417 lines
16 KiB
Python
417 lines
16 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 os
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import torch
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torch.backends.cudnn.benchmark = False
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torch.set_grad_enabled(False)
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import numpy as np
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import warnings
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warnings.filterwarnings('ignore')
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import warnings
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import torch
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from PIL import Image
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import torch
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from typing import Optional
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from torch import autocast
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from contextlib import nullcontext
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from utils import interpolate_spherical
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from diffusers import DiffusionPipeline
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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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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_type_pipe()
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self.init_dtype()
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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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def init_type_pipe(self):
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self.type_pipe = "StableDiffusionXLPipeline"
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if self.type_pipe == "StableDiffusionXLPipeline":
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self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
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self.use_sd_xl = True
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else:
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self.use_sd_xl = False
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def init_dtype(self):
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if self.type_pipe == "StableDiffusionXLPipeline":
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prompt_embeds, _, _, _ = self.pipe.encode_prompt("test")
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self.dtype = prompt_embeds.dtype
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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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if self.use_sd_xl:
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self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
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def set_dimensions(self, width, height):
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s = self.pipe.vae_scale_factor
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if width is None:
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self.width_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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else:
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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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if height is None:
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self.height_latent = self.pipe.unet.config.sample_size
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self.height_img = self.width_latent * self.pipe.vae_scale_factor
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else:
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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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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, do_classifier_free_guidance=True):
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if self.use_sd_xl:
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pr_encoder = self.pipe.encode_prompt
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else:
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pr_encoder = self.pipe._encode_prompt
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prompt_embeds = pr_encoder(
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prompt,
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self.device,
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1,
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do_classifier_free_guidance,
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negative_prompt=self.negative_prompt,
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prompt_embeds=None,
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negative_prompt_embeds=None,
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lora_scale=None,
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)
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return prompt_embeds
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def get_noise(self, seed=420, mode=None):
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H = self.height_latent
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W = self.width_latent
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C = self.pipe.unet.config.in_channels
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generator = torch.Generator(device=self.device).manual_seed(int(seed))
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latents = torch.randn((1, C, H, W), generator=generator, dtype=self.dtype, device=self.device)
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if self.use_sd_xl:
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latents = latents * self.pipe.scheduler.init_noise_sigma
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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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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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"""
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if self.use_sd_xl:
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# make sure the VAE is in float32 mode, as it overflows in float16
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self.pipe.vae.to(dtype=torch.float32)
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use_torch_2_0_or_xformers = isinstance(
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self.pipe.vae.decoder.mid_block.attentions[0].processor,
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(
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AttnProcessor2_0,
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XFormersAttnProcessor,
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LoRAXFormersAttnProcessor,
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LoRAAttnProcessor2_0,
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),
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)
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# if xformers or torch_2_0 is used attention block does not need
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# to be in float32 which can save lots of memory
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if use_torch_2_0_or_xformers:
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self.pipe.vae.post_quant_conv.to(latents.dtype)
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self.pipe.vae.decoder.conv_in.to(latents.dtype)
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self.pipe.vae.decoder.mid_block.to(latents.dtype)
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else:
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latents = latents.float()
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image = self.pipe.vae.decode(latents / self.pipe.vae.config.scaling_factor, return_dict=False)[0]
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image = self.pipe.image_processor.postprocess(image, output_type="pil", do_denormalize=[True] * image.shape[0])
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return np.asarray(image[0])
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@torch.no_grad()
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def run_diffusion_standard(
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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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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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do_classifier_free_guidance = self.guidance_scale > 1.0
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# diffusers bit wiggly
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self.pipe.scheduler.set_timesteps(self.num_inference_steps-1, device=self.device)
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timesteps = self.pipe.scheduler.timesteps
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if len(timesteps) != self.num_inference_steps:
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self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
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timesteps = self.pipe.scheduler.timesteps
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latents = latents_start.clone()
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list_latents_out = []
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num_warmup_steps = len(timesteps) - self.num_inference_steps * self.pipe.scheduler.order
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for i, t in enumerate(timesteps):
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# Set the right starting latents
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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
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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 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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noise_pred = self.pipe.unet(
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latent_model_input,
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t,
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encoder_hidden_states=text_embeddings,
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return_dict=False,
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)[0]
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if 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.guidance_scale * (noise_pred_text - noise_pred_uncond)
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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, return_dict=False)[0]
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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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@torch.no_grad()
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def run_diffusion_sd_xl(
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self,
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text_embeddings: list,
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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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# prompt = "photo of a house"
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# self.num_inference_steps = 50
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# mixing_coeffs= 0.0
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# idx_start= 0
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# latents_start = self.get_noise()
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# text_embeddings = self.pipe.encode_prompt(
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# prompt,
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# self.device,
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# num_images_per_prompt=1,
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# do_classifier_free_guidance=True,
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# 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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# )
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# 0. Default height and width to unet
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original_size = (1024, 1024) # FIXME
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crops_coords_top_left = (0, 0) # FIXME
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target_size = original_size
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batch_size = 1
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eta = 0.0
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num_images_per_prompt = 1
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cross_attention_kwargs = None
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generator = torch.Generator(device=self.device) # dummy generator
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do_classifier_free_guidance = self.guidance_scale > 1.0
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# 1. Check inputs. Raise error if not correct & 2. Define call parameters
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# FIXME see if check_inputs use
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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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# 3. Encode input prompt (already encoded outside bc of mixing, just split here)
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prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = text_embeddings
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# 4. Prepare timesteps
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self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
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timesteps = self.pipe.scheduler.timesteps
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# 5. Prepare latent variables
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latents = latents_start.clone()
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list_latents_out = []
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# 6. Prepare extra step kwargs. usedummy generator
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extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta) # dummy
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# 7. Prepare added time ids & embeddings
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add_text_embeds = pooled_prompt_embeds
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add_time_ids = self.pipe._get_add_time_ids(
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original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
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)
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if do_classifier_free_guidance:
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prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
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add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
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add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
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prompt_embeds = prompt_embeds.to(self.device)
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add_text_embeds = add_text_embeds.to(self.device)
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add_time_ids = add_time_ids.to(self.device).repeat(batch_size * num_images_per_prompt, 1)
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# 8. Denoising loop
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for i, t in enumerate(timesteps):
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# Set the right starting latents
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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 do_classifier_free_guidance else latents
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# Always scale 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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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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cross_attention_kwargs=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 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.guidance_scale * (noise_pred_text - noise_pred_uncond)
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# FIXME guidance_rescale disabled
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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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pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-0.9"
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16)
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pipe.to('cuda')
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# xxx
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self = DiffusersHolder(pipe)
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# xxx
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self.set_num_inference_steps(50)
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self.set_dimensions(1536, 1024)
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prompt = "photo of a beautiful cherry forest covered in white flowers, ambient light, very detailed, magic"
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text_embeddings = self.get_text_embedding(prompt)
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generator = torch.Generator(device=self.device).manual_seed(int(420))
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latents_start = self.get_noise()
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list_latents_1 = self.run_diffusion_sd_xl(text_embeddings, latents_start)
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img_orig = self.latent2image(list_latents_1[-1])
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# %%
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"""
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OPEN
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- other examples
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- kill upscaling? or keep?
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- cleanup
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- ldh
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- sdh class
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- diffusion holder
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- check linting
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- check docstrings
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- fix readme
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"""
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