mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
a626533cd4
Add message about interpolation size Fix crash if sampler not set to DDIM, change parameter name to hires_fix Hi res mode fix duplicates with img2img scaling
127 lines
4.5 KiB
Python
127 lines
4.5 KiB
Python
'''
|
|
ldm.dream.generator.txt2img inherits from ldm.dream.generator
|
|
'''
|
|
|
|
import torch
|
|
import numpy as np
|
|
import math
|
|
from ldm.dream.generator.base import Generator
|
|
from ldm.models.diffusion.ddim import DDIMSampler
|
|
|
|
|
|
class Txt2Img2Img(Generator):
|
|
def __init__(self, model, precision):
|
|
super().__init__(model, precision)
|
|
self.init_latent = None # for get_noise()
|
|
|
|
@torch.no_grad()
|
|
def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
|
|
conditioning,width,height,strength,step_callback=None,**kwargs):
|
|
"""
|
|
Returns a function returning an image derived from the prompt and the initial image
|
|
Return value depends on the seed at the time you call it
|
|
kwargs are 'width' and 'height'
|
|
"""
|
|
uc, c = conditioning
|
|
|
|
@torch.no_grad()
|
|
def make_image(x_T):
|
|
|
|
trained_square = 512 * 512
|
|
actual_square = width * height
|
|
scale = math.sqrt(trained_square / actual_square)
|
|
|
|
init_width = math.ceil(scale * width / 64) * 64
|
|
init_height = math.ceil(scale * height / 64) * 64
|
|
|
|
shape = [
|
|
self.latent_channels,
|
|
init_height // self.downsampling_factor,
|
|
init_width // self.downsampling_factor,
|
|
]
|
|
|
|
x = self.get_noise(init_width, init_height)
|
|
|
|
if self.free_gpu_mem and self.model.model.device != self.model.device:
|
|
self.model.model.to(self.model.device)
|
|
|
|
samples, _ = sampler.sample(
|
|
batch_size = 1,
|
|
S = steps,
|
|
x_T = x,
|
|
conditioning = c,
|
|
shape = shape,
|
|
verbose = False,
|
|
unconditional_guidance_scale = cfg_scale,
|
|
unconditional_conditioning = uc,
|
|
eta = ddim_eta,
|
|
img_callback = step_callback
|
|
)
|
|
|
|
print(
|
|
f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height}"
|
|
)
|
|
|
|
# resizing
|
|
samples = torch.nn.functional.interpolate(
|
|
samples,
|
|
size=(height // self.downsampling_factor, width // self.downsampling_factor),
|
|
mode="bilinear"
|
|
)
|
|
|
|
t_enc = int(strength * steps)
|
|
|
|
x = None
|
|
|
|
# Other samplers not supported yet, so ignore previous sampler
|
|
if not isinstance(sampler,DDIMSampler):
|
|
print(
|
|
f"\n>> Sampler '{sampler.__class__.__name__}' is not yet supported for img2img. Using DDIM sampler"
|
|
)
|
|
img_sampler = DDIMSampler(self.model, device=self.model.device)
|
|
img_sampler.make_schedule(
|
|
ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
|
|
)
|
|
else:
|
|
img_sampler = sampler
|
|
|
|
z_enc = img_sampler.stochastic_encode(
|
|
samples,
|
|
torch.tensor([t_enc]).to(self.model.device),
|
|
noise=x_T
|
|
)
|
|
|
|
# decode it
|
|
samples = img_sampler.decode(
|
|
z_enc,
|
|
c,
|
|
t_enc,
|
|
img_callback = step_callback,
|
|
unconditional_guidance_scale=cfg_scale,
|
|
unconditional_conditioning=uc,
|
|
)
|
|
|
|
if self.free_gpu_mem:
|
|
self.model.model.to("cpu")
|
|
|
|
return self.sample_to_image(samples)
|
|
|
|
return make_image
|
|
|
|
|
|
# returns a tensor filled with random numbers from a normal distribution
|
|
def get_noise(self,width,height):
|
|
device = self.model.device
|
|
if device.type == 'mps':
|
|
return torch.randn([1,
|
|
self.latent_channels,
|
|
height // self.downsampling_factor,
|
|
width // self.downsampling_factor],
|
|
device='cpu').to(device)
|
|
else:
|
|
return torch.randn([1,
|
|
self.latent_channels,
|
|
height // self.downsampling_factor,
|
|
width // self.downsampling_factor],
|
|
device=device)
|