822 lines
33 KiB
Python
822 lines
33 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 Optional
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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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self.use_sd_xl = True
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prompt_embeds, _, _, _ = self.pipe.encode_prompt("test")
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else:
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self.use_sd_xl = False
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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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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, 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, 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=prompt,
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prompt_2=prompt,
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device=self.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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lora_scale=None,
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clip_skip=False,
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)
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return prompt_embeds
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def get_noise(self, seed=420):
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generator = torch.Generator(device=self.device).manual_seed(int(seed))
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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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generator,
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None,
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)
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return latents
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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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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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# if output_type == "np":
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# return np.asarray(image)
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# else:
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# return image
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# # xxx
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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])[0]
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# if output_type == "np":
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# return np.asarray(image)
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# else:
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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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if self.pipe.__class__.__name__ == 'StableDiffusionXLPipeline':
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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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elif self.pipe.__class__.__name__ == 'StableDiffusionPipeline':
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return self.run_diffusion_sd12x(text_embeddings, latents_start, idx_start, list_latents_mixing, mixing_coeffs, return_image)
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elif self.pipe.__class__.__name__ == 'StableDiffusionControlNetPipeline':
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pass
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@torch.no_grad()
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def run_diffusion_sd12x(
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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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list_mixing_coeffs = self.prepare_mixing()
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do_classifier_free_guidance = self.guidance_scale > 1.0
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# accomodate different sd model types
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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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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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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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if i < idx_start:
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list_latents_out.append(latents)
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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_controlnet(
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self,
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conditioning: 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_embeds = conditioning[0]
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image = conditioning[1]
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list_mixing_coeffs = self.prepare_mixing()
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controlnet = self.pipe.controlnet
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control_guidance_start = [0.0]
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control_guidance_end = [1.0]
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guess_mode = False
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num_images_per_prompt = 1
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batch_size = 1
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eta = 0.0
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controlnet_conditioning_scale = 1.0
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# align format for control guidance
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if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
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control_guidance_start = len(control_guidance_end) * [control_guidance_start]
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elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
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control_guidance_end = len(control_guidance_start) * [control_guidance_end]
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# 2. Define call parameters
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device = self.pipe._execution_device
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = self.guidance_scale > 1.0
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# 4. Prepare image
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image = self.pipe.prepare_image(
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image=image,
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width=None,
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height=None,
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batch_size=batch_size * num_images_per_prompt,
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num_images_per_prompt=num_images_per_prompt,
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device=self.device,
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dtype=controlnet.dtype,
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do_classifier_free_guidance=do_classifier_free_guidance,
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guess_mode=guess_mode,
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)
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height, width = image.shape[-2:]
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# 5. 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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# 6. Prepare latent variables
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generator = torch.Generator(device=self.device).manual_seed(int(420))
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latents = latents_start.clone()
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list_latents_out = []
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# 7. 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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# 7.1 Create tensor stating which controlnets to keep
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controlnet_keep = []
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for i in range(len(timesteps)):
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keeps = [
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1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
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for s, e in zip(control_guidance_start, control_guidance_end)
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]
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controlnet_keep.append(keeps[0] if len(keeps) == 1 else keeps)
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# 8. Denoising loop
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for i, t in enumerate(timesteps):
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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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latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t)
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control_model_input = latent_model_input
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controlnet_prompt_embeds = prompt_embeds
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if isinstance(controlnet_keep[i], list):
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cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
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else:
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cond_scale = controlnet_conditioning_scale * controlnet_keep[i]
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down_block_res_samples, mid_block_res_sample = self.pipe.controlnet(
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control_model_input,
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t,
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encoder_hidden_states=controlnet_prompt_embeds,
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controlnet_cond=image,
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conditioning_scale=cond_scale,
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guess_mode=guess_mode,
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return_dict=False,
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)
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if guess_mode and do_classifier_free_guidance:
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# Infered ControlNet only for the conditional batch.
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# To apply the output of ControlNet to both the unconditional and conditional batches,
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# add 0 to the unconditional batch to keep it unchanged.
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down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
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mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
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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=prompt_embeds,
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cross_attention_kwargs=None,
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down_block_additional_residuals=down_block_res_samples,
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mid_block_additional_residual=mid_block_res_sample,
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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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# 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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@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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**kwargs,
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):
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timesteps = None
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|
denoising_end = None
|
|
guidance_scale = 0.0
|
|
negative_prompt = 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_on_step_end = None
|
|
callback_on_step_end_tensor_inputs = ["latents"]
|
|
|
|
# 0. Default height and width to unet
|
|
height = self.pipe.default_sample_size * self.pipe.vae_scale_factor
|
|
width = self.pipe.default_sample_size * self.pipe.vae_scale_factor
|
|
list_mixing_coeffs = self.prepare_mixing(mixing_coeffs, list_latents_mixing)
|
|
|
|
original_size = (height, width)
|
|
target_size = (height, width)
|
|
|
|
# 1. (skipped) Check inputs. Raise error if not correct
|
|
|
|
|
|
self.pipe._guidance_scale = 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
|
|
batch_size = 1
|
|
|
|
device = self.pipe._execution_device
|
|
|
|
# 3. Encode input prompt
|
|
prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = text_embeddings
|
|
|
|
# 4. Prepare timesteps
|
|
timesteps, self.num_inference_steps = retrieve_timesteps(self.pipe.scheduler, self.num_inference_steps, device, timesteps)
|
|
|
|
# 5. Prepare latent variables
|
|
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) - 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:
|
|
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
|
|
if 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])
|
|
|
|
# Write latents out and skip
|
|
if i < idx_start:
|
|
list_latents_out.append(None)
|
|
continue
|
|
|
|
# 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_old(
|
|
self,
|
|
text_embeddings: list,
|
|
latents_start: torch.FloatTensor,
|
|
idx_start: int = 0,
|
|
list_latents_mixing=None,
|
|
mixing_coeffs=0.0,
|
|
return_image: Optional[bool] = False,
|
|
**kwargs,
|
|
):
|
|
# 0. Default height and width to unet
|
|
original_size = (self.width_img, self.height_img)
|
|
crops_coords_top_left = (0, 0)
|
|
target_size = original_size
|
|
batch_size = 1
|
|
eta = 0.0
|
|
num_images_per_prompt = 1
|
|
cross_attention_kwargs = None
|
|
generator = torch.Generator(device=self.device) # dummy generator
|
|
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(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
|
|
|
|
# 4. Prepare timesteps
|
|
self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
|
|
timesteps = self.pipe.scheduler.timesteps
|
|
|
|
# 5. Prepare latent variables
|
|
latents = latents_start.clone()
|
|
list_latents_out = []
|
|
|
|
# 6. Prepare extra step kwargs. usedummy generator
|
|
extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta) # dummy
|
|
|
|
# 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,
|
|
)
|
|
|
|
negative_add_time_ids = add_time_ids
|
|
|
|
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(self.device)
|
|
add_text_embeds = add_text_embeds.to(self.device)
|
|
add_time_ids = add_time_ids.to(self.device).repeat(batch_size * num_images_per_prompt, 1)
|
|
|
|
# 8. Denoising loop
|
|
for i, t in enumerate(timesteps):
|
|
# Set the right starting latents
|
|
if 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])
|
|
|
|
# Write latents out and skip
|
|
if i < idx_start:
|
|
list_latents_out.append(latents)
|
|
continue
|
|
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = torch.cat([latents] * 2)# if do_classifier_free_guidance else latents
|
|
# Always scale 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}
|
|
noise_pred = self.pipe.unet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
added_cond_kwargs=added_cond_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# perform guidance
|
|
if do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, 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()
|
|
#%%
|
|
self = DiffusersHolder(pipe)
|
|
self.set_num_inference_steps(4)
|
|
prompt1 = "Photo of a colorful landscape with a blue sky with clouds"
|
|
text_embeddings1 = self.get_text_embedding(prompt1)
|
|
latents_start = self.get_noise(seed=420)
|
|
latents = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=False)[-1]
|
|
image = self.latent2image(latents)
|
|
|
|
|
|
|
|
xxxx
|
|
# # 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])
|
|
|
|
#%%
|
|
|
|
self = DiffusersHolder(pipe)
|
|
num_inference_steps = 4
|
|
self.set_num_inference_steps(num_inference_steps)
|
|
latents_start = self.get_noise()
|
|
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)
|
|
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)
|
|
|
|
# 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)
|
|
|
|
|
|
|
|
|