From 7c48f5e2347776496489010d867ad83f0f07c136 Mon Sep 17 00:00:00 2001 From: Johannes Stelzer Date: Tue, 9 Jan 2024 14:08:40 +0100 Subject: [PATCH] fixed diffusion call --- diffusers_holder.py | 422 +++++++++++++++++++++++++++++++------------- 1 file changed, 302 insertions(+), 120 deletions(-) diff --git a/diffusers_holder.py b/diffusers_holder.py index a65cdfe..71f4d41 100644 --- a/diffusers_holder.py +++ b/diffusers_holder.py @@ -17,7 +17,7 @@ import torch import numpy as np import warnings -from typing import Optional +from typing import Any, Callable, Dict, List, Optional, Tuple, Union from utils import interpolate_spherical from diffusers import DiffusionPipeline, StableDiffusionControlNetPipeline, ControlNetModel from diffusers.models.attention_processor import ( @@ -48,6 +48,8 @@ class DiffusersHolder(): self.height_latent = self.pipe.unet.config.sample_size self.width_img = self.width_latent * self.pipe.vae_scale_factor self.height_img = self.height_latent * self.pipe.vae_scale_factor + + self.is_sdxl_turbo = False def init_types(self): assert hasattr(self.pipe, "__class__"), "No valid diffusers pipeline found." @@ -90,27 +92,23 @@ class DiffusersHolder(): if len(self.negative_prompt) > 1: self.negative_prompt = [self.negative_prompt[0]] - def get_text_embedding(self, prompt, do_classifier_free_guidance=True): - if self.use_sd_xl: - pr_encoder = self.pipe.encode_prompt - else: - pr_encoder = self.pipe._encode_prompt - - prompt_embeds = pr_encoder( + def get_text_embedding(self, prompt): + text_embeddings = self.pipe.encode_prompt( prompt=prompt, - prompt_2=prompt, - device=self.device, + prompt_2=None, + device=self.pipe._execution_device, num_images_per_prompt=1, - do_classifier_free_guidance=do_classifier_free_guidance, + do_classifier_free_guidance=self.pipe.do_classifier_free_guidance, negative_prompt=self.negative_prompt, - negative_prompt_2=self.negative_prompt, + negative_prompt_2=None, prompt_embeds=None, negative_prompt_embeds=None, pooled_prompt_embeds=None, + negative_pooled_prompt_embeds=None, lora_scale=None, - clip_skip=False, + clip_skip=self.pipe.clip_skip, ) - return prompt_embeds + return text_embeddings def get_noise(self, seed=420): @@ -126,16 +124,7 @@ class DiffusersHolder(): ) return latents - - - # H = self.height_latent - # W = self.width_latent - # C = self.pipe.unet.config.in_channels - # generator = torch.Generator(device=self.device).manual_seed(int(seed)) - # latents = torch.randn((1, C, H, W), generator=generator, dtype=self.dtype, device=self.device) - # if self.use_sd_xl: - # latents = latents * self.pipe.scheduler.init_noise_sigma - return latents + @torch.no_grad() def latent2image( @@ -168,41 +157,6 @@ class DiffusersHolder(): return image - # if output_type == "np": - # return np.asarray(image) - # else: - # return image - - - # # xxx - # if self.use_sd_xl: - # # make sure the VAE is in float32 mode, as it overflows in float16 - # self.pipe.vae.to(dtype=torch.float32) - - # use_torch_2_0_or_xformers = isinstance( - # self.pipe.vae.decoder.mid_block.attentions[0].processor, - # ( - # AttnProcessor2_0, - # XFormersAttnProcessor, - # LoRAXFormersAttnProcessor, - # LoRAAttnProcessor2_0, - # ), - # ) - # # if xformers or torch_2_0 is used attention block does not need - # # to be in float32 which can save lots of memory - # if use_torch_2_0_or_xformers: - # self.pipe.vae.post_quant_conv.to(latents.dtype) - # self.pipe.vae.decoder.conv_in.to(latents.dtype) - # self.pipe.vae.decoder.mid_block.to(latents.dtype) - # else: - # 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])[0] - # if output_type == "np": - # return np.asarray(image) - # else: - # return image def prepare_mixing(self, mixing_coeffs, list_latents_mixing): if type(mixing_coeffs) == float: @@ -438,7 +392,7 @@ class DiffusersHolder(): @torch.no_grad() - def run_diffusion_sd_xl( + def run_diffusion_sd_xl_turbo( self, text_embeddings: list, latents_start: torch.FloatTensor, @@ -457,14 +411,12 @@ class DiffusersHolder(): negative_prompt_2 = None num_images_per_prompt = 1 eta = 0.0 - generator = None latents = None prompt_embeds = None negative_prompt_embeds = None pooled_prompt_embeds = None negative_pooled_prompt_embeds = None ip_adapter_image = None - output_type = "pil" return_dict = True cross_attention_kwargs = None guidance_rescale = 0.0 @@ -475,8 +427,6 @@ class DiffusersHolder(): negative_crops_coords_top_left = (0, 0) negative_target_size = None clip_skip = None - callback_on_step_end = None - callback_on_step_end_tensor_inputs = ["latents"] # 0. Default height and width to unet @@ -561,7 +511,6 @@ class DiffusersHolder(): num_warmup_steps = max(len(timesteps) - self.num_inference_steps * self.pipe.scheduler.order, 0) - # 9. Optionally get Guidance Scale Embedding timestep_cond = None if self.pipe.unet.config.time_cond_proj_dim is not None: @@ -586,7 +535,202 @@ class DiffusersHolder(): if i > 0 and list_mixing_coeffs[i] > 0: latents_mixtarget = list_latents_mixing[i - 1].clone() latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i]) + + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if self.pipe.do_classifier_free_guidance else latents + latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t) + + # predict the noise residual + added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} + if ip_adapter_image is not None: + added_cond_kwargs["image_embeds"] = image_embeds + + noise_pred = self.pipe.unet( + latent_model_input, + t, + encoder_hidden_states=prompt_embeds, + timestep_cond=timestep_cond, + cross_attention_kwargs=self.pipe.cross_attention_kwargs, + added_cond_kwargs=added_cond_kwargs, + return_dict=False, + )[0] + + # perform guidance + if self.pipe.do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + self.pipe.guidance_scale * (noise_pred_text - noise_pred_uncond) + + if self.pipe.do_classifier_free_guidance and self.pipe.guidance_rescale > 0.0: + # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf + noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.pipe.guidance_rescale) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] + # Append latents + list_latents_out.append(latents.clone()) + + if return_image: + return self.latent2image(latents) + else: + return list_latents_out + + + + @torch.no_grad() + def run_diffusion_sd_xl( + self, + text_embeddings: tuple, + latents_start: torch.FloatTensor, + idx_start: int = 0, + list_latents_mixing=None, + mixing_coeffs=0.0, + return_image: Optional[bool] = False, + ): + + + prompt_2 = None + height = None + width = None + timesteps = None + denoising_end = None + negative_prompt_2 = None + num_images_per_prompt = 1 + eta = 0.0 + generator = None + latents = None + prompt_embeds = None + negative_prompt_embeds = None + pooled_prompt_embeds = None + negative_pooled_prompt_embeds = None + ip_adapter_image = None + output_type = "pil" + return_dict = True + cross_attention_kwargs = None + guidance_rescale = 0.0 + original_size = None + crops_coords_top_left = (0, 0) + target_size = None + negative_original_size = None + negative_crops_coords_top_left = (0, 0) + negative_target_size = None + clip_skip = None + callback = None + callback_on_step_end = None + callback_on_step_end_tensor_inputs = ["latents"] + # kwargs are additional keyword arguments and don't need a default value set here. + + # 0. Default height and width to unet + height = height or self.pipe.default_sample_size * self.pipe.vae_scale_factor + width = width or self.pipe.default_sample_size * self.pipe.vae_scale_factor + + original_size = original_size or (height, width) + target_size = target_size or (height, width) + + # 1. Check inputs. skipped. + + self.pipe._guidance_scale = self.guidance_scale + self.pipe._guidance_rescale = guidance_rescale + self.pipe._clip_skip = clip_skip + self.pipe._cross_attention_kwargs = cross_attention_kwargs + self.pipe._denoising_end = denoising_end + self.pipe._interrupt = False + + # 2. Define call parameters + list_mixing_coeffs = self.prepare_mixing(mixing_coeffs, list_latents_mixing) + batch_size = 1 + + device = self.pipe._execution_device + + # 3. Encode input prompt + lora_scale = None + ( + prompt_embeds, + negative_prompt_embeds, + pooled_prompt_embeds, + negative_pooled_prompt_embeds, + ) = text_embeddings + + # 4. Prepare timesteps + timesteps, num_inference_steps = retrieve_timesteps(self.pipe.scheduler, self.num_inference_steps, device, timesteps) + + # 5. Prepare latent variables + num_channels_latents = self.pipe.unet.config.in_channels + latents = latents_start.clone() + list_latents_out = [] + + # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta) + + # 7. Prepare added time ids & embeddings + add_text_embeds = pooled_prompt_embeds + if self.pipe.text_encoder_2 is None: + text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) + else: + text_encoder_projection_dim = self.pipe.text_encoder_2.config.projection_dim + + add_time_ids = self.pipe._get_add_time_ids( + original_size, + crops_coords_top_left, + target_size, + dtype=prompt_embeds.dtype, + text_encoder_projection_dim=text_encoder_projection_dim, + ) + if negative_original_size is not None and negative_target_size is not None: + negative_add_time_ids = self.pipe._get_add_time_ids( + negative_original_size, + negative_crops_coords_top_left, + negative_target_size, + dtype=prompt_embeds.dtype, + text_encoder_projection_dim=text_encoder_projection_dim, + ) + else: + negative_add_time_ids = add_time_ids + + if self.pipe.do_classifier_free_guidance: + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) + add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0) + + prompt_embeds = prompt_embeds.to(device) + add_text_embeds = add_text_embeds.to(device) + add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) + + if ip_adapter_image is not None: + output_hidden_state = False if isinstance(self.pipe.unet.encoder_hid_proj, ImageProjection) else True + image_embeds, negative_image_embeds = self.pipe.encode_image( + ip_adapter_image, device, num_images_per_prompt, output_hidden_state + ) + if self.pipe.do_classifier_free_guidance: + image_embeds = torch.cat([negative_image_embeds, image_embeds]) + image_embeds = image_embeds.to(device) + + # 8. Denoising loop + num_warmup_steps = max(len(timesteps) - num_inference_steps * self.pipe.scheduler.order, 0) + + # 9. Optionally get Guidance Scale Embedding + timestep_cond = None + if self.pipe.unet.config.time_cond_proj_dim is not None: + guidance_scale_tensor = torch.tensor(self.pipe.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) + timestep_cond = self.pipe.get_guidance_scale_embedding( + guidance_scale_tensor, embedding_dim=self.pipe.unet.config.time_cond_proj_dim + ).to(device=device, dtype=latents.dtype) + + self.pipe._num_timesteps = len(timesteps) + for i, t in enumerate(timesteps): + # Set the right starting latents + # Write latents out and skip + if i < idx_start: + list_latents_out.append(None) + continue + elif i == idx_start: + latents = latents_start.clone() + + # Mix latents for crossfeeding + if i > 0 and list_mixing_coeffs[i] > 0: + latents_mixtarget = list_latents_mixing[i - 1].clone() + latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i]) + # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if self.pipe.do_classifier_free_guidance else latents @@ -618,86 +762,124 @@ class DiffusersHolder(): # compute the previous noisy sample x_t -> x_t-1 latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] - + # Append latents list_latents_out.append(latents.clone()) - + + + if return_image: return self.latent2image(latents) else: return list_latents_out - + #%% if __name__ == "__main__": from PIL import Image - #%% from diffusers import AutoencoderTiny - # pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0" - pretrained_model_name_or_path = "stabilityai/sdxl-turbo" - pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16) - pipe.to('cuda') # xxx - - #% - pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16) - pipe.vae = pipe.vae.cuda() - #%% - - # # xxx - # self.set_dimensions((512, 512)) - # self.set_num_inference_steps(4) - # self.guidance_scale = 2 - # # self.set_dimensions(1536, 1024) - # latents_start = torch.randn((1,4,64//1,64)).half().cuda() - # # latents_start = self.get_noise() - # list_latents_1 = self.run_diffusion_sd_xl(text_embeddings, latents_start) - # img_orig = self.latent2image(list_latents_1[-1]) - - #%% - + pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0" + # pretrained_model_name_or_path = "stabilityai/sdxl-turbo" + pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16") + pipe.to("cuda") + #% + # pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16) + # pipe.vae = pipe.vae.cuda() + #%% resanity + import time self = DiffusersHolder(pipe) - num_inference_steps = 4 + prompt1 = "photo of underwater landscape, fish, und the sea, incredible detail, high resolution" + negative_prompt = "blurry, ugly, pale" + num_inference_steps = 30 + guidance_scale = 4 + self.set_num_inference_steps(num_inference_steps) - latents_start = self.get_noise() - guidance_scale = 0 - self.guidance_scale = 0 + self.guidance_scale = guidance_scale - #% get embeddings1 - prompt1 = "Photo of a colorful landscape with a blue sky with clouds" - text_embeddings1 = self.get_text_embedding(prompt1) - prompt_embeds1, negative_prompt_embeds1, pooled_prompt_embeds1, negative_pooled_prompt_embeds1 = text_embeddings1 + prefix='full' + for i in range(10): + self.set_negative_prompt(negative_prompt) + + text_embeddings = self.get_text_embedding(prompt1) + latents_start = self.get_noise(np.random.randint(111111)) + + t0 = time.time() - #% get embeddings2 - prompt2 = "Photo of a tree" - text_embeddings2 = self.get_text_embedding(prompt2) - prompt_embeds2, negative_prompt_embeds2, pooled_prompt_embeds2, negative_pooled_prompt_embeds2 = text_embeddings2 + # img_refx = self.pipe(prompt=prompt1, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale)[0] + + img_refx = self.run_diffusion_sd_xl_resanity(text_embeddings=text_embeddings, latents_start=latents_start, return_image=True) + + dt_ref = time.time() - t0 + img_refx.save(f"x_{prefix}_{i}.jpg") + + + - latents1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=False) - img1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True) - img1B = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True) + # xxx + + # self.set_negative_prompt(negative_prompt) + # self.set_num_inference_steps(num_inference_steps) + # text_embeddings1 = self.get_text_embedding(prompt1) + # prompt_embeds1, negative_prompt_embeds1, pooled_prompt_embeds1, negative_pooled_prompt_embeds1 = text_embeddings1 + # latents_start = self.get_noise(420) + # t0 = time.time() + # img_dh = self.run_diffusion_sd_xl_resanity(text_embeddings1, latents_start, idx_start=0, return_image=True) + # dt_dh = time.time() - t0 + + + """ + sth bad in call + sth bad in cond + sth bad in noise + """ + + # xxxx + # #%% + + # self = DiffusersHolder(pipe) + # num_inference_steps = 4 + # self.set_num_inference_steps(num_inference_steps) + # latents_start = self.get_noise(420) + # guidance_scale = 0 + # self.guidance_scale = 0 + + # #% get embeddings1 + # prompt1 = "Photo of a colorful landscape with a blue sky with clouds" + # text_embeddings1 = self.get_text_embedding(prompt1) + # prompt_embeds1, negative_prompt_embeds1, pooled_prompt_embeds1, negative_pooled_prompt_embeds1 = text_embeddings1 + + # #% get embeddings2 + # prompt2 = "Photo of a tree" + # text_embeddings2 = self.get_text_embedding(prompt2) + # prompt_embeds2, negative_prompt_embeds2, pooled_prompt_embeds2, negative_pooled_prompt_embeds2 = text_embeddings2 + + # latents1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=False) + + # img1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True) + # img1B = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True) - # latents2 = self.run_diffusion_sd_xl(text_embeddings2, latents_start, idx_start=0, return_image=False) + # # latents2 = self.run_diffusion_sd_xl(text_embeddings2, latents_start, idx_start=0, return_image=False) - # # check if brings same image if restarted - # img1_return = self.run_diffusion_sd_xl(text_embeddings1, latents1[idx_mix-1], idx_start=idx_start, return_image=True) + # # # check if brings same image if restarted + # # img1_return = self.run_diffusion_sd_xl(text_embeddings1, latents1[idx_mix-1], idx_start=idx_start, return_image=True) - # mix latents - #%% - idx_mix = 2 - fract=0.8 - latents_start_mixed = interpolate_spherical(latents1[idx_mix-1], latents2[idx_mix-1], fract) - prompt_embeds = interpolate_spherical(prompt_embeds1, prompt_embeds2, fract) - pooled_prompt_embeds = interpolate_spherical(pooled_prompt_embeds1, pooled_prompt_embeds2, fract) - negative_prompt_embeds = negative_prompt_embeds1 - negative_pooled_prompt_embeds = negative_pooled_prompt_embeds1 - text_embeddings_mix = [prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds] + # # mix latents + # #%% + # idx_mix = 2 + # fract=0.8 + # latents_start_mixed = interpolate_spherical(latents1[idx_mix-1], latents2[idx_mix-1], fract) + # prompt_embeds = interpolate_spherical(prompt_embeds1, prompt_embeds2, fract) + # pooled_prompt_embeds = interpolate_spherical(pooled_prompt_embeds1, pooled_prompt_embeds2, fract) + # negative_prompt_embeds = negative_prompt_embeds1 + # negative_pooled_prompt_embeds = negative_pooled_prompt_embeds1 + # text_embeddings_mix = [prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds] - self.run_diffusion_sd_xl(text_embeddings_mix, latents_start_mixed, idx_start=idx_start, return_image=True) + # self.run_diffusion_sd_xl(text_embeddings_mix, latents_start_mixed, idx_start=idx_start, return_image=True)