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cleanup

This commit is contained in:
Johannes Stelzer 2023-02-20 08:29:21 +01:00
parent d186572b0a
commit 1f761f360b
2 changed files with 104 additions and 154 deletions
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214
gradio_ui.py
View File

@ -59,6 +59,7 @@ class BlendingFrontend():
self.init_save_dir()
self.save_empty_image()
self.share = False
self.transition_can_be_computed = False
self.depth_strength = 0.25
self.seed1 = 420
self.seed2 = 420
@ -69,14 +70,12 @@ class BlendingFrontend():
self.prompt2 = ""
self.negative_prompt = ""
self.state_current = {}
self.branch1_influence = 0.3
self.branch1_max_depth_influence = 0.6
self.branch1_influence_decay = 0.3
self.parental_influence = 0.1
self.parental_max_depth_influence = 1.0
self.parental_influence_decay = 1.0
self.nmb_branches_final = 9
self.nmb_imgs_show = 5 # don't change
self.branch1_crossfeed_power = self.lb.branch1_crossfeed_power
self.branch1_crossfeed_range = self.lb.branch1_crossfeed_range
self.branch1_crossfeed_decay = self.lb.branch1_crossfeed_decay
self.parental_crossfeed_power = self.lb.parental_crossfeed_power
self.parental_crossfeed_range = self.lb.parental_crossfeed_range
self.parental_crossfeed_power_decay = self.lb.parental_crossfeed_power_decay
self.fps = 30
self.duration_video = 10
self.t_compute_max_allowed = 10
@ -87,15 +86,14 @@ class BlendingFrontend():
self.fp_img1 = None
self.fp_img2 = None
self.multi_idx_current = -1
self.multi_list_concat = []
self.list_imgs_shown_last = 5*[self.fp_img_empty]
self.nmb_trans_stack = 6
self.list_all_segments = []
self.dp_session = ""
def init_save_dir(self):
load_dotenv(find_dotenv(), verbose=False)
self.dp_out = os.getenv("dp_out")
self.dp_out = os.getenv("DIR_OUT")
if self.dp_out is None:
self.dp_out = ""
self.dp_imgs = os.path.join(self.dp_out, "imgs")
@ -104,7 +102,6 @@ class BlendingFrontend():
os.makedirs(self.dp_movies, exist_ok=True)
# make dummy image
def save_empty_image(self):
self.fp_img_empty = os.path.join(self.dp_imgs, 'empty.jpg')
@ -112,7 +109,11 @@ class BlendingFrontend():
def randomize_seed1(self):
seed = np.random.randint(0, 10000000)
# Dont randomize seed if we are in a multi concat mode. we don't want to change this one otherwise the movie breaks
if len(self.list_all_segments) > 0:
seed = self.seed1
else:
seed = np.random.randint(0, 10000000)
self.seed1 = int(seed)
print(f"randomize_seed1: new seed = {self.seed1}")
return seed
@ -141,12 +142,12 @@ class BlendingFrontend():
self.lb.seed1 = list_ui_elem[list_ui_keys.index('seed1')] #seed
self.lb.seed2 = list_ui_elem[list_ui_keys.index('seed2')]
self.lb.branch1_influence = list_ui_elem[list_ui_keys.index('branch1_influence')]
self.lb.branch1_max_depth_influence = list_ui_elem[list_ui_keys.index('branch1_max_depth_influence')]
self.lb.branch1_influence_decay = list_ui_elem[list_ui_keys.index('branch1_influence_decay')]
self.lb.parental_influence = list_ui_elem[list_ui_keys.index('parental_influence')]
self.lb.parental_max_depth_influence = list_ui_elem[list_ui_keys.index('parental_max_depth_influence')]
self.lb.parental_influence_decay = list_ui_elem[list_ui_keys.index('parental_influence_decay')]
self.lb.branch1_crossfeed_power = list_ui_elem[list_ui_keys.index('branch1_crossfeed_power')]
self.lb.branch1_crossfeed_range = list_ui_elem[list_ui_keys.index('branch1_crossfeed_range')]
self.lb.branch1_crossfeed_decay = list_ui_elem[list_ui_keys.index('branch1_crossfeed_decay')]
