latentblending/diffusers_holder.py

818 lines
33 KiB
Python

# Copyright 2022 Lunar Ring. All rights reserved.
# Written by Johannes Stelzer, email stelzer@lunar-ring.ai twitter @j_stelzer
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import numpy as np
import warnings
from typing import Optional
from utils import interpolate_spherical
from diffusers import DiffusionPipeline, StableDiffusionControlNetPipeline, ControlNetModel
from diffusers.models.attention_processor import (
AttnProcessor2_0,
LoRAAttnProcessor2_0,
LoRAXFormersAttnProcessor,
XFormersAttnProcessor,
)
from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import retrieve_timesteps
warnings.filterwarnings('ignore')
torch.backends.cudnn.benchmark = False
torch.set_grad_enabled(False)
class DiffusersHolder():
def __init__(self, pipe):
# Base settings
self.negative_prompt = ""
self.guidance_scale = 5.0
self.num_inference_steps = 30
# Check if valid pipe
self.pipe = pipe
self.device = str(pipe._execution_device)
self.init_types()
self.width_latent = self.pipe.unet.config.sample_size
self.height_latent = self.pipe.unet.config.sample_size
self.width_img = self.width_latent * self.pipe.vae_scale_factor
self.height_img = self.height_latent * self.pipe.vae_scale_factor
def init_types(self):
assert hasattr(self.pipe, "__class__"), "No valid diffusers pipeline found."
assert hasattr(self.pipe.__class__, "__name__"), "No valid diffusers pipeline found."
if self.pipe.__class__.__name__ == 'StableDiffusionXLPipeline':
self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
self.use_sd_xl = True
prompt_embeds, _, _, _ = self.pipe.encode_prompt("test")
else:
self.use_sd_xl = False
prompt_embeds = self.pipe._encode_prompt("test", self.device, 1, True)
self.dtype = prompt_embeds.dtype
def set_num_inference_steps(self, num_inference_steps):
self.num_inference_steps = num_inference_steps
if self.use_sd_xl:
self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
def set_dimensions(self, size_output):
s = self.pipe.vae_scale_factor
if size_output is None:
width = self.pipe.unet.config.sample_size
height = self.pipe.unet.config.sample_size
else:
width, height = size_output
self.width_img = int(round(width / s) * s)
self.width_latent = int(self.width_img / s)
self.height_img = int(round(height / s) * s)
self.height_latent = int(self.height_img / s)
print(f"set_dimensions to width={width} and height={height}")
def set_negative_prompt(self, negative_prompt):
r"""Set the negative prompt. Currenty only one negative prompt is supported
"""
if isinstance(negative_prompt, str):
self.negative_prompt = [negative_prompt]
else:
self.negative_prompt = negative_prompt
if len(self.negative_prompt) > 1:
self.negative_prompt = [self.negative_prompt[0]]
def get_text_embedding(self, prompt, do_classifier_free_guidance=True):
if self.use_sd_xl:
pr_encoder = self.pipe.encode_prompt
else:
pr_encoder = self.pipe._encode_prompt
prompt_embeds = pr_encoder(
prompt=prompt,
prompt_2=prompt,
device=self.device,
num_images_per_prompt=1,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=self.negative_prompt,
negative_prompt_2=self.negative_prompt,
prompt_embeds=None,
negative_prompt_embeds=None,
pooled_prompt_embeds=None,
lora_scale=None,
clip_skip=False,
)
return prompt_embeds
def get_noise(self, seed=420):
generator = torch.Generator(device=self.device).manual_seed(int(seed))
latents = self.pipe.prepare_latents(
1,
self.pipe.unet.config.in_channels,
self.height_img,
self.width_img,
torch.float16,
self.pipe._execution_device,
generator,
None,
)
return latents
# H = self.height_latent
# W = self.width_latent
# C = self.pipe.unet.config.in_channels
# generator = torch.Generator(device=self.device).manual_seed(int(seed))
# latents = torch.randn((1, C, H, W), generator=generator, dtype=self.dtype, device=self.device)
# if self.use_sd_xl:
# latents = latents * self.pipe.scheduler.init_noise_sigma
return latents
@torch.no_grad()
def latent2image(
self,
latents: torch.FloatTensor,
output_type="pil"):
r"""
Returns an image provided a latent representation from diffusion.
