diff --git a/example1_standard.py b/example1_standard.py index ae9c138..3ed30e1 100644 --- a/example1_standard.py +++ b/example1_standard.py @@ -29,44 +29,33 @@ import torch from movie_util import MovieSaver from typing import Callable, List, Optional, Union from latent_blending import LatentBlending, add_frames_linear_interp +from stable_diffusion_holder import StableDiffusionHolder torch.set_grad_enabled(False) -#%% First let us spawn a diffusers pipe using DDIMScheduler +#%% First let us spawn a stable diffusion holder device = "cuda:0" -model_path = "../stable_diffusion_models/stable-diffusion-v1-5" +num_inference_steps = 20 # Number of diffusion interations +fp_ckpt = "../stable_diffusion_models/ckpt/768-v-ema.ckpt" +fp_config = '../stablediffusion/configs/stable-diffusion/v2-inference-v.yaml' + +sdh = StableDiffusionHolder(fp_ckpt, fp_config, device, num_inference_steps=num_inference_steps) -scheduler = DDIMScheduler(beta_start=0.00085, - beta_end=0.012, - beta_schedule="scaled_linear", - clip_sample=False, - set_alpha_to_one=False) - -pipe = StableDiffusionPipeline.from_pretrained( - model_path, - revision="fp16", - torch_dtype=torch.float16, - scheduler=scheduler, - use_auth_token=True -) -pipe = pipe.to(device) #%% Next let's set up all parameters # FIXME below fix numbers # We want 20 diffusion steps in total, begin with 2 branches, have 3 branches at step 12 (=0.6*20) # 10 branches at step 16 (=0.8*20) and 24 branches at step 18 (=0.9*20) # Furthermore we want seed 993621550 for keyframeA and seed 54878562 for keyframeB () - -num_inference_steps = 20 # Number of diffusion interations list_nmb_branches = [2, 3, 10, 24] # Branching structure: how many branches list_injection_strength = [0.0, 0.6, 0.8, 0.9] # Branching structure: how deep is the blending -width = 512 -height = 512 +width = 768 +height = 768 guidance_scale = 5 fixed_seeds = [993621550, 280335986] -lb = LatentBlending(pipe, device, height, width, num_inference_steps, guidance_scale) +lb = LatentBlending(sdh, num_inference_steps, guidance_scale) prompt1 = "photo of a beautiful forest covered in white flowers, ambient light, very detailed, magic" -prompt2 = "photo of an eerie statue surrounded by ferns and vines, analog photograph kodak portra, mystical ambience, incredible detail" +prompt2 = "photo of an golden statue with a funny hat, surrounded by ferns and vines, grainy analog photograph,, mystical ambience, incredible detail" lb.set_prompt1(prompt1) lb.set_prompt2(prompt2) @@ -78,7 +67,7 @@ fps = 60 imgs_transition_ext = add_frames_linear_interp(imgs_transition, duration_transition, fps) # movie saving -fp_movie = f"/home/lugo/tmp/latentblending/bobo_incoming.mp4" +fp_movie = "/home/lugo/tmp/latentblending/bobo_incoming.mp4" if os.path.isfile(fp_movie): os.remove(fp_movie) ms = MovieSaver(fp_movie, fps=fps) diff --git a/example2_inpaint.py b/example2_inpaint.py index ca2552f..89e01f4 100644 --- a/example2_inpaint.py +++ b/example2_inpaint.py @@ -27,22 +27,19 @@ from diffusers import StableDiffusionInpaintPipeline from PIL import Image import matplotlib.pyplot as plt import torch -from movie_man import MovieSaver +from movie_util import MovieSaver from typing import Callable, List, Optional, Union from latent_blending import LatentBlending, add_frames_linear_interp +from stable_diffusion_holder import StableDiffusionHolder torch.set_grad_enabled(False) -#%% First let us spawn a diffusers pipe using DDIMScheduler +#%% First let us spawn a stable diffusion holder device = "cuda:0" -model_path = "../stable_diffusion_models/stable-diffusion-inpainting" +num_inference_steps = 20 # Number of diffusion interations +fp_ckpt= "../stable_diffusion_models/ckpt/512-inpainting-ema.ckpt" +fp_config = '../stablediffusion/configs//stable-diffusion/v2-inpainting-inference.yaml' -pipe = StableDiffusionInpaintPipeline.from_pretrained( - model_path, - revision="fp16", - torch_dtype=torch.float16, - safety_checker=None -) -pipe = pipe.to(device) +sdh = StableDiffusionHolder(fp_ckpt, fp_config, device, num_inference_steps=num_inference_steps) #%% Let's make a source image and mask. @@ -52,7 +49,7 @@ num_inference_steps = 30 guidance_scale = 5 fixed_seeds = [629575320, 670154945] -lb = LatentBlending(pipe, device, height, width, num_inference_steps, guidance_scale) +lb = LatentBlending(sdh, num_inference_steps, guidance_scale) prompt1 = "photo of a futuristic alien temple in a desert, mystic, glowing, organic, intricate, sci-fi movie, mesmerizing, scary" lb.set_prompt1(prompt1) lb.init_inpainting(init_empty=True) @@ -77,7 +74,6 @@ height = 512 guidance_scale = 5 fixed_seeds = [993621550, 280335986] -lb = LatentBlending(pipe, device, height, width, num_inference_steps, guidance_scale) prompt1 = "photo of a futuristic alien temple in a desert, mystic, glowing, organic, intricate, sci-fi movie, mesmerizing, scary" prompt2 = "aerial photo of a futuristic alien temple in a coastal area, waves clashing" lb.set_prompt1(prompt1) @@ -92,12 +88,11 @@ fps = 60 imgs_transition_ext = add_frames_linear_interp(imgs_transition, duration_transition, fps) # movie saving -fp_movie = f"/home/lugo/tmp/latentblending/bobo_incoming.mp4" +fp_movie = "/home/lugo/tmp/latentblending/bobo_incoming.mp4" if os.path.isfile(fp_movie): os.remove(fp_movie) -ms = MovieSaver(fp_movie, fps=fps, profile='save') +ms = MovieSaver(fp_movie, fps=fps, shape_hw=[lb.height, lb.width]) for img in tqdm(imgs_transition_ext): ms.write_frame(img) ms.finalize() - diff --git a/latent_blending.py b/latent_blending.py index 5a3e710..f21e286 100644 --- a/latent_blending.py +++ b/latent_blending.py @@ -38,15 +38,14 @@ from typing import Callable, List, Optional, Union import inspect from threading import Thread torch.set_grad_enabled(False) - +from omegaconf import OmegaConf +from torch import autocast +from contextlib import nullcontext #%% class LatentBlending(): def __init__( self, - pipe: Union[StableDiffusionInpaintPipeline, StableDiffusionPipeline], - device: str, - height: int = 512, - width: int = 512, + sdh: None, num_inference_steps: int = 30, guidance_scale: float = 7.5, seed: int = 420, @@ -54,8 +53,7 @@ class LatentBlending(): r""" Initializes the latent blending class. Args: - device: str - Compute device, e.g. cuda:0 + FIXME XXX height: int Height of the desired output image. The model was trained on 512. width: int @@ -72,19 +70,15 @@ class LatentBlending(): Random seed. """ - - self.pipe = pipe - self.device = device - self.guidance_scale = guidance_scale + self.sdh = sdh self.num_inference_steps = num_inference_steps - self.width = width - self.height = height + self.sdh.num_inference_steps = num_inference_steps + self.device = self.sdh.device + self.guidance_scale = guidance_scale + self.width = self.sdh.width + self.height = self.sdh.height self.seed = seed - # Inits - self.check_asserts() - self.init_mode() - # Initialize vars self.prompt1 = "" self.prompt2 = "" @@ -99,59 +93,21 @@ class LatentBlending(): self.stop_diffusion = False self.negative_prompt = None - - def check_asserts(self): - r""" - Runs Minimal set of sanity checks. - """ - assert self.pipe.scheduler._class_name == 'DDIMScheduler', 'Currently only the DDIMScheduler is supported.' + self.init_mode() - - def init_mode(self): + + def init_mode(self, mode='standard'): r""" Automatically sets the mode of this class, depending on the supplied pipeline. + FIXME XXX """ - if self.pipe._class_name == 'StableDiffusionInpaintPipeline': - self.mask_empty = Image.fromarray(255*np.ones([self.width, self.height], dtype=np.uint8)) - self.image_empty = Image.fromarray(np.zeros([self.width, self.height, 3], dtype=np.uint8)) - self.image_source = None - self.mask_image = None + if mode == 