self.lb.parental_crossfeed_power = list_ui_elem[list_ui_keys.index('parental_crossfeed_power')]
self.lb.parental_crossfeed_range = list_ui_elem[list_ui_keys.index('parental_crossfeed_range')]
self.lb.parental_crossfeed_power_decay = list_ui_elem[list_ui_keys.index('parental_crossfeed_power_decay')]
self.num_inference_steps = list_ui_elem[list_ui_keys.index('num_inference_steps')]
self.depth_strength = list_ui_elem[list_ui_keys.index('depth_strength')]
@ -162,27 +163,26 @@ class BlendingFrontend():
return [self.fp_img1, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty]
def compute_img2(self, *args):
if self.fp_img1 is None: # don't do anything
return [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty]
list_ui_elem = args
self.setup_lb(list_ui_elem)
self.fp_img2 = os.path.join(self.dp_imgs, f"img2_{get_time('second')}.jpg")
img2 = Image.fromarray(self.lb.compute_latents2(return_image=True))
img2.save(self.fp_img2)
self.recycle_img2 = True
self.transition_can_be_computed = True
return [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img2]
def compute_transition(self, *args):
if not self.recycle_img1:
print("compute first image before transition")
return
if not self.recycle_img2:
print("compute last image before transition")
return
if not self.transition_can_be_computed:
list_return = [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty]
return list_return
list_ui_elem = args
self.setup_lb(list_ui_elem)
print("STARTING DIFFUSION!")
print("STARTING TRANSITION...")
if self.use_debug:
list_imgs = [(255*np.random.rand(self.height,self.width,3)).astype(np.uint8) for l in range(5)]
list_imgs = [Image.fromarray(l) for l in list_imgs]
@ -236,14 +236,21 @@ class BlendingFrontend():
def stack_forward(self, prompt2, seed2):
# Save preview images, prompts and seeds into dictionary for stacking
# self.list_imgs_shown_last = self.get_multi_trans_imgs_preview(f"lowres_{self.current_timestamp}")[0:5]
timestamp_section = get_time('second')
self.lb.write_imgs_transition(os.path.join(self.dp_out, f"lowres_{timestamp_section}"))
self.lb.write_imgs_transition(os.path.join(self.dp_out, "lowres_current"))
shutil.copyfile(self.fp_movie, os.path.join(self.dp_out, f"lowres_{timestamp_section}", "movie.mp4"))
if len(self.list_all_segments) == 0:
timestamp_session = get_time('second')
self.dp_session = os.path.join(self.dp_out, f"session_{timestamp_session}")
os.makedirs(self.dp_session)
self.transition_can_be_computed = False
idx_segment = len(self.list_all_segments)
dp_segment = os.path.join(self.dp_session, f"segment_{str(idx_segment).zfill(3)}")
self.list_all_segments.append(dp_segment)
self.lb.write_imgs_transition(dp_segment)
shutil.copyfile(self.fp_movie, os.path.join(dp_segment, "movie.mp4"))
self.lb.swap_forward()
self.multi_append()
fp_multi = self.multi_concat()
list_out = [fp_multi]
list_out.extend([self.fp_img2])
@ -252,52 +259,19 @@ class BlendingFrontend():
list_out.append(gr.update(interactive=False, value=seed2))
list_out.append("")
list_out.append(np.random.randint(0, 10000000))
print(f"stack_forward: fp_multi {fp_multi}")
return list_out
def get_list_all_stacked(self):
list_all = os.listdir(os.path.join(self.dp_out))
list_all = [l for l in list_all if l[:8]=="lowres_2"]
list_all.sort()
return list_all
def multi_append(self):
list_all = self.get_list_all_stacked()
dn = list_all[self.multi_idx_current]
self.multi_list_concat.append(dn)
list_short = [dn[7:] for dn in self.multi_list_concat]