Args:
latents: torch.FloatTensor
Result of the diffusion process.
output_type: "pil" or "np"
"""
assert output_type in ["pil", "np"]
# make sure the VAE is in float32 mode, as it overflows in float16
needs_upcasting = self.pipe.vae.dtype == torch.float16 and self.pipe.vae.config.force_upcast
if needs_upcasting:
self.pipe.upcast_vae()
latents = latents.to(next(iter(self.pipe.vae.post_quant_conv.parameters())).dtype)
image = self.pipe.vae.decode(latents / self.pipe.vae.config.scaling_factor, return_dict=False)[0]
# cast back to fp16 if needed
if needs_upcasting:
self.pipe.vae.to(dtype=torch.float16)
image = self.pipe.image_processor.postprocess(image, output_type=output_type)[0]
return image
# if output_type == "np":
# return np.asarray(image)
# else:
# return image
# # xxx
# if self.use_sd_xl:
# # make sure the VAE is in float32 mode, as it overflows in float16
# self.pipe.vae.to(dtype=torch.float32)
# use_torch_2_0_or_xformers = isinstance(
# self.pipe.vae.decoder.mid_block.attentions[0].processor,
# (
# AttnProcessor2_0,
# XFormersAttnProcessor,
# LoRAXFormersAttnProcessor,
# LoRAAttnProcessor2_0,
# ),
# )
# # if xformers or torch_2_0 is used attention block does not need
# # to be in float32 which can save lots of memory
# if use_torch_2_0_or_xformers:
# self.pipe.vae.post_quant_conv.to(latents.dtype)
# self.pipe.vae.decoder.conv_in.to(latents.dtype)
# self.pipe.vae.decoder.mid_block.to(latents.dtype)
# else:
# latents = latents.float()
# image = self.pipe.vae.decode(latents / self.pipe.vae.config.scaling_factor, return_dict=False)[0]
# image = self.pipe.image_processor.postprocess(image, output_type="pil", do_denormalize=[True] * image.shape[0])[0]
# if output_type == "np":
# return np.asarray(image)
# else:
# return image
def prepare_mixing(self, mixing_coeffs, list_latents_mixing):
if type(mixing_coeffs) == float:
list_mixing_coeffs = (1 + self.num_inference_steps) * [mixing_coeffs]
elif type(mixing_coeffs) == list:
assert len(mixing_coeffs) == self.num_inference_steps, f"len(mixing_coeffs) {len(mixing_coeffs)} != self.num_inference_steps {self.num_inference_steps}"
list_mixing_coeffs = mixing_coeffs
else:
raise ValueError("mixing_coeffs should be float or list with len=num_inference_steps")
if np.sum(list_mixing_coeffs) > 0:
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}"
return list_mixing_coeffs
@torch.no_grad()
def run_diffusion(
self,
text_embeddings: torch.FloatTensor,
latents_start: torch.FloatTensor,
idx_start: int = 0,
list_latents_mixing=None,
mixing_coeffs=0.0,
return_image: Optional[bool] = False):
if self.pipe.__class__.__name__ == 'StableDiffusionXLPipeline':
return self.run_diffusion_sd_xl(text_embeddings, latents_start, idx_start, list_latents_mixing, mixing_coeffs, return_image)
elif self.pipe.__class__.__name__ == 'StableDiffusionPipeline':
return self.run_diffusion_sd12x(text_embeddings, latents_start, idx_start, list_latents_mixing, mixing_coeffs, return_image)
elif self.pipe.__class__.__name__ == 'StableDiffusionControlNetPipeline':
pass
@torch.no_grad()
def run_diffusion_sd12x(
self,
text_embeddings: torch.FloatTensor,
latents_start: torch.FloatTensor,
idx_start: int = 0,
list_latents_mixing=None,
mixing_coeffs=0.0,
return_image: Optional[bool] = False):
list_mixing_coeffs = self.prepare_mixing()
do_classifier_free_guidance = self.guidance_scale > 1.0
# accomodate different sd model types
self.pipe.scheduler.set_timesteps(self.num_inference_steps - 1, device=self.device)
timesteps = self.pipe.scheduler.timesteps
if len(timesteps) != self.num_inference_steps:
self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
timesteps = self.pipe.scheduler.timesteps
latents = latents_start.clone()
list_latents_out = []
for i, t in enumerate(timesteps):
# Set the right starting latents
elif i == idx_start:
latents = latents_start.clone()
# Mix latents
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])
if i < idx_start:
list_latents_out.append(latents)
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.pipe.unet(
latent_model_input,
t,
encoder_hidden_states=text_embeddings,
return_dict=False,
)[0]
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, return_dict=False)[0]
list_latents_out.append(latents.clone())
if return_image:
return self.latent2image(latents)
else:
return list_latents_out
@torch.no_grad()