'inpaint': + self.sdh.image_source = None + self.sdh.mask_image = None self.mode = 'inpaint' else: self.mode = 'standard' - - def init_inpainting( - self, - image_source: Union[Image.Image, np.ndarray] = None, - mask_image: Union[Image.Image, np.ndarray] = None, - init_empty: Optional[bool] = False, - ): - r""" - Initializes inpainting with a source and maks image. - Args: - image_source: Union[Image.Image, np.ndarray] - Source image onto which the mask will be applied. - mask_image: Union[Image.Image, np.ndarray] - Mask image, value = 0 will stay untouched, value = 255 subjet to diffusion - init_empty: Optional[bool]: - Initialize inpainting with an empty image and mask, effectively disabling inpainting. - """ - assert self.mode == 'inpaint', 'Initialize class with an inpainting pipeline!' - if not init_empty: - assert image_source is not None, "init_inpainting: you need to provide image_source" - assert mask_image is not None, "init_inpainting: you need to provide mask_image" - if type(image_source) == np.ndarray: - image_source = Image.fromarray(image_source) - self.image_source = image_source - - if type(mask_image) == np.ndarray: - mask_image = Image.fromarray(mask_image) - self.mask_image = mask_image - else: - self.mask_image = self.mask_empty - self.image_source = self.image_empty - def set_prompt1(self, prompt: str): r""" @@ -238,6 +194,9 @@ class LatentBlending(): # Process interruption variable self.stop_diffusion = False + # Ensure correct num_inference_steps in holder + self.sdh.num_inference_steps = self.num_inference_steps + # Recycling? There are requirements if recycle_img1 or recycle_img2: if self.list_nmb_branches_prev == []: @@ -291,11 +250,11 @@ class LatentBlending(): self.tree_status[t_block][idx_branch] = 'untouched' if recycle_img1: self.tree_status[t_block][0] = 'computed' - self.tree_final_imgs[0] = self.latent2image(self.tree_latents[-1][0][-1]) + self.tree_final_imgs[0] = self.sdh.latent2image(self.tree_latents[-1][0][-1]) self.tree_final_imgs_timing[0] = 0 if recycle_img2: self.tree_status[t_block][-1] = 'computed' - self.tree_final_imgs[-1] = self.latent2image(self.tree_latents[-1][-1][-1]) + self.tree_final_imgs[-1] = self.sdh.latent2image(self.tree_latents[-1][-1][-1]) self.tree_final_imgs_timing[-1] = 0 # setup compute order: goal: try to get last branch computed asap. @@ -365,7 +324,7 @@ class LatentBlending(): # Convert latents to image directly for the last t_block if t_block == nmb_blocks_time-1: - self.tree_final_imgs[idx_branch] = self.latent2image(list_latents[-1]) + self.tree_final_imgs[idx_branch] = self.sdh.latent2image(list_latents[-1]) self.tree_final_imgs_timing[idx_branch] = time.time() - time_start return self.tree_final_imgs @@ -406,6 +365,8 @@ class LatentBlending(): The duration of your movie will be duration_single_trans * len(list_prompts) """ + + # Ensure correct if list_seeds is None: list_seeds = list(np.random.randint(0, 10e10, len(list_prompts))) @@ -424,7 +385,7 @@ class LatentBlending(): recycle_img1 = True local_seeds = [list_seeds[i], list_seeds[i+1]] - list_imgs = lb.run_transition(list_nmb_branches, list_injection_strength=list_injection_strength, list_injection_idx=list_injection_idx, recycle_img1=recycle_img1, fixed_seeds=local_seeds) + list_imgs = self.run_transition(list_nmb_branches, list_injection_strength=list_injection_strength, list_injection_idx=list_injection_idx, recycle_img1=recycle_img1, fixed_seeds=local_seeds) list_imgs_interp = add_frames_linear_interp(list_imgs, fps, duration_single_trans) # Save movie frame @@ -462,257 +423,37 @@ class LatentBlending(): Optionally return image directly """ + # Ensure correct num_inference_steps in Holder + self.sdh.num_inference_steps = self.num_inference_steps if self.mode == 'standard': - return self.run_diffusion_standard(text_embeddings, latents_for_injection=latents_for_injection, idx_start=idx_start, idx_stop=idx_stop, return_image=return_image) + return self.sdh.run_diffusion_standard(text_embeddings, latents_for_injection=latents_for_injection, idx_start=idx_start, idx_stop=idx_stop, return_image=return_image) elif self.mode == 'inpaint': - assert self.image_source is not None, "image_source is None. Please run init_inpainting first." - assert self.mask_image is not None, "image_source is None. Please run init_inpainting first." - return self.run_diffusion_inpaint(text_embeddings, latents_for_injection=latents_for_injection, idx_start=idx_start, idx_stop=idx_stop, return_image=return_image) + assert self.sdh.image_source is not None, "image_source is None. Please run init_inpainting first." + assert self.sdh.mask_image is not None, "image_source is None. Please run init_inpainting first." + return self.sdh.run_diffusion_inpaint(text_embeddings, latents_for_injection=latents_for_injection, idx_start=idx_start, idx_stop=idx_stop, return_image=return_image) - - @torch.no_grad() - def run_diffusion_standard( + def init_inpainting( self, - text_embeddings: torch.FloatTensor, - latents_for_injection: torch.FloatTensor = None, - idx_start: int = -1, - idx_stop: int = -1, - return_image: Optional[bool] = False + image_source: Union[Image.Image, np.ndarray] = None, + mask_image: Union[Image.Image, np.ndarray] = None, + init_empty: Optional[bool] = False, ): r""" - Runs regular diffusion. Returns a list of latents that were computed. - Adaptations allow to supply - a) starting index for diffusion - b) stopping index for diffusion - c) latent representations that are injected at the starting index - Furthermore the intermittent latents are collected and returned. - Adapted from diffusers (https://github.com/huggingface/diffusers) - + Initializes inpainting with a source and maks image. Args: - text_embeddings: torch.FloatTensor - Text embeddings used for diffusion - latents_for_injection: torch.FloatTensor - Latents that are used for injection - idx_start: int - Index of the diffusion process start and where the latents_for_injection are injected - idx_stop: int - Index of the diffusion process end. - return_image: Optional[bool] - Optionally return image directly - - """ - if latents_for_injection is None: - do_inject_latents = False - else: - do_inject_latents = True - - generator = torch.Generator(device=self.device).manual_seed(int(self.seed)) - batch_size = 1 - height = self.height - width = self.width - num_inference_steps = self.num_inference_steps - num_images_per_prompt = 1 - do_classifier_free_guidance = True - - # duplicate text embeddings for each generation per prompt, using mps friendly method - bs_embed, seq_len, _ = text_embeddings.shape - text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1) - text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) - - # set timesteps - self.pipe.scheduler.set_timesteps(num_inference_steps) - - # Some schedulers like PNDM have timesteps as arrays - # It's more optimized to move all timesteps to correct device beforehand - timesteps_tensor = self.pipe.scheduler.timesteps.to(self.pipe.device) - - if not do_inject_latents: - # get the initial random noise unless the user supplied it - latents_shape = (batch_size * num_images_per_prompt, self.pipe.unet.in_channels, height // 8, width // 8) - latents_dtype = text_embeddings.dtype - latents = torch.randn(latents_shape, generator=generator, device=self.pipe.device, dtype=latents_dtype) - - # scale the initial noise by the standard deviation required by the scheduler - latents = latents * self.pipe.scheduler.init_noise_sigma - extra_step_kwargs = {} - - # collect latents - list_latents_out = [] - for i, t in enumerate(timesteps_tensor): - - - if do_inject_latents: - # Inject latent at right place - if i < idx_start: - continue - elif i == idx_start: - latents = latents_for_injection.clone() - - if i == idx_stop: - return list_latents_out - - # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents - latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t) - - # predict the noise residual - noise_pred = self.pipe.