_nmb_stems[s_idx]
+            nmb_stems = self.list_nmb_stems[s_idx]
-            idx_injection = list_idx_injection[s_idx]
+            idx_injection = self.list_idx_injection[s_idx]
 
             for i in range(nmb_stems):
                 fract_mixing, b_parent1, b_parent2 = self.get_mixing_parameters(idx_injection)
@@ -310,6 +351,9 @@ class LatentBlending():
                 # print(f"fract_mixing: {fract_mixing} idx_injection {idx_injection} bp1 {b_parent1} bp2 {b_parent2}")
 
         return self.tree_final_imgs
+    
+
+        
 
     def compute_latents1(self, return_image=False):
         r"""
@@ -327,7 +371,7 @@ class LatentBlending():
             latents_start=latents_start,
             idx_start=0)
         t1 = time.time()
-        self.dt_per_diff = (t1 - t0) / self.num_inference_steps
+        self.dt_unet_step = (t1 - t0) / self.num_inference_steps
         self.tree_latents[0] = list_latents1
         if return_image:
             return self.dh.latent2image(list_latents1[-1])
@@ -447,8 +491,8 @@ class LatentBlending():
         while not stop_criterion_reached:
             list_compute_steps = self.num_inference_steps - list_idx_injection
             list_compute_steps *= list_nmb_stems
-            t_compute = np.sum(list_compute_steps) * self.dt_per_diff + 0.15 * np.sum(list_nmb_stems)
+            t_compute = np.sum(list_compute_steps) * self.dt_unet_step + self.dt_vae * np.sum(list_nmb_stems)
-            t_compute += 2 * self.num_inference_steps * self.dt_per_diff  # outer branches
+            t_compute += 2 * (self.num_inference_steps * self.dt_unet_step + self.dt_vae) # outer branches
             increase_done = False
             for s_idx in range(len(list_nmb_stems) - 1):
                 if list_nmb_stems[s_idx + 1] / list_nmb_stems[s_idx] >= 1:
@@ -765,8 +809,8 @@ if __name__ == "__main__":
     from diffusers_holder import DiffusersHolder
     from diffusers import DiffusionPipeline
     from diffusers import AutoencoderTiny
-    # pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
+    pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
-    pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
+    # pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
     
     
     pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16")
@@ -776,8 +820,8 @@ if __name__ == "__main__":
 
     dh = DiffusersHolder(pipe)
     # %% Next let's set up all parameters
-    size_output = (512, 512)
+    # size_output = (512, 512)
-    # size_output = (1024, 1024)
+    size_output = (1024, 1024)
     prompt1 = "photo of underwater landscape, fish, und the sea, incredible detail, high resolution"
     prompt2 = "rendering of an alien planet, strange plants, strange creatures, surreal"
     negative_prompt = "blurry, ugly, pale"  # Optional
@@ -793,10 +837,9 @@ if __name__ == "__main__":
     lb.set_prompt2(prompt2)
     lb.set_dimensions(size_output)
     lb.set_negative_prompt(negative_prompt)
-    
 
     # Run latent blending
-    lb.run_transition(fixed_seeds=[420, 421], t_compute_max_allowed=15)
+    lb.run_transition(fixed_seeds=[420, 421])
 
     # Save movie
     fp_movie = f'test.mp4'
@@ -804,4 +847,4 @@ if __name__ == "__main__":
     
 
 
-    #%%
+