upscaling model

README.md

@@ -1,4 +1,4 @@
-Latent blending enables the creation of super-smooth video transitions between prompts. Powered by [stable diffusion 2.1](https://stability.ai/blog/stablediffusion2-1-release7-dec-2022), this method involves specific mixing of intermediate latent representations to create a seamless transition – with users having the option to choose full customization or preset options.
+Latent blending enables lightning-fast video transitions with incredible smoothness between prompts. Powered by [stable diffusion 2.1](https://stability.ai/blog/stablediffusion2-1-release7-dec-2022), this method involves specific mixing of intermediate latent representations to create a seamless transition – with users having the option to fully customize the transition and run high-resolution upscaling.
 
 # Quickstart
 ```python
@@ -28,6 +28,10 @@ To run a transition between two prompts where you want some part of the image to
 ## Example 3: Multi transition
 To run multiple transition between K prompts, resulting in a stitched video, run `example3_multitrans.py`
 
+## Example 4: High-resolution with upscaling
+![](example4.jpg)
+You can run a high-res transition using the x4 upscaling model in a two-stage procedure, see `example4_upscaling.py`
+
 # Customization
 
 ## Most relevant parameters

example4_upscaling.py

@@ -0,0 +1,67 @@
+# Copyright 2022 Lunar Ring. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os, sys
+import torch
+torch.backends.cudnn.benchmark = False
+import numpy as np
+import warnings
+warnings.filterwarnings('ignore')
+import warnings
+import torch
+from tqdm.auto import tqdm
+from PIL import Image
+# import matplotlib.pyplot as plt
+import torch
+from movie_util import MovieSaver
+from typing import Callable, List, Optional, Union
+from latent_blending import LatentBlending, add_frames_linear_interp
+from stable_diffusion_holder import StableDiffusionHolder
+torch.set_grad_enabled(False)
+#%% Define vars for low-resoltion pass
+dp_img = "upscaling_bleding" # the results will be saved in this folder
+prompt1 = "photo of mount vesuvius erupting a terrifying pyroclastic ash cloud"
+prompt2 = "photo of a inside a building full of ash, fire, death, destruction, explosions"
+fixed_seeds = [5054613, 1168652]
+
+width = 512
+height = 384
+num_inference_steps_lores = 40
+nmb_branches_final_lores = 10
+depth_strength_lores = 0.5
+
+device = "cuda" 
+fp_ckpt_lores = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt" 
+fp_config_lores = 'configs/v2-inference.yaml'
+
+#%% Define vars for high-resoltion pass
+fp_ckpt_hires = "../stable_diffusion_models/ckpt/x4-upscaler-ema.ckpt"
+fp_config_hires = 'configs/x4-upscaling.yaml'
+depth_strength_hires = 0.65
+num_inference_steps_hires = 100
+nmb_branches_final_hires = 6
+
+#%% Run low-res pass
+sdh = StableDiffusionHolder(fp_ckpt_lores, fp_config_lores, device)
+lb = LatentBlending(sdh)
+lb.set_prompt1(prompt1)
+lb.set_prompt2(prompt2)
+lb.set_width(width)
+lb.set_height(height)
+lb.run_upscaling_step1(dp_img, depth_strength_lores, num_inference_steps_lores, nmb_branches_final_lores, fixed_seeds)
+
+#%% Run high-res pass
+sdh = StableDiffusionHolder(fp_ckpt_hires, fp_config_hires)
+lb = LatentBlending(sdh) 
+lb.run_upscaling_step2(dp_img, depth_strength_hires, num_inference_steps_hires, nmb_branches_final_hires)

latent_blending.py

@@ -614,10 +614,10 @@ class LatentBlending():
     def run_upscaling_step1(
             self, 
             dp_img: str,
-            quality: str = 'upscaling_step1',
             depth_strength: float = 0.65,
+            num_inference_steps: int = 30,
+            nmb_branches_final: int = 10,
             fixed_seeds: Optional[List[int]] = None,
-            overwrite_folder: bool = False,
             ):
         r"""
         Initializes inpainting with a source and maks image.
@@ -644,20 +644,19 @@ class LatentBlending():
             fixed_seeds = list(np.random.randint(0, 1000000, 2).astype(np.int32))
 
         # Run latent blending
-        self.load_branching_profile(quality='upscaling_step1', depth_strength=depth_strength)
+        self.autosetup_branching(depth_strength, num_inference_steps, nmb_branches_final)
         imgs_transition = self.run_transition(fixed_seeds=fixed_seeds)
-        
         self.write_imgs_transition(dp_img, imgs_transition)
 
-
         print(f"run_upscaling_step1: completed! {dp_img}")
         
         
     def run_upscaling_step2(
             self, 
             dp_img: str,
-            quality: str = 'upscaling_step2',
+            depth_strength: float = 0.65,
-            depth_strength: float = 0.6,
+            num_inference_steps: int = 30,
+            nmb_branches_final: int = 10,
             fixed_seeds: Optional[List[int]] = None,
             ):
         
@@ -683,12 +682,7 @@ class LatentBlending():
         text_embeddingA = self.sdh.get_text_embedding(prompt1)
         text_embeddingB = self.sdh.get_text_embedding(prompt2)
         
-        self.load_branching_profile(quality='upscaling_step2', depth_strength=depth_strength)
+        self.autosetup_branching(depth_strength, num_inference_steps, nmb_branches_final)
-        
-        # list_nmb_branches = [2, 3, 4]
-        # list_injection_strength = [0.0, 0.6, 0.95]
-        # num_inference_steps = 100
-        # self.setup_branching(num_inference_steps, list_nmb_branches, list_injection_strength)
         
         duration_single_trans = 3
         list_fract_mixing = np.linspace(0, 1, nmb_images_lowres-1)