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experimental feature, branch2 independence

This commit is contained in:
Johannes Stelzer 2023-01-10 11:00:14 +01:00
parent 3b5f079d01
commit 607961feae
2 changed files with 57 additions and 30 deletions
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40
gradio_ui.py
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@ -33,11 +33,6 @@ import copy
"""
experiment with slider as output -> does it change in the browser?
"""
#%% #%%
def compare_dicts(a, b): def compare_dicts(a, b):
@ -80,6 +75,7 @@ class BlendingFrontend():
self.state_prev = {} self.state_prev = {}
self.state_current = {} self.state_current = {}
self.showing_current = True self.showing_current = True
self.branch2_independence = False
self.imgs_show_last = [] self.imgs_show_last = []
self.imgs_show_current = [] self.imgs_show_current = []
self.nmb_branches_final = 13 self.nmb_branches_final = 13
@ -91,13 +87,16 @@ class BlendingFrontend():
self.init_diffusion() self.init_diffusion()
self.height = self.lb.sdh.height self.height = self.lb.sdh.height
self.width = self.lb.sdh.width self.width = self.lb.sdh.width
else:
self.height = 420
self.width = 420
def init_diffusion(self): def init_diffusion(self):
fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt" # fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt"
fp_config = 'configs/v2-inference.yaml' # fp_config = 'configs/v2-inference.yaml'
# fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt" fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt"
# fp_config = 'configs/v2-inference-v.yaml' fp_config = 'configs/v2-inference-v.yaml'
sdh = StableDiffusionHolder(fp_ckpt, fp_config, num_inference_steps=self.num_inference_steps) sdh = StableDiffusionHolder(fp_ckpt, fp_config, num_inference_steps=self.num_inference_steps)
self.lb = LatentBlending(sdh) self.lb = LatentBlending(sdh)
@ -124,6 +123,11 @@ class BlendingFrontend():
self.mid_compression_scaler = value self.mid_compression_scaler = value
print(f"changed mid_compression_scaler to {value}") print(f"changed mid_compression_scaler to {value}")
def change_branch2_independence(self):
self.branch2_independence = not self.branch2_independence
self.lb.branch2_independence = self.branch2_independence
print(f"changed branch2_independence to {self.branch2_independence}")
def change_height(self, value): def change_height(self, value):
self.height = value self.height = value
print(f"changed height to {value}") print(f"changed height to {value}")
@ -181,7 +185,7 @@ class BlendingFrontend():
self.imgs_show_last = copy.deepcopy(self.imgs_show_current) self.imgs_show_last = copy.deepcopy(self.imgs_show_current)
if self.use_debug: if self.use_debug:
list_imgs = [(255*np.random.rand(200,200,3)).astype(np.uint8) for l in range(5)] list_imgs = [(255*np.random.rand(self.height,self.width,3)).astype(np.uint8) for l in range(5)]
self.imgs_show_current = copy.deepcopy(list_imgs) self.imgs_show_current = copy.deepcopy(list_imgs)
return list_imgs return list_imgs
# FIXME TODO ASSERTS # FIXME TODO ASSERTS
@ -290,21 +294,22 @@ with gr.Blocks() as demo:
prompt2 = gr.Textbox(label="prompt 2") prompt2 = gr.Textbox(label="prompt 2")
negative_prompt = gr.Textbox(label="negative prompt") negative_prompt = gr.Textbox(label="negative prompt")
with gr.Row():
depth_strength = gr.Slider(0.01, 0.99, self.depth_strength, step=0.01, label='depth_strength', interactive=True)
guidance_scale = gr.Slider(1, 25, self.guidance_scale, step=0.1, label='guidance_scale', 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)
mid_compression_scaler = gr.Slider(1.0, 2.0, self.mid_compression_scaler, step=0.01, label='mid_compression_scaler', interactive=True)
with gr.Row(): with gr.Row():
num_inference_steps = gr.Slider(5, 100, self.num_inference_steps, step=1, label='num_inference_steps', interactive=True) num_inference_steps = gr.Slider(5, 100, self.num_inference_steps, step=1, label='num_inference_steps', interactive=True)
nmb_branches_final = gr.Slider(5, 125, self.nmb_branches_final, step=4, label='nmb trans images', interactive=True) guidance_scale = gr.Slider(1, 25, self.guidance_scale, step=0.1, label='guidance_scale', interactive=True)
height = gr.Slider(256, 2048, self.height, step=128, label='height', 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) width = gr.Slider(256, 2048, self.width, step=128, label='width', interactive=True)
with gr.Row():
depth_strength = gr.Slider(0.01, 0.99, self.depth_strength, step=0.01, label='depth_strength', interactive=True)
nmb_branches_final = gr.Slider(5, 125, self.nmb_branches_final, step=4, label='nmb trans images', 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)
mid_compression_scaler = gr.Slider(1.0, 2.0, self.mid_compression_scaler, step=0.01, label='mid_compression_scaler', interactive=True)
with gr.Row(): with gr.Row():
b_newseed1 = gr.Button("rand seed 1") b_newseed1 = gr.Button("rand seed 1")
seed1 = gr.Number(42, label="seed 1", interactive=True) seed1 = gr.Number(42, label="seed 1", interactive=True)
branch2_independence = gr.Checkbox(label="branch2 independence", interactive=True)
b_newseed2 = gr.Button("rand seed 2") b_newseed2 = gr.Button("rand seed 2")
seed2 = gr.Number(420, label="seed 2", interactive=True) seed2 = gr.Number(420, label="seed 2", interactive=True)
b_compare = gr.Button("compare") b_compare = gr.Button("compare")
@ -348,6 +353,7 @@ with gr.Blocks() as demo:
seed2.change(fn=self.change_seed2, inputs=seed2) seed2.change(fn=self.change_seed2, inputs=seed2)
fps.change(fn=self.change_fps, inputs=fps) fps.change(fn=self.change_fps, inputs=fps)
duration.change(fn=self.change_duration, inputs=duration) duration.change(fn=self.change_duration, inputs=duration)
branch2_independence.change(fn=self.change_branch2_independence)
b_newseed1.click(self.randomize_seed1, outputs=seed1) b_newseed1.click(self.randomize_seed1, outputs=seed1)
b_newseed2.click(self.randomize_seed2, outputs=seed2) b_newseed2.click(self.randomize_seed2, outputs=seed2)