str_out = "\n".join(list_short)
return str_out
def multi_reset(self):
self.multi_list_concat = []
str_out = ""
return str_out
def multi_concat(self):
# Make new output directory
dp_multi = os.path.join(self.dp_out, f"multi_{get_time('second')}")
os.makedirs(dp_multi, exist_ok=False)
# Copy all low-res folders (prepending multi001_xxxx), however leave out the movie.mp4
# also collect all movie.mp4
list_fp_movies = []
for i, dn in enumerate(self.multi_list_concat):
dp_source = os.path.join(self.dp_out, dn)
dp_sequence = os.path.join(dp_multi, f"{str(i).zfill(3)}_{dn}")
os.makedirs(dp_sequence, exist_ok=False)
list_source = os.listdir(dp_source)
list_source = [l for l in list_source if not l.endswith(".mp4")]
for fn in list_source:
shutil.copyfile(os.path.join(dp_source, fn), os.path.join(dp_sequence, fn))
list_fp_movies.append(os.path.join(dp_source, "movie.mp4"))
for dp_segment in self.list_all_segments:
list_fp_movies.append(os.path.join(dp_segment, "movie.mp4"))
# Concatenate movies and save
fp_final = os.path.join(dp_multi, "movie.mp4")
fp_final = os.path.join(self.dp_session, "movie.mp4")
concatenate_movies(fp_final, list_fp_movies)
return fp_final
@ -312,36 +286,13 @@ class BlendingFrontend():
return state_dict
def get_img_rand():
return (255*np.random.rand(self.height,self.width,3)).astype(np.uint8)
def generate_list_output(
prompt1,
prompt2,
seed1,
seed2,
list_fp_imgs,
):
list_output = []
list_output.append(prompt1)
list_output.append(prompt2)
list_output.append(seed1)
list_output.append(seed2)
for fp_img in list_fp_imgs:
list_output.append(fp_img)
return list_output
if __name__ == "__main__":
# fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt"
fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt"
self = BlendingFrontend(StableDiffusionHolder(fp_ckpt)) # Yes this is possible in python and yes it is an awesome trick
# self = BlendingFrontend(None) # Yes this is possible in python and yes it is an awesome trick
dict_ui_elem = {}
bf = BlendingFrontend(StableDiffusionHolder(fp_ckpt))
# self = BlendingFrontend(None)
with gr.Blocks() as demo:
with gr.Tab("Single Transition"):
@ -350,40 +301,40 @@ if __name__ == "__main__":
prompt2 = gr.Textbox(label="prompt 2")
with gr.Row():
duration_compute = gr.Slider(5, 45, self.t_compute_max_allowed, step=1, label='compute budget for transition (seconds)', interactive=True)
duration_video = gr.Slider(0.1, 30, self.duration_video, step=0.1, label='result video duration (seconds)', interactive=True)
height = gr.Slider(256, 2048, self.height, step=128, label='height', interactive=True)
width = gr.Slider(256, 2048, self.width, step=128, label='width', interactive=True)
duration_compute = gr.Slider(5, 200, bf.t_compute_max_allowed, step=1, label='compute budget for transition (seconds)', interactive=True)
duration_video = gr.Slider(1, 100, bf.duration_video, step=0.1, label='result video duration (seconds)', interactive=True)
height = gr.Slider(256, 2048, bf.height, step=128, label='height', interactive=True)
width = gr.Slider(256, 2048, bf.width, step=128, label='width', interactive=True)
with gr.Accordion("Advanced Settings (click to expand)", open=False):
with gr.Accordion("Diffusion settings", open=True):
with gr.Row():
num_inference_steps = gr.Slider(5, 100, self.num_inference_steps, step=1, label='num_inference_steps', interactive=True)
guidance_scale = gr.Slider(1, 25, self.guidance_scale, step=0.1, label='guidance_scale', interactive=True)
