490 lines
19 KiB
Python
490 lines
19 KiB
Python
# 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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import numpy as np
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import warnings
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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from utils import interpolate_spherical
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from diffusers import DiffusionPipeline, StableDiffusionControlNetPipeline, ControlNetModel
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from diffusers.models.attention_processor import (
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AttnProcessor2_0,
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LoRAAttnProcessor2_0,
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LoRAXFormersAttnProcessor,
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XFormersAttnProcessor,
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)
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from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import retrieve_timesteps
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warnings.filterwarnings('ignore')
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torch.backends.cudnn.benchmark = False
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torch.set_grad_enabled(False)
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class DiffusersHolder():
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def __init__(self, pipe):
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# Base settings
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self.negative_prompt = ""
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self.guidance_scale = 5.0
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self.num_inference_steps = 30
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# Check if valid pipe
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self.pipe = pipe
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self.device = str(pipe._execution_device)
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self.init_types()
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self.width_latent = self.pipe.unet.config.sample_size
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self.height_latent = self.pipe.unet.config.sample_size
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self.width_img = self.width_latent * self.pipe.vae_scale_factor
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self.height_img = self.height_latent * self.pipe.vae_scale_factor
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def init_types(self):
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assert hasattr(self.pipe, "__class__"), "No valid diffusers pipeline found."
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assert hasattr(self.pipe.__class__, "__name__"), "No valid diffusers pipeline found."
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if self.pipe.__class__.__name__ == 'StableDiffusionXLPipeline':
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self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
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prompt_embeds, _, _, _ = self.pipe.encode_prompt("test")
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else:
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prompt_embeds = self.pipe._encode_prompt("test", self.device, 1, True)
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self.dtype = prompt_embeds.dtype
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self.is_sdxl_turbo = 'turbo' in self.pipe._name_or_path
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def set_num_inference_steps(self, num_inference_steps):
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self.num_inference_steps = num_inference_steps
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self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
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def set_dimensions(self, size_output):
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s = self.pipe.vae_scale_factor
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if size_output is None:
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width = self.pipe.unet.config.sample_size
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height = self.pipe.unet.config.sample_size
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else:
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width, height = size_output
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self.width_img = int(round(width / s) * s)
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self.width_latent = int(self.width_img / s)
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self.height_img = int(round(height / s) * s)
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self.height_latent = int(self.height_img / s)
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print(f"set_dimensions to width={width} and height={height}")
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def set_negative_prompt(self, negative_prompt):
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r"""Set the negative prompt. Currenty only one negative prompt is supported
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"""
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if isinstance(negative_prompt, str):
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self.negative_prompt = [negative_prompt]
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else:
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self.negative_prompt = negative_prompt
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if len(self.negative_prompt) > 1:
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self.negative_prompt = [self.negative_prompt[0]]
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def get_text_embedding(self, prompt):
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do_classifier_free_guidance = self.guidance_scale > 1 and self.pipe.unet.config.time_cond_proj_dim is None
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text_embeddings = self.pipe.encode_prompt(
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prompt=prompt,
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prompt_2=prompt,
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device=self.pipe._execution_device,
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num_images_per_prompt=1,
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do_classifier_free_guidance=do_classifier_free_guidance,
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negative_prompt=self.negative_prompt,
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negative_prompt_2=self.negative_prompt,
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prompt_embeds=None,
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negative_prompt_embeds=None,
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pooled_prompt_embeds=None,
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negative_pooled_prompt_embeds=None,
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lora_scale=None,
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clip_skip=None,#self.pipe._clip_skip,
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)
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return text_embeddings
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def get_noise(self, seed=420):
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latents = self.pipe.prepare_latents(
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1,
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self.pipe.unet.config.in_channels,
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self.height_img,
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self.width_img,
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torch.float16,
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self.pipe._execution_device,
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torch.Generator(device=self.device).manual_seed(int(seed)),
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None,
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)
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return latents
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@torch.no_grad()
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def latent2image(
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self,
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latents: torch.FloatTensor,
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output_type="pil"):
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r"""
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Returns an image provided a latent representation from diffusion.
