mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
7e8f364d8d
- All tensors in diffusers code path are now set explicitly to float32 or float16, depending on the --precision flag. - autocast is still used in the ckpt path, since it is being deprecated.
91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
'''
|
|
ldm.invoke.ckpt_generator.txt2img inherits from ldm.invoke.ckpt_generator
|
|
'''
|
|
|
|
import torch
|
|
import numpy as np
|
|
from ldm.invoke.ckpt_generator.base import CkptGenerator
|
|
from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
|
|
import gc
|
|
|
|
|
|
class CkptTxt2Img(CkptGenerator):
|
|
def __init__(self, model, precision):
|
|
super().__init__(model, precision)
|
|
|
|
@torch.no_grad()
|
|
def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
|
|
conditioning,width,height,step_callback=None,threshold=0.0,perlin=0.0,
|
|
attention_maps_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'
|
|
"""
|
|
self.perlin = perlin
|
|
uc, c, extra_conditioning_info = conditioning
|
|
|
|
@torch.no_grad()
|
|
def make_image(x_T):
|
|
shape = [
|
|
self.latent_channels,
|
|
height // self.downsampling_factor,
|
|
width // self.downsampling_factor,
|
|
]
|
|
|
|
if self.free_gpu_mem and self.model.model.device != self.model.device:
|
|
self.model.model.to(self.model.device)
|
|
|
|
sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
|
|
|
|
samples, _ = sampler.sample(
|
|
batch_size = 1,
|
|
S = steps,
|
|
x_T = x_T,
|
|
conditioning = c,
|
|
shape = shape,
|
|
verbose = False,
|
|
unconditional_guidance_scale = cfg_scale,
|
|
unconditional_conditioning = uc,
|
|
extra_conditioning_info = extra_conditioning_info,
|
|
eta = ddim_eta,
|
|
img_callback = step_callback,
|
|
threshold = threshold,
|
|
attention_maps_callback = attention_maps_callback,
|
|
)
|
|
|
|
if self.free_gpu_mem:
|
|
self.model.model.to('cpu')
|
|
self.model.cond_stage_model.device = 'cpu'
|
|
self.model.cond_stage_model.to('cpu')
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
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 self.use_mps_noise or device.type == 'mps':
|
|
x = torch.randn([1,
|
|
self.latent_channels,
|
|
height // self.downsampling_factor,
|
|
width // self.downsampling_factor],
|
|
dtype=self.torch_dtype(),
|
|
device='cpu').to(device)
|
|
else:
|
|
x = torch.randn([1,
|
|
self.latent_channels,
|
|
height // self.downsampling_factor,
|
|
width // self.downsampling_factor],
|
|
dtype=self.torch_dtype(),
|
|
device=device)
|
|
if self.perlin > 0.0:
|
|
x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width // self.downsampling_factor, height // self.downsampling_factor)
|
|
return x
|
|
|