num_inference_steps = gr.Slider(5, 100, bf.num_inference_steps, step=1, label='num_inference_steps', interactive=True)
guidance_scale = gr.Slider(1, 25, bf.guidance_scale, step=0.1, label='guidance_scale', interactive=True)
negative_prompt = gr.Textbox(label="negative prompt")
with gr.Accordion("Seeds control", open=True):
with gr.Accordion("Seed control: adjust seeds for first and last images", open=True):
with gr.Row():
b_newseed1 = gr.Button("randomize seed 1", variant='secondary')
seed1 = gr.Number(self.seed1, label="seed 1", interactive=True)
seed2 = gr.Number(self.seed2, label="seed 2", interactive=True)
seed1 = gr.Number(bf.seed1, label="seed 1", interactive=True)
seed2 = gr.Number(bf.seed2, label="seed 2", interactive=True)
b_newseed2 = gr.Button("randomize seed 2", variant='secondary')
with gr.Accordion("Crossfeeding for last image", open=True):
with gr.Accordion("Last image crossfeeding.", open=True):
with gr.Row():
branch1_influence = gr.Slider(0.0, 1.0, self.branch1_influence, step=0.01, label='crossfeed power', interactive=True)
branch1_max_depth_influence = gr.Slider(0.0, 1.0, self.branch1_max_depth_influence, step=0.01, label='crossfeed range', interactive=True)
branch1_influence_decay = gr.Slider(0.0, 1.0, self.branch1_influence_decay, step=0.01, label='crossfeed decay', interactive=True)
branch1_crossfeed_power = gr.Slider(0.0, 1.0, bf.branch1_crossfeed_power, step=0.01, label='branch1 crossfeed power', interactive=True)
branch1_crossfeed_range = gr.Slider(0.0, 1.0, bf.branch1_crossfeed_range, step=0.01, label='branch1 crossfeed range', interactive=True)
branch1_crossfeed_decay = gr.Slider(0.0, 1.0, bf.branch1_crossfeed_decay, step=0.01, label='branch1 crossfeed decay', interactive=True)
with gr.Accordion("Transition settings", open=True):
with gr.Row():
parental_influence = gr.Slider(0.0, 1.0, self.parental_influence, step=0.01, label='parental power', interactive=True)
parental_max_depth_influence = gr.Slider(0.0, 1.0, self.parental_max_depth_influence, step=0.01, label='parental range', interactive=True)
parental_influence_decay = gr.Slider(0.0, 1.0, self.parental_influence_decay, step=0.01, label='parental decay', interactive=True)
parental_crossfeed_power = gr.Slider(0.0, 1.0, bf.parental_crossfeed_power, step=0.01, label='parental crossfeed power', interactive=True)
parental_crossfeed_range = gr.Slider(0.0, 1.0, bf.parental_crossfeed_range, step=0.01, label='parental crossfeed range', interactive=True)
parental_crossfeed_power_decay = gr.Slider(0.0, 1.0, bf.parental_crossfeed_power_decay, step=0.01, label='parental crossfeed decay', interactive=True)
with gr.Row():
depth_strength = gr.Slider(0.01, 0.99, self.depth_strength, step=0.01, label='depth_strength', interactive=True)
guidance_scale_mid_damper = gr.Slider(0.01, 2.0, self.guidance_scale_mid_damper, step=0.01, label='guidance_scale_mid_damper', interactive=True)
depth_strength = gr.Slider(0.01, 0.99, bf.depth_strength, step=0.01, label='depth_strength', interactive=True)
guidance_scale_mid_damper = gr.Slider(0.01, 2.0, bf.guidance_scale_mid_damper, step=0.01, label='guidance_scale_mid_damper', interactive=True)
with gr.Row():
@ -404,10 +355,11 @@ if __name__ == "__main__":
with gr.Row():
# b_restart = gr.Button("RESTART EVERYTHING")
b_stackforward = gr.Button('multi-movie start next segment (move last image -> first image)', variant='primary')
b_stackforward = gr.Button('append last movie segment (left) to multi movie (right)', variant='primary')
