diff --git a/diffusers_holder.py b/diffusers_holder.py index a732c6b..f603ff5 100644 --- a/diffusers_holder.py +++ b/diffusers_holder.py @@ -108,10 +108,10 @@ class DiffusersHolder(): pr_encoder = self.pipe._encode_prompt prompt_embeds = pr_encoder( - prompt, - self.device, - 1, - do_classifier_free_guidance, + prompt=prompt, + device=self.device, + num_images_per_prompt=1, + do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=self.negative_prompt, prompt_embeds=None, negative_prompt_embeds=None, @@ -132,12 +132,14 @@ class DiffusersHolder(): @torch.no_grad() def latent2image( self, - latents: torch.FloatTensor): + latents: torch.FloatTensor, + convert_numpy=True): r""" Returns an image provided a latent representation from diffusion. Args: latents: torch.FloatTensor Result of the diffusion process. + convert_numpy: if converting to numpy """ if self.use_sd_xl: # make sure the VAE is in float32 mode, as it overflows in float16 @@ -162,8 +164,12 @@ class DiffusersHolder(): latents = latents.float() image = self.pipe.vae.decode(latents / self.pipe.vae.config.scaling_factor, return_dict=False)[0] - image = self.pipe.image_processor.postprocess(image, output_type="pil", do_denormalize=[True] * image.shape[0]) - return np.asarray(image[0]) + image = self.pipe.image_processor.postprocess(image, output_type="pil", do_denormalize=[True] * image.shape[0])[0] + if convert_numpy: + return np.asarray(image) + else: + return image + def prepare_mixing(self, mixing_coeffs, list_latents_mixing): if type(mixing_coeffs) == float: @@ -266,7 +272,7 @@ class DiffusersHolder(): return_image: Optional[bool] = False): # 0. Default height and width to unet - original_size = (1024, 1024) # FIXME + original_size = (self.width_img, self.height_img) # FIXME crops_coords_top_left = (0, 0) # FIXME target_size = original_size batch_size = 1 @@ -277,7 +283,7 @@ class DiffusersHolder(): do_classifier_free_guidance = self.guidance_scale > 1.0 # 1. Check inputs. Raise error if not correct & 2. Define call parameters - list_mixing_coeffs = self.prepare_mixing() + list_mixing_coeffs = self.prepare_mixing(mixing_coeffs, list_latents_mixing) # 3. Encode input prompt (already encoded outside bc of mixing, just split here) prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = text_embeddings @@ -517,39 +523,27 @@ steps: if __name__ == "__main__": - controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble", torch_dtype=torch.float16) - pipe = StableDiffusionControlNetPipeline.from_pretrained( - "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16 - ).to("cuda") + #%% + pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0" + pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16) + pipe.to('cuda:1') # xxx + #%% self = DiffusersHolder(pipe) + # xxx + self.set_dimensions(1024, 704) + self.set_num_inference_steps(40) + # self.set_dimensions(1536, 1024) + prompt = "Surreal painting of eerie, nebulous glow of an indigo moon, a spine-chilling spectacle unfolds; a baroque, marbled hand reaches out from a viscous, purple lake clutching a melting clock, its face distorted in a never-ending scream of hysteria, while a cluster of laughing orchids, their petals morphed into grotesque human lips, festoon a crimson tree weeping blood instead of sap, a psychedelic cat with an unnaturally playful grin and mismatched eyes lounges atop a floating vintage television showing static, an albino peacock with iridescent, crystalline feathers dances around a towering, inverted pyramid on top of which a humanoid figure with an octopus head lounges seductively, all against the backdrop of a sprawling cityscape where buildings are inverted and writhing as if alive, and the sky is punctuated by floating aquatic creatures glowing neon, adding a touch of haunting beauty to this otherwise deeply unsettling tableau" + text_embeddings = self.get_text_embedding(prompt) + generator = torch.Generator(device=self.device).manual_seed(int(420)) + latents_start = self.get_noise() + list_latents_1 = self.run_diffusion(text_embeddings, latents_start) + img_orig = self.latent2image(list_latents_1[-1]) - # get text encoding - - # get image encoding - - #%% - # # pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-0.9" - # pretrained_model_name_or_path = "stabilityai/stable-diffusion-2-1" - # pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16) - # pipe.to('cuda') - # # xxx - # self = DiffusersHolder(pipe) - # # xxx - # self.set_num_inference_steps(50) - # # self.set_dimensions(1536, 1024) - # prompt = "photo of a beautiful cherry forest covered in white flowers, ambient light, very detailed, magic" - # text_embeddings = self.get_text_embedding(prompt) - # generator = torch.Generator(device=self.device).manual_seed(int(420)) - # latents_start = self.get_noise() - # list_latents_1 = self.run_diffusion(text_embeddings, latents_start) - # img_orig = self.latent2image(list_latents_1[-1]) - - - # %% """ diff --git a/example2_multitrans.py b/example2_multitrans.py index 2b8d393..ef492f9 100644 --- a/example2_multitrans.py +++ b/example2_multitrans.py @@ -20,34 +20,37 @@ import warnings warnings.filterwarnings('ignore') import warnings from latent_blending import LatentBlending -from stable_diffusion_holder import StableDiffusionHolder +from diffusers_holder import DiffusersHolder +from diffusers import DiffusionPipeline from movie_util import concatenate_movies from huggingface_hub import hf_hub_download # %% First let us spawn a stable diffusion holder. Uncomment your version of choice. -fp_ckpt = hf_hub_download(repo_id="stabilityai/stable-diffusion-2-1-base", filename="v2-1_512-ema-pruned.ckpt") -# fp_ckpt = hf_hub_download(repo_id="stabilityai/stable-diffusion-2-1", filename="v2-1_768-ema-pruned.ckpt") -sdh = StableDiffusionHolder(fp_ckpt) +pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0" +pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16) +pipe.to('cuda:1') +dh = DiffusersHolder(pipe) # %% Let's setup the multi transition fps = 30 -duration_single_trans = 6 -depth_strength = 0.55 # Specifies how deep (in terms of diffusion iterations the first branching happens) +duration_single_trans = 20 +depth_strength = 0.25 # Specifies how deep (in terms of diffusion iterations the first branching happens) # Specify a list of prompts below list_prompts = [] -list_prompts.append("surrealistic statue made of glitter and dirt, standing in a lake, atmospheric light, strange glow") -list_prompts.append("statue of a mix between a tree and human, made of marble, incredibly detailed") -list_prompts.append("weird statue of a frog monkey, many colors, standing next to the ruins of an ancient city") -# list_prompts.append("statue of a spider that looked like a human") -# list_prompts.append("statue of a bird that looked like a scorpion") -# list_prompts.append("statue of an ancient cybernetic messenger annoucing good news, golden, futuristic") +list_prompts.append("A panoramic photo of a sentient mirror maze amidst a neon-lit forest, where bioluminescent mushrooms glow eerily, reflecting off the mirrors, and cybernetic crows, with silver wings and ruby eyes, perch ominously, David Lynch, Gaspar Noé, Photograph.") +list_prompts.append("An unsettling tableau of spectral butterflies with clockwork wings, swirling around an antique typewriter perched precariously atop a floating, gnarled tree trunk, a stormy twilight sky, David Lynch's dreamscape, meticulously crafted.") +# list_prompts.append("A haunting tableau of an antique dollhouse swallowed by a giant venus flytrap under the neon glow of an alien moon, its uncanny light reflecting from shattered porcelain faces and marbles, in a quiet, abandoned amusement park.") + # You can optionally specify the seeds -list_seeds = [954375479, 332539350, 956051013, 408831845, 250009012, 675588737] -t_compute_max_allowed = 12 # per segment +list_seeds = [95437579, 33259350, 956051013, 408831845, 250009012, 675588737] +t_compute_max_allowed = 20 # per segment fp_movie = 'movie_example2.mp4' -lb = LatentBlending(sdh) +lb = LatentBlending(dh) +lb.dh.set_dimensions(1024, 704) +lb.dh.set_num_inference_steps(40) + list_movie_parts = [] for i in range(len(list_prompts) - 1): diff --git a/latent_blending.py b/latent_blending.py index 3645c0a..70b2811 100644 --- a/latent_blending.py +++ b/latent_blending.py @@ -111,18 +111,6 @@ class LatentBlending(): self.set_prompt1("") self.set_prompt2("") - # def init_mode(self): - # r""" - # Sets the operational mode. Currently supported are standard, inpainting and x4 upscaling. - # """ - # if isinstance(self.dh.model, LatentUpscaleDiffusion): - # self.mode = 'upscale' - # elif isinstance(self.dh.model, LatentInpaintDiffusion): - # self.dh.image_source = None - # self.dh.mask_image = None - # self.mode = 'inpaint' - # else: - # self.mode = 'standard' def set_dimensions(self, width=None, height=None): self.dh.set_dimensions(width, height) @@ -449,6 +437,7 @@ class LatentBlending(): list_compute_steps = self.num_inference_steps - list_idx_injection list_compute_steps *= list_nmb_stems t_compute = np.sum(list_compute_steps) * self.dt_per_diff + 0.15 * np.sum(list_nmb_stems) + t_compute += 2*self.num_inference_steps*self.dt_per_diff # outer branches increase_done = False for s_idx in range(len(list_nmb_stems) - 1): if list_nmb_stems[s_idx + 1] / list_nmb_stems[s_idx] >= 2: