intermediate progress

cherry_picknick.py

@@ -0,0 +1,103 @@
+# 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 diffusers import StableDiffusionPipeline
+from diffusers.schedulers import DDIMScheduler
+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
+torch.set_grad_enabled(False)
+
+#%% First let us spawn a diffusers pipe using DDIMScheduler
+device = "cuda:0"
+model_path = "../stable_diffusion_models/stable-diffusion-v1-5"
+
+scheduler = DDIMScheduler(beta_start=0.00085,
+            beta_end=0.012,
+            beta_schedule="scaled_linear",
+            clip_sample=False,
+            set_alpha_to_one=False)
+            
+pipe = StableDiffusionPipeline.from_pretrained(
+    model_path,
+    revision="fp16", 
+    torch_dtype=torch.float16,
+    scheduler=scheduler,
+    use_auth_token=True
+)
+pipe = pipe.to(device)
+    
+#%% Next let's set up all parameters
+num_inference_steps = 30 # Number of diffusion interations
+list_nmb_branches = [2, 3, 10, 24]#, 50] # Branching structure: how many branches
+list_injection_strength = [0.0, 0.6, 0.8, 0.9]#, 0.95] # Branching structure: how deep is the blending
+
+width = 512
+height = 512
+guidance_scale = 5
+fps = 30
+duration_target = 10
+width = 512
+height = 512
+
+lb = LatentBlending(pipe, device, height, width, num_inference_steps, guidance_scale)
+
+
+list_prompts = []
+list_prompts.append("surrealistic statue made of glitter and dirt, standing in a lake, atmospheric light, strange glow")
+list_prompts.append("weird statue of a frog monkey, many colors, standing next to the ruins of an ancient city")
+list_prompts.append("statue of a mix between a tree and human, made of marble, incredibly detailed")
+list_prompts.append("statue made of hot metal, bizzarre, dark clouds in the sky")
+list_prompts.append("statue of a spider that looked like a human")
+list_prompts.append("statue of a bird that looked like a scorpion")
+list_prompts.append("statue of an ancient cybernetic messenger annoucing good news, golden, futuristic")
+
+k = 6
+
+prompt = list_prompts[k]
+for i in range(4):
+    lb.set_prompt1(prompt)
+    
+    seed = np.random.randint(999999999)
+    lb.set_seed(seed)
+    plt.imshow(lb.run_diffusion(lb.text_embedding1, return_image=True))
+    plt.title(f"{i} seed {seed}")
+    plt.show()
+    print(f"prompt {k} seed {seed} trial {i}")
+    
+#%%
+
+"""
+
+prompt 3 seed 28652396 trial 2
+prompt 4 seed 783279867 trial 3
+prompt 5 seed 831049796 trial 3
+
+prompt 6 seed 798876383 trial 2
+prompt 6 seed 750494819 trial 2
+prompt 6 seed 416472011 trial 1
+
+"""

example1_standard.py

@@ -26,7 +26,7 @@ from diffusers.schedulers import DDIMScheduler
 from PIL import Image
 import matplotlib.pyplot as plt
 import torch
-from movie_man import MovieSaver
+from movie_util import MovieSaver
 from typing import Callable, List, Optional, Union
 from latent_blending import LatentBlending, add_frames_linear_interp
 torch.set_grad_enabled(False)
@@ -81,7 +81,7 @@ imgs_transition_ext = add_frames_linear_interp(imgs_transition, duration_transit
 fp_movie = f"/home/lugo/tmp/latentblending/bobo_incoming.mp4"
 if os.path.isfile(fp_movie):
     os.remove(fp_movie)
-ms = MovieSaver(fp_movie, fps=fps, profile='save')
+ms = MovieSaver(fp_movie, fps=fps)
 for img in tqdm(imgs_transition_ext):
     ms.write_frame(img)
 ms.finalize()