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample - - # perform guidance - if do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) - - # compute the previous noisy sample x_t -> x_t-1 - latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample - - list_latents_out.append(latents.clone()) - - if return_image: - return self.latent2image(latents) - else: - return list_latents_out - - - @torch.no_grad() - def run_diffusion_inpaint( - self, - text_embeddings: torch.FloatTensor, - latents_for_injection: torch.FloatTensor = None, - idx_start: int = -1, - idx_stop: int = -1, - return_image: Optional[bool] = False - ): - r""" - Runs inpaint-based diffusion. Returns a list of latents that were computed. - Adaptations allow to supply - a) starting index for diffusion - b) stopping index for diffusion - c) latent representations that are injected at the starting index - Furthermore the intermittent latents are collected and returned. - - Adapted from diffusers (https://github.com/huggingface/diffusers) - Args: - text_embeddings: torch.FloatTensor - Text embeddings used for diffusion - latents_for_injection: torch.FloatTensor - Latents that are used for injection - idx_start: int - Index of the diffusion process start and where the latents_for_injection are injected - idx_stop: int - Index of the diffusion process end. - return_image: Optional[bool] - Optionally return image directly - - """ - - if latents_for_injection is None: - do_inject_latents = False - else: - do_inject_latents = True - - generator = torch.Generator(device=self.device).manual_seed(int(self.seed)) - batch_size = 1 - height = self.height - width = self.width - num_inference_steps = self.num_inference_steps - num_images_per_prompt = 1 - do_classifier_free_guidance = True - - # prepare mask and masked_image - mask, masked_image = self.prepare_mask_and_masked_image(self.image_source, self.mask_image) - mask = mask.to(device=self.pipe.device, dtype=text_embeddings.dtype) - masked_image = masked_image.to(device=self.pipe.device, dtype=text_embeddings.dtype) - - # resize the mask to latents shape as we concatenate the mask to the latents - mask = torch.nn.functional.interpolate(mask, size=(height // 8, width // 8)) - - # encode the mask image into latents space so we can concatenate it to the latents - masked_image_latents = self.pipe.vae.encode(masked_image).latent_dist.sample(generator=generator) - masked_image_latents = 0.18215 * masked_image_latents - - # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method - mask = mask.repeat(num_images_per_prompt, 1, 1, 1) - masked_image_latents = masked_image_latents.repeat(num_images_per_prompt, 1, 1, 1) - - mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask - masked_image_latents = ( - torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents - ) - - num_channels_mask = mask.shape[1] - num_channels_masked_image = masked_image_latents.shape[1] - - num_channels_latents = self.pipe.vae.config.latent_channels - latents_shape = (batch_size * num_images_per_prompt, num_channels_latents, height // 8, width // 8) - latents_dtype = text_embeddings.dtype - latents = torch.randn(latents_shape, generator=generator, device=self.pipe.device, dtype=latents_dtype) - latents = latents.to(self.pipe.device) - # set timesteps - self.pipe.scheduler.set_timesteps(num_inference_steps) - timesteps_tensor = self.pipe.scheduler.timesteps.to(self.pipe.device) - latents = latents * self.pipe.scheduler.init_noise_sigma - extra_step_kwargs = {} - # collect latents - list_latents_out = [] - - for i, t in enumerate(timesteps_tensor): - if do_inject_latents: - # Inject latent at right place - if i < idx_start: - continue - elif i == idx_start: - latents = latents_for_injection.clone() - - if i == idx_stop: - return list_latents_out - - # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents - # concat latents, mask, masked_image_latents in the channel dimension - latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1) - - latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t) - - # predict the noise residual - noise_pred = self.pipe.