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Args:
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latents: torch.FloatTensor
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Result of the diffusion process.
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output_type: "pil" or "np"
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"""
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assert output_type in ["pil", "np"]
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# make sure the VAE is in float32 mode, as it overflows in float16
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needs_upcasting = self.pipe.vae.dtype == torch.float16 and self.pipe.vae.config.force_upcast
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if needs_upcasting:
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self.pipe.upcast_vae()
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latents = latents.to(next(iter(self.pipe.vae.post_quant_conv.parameters())).dtype)
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image = self.pipe.vae.decode(latents / self.pipe.vae.config.scaling_factor, return_dict=False)[0]
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# cast back to fp16 if needed
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if needs_upcasting:
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self.pipe.vae.to(dtype=torch.float16)
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image = self.pipe.image_processor.postprocess(image, output_type=output_type)[0]
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return image
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def prepare_mixing(self, mixing_coeffs, list_latents_mixing):
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if type(mixing_coeffs) == float:
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list_mixing_coeffs = (1 + self.num_inference_steps) * [mixing_coeffs]
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elif type(mixing_coeffs) == list:
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assert len(mixing_coeffs) == self.num_inference_steps, f"len(mixing_coeffs) {len(mixing_coeffs)} != self.num_inference_steps {self.num_inference_steps}"
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list_mixing_coeffs = mixing_coeffs
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else:
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raise ValueError("mixing_coeffs should be float or list with len=num_inference_steps")
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if np.sum(list_mixing_coeffs) > 0:
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assert len(list_latents_mixing) == self.num_inference_steps, f"len(list_latents_mixing) {len(list_latents_mixing)} != self.num_inference_steps {self.num_inference_steps}"
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return list_mixing_coeffs
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@torch.no_grad()
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def run_diffusion(
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self,
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text_embeddings: torch.FloatTensor,
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latents_start: torch.FloatTensor,
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idx_start: int = 0,
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list_latents_mixing=None,
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mixing_coeffs=0.0,
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return_image: Optional[bool] = False):
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return self.run_diffusion_sd_xl(text_embeddings, latents_start, idx_start, list_latents_mixing, mixing_coeffs, return_image)
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@torch.no_grad()
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def run_diffusion_sd_xl(
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self,
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text_embeddings: tuple,
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latents_start: torch.FloatTensor,
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idx_start: int = 0,
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list_latents_mixing=None,
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mixing_coeffs=0.0,
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return_image: Optional[bool] = False,
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):
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prompt_2 = None
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height = None
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width = None
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timesteps = None
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denoising_end = None
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negative_prompt_2 = None
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num_images_per_prompt = 1
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eta = 0.0
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generator = None
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latents = None
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prompt_embeds = None
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negative_prompt_embeds = None
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pooled_prompt_embeds = None
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negative_pooled_prompt_embeds = None
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ip_adapter_image = None
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output_type = "pil"
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return_dict = True
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cross_attention_kwargs = None
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guidance_rescale = 0.0
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original_size = None
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crops_coords_top_left = (0, 0)
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target_size = None
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negative_original_size = None
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negative_crops_coords_top_left = (0, 0)
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negative_target_size = None
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clip_skip = None
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callback = None
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callback_on_step_end = None
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callback_on_step_end_tensor_inputs = ["latents"]
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# kwargs are additional keyword arguments and don't need a default value set here.
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# 0. Default height and width to unet
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height = height or self.pipe.default_sample_size * self.pipe.vae_scale_factor
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width = width or self.pipe.default_sample_size * self.pipe.vae_scale_factor
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original_size = original_size or (height, width)
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target_size = target_size or (height, width)
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# 1. Check inputs. skipped.