# Collect all UI elemts in list to easily pass as inputs
# Collect all UI elemts in list to easily pass as inputs in gradio
dict_ui_elem = {}
dict_ui_elem["prompt1"] = prompt1
dict_ui_elem["negative_prompt"] = negative_prompt
dict_ui_elem["prompt2"] = prompt2
@ -418,9 +370,9 @@ if __name__ == "__main__":
dict_ui_elem["width"] = width
dict_ui_elem["depth_strength"] = depth_strength
dict_ui_elem["branch1_influence"] = branch1_influence
dict_ui_elem["branch1_max_depth_influence"] = branch1_max_depth_influence
dict_ui_elem["branch1_influence_decay"] = branch1_influence_decay
dict_ui_elem["branch1_crossfeed_power"] = branch1_crossfeed_power
dict_ui_elem["branch1_crossfeed_range"] = branch1_crossfeed_range
dict_ui_elem["branch1_crossfeed_decay"] = branch1_crossfeed_decay
dict_ui_elem["num_inference_steps"] = num_inference_steps
dict_ui_elem["guidance_scale"] = guidance_scale
@ -428,9 +380,9 @@ if __name__ == "__main__":
dict_ui_elem["seed1"] = seed1
dict_ui_elem["seed2"] = seed2
dict_ui_elem["parental_max_depth_influence"] = parental_max_depth_influence
dict_ui_elem["parental_influence"] = parental_influence
dict_ui_elem["parental_influence_decay"] = parental_influence_decay
dict_ui_elem["parental_crossfeed_range"] = parental_crossfeed_range
dict_ui_elem["parental_crossfeed_power"] = parental_crossfeed_power
dict_ui_elem["parental_crossfeed_power_decay"] = parental_crossfeed_power_decay
# Convert to list, as gradio doesn't seem to accept dicts
list_ui_elem = []
@ -438,21 +390,19 @@ if __name__ == "__main__":
for k in dict_ui_elem.keys():
list_ui_elem.append(dict_ui_elem[k])
list_ui_keys.append(k)
self.list_ui_keys = list_ui_keys
bf.list_ui_keys = list_ui_keys
b_newseed1.click(self.randomize_seed1, outputs=seed1)
b_newseed2.click(self.randomize_seed2, outputs=seed2)
b_compute1.click(self.compute_img1, inputs=list_ui_elem, outputs=[img1, img2, img3, img4, img5])
b_compute2.click(self.compute_img2, inputs=list_ui_elem, outputs=[img2, img3, img4, img5])
b_compute_transition.click(self.compute_transition,
b_newseed1.click(bf.randomize_seed1, outputs=seed1)
b_newseed2.click(bf.randomize_seed2, outputs=seed2)
b_compute1.click(bf.compute_img1, inputs=list_ui_elem, outputs=[img1, img2, img3, img4, img5])
b_compute2.click(bf.compute_img2, inputs=list_ui_elem, outputs=[img2, img3, img4, img5])
b_compute_transition.click(bf.compute_transition,
inputs=list_ui_elem,
outputs=[img2, img3, img4, vid_single])
b_stackforward.click(self.stack_forward,
b_stackforward.click(bf.stack_forward,
inputs=[prompt2, seed2],
outputs=[vid_multi, img1, img2, img3, img4, img5, prompt1, seed1, prompt2])
# b_restart.click(self.multi_reset)
demo.launch(share=self.share, inbrowser=True, inline=False)
demo.launch(share=bf.share, inbrowser=True, inline=False)

44
latent_blending.py
View File

@ -109,13 +109,13 @@ class LatentBlending():
self.list_nmb_branches = None
# Mixing parameters
self.branch1_influence = 0.0
self.branch1_max_depth_influence = 0.65
self.branch1_influence_decay = 0.8
self.branch1_crossfeed_power = 0.1
self.branch1_crossfeed_range = 0.6
self.branch1_crossfeed_decay = 0.8
self.parental_influence = 0.0
self.parental_max_depth_influence = 1.0
self.parental_influence_decay = 1.0
self.parental_crossfeed_power = 0.1
self.parental_crossfeed_range = 0.8
self.parental_crossfeed_power_decay = 0.8
self.branch1_insertion_completed = False
self.set_guidance_scale(guidance_scale)
@ -335,10 +335,10 @@ class LatentBlending():
