From 607961feae46b9dda343d45e727fac64a1f05546 Mon Sep 17 00:00:00 2001 From: Johannes Stelzer Date: Tue, 10 Jan 2023 11:00:14 +0100 Subject: [PATCH] experimental feature, branch2 independence --- gradio_ui.py | 40 ++++++++++++++++++++++----------------- latent_blending.py | 47 +++++++++++++++++++++++++++++++++------------- 2 files changed, 57 insertions(+), 30 deletions(-) diff --git a/gradio_ui.py b/gradio_ui.py index 2c5180d..0d1971c 100644 --- a/gradio_ui.py +++ b/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) diff --git a/latent_blending.py b/latent_blending.py index 08eebca..0c247c7 100644 --- a/latent_blending.py +++ b/latent_blending.py @@ -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() @@ -487,7 +488,12 @@ class LatentBlending(): self.set_seed(fixed_seeds[0]) elif idx_branch == self.list_nmb_branches[0] -1: self.set_seed(fixed_seeds[1]) - list_latents = self.run_diffusion(list_conditionings, idx_stop=idx_stop) + + # 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 b_parent1, b_parent2 = get_closest_idx(fract_mixing, self.tree_fracts[t_block-1]) @@ -1099,17 +1105,32 @@ def yml_save(fp_yml, dict_stuff): #%% le main if __name__ == "__main__": # xxxx - - #%% RUN UPSCALING_STEP2 (highres) - - 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 / - self = LatentBlending(sdh) - dp_img = "/home/lugo/latentblending/230107_144533" - self.run_upscaling_step2(dp_img) - - + #%% 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' + + sdh = StableDiffusionHolder(fp_ckpt, fp_config, device) + + + #%% 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) + 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)