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self.pipe._guidance_scale = self.guidance_scale
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self.pipe._guidance_rescale = guidance_rescale
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self.pipe._clip_skip = clip_skip
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self.pipe._cross_attention_kwargs = cross_attention_kwargs
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self.pipe._denoising_end = denoising_end
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self.pipe._interrupt = False
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# 2. Define call parameters
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list_mixing_coeffs = self.prepare_mixing(mixing_coeffs, list_latents_mixing)
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batch_size = 1
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device = self.pipe._execution_device
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# 3. Encode input prompt
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lora_scale = None
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(
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prompt_embeds,
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negative_prompt_embeds,
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pooled_prompt_embeds,
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negative_pooled_prompt_embeds,
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) = text_embeddings
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# 4. Prepare timesteps
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timesteps, num_inference_steps = retrieve_timesteps(self.pipe.scheduler, self.num_inference_steps, device, timesteps)
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# 5. Prepare latent variables
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num_channels_latents = self.pipe.unet.config.in_channels
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latents = latents_start.clone()
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list_latents_out = []
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# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
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extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta)
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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 self.pipe.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([negative_add_time_ids, add_time_ids], dim=0)
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prompt_embeds = prompt_embeds.to(device)
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add_text_embeds = add_text_embeds.to(device)
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add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
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if ip_adapter_image is not None:
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output_hidden_state = False if isinstance(self.pipe.unet.encoder_hid_proj, ImageProjection) else True
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image_embeds, negative_image_embeds = self.pipe.encode_image(
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ip_adapter_image, device, num_images_per_prompt, output_hidden_state
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)
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if self.pipe.do_classifier_free_guidance:
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image_embeds = torch.cat([negative_image_embeds, image_embeds])
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image_embeds = image_embeds.to(device)
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# 8. Denoising loop
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num_warmup_steps = max(len(timesteps) - num_inference_steps * self.pipe.scheduler.order, 0)
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# 9. Optionally get Guidance Scale Embedding
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timestep_cond = None
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if self.pipe.unet.config.time_cond_proj_dim is not None:
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guidance_scale_tensor = torch.tensor(self.pipe.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
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timestep_cond = self.pipe.get_guidance_scale_embedding(
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guidance_scale_tensor, embedding_dim=self.pipe.unet.config.time_cond_proj_dim
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).to(device=device, dtype=latents.dtype)
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self.pipe._num_timesteps = len(timesteps)
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for i, t in enumerate(timesteps):
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# Set the right starting latents
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# Write latents out and skip
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if i < idx_start:
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list_latents_out.append(None)
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continue
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elif i == idx_start:
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latents = latents_start.clone()
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# Mix latents for crossfeeding
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if i > 0 and list_mixing_coeffs[i] > 0:
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latents_mixtarget = list_latents_mixing[i - 1].clone()
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latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i])
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# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([latents] * 2) if self.pipe.do_classifier_free_guidance else latents
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latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t)
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# predict the noise residual
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added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
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if ip_adapter_image is not None:
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added_cond_kwargs["image_embeds"] = image_embeds
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noise_pred = self.pipe.unet(
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latent_model_input,
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t,
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encoder_hidden_states=prompt_embeds,
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timestep_cond=timestep_cond,
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cross_attention_kwargs=self.pipe.cross_attention_kwargs,
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added_cond_kwargs=added_cond_kwargs,
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return_dict=False,
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)[0]
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# perform guidance
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if self.pipe.do_classifier_free_guidance:
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noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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noise_pred = noise_pred_uncond + self.pipe.guidance_scale * (noise_pred_text - noise_pred_uncond)
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if self.pipe.do_classifier_free_guidance and self.pipe.guidance_rescale > 0.0:
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# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
