mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
4c3858e079
and update associated things in Generate & Generator to not instantly fail when that happens
77 lines
2.9 KiB
Python
77 lines
2.9 KiB
Python
'''
|
|
ldm.invoke.generator.txt2img inherits from ldm.invoke.generator
|
|
'''
|
|
import PIL.Image
|
|
import torch
|
|
|
|
from .base import Generator
|
|
from .diffusers_pipeline import StableDiffusionGeneratorPipeline
|
|
|
|
|
|
class Txt2Img(Generator):
|
|
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,
|
|
**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
|
|
|
|
pipeline = self.model
|
|
# TODO: customize a new pipeline for the given sampler (Scheduler)
|
|
|
|
def make_image(x_T) -> PIL.Image.Image:
|
|
# FIXME: restore free_gpu_mem functionality
|
|
# if self.free_gpu_mem and self.model.model.device != self.model.device:
|
|
# self.model.model.to(self.model.device)
|
|
|
|
# FIXME: how the embeddings are combined should be internal to the pipeline
|
|
combined_text_embeddings = torch.cat([uc, c])
|
|
|
|
pipeline_output = pipeline.image_from_embeddings(
|
|
latents=x_T,
|
|
num_inference_steps=steps,
|
|
text_embeddings=combined_text_embeddings,
|
|
guidance_scale=cfg_scale,
|
|
callback=step_callback,
|
|
# TODO: extra_conditioning_info = extra_conditioning_info,
|
|
# TODO: eta = ddim_eta,
|
|
# TODO: threshold = threshold,
|
|
)
|
|
|
|
# FIXME: restore free_gpu_mem functionality
|
|
# if self.free_gpu_mem:
|
|
# self.model.model.to("cpu")
|
|
|
|
return pipeline.numpy_to_pil(pipeline_output.images)[0]
|
|
|
|
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],
|
|
device='cpu').to(device)
|
|
else:
|
|
x = torch.randn([1,
|
|
self.latent_channels,
|
|
height // self.downsampling_factor,
|
|
width // self.downsampling_factor],
|
|
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
|
|
|