experimental feature, branch2 independence
This commit is contained in:
Johannes Stelzer
parent
3b5f079d01
commit
607961feae
40
gradio_ui.py
40
gradio_ui.py
|
|
@ -33,11 +33,6 @@ import copy
|
|||
|
||||
|
||||
|
||||
"""
|
||||
experiment with slider as output -> does it change in the browser?
|
||||
"""
|
||||
|
||||
|
||||
#%%
|
||||
|
||||
def compare_dicts(a, b):
|
||||
|
|
@ -80,6 +75,7 @@ class BlendingFrontend():
|
|||
self.state_prev = {}
|
||||
self.state_current = {}
|
||||
self.showing_current = True
|
||||
self.branch2_independence = False
|
||||
self.imgs_show_last = []
|
||||
self.imgs_show_current = []
|
||||
self.nmb_branches_final = 13
|
||||
|
|
@ -91,13 +87,16 @@ class BlendingFrontend():
|
|||
self.init_diffusion()
|
||||
self.height = self.lb.sdh.height
|
||||
self.width = self.lb.sdh.width
|
||||
else:
|
||||
self.height = 420
|
||||
self.width = 420
|
||||
|
||||
def init_diffusion(self):
|
||||
fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt"
|
||||
fp_config = 'configs/v2-inference.yaml'
|
||||
# fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt"
|
||||
# fp_config = 'configs/v2-inference.yaml'
|
||||
|
||||
# fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt"
|
||||
# fp_config = 'configs/v2-inference-v.yaml'
|
||||
fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt"
|
||||
fp_config = 'configs/v2-inference-v.yaml'
|
||||
|
||||
sdh = StableDiffusionHolder(fp_ckpt, fp_config, num_inference_steps=self.num_inference_steps)
|
||||
self.lb = LatentBlending(sdh)
|
||||
|
|
@ -124,6 +123,11 @@ class BlendingFrontend():
|
|||
self.mid_compression_scaler = 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):
|
||||
self.height = value
|
||||
print(f"changed height to {value}")
|
||||
|
|
@ -181,7 +185,7 @@ class BlendingFrontend():
|
|||
self.imgs_show_last = copy.deepcopy(self.imgs_show_current)
|
||||
|
||||
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)
|
||||
return list_imgs
|
||||
# FIXME TODO ASSERTS
|
||||
|
|
@ -290,21 +294,22 @@ with gr.Blocks() as demo:
|
|||
prompt2 = gr.Textbox(label="prompt 2")
|
||||
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():
|
||||
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)
|
||||
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():
|
||||
b_newseed1 = gr.Button("rand seed 1")
|
||||
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")
|
||||
seed2 = gr.Number(420, label="seed 2", interactive=True)
|
||||
b_compare = gr.Button("compare")
|
||||
|
|
@ -348,6 +353,7 @@ with gr.Blocks() as demo:
|
|||
seed2.change(fn=self.change_seed2, inputs=seed2)
|
||||
fps.change(fn=self.change_fps, inputs=fps)
|
||||
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_newseed2.click(self.randomize_seed2, outputs=seed2)
|
||||
|
|
|
|||
|
|
@ -103,6 +103,7 @@ class LatentBlending():
|
|||
self.noise_level_upscaling = 20
|
||||
self.list_injection_idx = None
|
||||
self.list_nmb_branches = None
|
||||
self.branch2_independence = False
|
||||
self.set_guidance_scale(guidance_scale)
|
||||
self.init_mode()
|
||||
|
||||
|
|
@ -487,6 +488,11 @@ class LatentBlending():
|
|||
self.set_seed(fixed_seeds[0])
|
||||
elif idx_branch == self.list_nmb_branches[0] -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)
|
||||
else:
|
||||
# find parents latents
|
||||
|
|
@ -1100,16 +1106,31 @@ def yml_save(fp_yml, dict_stuff):
|
|||
if __name__ == "__main__":
|
||||
# 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'
|
||||
sdh = StableDiffusionHolder(fp_ckpt, fp_config)
|
||||
#%% /home/lugo/latentblending/230106_210812 /
|
||||
|
||||
#%% Next let's set up all parameters
|
||||
quality = 'medium'
|
||||
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)
|
||||
dp_img = "/home/lugo/latentblending/230107_144533"
|
||||
self.run_upscaling_step2(dp_img)
|
||||
|
||||
self.load_branching_profile(quality=quality, depth_strength=0.3)
|
||||
self.set_prompt1(prompt1)
|
||||
self.set_prompt2(prompt2)
|
||||
|
||||
# Run latent blending
|
||||
imgs_transition = self.run_transition(fixed_seeds=fixed_seeds)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue