experimental feature, branch2 independence
This commit is contained in:
Johannes Stelzer
parent
3b5f079d01
commit
607961feae
40
gradio_ui.py
40
gradio_ui.py
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@ -33,11 +33,6 @@ import copy
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"""
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experiment with slider as output -> does it change in the browser?
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"""
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#%%
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def compare_dicts(a, b):
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@ -80,6 +75,7 @@ class BlendingFrontend():
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self.state_prev = {}
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self.state_current = {}
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self.showing_current = True
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self.branch2_independence = False
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self.imgs_show_last = []
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self.imgs_show_current = []
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self.nmb_branches_final = 13
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@ -91,13 +87,16 @@ class BlendingFrontend():
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self.init_diffusion()
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self.height = self.lb.sdh.height
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self.width = self.lb.sdh.width
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else:
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self.height = 420
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self.width = 420
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def init_diffusion(self):
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fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt"
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fp_config = 'configs/v2-inference.yaml'
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# fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt"
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# fp_config = 'configs/v2-inference.yaml'
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# fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt"
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# fp_config = 'configs/v2-inference-v.yaml'
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fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt"
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fp_config = 'configs/v2-inference-v.yaml'
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sdh = StableDiffusionHolder(fp_ckpt, fp_config, num_inference_steps=self.num_inference_steps)
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self.lb = LatentBlending(sdh)
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@ -124,6 +123,11 @@ class BlendingFrontend():
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self.mid_compression_scaler = value
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print(f"changed mid_compression_scaler to {value}")
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def change_branch2_independence(self):
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self.branch2_independence = not self.branch2_independence
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self.lb.branch2_independence = self.branch2_independence
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print(f"changed branch2_independence to {self.branch2_independence}")
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def change_height(self, value):
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self.height = value
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print(f"changed height to {value}")
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@ -181,7 +185,7 @@ class BlendingFrontend():
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self.imgs_show_last = copy.deepcopy(self.imgs_show_current)
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if self.use_debug:
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list_imgs = [(255*np.random.rand(200,200,3)).astype(np.uint8) for l in range(5)]
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list_imgs = [(255*np.random.rand(self.height,self.width,3)).astype(np.uint8) for l in range(5)]
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self.imgs_show_current = copy.deepcopy(list_imgs)
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return list_imgs
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# FIXME TODO ASSERTS
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@ -290,21 +294,22 @@ with gr.Blocks() as demo:
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prompt2 = gr.Textbox(label="prompt 2")
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negative_prompt = gr.Textbox(label="negative prompt")
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with gr.Row():
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depth_strength = gr.Slider(0.01, 0.99, self.depth_strength, step=0.01, label='depth_strength', interactive=True)
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guidance_scale = gr.Slider(1, 25, self.guidance_scale, step=0.1, label='guidance_scale', interactive=True)
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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)
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mid_compression_scaler = gr.Slider(1.0, 2.0, self.mid_compression_scaler, step=0.01, label='mid_compression_scaler', interactive=True)
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with gr.Row():
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num_inference_steps = gr.Slider(5, 100, self.num_inference_steps, step=1, label='num_inference_steps', interactive=True)
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nmb_branches_final = gr.Slider(5, 125, self.nmb_branches_final, step=4, label='nmb trans images', interactive=True)
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guidance_scale = gr.Slider(1, 25, self.guidance_scale, step=0.1, label='guidance_scale', interactive=True)
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height = gr.Slider(256, 2048, self.height, step=128, label='height', interactive=True)
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width = gr.Slider(256, 2048, self.width, step=128, label='width', interactive=True)
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with gr.Row():
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depth_strength = gr.Slider(0.01, 0.99, self.depth_strength, step=0.01, label='depth_strength', interactive=True)
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nmb_branches_final = gr.Slider(5, 125, self.nmb_branches_final, step=4, label='nmb trans images', interactive=True)
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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)
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mid_compression_scaler = gr.Slider(1.0, 2.0, self.mid_compression_scaler, step=0.01, label='mid_compression_scaler', interactive=True)
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with gr.Row():
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b_newseed1 = gr.Button("rand seed 1")
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seed1 = gr.Number(42, label="seed 1", interactive=True)
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branch2_independence = gr.Checkbox(label="branch2 independence", interactive=True)
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b_newseed2 = gr.Button("rand seed 2")
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seed2 = gr.Number(420, label="seed 2", interactive=True)
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b_compare = gr.Button("compare")
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@ -348,6 +353,7 @@ with gr.Blocks() as demo:
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seed2.change(fn=self.change_seed2, inputs=seed2)
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fps.change(fn=self.change_fps, inputs=fps)
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duration.change(fn=self.change_duration, inputs=duration)
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branch2_independence.change(fn=self.change_branch2_independence)
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b_newseed1.click(self.randomize_seed1, outputs=seed1)
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b_newseed2.click(self.randomize_seed2, outputs=seed2)
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@ -103,6 +103,7 @@ class LatentBlending():
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self.noise_level_upscaling = 20
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self.list_injection_idx = None
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self.list_nmb_branches = None
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self.branch2_independence = False
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self.set_guidance_scale(guidance_scale)
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self.init_mode()
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@ -487,7 +488,12 @@ class LatentBlending():
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self.set_seed(fixed_seeds[0])
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elif idx_branch == self.list_nmb_branches[0] -1:
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self.set_seed(fixed_seeds[1])
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list_latents = self.run_diffusion(list_conditionings, idx_stop=idx_stop)
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# Inject latents from first branch for very first block
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if not self.branch2_independence and idx_branch==1:
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list_latents = self.tree_latents[0][0]
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else:
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list_latents = self.run_diffusion(list_conditionings, idx_stop=idx_stop)
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else:
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# find parents latents
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b_parent1, b_parent2 = get_closest_idx(fract_mixing, self.tree_fracts[t_block-1])
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@ -1100,16 +1106,31 @@ def yml_save(fp_yml, dict_stuff):
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if __name__ == "__main__":
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# xxxx
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#%% First let us spawn a stable diffusion holder
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device = "cuda"
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fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt"
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fp_config = 'configs/v2-inference-v.yaml'
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#%% RUN UPSCALING_STEP2 (highres)
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sdh = StableDiffusionHolder(fp_ckpt, fp_config, device)
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fp_ckpt= "../stable_diffusion_models/ckpt/x4-upscaler-ema.ckpt"
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fp_config = 'configs/x4-upscaling.yaml'
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sdh = StableDiffusionHolder(fp_ckpt, fp_config)
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#%% /home/lugo/latentblending/230106_210812 /
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#%% Next let's set up all parameters
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quality = 'medium'
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depth_strength = 0.65 # Specifies how deep (in terms of diffusion iterations the first branching happens)
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fixed_seeds = [69731932, 504430820]
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prompt1 = "photo of a beautiful cherry forest covered in white flowers, ambient light, very detailed, magic"
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prompt2 = "photo of an golden statue with a funny hat, surrounded by ferns and vines, grainy analog photograph, mystical ambience, incredible detail"
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duration_transition = 12 # In seconds
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fps = 30
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# Spawn latent blending
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self = LatentBlending(sdh)
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dp_img = "/home/lugo/latentblending/230107_144533"
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self.run_upscaling_step2(dp_img)
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self.load_branching_profile(quality=quality, depth_strength=0.3)
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self.set_prompt1(prompt1)
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self.set_prompt2(prompt2)
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# Run latent blending
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imgs_transition = self.run_transition(fixed_seeds=fixed_seeds)
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