566 lines
22 KiB
Python
566 lines
22 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 os
|
|
import torch
|
|
torch.backends.cudnn.benchmark = False
|
|
torch.set_grad_enabled(False)
|
|
import numpy as np
|
|
import warnings
|
|
warnings.filterwarnings('ignore')
|
|
import warnings
|
|
import torch
|
|
from PIL import Image
|
|
import torch
|
|
from typing import Optional
|
|
from torch import autocast
|
|
from contextlib import nullcontext
|
|
from utils import interpolate_spherical
|
|
from diffusers import DiffusionPipeline, StableDiffusionControlNetPipeline, ControlNetModel
|
|
from diffusers.models.attention_processor import (
|
|
AttnProcessor2_0,
|
|
LoRAAttnProcessor2_0,
|
|
LoRAXFormersAttnProcessor,
|
|
XFormersAttnProcessor,
|
|
)
|
|
|
|
|
|
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
|
|
|
|
|
|
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, width, height):
|
|
s = self.pipe.vae_scale_factor
|
|
if width is None:
|
|
self.width_latent = self.pipe.unet.config.sample_size
|
|
self.width_img = self.width_latent * self.pipe.vae_scale_factor
|
|
else:
|
|
self.width_img = int(round(width / s) * s)
|
|
self.width_latent = int(self.width_img / s)
|
|
|
|
if height is None:
|
|
self.height_latent = self.pipe.unet.config.sample_size
|
|
self.height_img = self.width_latent * self.pipe.vae_scale_factor
|
|
else:
|
|
self.height_img = int(round(height / s) * s)
|
|
self.height_latent = int(self.height_img / s)
|
|
|
|
|
|
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,
|
|
self.device,
|
|
1,
|
|
do_classifier_free_guidance,
|
|
negative_prompt=self.negative_prompt,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
lora_scale=None,
|
|
)
|
|
return prompt_embeds
|
|
|
|
def get_noise(self, seed=420, mode=None):
|
|
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):
|
|
r"""
|
|
Returns an image provided a latent representation from diffusion.
|
|
Args:
|
|
latents: torch.FloatTensor
|
|
Result of the diffusion process.
|
|
"""
|
|
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])
|
|
|
|
return np.asarray(image[0])
|
|
|
|
@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):
|
|
|
|
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}"
|
|
|
|
do_classifier_free_guidance = self.guidance_scale > 1.0
|
|
|
|
# diffusers bit wiggly
|
|
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
|
|
if i < idx_start:
|
|
list_latents_out.append(None)
|
|
continue
|
|
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])
|
|
|
|
# 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_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):
|
|
|
|
# 0. Default height and width to unet
|
|
original_size = (1024, 1024) # FIXME
|
|
crops_coords_top_left = (0, 0) # FIXME
|
|
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
|
|
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}"
|
|
|
|
# 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
|
|
add_time_ids = self.pipe._get_add_time_ids(
|
|
original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
|
|
)
|
|
|
|
if 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([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)
|
|
|
|
# FIXME guidance_rescale disabled
|
|
|
|
# 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_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]
|
|
|
|
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
|
|
image = Image.open("/home/lugo/glif/lora_models/pretrained_model_name_or_path/value_runwayml_stable-diffusion-v1-5_fabian/fabian_in_the_desert/img_001.jpg")
|
|
|
|
# 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()
|
|
|
|
# 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):
|
|
# 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]
|
|
|
|
image = self.pipe.vae.decode(latents / self.pipe.vae.config.scaling_factor, return_dict=False)[0]
|
|
image, has_nsfw_concept = self.pipe.run_safety_checker(image, device, prompt_embeds.dtype)
|
|
image = self.pipe.image_processor.postprocess(image, output_type="pil")
|
|
return image
|
|
|
|
#%%
|
|
|
|
"""
|
|
steps:
|
|
x get controlnet vanilla running.
|
|
- externalize conditions
|
|
- have conditions as input (use one list)
|
|
- include latent blending
|
|
- test latent blending
|
|
- have lora and latent blending
|
|
|
|
"""
|
|
|
|
|
|
|
|
#%%
|
|
|
|
if __name__ == "__main__":
|
|
|
|
|
|
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble", torch_dtype=torch.float16)
|
|
pipe = StableDiffusionControlNetPipeline.from_pretrained(
|
|
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
|
|
).to("cuda")
|
|
|
|
self = DiffusersHolder(pipe)
|
|
|
|
# get text encoding
|
|
|
|
# get image encoding
|
|
|
|
|
|
|
|
|
|
#%%
|
|
# # pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-0.9"
|
|
# pretrained_model_name_or_path = "stabilityai/stable-diffusion-2-1"
|
|
# pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16)
|
|
# pipe.to('cuda')
|
|
# # xxx
|
|
# self = DiffusersHolder(pipe)
|
|
# # xxx
|
|
# self.set_num_inference_steps(50)
|
|
# # self.set_dimensions(1536, 1024)
|
|
# prompt = "photo of a beautiful cherry forest covered in white flowers, ambient light, very detailed, magic"
|
|
# text_embeddings = self.get_text_embedding(prompt)
|
|
# generator = torch.Generator(device=self.device).manual_seed(int(420))
|
|
# latents_start = self.get_noise()
|
|
# list_latents_1 = self.run_diffusion(text_embeddings, latents_start)
|
|
# img_orig = self.latent2image(list_latents_1[-1])
|
|
|
|
|
|
|
|
# %%
|
|
|
|
"""
|
|
OPEN
|
|
- rename text encodings to conditionings
|
|
- other examples
|
|
- kill upscaling? or keep?
|
|
- cleanup
|
|
- ldh
|
|
- sdh class
|
|
- diffusion holder
|
|
- check linting
|
|
- check docstrings
|
|
- fix readme
|
|
"""
|
|
|
|
|
|
|