list_conditionings = self.get_mixed_conditioning(1)
latents_start = self.get_noise(self.seed2)
# Influence from branch1
if self.branch1_influence > 0.0:
if self.branch1_crossfeed_power > 0.0:
# Set up the mixing_coeffs
idx_mixing_stop = int(round(self.num_inference_steps*self.branch1_max_depth_influence))
mixing_coeffs = list(np.linspace(self.branch1_influence, self.branch1_influence*self.branch1_influence_decay, idx_mixing_stop))
idx_mixing_stop = int(round(self.num_inference_steps*self.branch1_crossfeed_range))
mixing_coeffs = list(np.linspace(self.branch1_crossfeed_power, self.branch1_crossfeed_power*self.branch1_crossfeed_decay, idx_mixing_stop))
mixing_coeffs.extend((self.num_inference_steps-idx_mixing_stop)*[0])
list_latents_mixing = self.tree_latents[0]
list_latents2 = self.run_diffusion(
@ -385,11 +385,11 @@ class LatentBlending():
latents_parental = interpolate_spherical(latents_p1, latents_p2, fract_mixing_parental)
list_latents_parental_mix.append(latents_parental)
idx_mixing_stop = int(round(self.num_inference_steps*self.parental_max_depth_influence))
mixing_coeffs = idx_injection*[self.parental_influence]
idx_mixing_stop = int(round(self.num_inference_steps*self.parental_crossfeed_range))
mixing_coeffs = idx_injection*[self.parental_crossfeed_power]
nmb_mixing = idx_mixing_stop - idx_injection
if nmb_mixing > 0:
mixing_coeffs.extend(list(np.linspace(self.parental_influence, self.parental_influence*self.parental_influence_decay, nmb_mixing)))
mixing_coeffs.extend(list(np.linspace(self.parental_crossfeed_power, self.parental_crossfeed_power*self.parental_crossfeed_power_decay, nmb_mixing)))
mixing_coeffs.extend((self.num_inference_steps-len(mixing_coeffs))*[0])
latents_start = list_latents_parental_mix[idx_injection-1]
@ -793,8 +793,8 @@ class LatentBlending():
grab_vars = ['prompt1', 'prompt2', 'seed1', 'seed2', 'height', 'width',
'num_inference_steps', 'depth_strength', 'guidance_scale',
'guidance_scale_mid_damper', 'mid_compression_scaler', 'negative_prompt',
'branch1_influence', 'branch1_max_depth_influence', 'branch1_influence_decay'
'parental_influence', 'parental_max_depth_influence', 'parental_influence_decay']
'branch1_crossfeed_power', 'branch1_crossfeed_range', 'branch1_crossfeed_decay'
'parental_crossfeed_power', 'parental_crossfeed_range', 'parental_crossfeed_power_decay']
for v in grab_vars:
if hasattr(self, v):
if v == 'seed1' or v == 'seed2':
@ -1167,25 +1167,25 @@ if __name__ == "__main__":
self.set_prompt2(prompt2)
# Run latent blending
self.branch1_influence = 0.3
self.branch1_max_depth_influence = 0.4
self.branch1_crossfeed_power = 0.3
self.branch1_crossfeed_range = 0.4
# self.run_transition(depth_strength=depth_strength, fixed_seeds=fixed_seeds)
self.seed1=21312
img1 =self.compute_latents1(True)
#%
self.seed2=1234121
self.branch1_influence = 0.7
self.branch1_max_depth_influence = 0.3
self.branch1_influence_decay = 0.3
self.branch1_crossfeed_power = 0.7
self.branch1_crossfeed_range = 0.3
self.branch1_crossfeed_decay = 0.3
img2 =self.compute_latents2(True)
# Image.fromarray(np.concatenate((img1, img2), axis=1))
#%%
t0 = time.time()
self.t_compute_max_allowed = 30
self.parental_max_depth_influence = 1.0
self.parental_influence = 0.0
self.parental_influence_decay = 1.0
self.parental_crossfeed_range = 1.0
self.parental_crossfeed_power = 0.0
self.parental_crossfeed_power_decay = 1.0
imgs_transition = self.run_transition(recycle_img1=True, recycle_img2=True)
t1 = time.time()
print(f"took: {t1-t0}s")