cleanup

cherry_picknick.py

@@ -32,23 +32,24 @@ torch.set_grad_enabled(False)
 
 
 #%% First let us spawn a stable diffusion holder
-device = "cuda:0"
+use_inpaint = True
-num_inference_steps = 20 # Number of diffusion interations
-fp_ckpt = "../stable_diffusion_models/ckpt/768-v-ema.ckpt"
-fp_config = '../stablediffusion/configs/stable-diffusion/v2-inference-v.yaml'
 
-sdh = StableDiffusionHolder(fp_ckpt, fp_config, device, num_inference_steps=num_inference_steps)
+device = "cuda"
+fp_ckpt= "../stable_diffusion_models/ckpt/512-inpainting-ema.ckpt"
+fp_config = '../stablediffusion/configs//stable-diffusion/v2-inpainting-inference.yaml'
     
+    
+# fp_ckpt = "../stable_diffusion_models/ckpt/768-v-ema.ckpt"
+# fp_config = '../stablediffusion/configs/stable-diffusion/v2-inference-v.yaml'
+
+sdh = StableDiffusionHolder(fp_ckpt, fp_config, 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
 
 guidance_scale = 5
-fps = 30
-duration_target = 10
-width = 512
-height = 512
 
 lb = LatentBlending(sdh, num_inference_steps, guidance_scale)
 
@@ -71,6 +72,32 @@ for k, prompt in enumerate(list_prompts):
         plt.show()
         print(f"prompt {k} seed {seed} trial {i}")
         
+#%%
+#%% Let's make a source image and mask.
+k=0
+for i in range(10):
+    seed = 190791709# np.random.randint(999999999)
+# seed0 = 629575320
+
+    lb = LatentBlending(sdh)
+    lb.autosetup_branching(quality='medium', deepth_strength=0.65)
+    
+    prompt1 = "photo of a futuristic alien temple in a desert, mystic, glowing, organic, intricate, sci-fi movie, mesmerizing, scary"
+    lb.set_prompt1(prompt1)
+    lb.init_inpainting(init_empty=True)
+    lb.set_seed(seed)
+    plt.imshow(lb.run_diffusion(lb.text_embedding1, return_image=True))
+    plt.title(f"prompt1 {k}, seed {i} {seed}")
+    plt.show()
+    print(f"prompt1 {k} seed {seed} trial {i}")
+    
+    xx
+
+#%%
+mask_image = 255*np.ones([512,512], dtype=np.uint8)
+mask_image[340:420, 170:280, ] = 0
+mask_image = Image.fromarray(mask_image)
+        
 #%%
 
 """

example1_standard.py

@@ -40,15 +40,17 @@ sdh = StableDiffusionHolder(fp_ckpt, fp_config, device)
     
 #%% Next let's set up all parameters
 quality = 'medium'
+deepth_strength = 0.65
 fixed_seeds = [69731932, 504430820]
     
-lb = LatentBlending(sdh)
 prompt1 = "photo of a beautiful forest covered in white flowers, ambient light, very detailed, magic"
 prompt2 = "photo of an golden statue with a funny hat, surrounded by ferns and vines, grainy analog photograph, mystical ambience, incredible detail"
+
+lb = LatentBlending(sdh)
+lb.autosetup_branching(quality=quality, deepth_strength=deepth_strength)
 lb.set_prompt1(prompt1)
 lb.set_prompt2(prompt2)
 
-lb.autosetup_branching(quality=quality)
 
 imgs_transition = lb.run_transition(fixed_seeds=fixed_seeds)
 
@@ -58,7 +60,7 @@ fps = 60
 imgs_transition_ext = add_frames_linear_interp(imgs_transition, duration_transition, fps)
 
 # movie saving
-fp_movie = f"movie_example1.mp4"
+fp_movie = "movie_example1.mp4"
 if os.path.isfile(fp_movie):
     os.remove(fp_movie)
 ms = MovieSaver(fp_movie, fps=fps, shape_hw=[sdh.height, sdh.width])

example2_inpaint.py

@@ -34,40 +34,32 @@ from stable_diffusion_holder import StableDiffusionHolder
 torch.set_grad_enabled(False)
 
 #%% First let us spawn a stable diffusion holder
-device = "cuda:0"
+device = "cuda"
-num_inference_steps = 20 # Number of diffusion interations
+quality = 'medium'
+deepth_strength = 0.65
 fp_ckpt= "../stable_diffusion_models/ckpt/512-inpainting-ema.ckpt"
 fp_config = '../stablediffusion/configs//stable-diffusion/v2-inpainting-inference.yaml'
 
-sdh = StableDiffusionHolder(fp_ckpt, fp_config, device, num_inference_steps=num_inference_steps)
+sdh = StableDiffusionHolder(fp_ckpt, fp_config, device)
 
 
 #%% Let's make a source image and mask.
-height = 512
+seed0 = 190791709
-width = 512
-num_inference_steps = 30
-guidance_scale = 5
-fixed_seeds = [629575320, 670154945]
 
 lb = LatentBlending(sdh)
-lb.autosetup_branching("low")
+lb.autosetup_branching(quality=quality, deepth_strength=deepth_strength)
 prompt1 = "photo of a futuristic alien temple in a desert, mystic, glowing, organic, intricate, sci-fi movie, mesmerizing, scary"
 lb.set_prompt1(prompt1)
 lb.init_inpainting(init_empty=True)
-lb.set_seed(fixed_seeds[0])
+lb.set_seed(seed0)
 image_source = lb.run_diffusion(lb.text_embedding1, return_image=True)
 mask_image = 255*np.ones([512,512], dtype=np.uint8)
-mask_image[160:250, 200:320] = 0
+mask_image[340:420, 170:280, ] = 0
 mask_image = Image.fromarray(mask_image)
 
 
 #%% Next let's set up all parameters
-# FIXME below fix numbers
+fixed_seeds = [seed0, 280335986]
-# We want 20 diffusion steps, begin with 2 branches, have 3 branches at step 12 (=0.6*20)
-# 10 branches at step 16 (=0.8*20) and 24 branches at step 18 (=0.9*20)
-# Furthermore we want seed 993621550 for keyframeA and seed 54878562 for keyframeB ()
-
-fixed_seeds = [993621550, 280335986]
     
 prompt1 = "photo of a futuristic alien temple in a desert, mystic, glowing, organic, intricate, sci-fi movie, mesmerizing, scary"
 prompt2 = "aerial photo of a futuristic alien temple in a coastal area, waves clashing"