diffusers update fix
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@ -295,20 +295,55 @@ class DiffusersHolder():
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extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta) # dummy
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extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta) # dummy
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# 7. Prepare added time ids & embeddings
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# 7. Prepare added time ids & embeddings
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add_text_embeds = pooled_prompt_embeds
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# add_text_embeds = pooled_prompt_embeds
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add_time_ids = self.pipe._get_add_time_ids(
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# add_time_ids = self.pipe._get_add_time_ids(
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original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
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# original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
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)
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# )
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# if do_classifier_free_guidance:
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# prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
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# add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
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# add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
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# prompt_embeds = prompt_embeds.to(self.device)
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# add_text_embeds = add_text_embeds.to(self.device)
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# add_time_ids = add_time_ids.to(self.device).repeat(batch_size * num_images_per_prompt, 1)
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# 7. Prepare added time ids & embeddings
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add_text_embeds = pooled_prompt_embeds
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if self.pipe.text_encoder_2 is None:
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text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
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else:
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text_encoder_projection_dim = self.pipe.text_encoder_2.config.projection_dim
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add_time_ids = self.pipe._get_add_time_ids(
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original_size,
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crops_coords_top_left,
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target_size,
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dtype=prompt_embeds.dtype,
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text_encoder_projection_dim=text_encoder_projection_dim,
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)
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# if negative_original_size is not None and negative_target_size is not None:
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# negative_add_time_ids = self.pipe._get_add_time_ids(
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# negative_original_size,
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# negative_crops_coords_top_left,
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# negative_target_size,
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# dtype=prompt_embeds.dtype,
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# text_encoder_projection_dim=text_encoder_projection_dim,
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# )
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# else:
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negative_add_time_ids = add_time_ids
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if do_classifier_free_guidance:
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prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
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prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
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add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
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add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
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add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
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add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
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prompt_embeds = prompt_embeds.to(self.device)
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prompt_embeds = prompt_embeds.to(self.device)
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add_text_embeds = add_text_embeds.to(self.device)
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add_text_embeds = add_text_embeds.to(self.device)
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add_time_ids = add_time_ids.to(self.device).repeat(batch_size * num_images_per_prompt, 1)
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add_time_ids = add_time_ids.to(self.device).repeat(batch_size * num_images_per_prompt, 1)
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# 8. Denoising loop
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# 8. Denoising loop
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for i, t in enumerate(timesteps):
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for i, t in enumerate(timesteps):
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# Set the right starting latents
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# Set the right starting latents
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@ -508,7 +543,7 @@ if __name__ == "__main__":
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#%%
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#%%
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pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
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pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16)
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16)
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pipe.to('cuda:1') # xxx
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pipe.to('cuda') # xxx
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#%%
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#%%
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self = DiffusersHolder(pipe)
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self = DiffusersHolder(pipe)
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@ -1,56 +0,0 @@
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# Copyright 2022 Lunar Ring. All rights reserved.
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# Written by Johannes Stelzer, email stelzer@lunar-ring.ai twitter @j_stelzer
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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torch.backends.cudnn.benchmark = False
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torch.set_grad_enabled(False)
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import warnings
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warnings.filterwarnings('ignore')
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import warnings
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from latent_blending import LatentBlending
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from diffusers_holder import DiffusersHolder
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from diffusers import DiffusionPipeline
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# %% First let us spawn a stable diffusion holder. Uncomment your version of choice.
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pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16)
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pipe.to('cuda')
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dh = DiffusersHolder(pipe)
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# %% Next let's set up all parameters
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depth_strength = 0.55 # Specifies how deep (in terms of diffusion iterations the first branching happens)
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t_compute_max_allowed = 60 # Determines the quality of the transition in terms of compute time you grant it
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num_inference_steps = 50
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size_output = (1024, 768)
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prompt1 = "underwater landscape, fish, und the sea, incredible detail, high resolution"
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prompt2 = "rendering of an alien planet, strange plants, strange creatures, surreal"
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fp_movie = 'movie_example1.mp4'
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duration_transition = 12 # In seconds
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# Spawn latent blending
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lb = LatentBlending(dh)
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lb.set_prompt1(prompt1)
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lb.set_prompt2(prompt2)
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lb.set_dimensions(size_output)
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# Run latent blending
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lb.run_transition(
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depth_strength=depth_strength,
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num_inference_steps=num_inference_steps,
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t_compute_max_allowed=t_compute_max_allowed)
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# Save movie
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lb.write_movie_transition(fp_movie, duration_transition)
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@ -28,7 +28,7 @@ from huggingface_hub import hf_hub_download
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# %% First let us spawn a stable diffusion holder. Uncomment your version of choice.
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# %% First let us spawn a stable diffusion holder. Uncomment your version of choice.
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pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
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pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16)
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16)
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pipe.to('cuda:1')
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pipe.to('cuda')
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dh = DiffusersHolder(pipe)
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dh = DiffusersHolder(pipe)
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# %% Let's setup the multi transition
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# %% Let's setup the multi transition
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