37
latent_blending.py
View File

@ -103,6 +103,7 @@ class LatentBlending():
self.noise_level_upscaling = 20 self.noise_level_upscaling = 20
self.list_injection_idx = None self.list_injection_idx = None
self.list_nmb_branches = None self.list_nmb_branches = None
self.branch2_independence = False
self.set_guidance_scale(guidance_scale) self.set_guidance_scale(guidance_scale)
self.init_mode() self.init_mode()
@ -487,6 +488,11 @@ class LatentBlending():
self.set_seed(fixed_seeds[0]) self.set_seed(fixed_seeds[0])
elif idx_branch == self.list_nmb_branches[0] -1: elif idx_branch == self.list_nmb_branches[0] -1:
self.set_seed(fixed_seeds[1]) self.set_seed(fixed_seeds[1])
# Inject latents from first branch for very first block
if not self.branch2_independence and idx_branch==1:
list_latents = self.tree_latents[0][0]
else:
list_latents = self.run_diffusion(list_conditionings, idx_stop=idx_stop) list_latents = self.run_diffusion(list_conditionings, idx_stop=idx_stop)
else: else:
# find parents latents # find parents latents
@ -1100,16 +1106,31 @@ def yml_save(fp_yml, dict_stuff):
if __name__ == "__main__": if __name__ == "__main__":
# xxxx # xxxx
#%% First let us spawn a stable diffusion holder
device = "cuda"
fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt"
fp_config = 'configs/v2-inference-v.yaml'
#%% RUN UPSCALING_STEP2 (highres) sdh = StableDiffusionHolder(fp_ckpt, fp_config, device)
fp_ckpt= "../stable_diffusion_models/ckpt/x4-upscaler-ema.ckpt"
fp_config = 'configs/x4-upscaling.yaml' #%% Next let's set up all parameters
sdh = StableDiffusionHolder(fp_ckpt, fp_config) quality = 'medium'
#%% /home/lugo/latentblending/230106_210812 / depth_strength = 0.65 # Specifies how deep (in terms of diffusion iterations the first branching happens)
fixed_seeds = [69731932, 504430820]
prompt1 = "photo of a beautiful cherry 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"
duration_transition = 12 # In seconds
fps = 30
# Spawn latent blending
self = LatentBlending(sdh) self = LatentBlending(sdh)
dp_img = "/home/lugo/latentblending/230107_144533" self.load_branching_profile(quality=quality, depth_strength=0.3)
self.run_upscaling_step2(dp_img) self.set_prompt1(prompt1)
self.set_prompt2(prompt2)
# Run latent blending
imgs_transition = self.run_transition(fixed_seeds=fixed_seeds)