example3_multitrans.py

@@ -0,0 +1,122 @@
+# 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 diffusers import StableDiffusionPipeline
+from diffusers.schedulers import DDIMScheduler
+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
+torch.set_grad_enabled(False)
+
+#%% First let us spawn a diffusers pipe using DDIMScheduler
+device = "cuda:0"
+model_path = "../stable_diffusion_models/stable-diffusion-v1-5"
+
+scheduler = DDIMScheduler(beta_start=0.00085,
+            beta_end=0.012,
+            beta_schedule="scaled_linear",
+            clip_sample=False,
+            set_alpha_to_one=False)
+            
+pipe = StableDiffusionPipeline.from_pretrained(
+    model_path,
+    revision="fp16", 
+    torch_dtype=torch.float16,
+    scheduler=scheduler,
+    use_auth_token=True
+)
+pipe = pipe.to(device)
+    
+#%% MULTITRANS
+# XXX FIXME AssertionError: Need to supply floats for list_injection_strength
+# GO AS DEEP AS POSSIBLE WITHOUT CAUSING MOTION
+
+num_inference_steps = 100 # Number of diffusion interations
+#list_nmb_branches = [2, 12, 24, 55, 77] # Branching structure: how many branches
+#list_injection_strength = [0.0, 0.35, 0.5, 0.65, 0.95] # Branching structure: how deep is the blending
+list_nmb_branches = list(np.linspace(2, 600, 15).astype(int)) #
+list_injection_strength = list(np.linspace(0.45, 0.97, 14).astype(np.float32)) # Branching structure: how deep is the blending
+list_injection_strength = [float(x) for x in list_injection_strength]
+list_injection_strength.insert(0,0.0)
+
+width = 512
+height = 512
+guidance_scale = 5
+fps = 30
+duration_target = 20
+width = 512
+height = 512
+
+lb = LatentBlending(pipe, device, height, width, num_inference_steps, guidance_scale)
+
+#list_nmb_branches = [2, 3, 10, 24] # Branching structure: how many branches
+#list_injection_strength = [0.0, 0.6, 0.8, 0.9] # 
+
+list_prompts = []
+list_prompts.append("surrealistic statue made of glitter and dirt, standing in a lake, atmospheric light, strange glow")
+list_prompts.append("statue of a mix between a tree and human, made of marble, incredibly detailed")
+list_prompts.append("weird statue of a frog monkey, many colors, standing next to the ruins of an ancient city")
+list_prompts.append("statue made of hot metal, bizzarre, dark clouds in the sky")
+list_prompts.append("statue of a spider that looked like a human")
+list_prompts.append("statue of a bird that looked like a scorpion")
+list_prompts.append("statue of an ancient cybernetic messenger annoucing good news, golden, futuristic")
+
+
+list_seeds = [234187386, 422209351, 241845736, 28652396, 783279867, 831049796, 234903931]
+
+
+fp_movie = "/home/lugo/tmp/latentblending/bubu.mp4"
+ms = MovieSaver(fp_movie, fps=fps)
+
+for i in range(len(list_prompts)-1):
+    print(f"Starting movie segment {i+1}/{len(list_prompts)-1}")
+    
+    if i==0:
+        lb.set_prompt1(list_prompts[i])
+        lb.set_prompt2(list_prompts[i+1])
+        recycle_img1 = False    
+    else:
+        lb.swap_forward()
+        lb.set_prompt2(list_prompts[i+1])
+        recycle_img1 = True    
+    
+    local_seeds = [list_seeds[i], list_seeds[i+1]]
+    list_imgs = lb.run_transition(list_nmb_branches, list_injection_strength, recycle_img1=recycle_img1, fixed_seeds=local_seeds)
+    list_imgs_interp = add_frames_linear_interp(list_imgs, fps, duration_target)
+    
+    # Save movie frame
+    for img in list_imgs_interp:
+        ms.write_frame(img)
+        
+ms.finalize()
+
+#%%
+#for img in lb.tree_final_imgs:
+#    if img is not None:
+#        ms.write_frame(img)
+#        
+#ms.finalize()      
+

latent_blending.py

@@ -314,7 +314,7 @@ class LatentBlending():
                 fract_mixing = self.tree_fracts[t_block][idx_leaf_deep]
                 list_fract_mixing_prev = self.tree_fracts[t_block_prev]
                 b_parent1, b_parent2 = get_closest_idx(fract_mixing, list_fract_mixing_prev)
-                assert self.tree_status[t_block_prev][b_parent1] != 'untouched', 'This should never happen!'
+                assert self.tree_status[t_block_prev][b_parent1] != 'untouched', 'Branch destruction??? This should never happen!'
                 if self.tree_status[t_block_prev][b_parent2] == 'untouched':
                     self.tree_status[t_block_prev][b_parent2] = 'prefetched'
                     list_local_stem.append([t_block_prev, b_parent2])
@@ -934,114 +934,17 @@ def get_time(resolution=None):
 #%% le main
 if __name__ == "__main__":
     