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noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.pipe.guidance_rescale)
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# compute the previous noisy sample x_t -> x_t-1
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latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
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# Append latents
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list_latents_out.append(latents.clone())
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if return_image:
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return self.latent2image(latents)
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else:
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return list_latents_out
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#%%
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if __name__ == "__main__":
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from PIL import Image
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from diffusers import AutoencoderTiny
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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/sdxl-turbo"
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16")
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pipe.to("cuda")
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#%
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# pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16)
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# pipe.vae = pipe.vae.cuda()
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#%% resanity
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import time
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self = DiffusersHolder(pipe)
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prompt1 = "photo of underwater landscape, fish, und the sea, incredible detail, high resolution"
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negative_prompt = "blurry, ugly, pale"
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num_inference_steps = 4
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guidance_scale = 0
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self.set_num_inference_steps(num_inference_steps)
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self.guidance_scale = guidance_scale
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prefix='turbo'
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for i in range(10):
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self.set_negative_prompt(negative_prompt)
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text_embeddings = self.get_text_embedding(prompt1)
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latents_start = self.get_noise(np.random.randint(111111))
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t0 = time.time()
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# img_refx = self.pipe(prompt=prompt1, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale)[0]
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img_refx = self.run_diffusion_sd_xl(text_embeddings=text_embeddings, latents_start=latents_start, return_image=False)
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dt_ref = time.time() - t0
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img_refx.save(f"x_{prefix}_{i}.jpg")
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# xxx
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# self.set_negative_prompt(negative_prompt)
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# self.set_num_inference_steps(num_inference_steps)
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# text_embeddings1 = self.get_text_embedding(prompt1)
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# prompt_embeds1, negative_prompt_embeds1, pooled_prompt_embeds1, negative_pooled_prompt_embeds1 = text_embeddings1
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# latents_start = self.get_noise(420)
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# t0 = time.time()
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# img_dh = self.run_diffusion_sd_xl_resanity(text_embeddings1, latents_start, idx_start=0, return_image=True)
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# dt_dh = time.time() - t0
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# xxxx
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# #%%
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# self = DiffusersHolder(pipe)
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# num_inference_steps = 4
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# self.set_num_inference_steps(num_inference_steps)
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# latents_start = self.get_noise(420)
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# guidance_scale = 0
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# self.guidance_scale = 0
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# #% get embeddings1
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# prompt1 = "Photo of a colorful landscape with a blue sky with clouds"
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# text_embeddings1 = self.get_text_embedding(prompt1)
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# prompt_embeds1, negative_prompt_embeds1, pooled_prompt_embeds1, negative_pooled_prompt_embeds1 = text_embeddings1
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# #% get embeddings2
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# prompt2 = "Photo of a tree"
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# text_embeddings2 = self.get_text_embedding(prompt2)
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# prompt_embeds2, negative_prompt_embeds2, pooled_prompt_embeds2, negative_pooled_prompt_embeds2 = text_embeddings2
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# latents1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=False)
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# img1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True)
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# img1B = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True)
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# # latents2 = self.run_diffusion_sd_xl(text_embeddings2, latents_start, idx_start=0, return_image=False)
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# # # check if brings same image if restarted
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# # img1_return = self.run_diffusion_sd_xl(text_embeddings1, latents1[idx_mix-1], idx_start=idx_start, return_image=True)
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# # mix latents
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# #%%
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# idx_mix = 2
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# fract=0.8
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# latents_start_mixed = interpolate_spherical(latents1[idx_mix-1], latents2[idx_mix-1], fract)
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# prompt_embeds = interpolate_spherical(prompt_embeds1, prompt_embeds2, fract)
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# pooled_prompt_embeds = interpolate_spherical(pooled_prompt_embeds1, pooled_prompt_embeds2, fract)
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# negative_prompt_embeds = negative_prompt_embeds1
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# negative_pooled_prompt_embeds = negative_pooled_prompt_embeds1
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# text_embeddings_mix = [prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds]
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# self.run_diffusion_sd_xl(text_embeddings_mix, latents_start_mixed, idx_start=idx_start, return_image=True)
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