def run_diffusion_controlnet(
self,
conditioning: list,
latents_start: torch.FloatTensor,
idx_start: int = 0,
list_latents_mixing=None,
mixing_coeffs=0.0,
return_image: Optional[bool] = False):
prompt_embeds = conditioning[0]
image = conditioning[1]
list_mixing_coeffs = self.prepare_mixing()
controlnet = self.pipe.controlnet
control_guidance_start = [0.0]
control_guidance_end = [1.0]
guess_mode = False
num_images_per_prompt = 1
batch_size = 1
eta = 0.0
controlnet_conditioning_scale = 1.0
# align format for control guidance
if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
control_guidance_start = len(control_guidance_end) * [control_guidance_start]
elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
control_guidance_end = len(control_guidance_start) * [control_guidance_end]
# 2. Define call parameters
device = self.pipe._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = self.guidance_scale > 1.0
# 4. Prepare image
image = self.pipe.prepare_image(
image=image,
width=None,
height=None,
batch_size=batch_size * num_images_per_prompt,
num_images_per_prompt=num_images_per_prompt,
device=self.device,
dtype=controlnet.dtype,
do_classifier_free_guidance=do_classifier_free_guidance,
guess_mode=guess_mode,
)
height, width = image.shape[-2:]
# 5. Prepare timesteps
self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
timesteps = self.pipe.scheduler.timesteps
# 6. Prepare latent variables
generator = torch.Generator(device=self.device).manual_seed(int(420))
latents = latents_start.clone()
list_latents_out = []
# 7. 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)
# 7.1 Create tensor stating which controlnets to keep
controlnet_keep = []
for i in range(len(timesteps)):
keeps = [
1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
for s, e in zip(control_guidance_start, control_guidance_end)
]
controlnet_keep.append(keeps[0] if len(keeps) == 1 else keeps)
# 8. Denoising loop
for i, t in enumerate(timesteps):
if i < idx_start:
list_latents_out.append(None)
continue
elif i == idx_start:
latents = latents_start.clone()
# Mix latents for crossfeeding
if i > 0 and list_mixing_coeffs[i] > 0:
latents_mixtarget = list_latents_mixing[i - 1].clone()
latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i])
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t)
control_model_input = latent_model_input
controlnet_prompt_embeds = prompt_embeds
if isinstance(controlnet_keep[i], list):
cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
else:
cond_scale = controlnet_conditioning_scale * controlnet_keep[i]
down_block_res_samples, mid_block_res_sample = self.pipe.controlnet(
control_model_input,
t,
encoder_hidden_states=controlnet_prompt_embeds,
controlnet_cond=image,
conditioning_scale=cond_scale,
guess_mode=guess_mode,
return_dict=False,
)
if guess_mode and do_classifier_free_guidance:
# Infered ControlNet only for the conditional batch.
# To apply the output of ControlNet to both the unconditional and conditional batches,
# add 0 to the unconditional batch to keep it unchanged.
down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
# predict the noise residual
noise_pred = self.pipe.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=None,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
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
@torch.no_grad()
def run_diffusion_sd_xl(
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,
):
timesteps = None
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:
list_latents_out.append(None)
continue
elif i == idx_start:
latents = latents_start.clone()
# Mix latents for crossfeeding
if i > 0 and list_mixing_coeffs[i] > 0:
latents_mixtarget = list_latents_mixing[i - 1].clone()
latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i])
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if self.pipe.do_classifier_free_guidance else latents
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:
list_latents_out.append(None)
continue
elif i == idx_start:
latents = latents_start.clone()
# Mix latents for crossfeeding
if i > 0 and list_mixing_coeffs[i] > 0:
latents_mixtarget = list_latents_mixing[i - 1].clone()
latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i])
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2)# if 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)