-    #%% TMP SURGERY
-    num_inference_steps = 20 # Number of diffusion interations
-    list_nmb_branches = [2, 3, 10, 24] # Branching structure: how many branches
-    list_injection_strength = [0.0, 0.6, 0.8, 0.9] # Branching structure: how deep is the blending
-    width = 512 
-    height = 512
-    guidance_scale = 5
-    fixed_seeds = [993621550, 280335986]
 
-    lb = LatentBlending(pipe, device, height, width, num_inference_steps, guidance_scale)
-    prompt1 = "photo of a beautiful forest covered in white flowers, ambient light, very detailed, magic"
-    prompt2 = "photo of an eerie statue surrounded by ferns and vines, analog photograph kodak portra, mystical ambience, incredible detail"
-    lb.set_prompt1(prompt1)
-    lb.set_prompt2(prompt2)
-    
-    imgs_transition = lb.run_transition(list_nmb_branches, list_injection_strength, fixed_seeds=fixed_seeds)    
-    
-
-    #%% LOOP
-    list_prompts = []
-    list_prompts.append("paiting of a medieval city")
-    list_prompts.append("paiting of a forest")
-    list_prompts.append("photo of a desert landscape")
-    list_prompts.append("photo of a jungle")
-    # we provide a mask for that image1
-    mask_image = 255*np.ones([512,512], dtype=np.uint8)
-    mask_image[200:300, 200:300] = 0
-    
-    list_nmb_branches = [2, 4, 12]
-    list_injection_idx = [0, 4, 12]
-    
-    # we provide a new prompt for image2
-    prompt2 = list_prompts[1]# "beautiful painting ocean sunset colorful"
-    # self.swap_forward()
-    self.randomize_seed()
-    self.set_prompt2(prompt2)
-    self.init_inpainting(image_source=img1, mask_image=mask_image)
-    list_imgs = self.run_transition(list_nmb_branches, list_injection_idx=list_injection_idx, recycle_img1=True, fixed_seeds='randomize')
-    
-    # now we switch them around so image2 becomes image1
-    img1 = list_imgs[-1]
-    
-
-
-    
-    #%% GOOD MOVIE ENGINE
-    num_inference_steps = 30
-    width = 512
-    height = 512
-    guidance_scale = 5
-    list_nmb_branches = [2, 4, 10, 50]
-    list_injection_idx = [0, 17, 24, 27]
-    fps_target = 30
-    duration_target = 10
-    width = 512
-    height = 512
-    
-    
-    list_prompts = []
-    list_prompts.append('painting of the first beer that was drunk in mesopotamia')
-    list_prompts.append('painting of a greek wine symposium')
-    
-    lb = LatentBlending(pipe, device, height, width, num_inference_steps, guidance_scale, seed)
-    dp_movie = "/home/lugo/tmp/movie"
-    
-
-
-        
-
-    
-    #%% EXAMPLE3 MOVIE ENGINE
-    list_injection_steps = [2, 3, 4, 5]
-    list_injection_strength = [0.55, 0.69, 0.8, 0.92]
-    num_inference_steps = 30
-    width = 768
-    height = 512
-    guidance_scale = 5
-    seed = 421
-    mode = 'standard'
-    fps_target = 30
-    duration_target = 15
-    gpu_id = 0
-    
-    device = "cuda:"+str(gpu_id)
-    model_path = "../stable_diffusion_models/stable-diffusion-v1-5"
-    pipe = StableDiffusionPipeline.from_pretrained(
-        model_path,
-        revision="fp16", 
-        torch_dtype=torch.float16,
-        scheduler=DDIMScheduler(),
-        use_auth_token=True
-    )
-    pipe = pipe.to(device)
         
 
 
     #%%
     """
     TODO Coding:
+        RUNNING WITHOUT PROMPT!
+        
         auto mode (quality settings)
-        refactor movie man
-        make movie combiner in movie man
-        check how default args handled in proper python code...
         save value ranges, can it be trashed?
-        documentation in code
-        example1: single transition
-        example2: single transition inpaint
-        example3: make movie
         set all variables in init! self.img2...
         
     TODO Other:

movie_util.py

@@ -202,6 +202,11 @@ class MovieReader():
 
 #%%
 if __name__ == "__main__": 
+    ms = MovieSaver("/tmp/bubu.mp4", fps=fps)
+    for img in list_imgs_interp:
+        ms.write_frame(img)
+    ms.finalize()
+if False:
     fps=2
     list_fp_movies = []
     for k in range(4):