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample - - # perform guidance - if do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) - - # compute the previous noisy sample x_t -> x_t-1 - latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample - - list_latents_out.append(latents.clone()) - - if return_image: - return self.latent2image(latents) - else: - return list_latents_out - - @torch.no_grad() - def latent2image( - self, - latents: torch.FloatTensor - ): - r""" - Returns an image provided a latent representation from diffusion. - Args: - latents: torch.FloatTensor - Result of the diffusion process. + image_source: Union[Image.Image, np.ndarray] + Source image onto which the mask will be applied. + mask_image: Union[Image.Image, np.ndarray] + Mask image, value = 0 will stay untouched, value = 255 subjet to diffusion + init_empty: Optional[bool]: + Initialize inpainting with an empty image and mask, effectively disabling inpainting, + useful for generating a first image for transitions using diffusion. """ - - latents = 1 / 0.18215 * latents - image = self.pipe.vae.decode(latents).sample - image = (image / 2 + 0.5).clamp(0, 1) - image = image.cpu().permute(0, 2, 3, 1).float().numpy() - image = (image[0,:,:,:] * 255).astype(np.uint8) - - return image + self.init_mode('inpaint') + self.sdh.init_inpainting(image_source, mask_image, init_empty) + @torch.no_grad() def get_text_embeddings( @@ -721,82 +462,14 @@ class LatentBlending(): ): r""" Computes the text embeddings provided a string with a prompts. - Adapted from diffusers (https://github.com/huggingface/diffusers) + Adapted from stable diffusion repo Args: prompt: str ABC trending on artstation painted by Old Greg. """ - if self.negative_prompt is None: - uncond_tokens = [""] - else: - if isinstance(self.negative_prompt, str): - uncond_tokens = [self.negative_prompt] - - batch_size = 1 - num_images_per_prompt = 1 - do_classifier_free_guidance = True - # get prompt text embeddings - text_inputs = self.pipe.tokenizer( - prompt, - padding="max_length", - max_length=self.pipe.tokenizer.model_max_length, - return_tensors="pt", - ) - text_input_ids = text_inputs.input_ids - # if text_input_ids.shape[-1] > self.pipe.tokenizer.modeLatentBlendingl_max_length: - # removed_text = self.pipe.tokenizer.batch_decode(text_input_ids[:, self.pipe.tokenizer.model_max_length :]) - # text_input_ids = text_input_ids[:, : self.pipe.tokenizer.model_max_length] - text_embeddings = self.pipe.text_encoder(text_input_ids.to(self.pipe.device))[0] - - # duplicate text embeddings for each generation per prompt, using mps friendly method - bs_embed, seq_len, _ = text_embeddings.shape - text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1) - text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1) - - # get unconditional embeddings for classifier free guidance - if do_classifier_free_guidance: - max_length = text_input_ids.shape[-1] - uncond_input = self.pipe.tokenizer( - uncond_tokens, - padding="max_length", - max_length=max_length, - truncation=True, - return_tensors="pt", - ) - uncond_embeddings = self.pipe.text_encoder(uncond_input.input_ids.to(self.pipe.device))[0] - - seq_len = uncond_embeddings.shape[1] - uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1) - uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1) - text_embeddings = torch.cat([uncond_embeddings, text_embeddings]) - return text_embeddings + return self.sdh.get_text_embedding(prompt) - - def prepare_mask_and_masked_image(self, image, mask): - r""" - Mask and image preparation for inpainting. - Adapted from diffusers (https://github.com/huggingface/diffusers) - Args: - image: - Source image - mask: - Mask image - """ - image = np.array(image.convert("RGB")) - image = image[None].transpose(0, 3, 1, 2) - image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0 - - mask = np.array(mask.convert("L")) - mask = mask.astype(np.float32) / 255.0 - mask = mask[None, None] - mask[mask < 0.5] = 0 - mask[mask >= 0.5] = 1 - mask = torch.from_numpy(mask) - - masked_image = image * (mask < 0.5) - - return mask, masked_image def randomize_seed(self): r""" @@ -858,7 +531,7 @@ def interpolate_spherical(p0, p1, fract_mixing: float): r""" Helper function to correctly mix two random variables using spherical interpolation. See https://en.wikipedia.org/wiki/Slerp - The function will always cast up to float64 for sake of extra precision. + The function will always cast up to float64 for sake of extra 4. Args: p0: First tensor for interpolation @@ -992,7 +665,7 @@ def add_frames_linear_interp( nmb_frames_to_insert = nmb_frames_to_insert.astype(np.int32) list_imgs_interp = [] - for i in tqdm(range(len(list_imgs_float)-1), desc="STAGE linear interp"): + for i in range(len(list_imgs_float)-1):#, desc="STAGE linear interp"): img0 = list_imgs_float[i] img1 = list_imgs_float[i+1] list_imgs_interp.append(img0.astype(np.uint8)) @@ -1051,8 +724,7 @@ def get_branching( """ - - +#%% if quality == 'lowest': num_inference_steps = 12 nmb_branches_final = 5 @@ -1095,12 +767,49 @@ def get_branching( print(f"list_injection_idx: {list_injection_idx_clean}") print(f"list_nmb_branches: {list_nmb_branches_clean}") - return num_inference_steps, list_injection_idx_clean, list_nmb_branches_clean + # return num_inference_steps, list_injection_idx_clean, list_nmb_branches_clean + + #%% le main if __name__ == "__main__": + sys.path.append('../stablediffusion/ldm') + from ldm.util import instantiate_from_config + from ldm.models.diffusion.ddim import DDIMSampler + from ldm.models.diffusion.dpm_solver import DPMSolverSampler + + num_inference_steps = 20 # Number of diffusion interations + sdh = StableDiffusionHolder(num_inference_steps) + # fp_ckpt = "../stable_diffusion_models/ckpt/768-v-ema.ckpt" + # fp_config = '../stablediffusion/configs/stable-diffusion/v2-inference-v.yaml' + + fp_ckpt= "../stable_diffusion_models/ckpt/512-base-ema.ckpt" + fp_config = '../stablediffusion/configs//stable-diffusion/v2-inference.yaml' + + sdh.init_model(fp_ckpt, fp_config) + + #%% + list_nmb_branches = [2, 3, 10, 24] # Branching structure: how many branches + list_injection_strength = [0.0, 0.6, 0.8, 0.9] # Branching structure: how deep is the blending + width = 512 + height = 512 + guidance_scale = 5 + fixed_seeds = [993621550, 280335986] + device = "cuda:0" + lb = LatentBlending(sdh, device, height, width, num_inference_steps, guidance_scale) + prompt1 = "photo of a forest covered in white flowers, ambient light, very detailed, magic" + prompt2 = "photo of an eerie statue surrounded by ferns and vines, analog photograph kodak portra, mystical ambience, incredible detail" + lb.set_prompt1(prompt1) + lb.set_prompt2(prompt2) + + lx = lb.run_transition(list_nmb_branches, list_injection_strength) + + + + #%% + xxx device = "cuda:0" model_path = "../stable_diffusion_models/stable-diffusion-v1-5" @@ -1110,7 +819,7 @@ if __name__ == "__main__": clip_sample=False, set_alpha_to_one=False) - pipe = StableDiffusionPipeline.from_pretrained( + pipe = StableDiffusionPipeline.from_Union[StableDiffusionInpaintPipeline, StableDiffusionPipeline],pretrained( model_path, revision="fp16", torch_dtype=torch.float16,