mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
0240656361
- get_perlin_noise() was returning 9 channels; fixed code to return noise for just the 4 image channels and not the mask ones. - Closes Issue #2541
66 lines
2.6 KiB
Python
66 lines
2.6 KiB
Python
'''
|
|
ldm.invoke.generator.img2img descends from ldm.invoke.generator
|
|
'''
|
|
|
|
import torch
|
|
from diffusers import logging
|
|
|
|
from ldm.invoke.generator.base import Generator
|
|
from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline, ConditioningData
|
|
from ldm.models.diffusion.shared_invokeai_diffusion import ThresholdSettings
|
|
|
|
|
|
class Img2Img(Generator):
|
|
def __init__(self, model, precision):
|
|
super().__init__(model, precision)
|
|
self.init_latent = None # by get_noise()
|
|
|
|
def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
|
|
conditioning,init_image,strength,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.
|
|
"""
|
|
self.perlin = perlin
|
|
|
|
# noinspection PyTypeChecker
|
|
pipeline: StableDiffusionGeneratorPipeline = self.model
|
|
pipeline.scheduler = sampler
|
|
|
|
uc, c, extra_conditioning_info = conditioning
|
|
conditioning_data = (
|
|
ConditioningData(
|
|
uc, c, cfg_scale, extra_conditioning_info,
|
|
threshold = ThresholdSettings(threshold, warmup=0.2) if threshold else None)
|
|
.add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
|
|
|
|
|
|
def make_image(x_T):
|
|
# FIXME: use x_T for initial seeded noise
|
|
# We're not at the moment because the pipeline automatically resizes init_image if
|
|
# necessary, which the x_T input might not match.
|
|
logging.set_verbosity_error() # quench safety check warnings
|
|
pipeline_output = pipeline.img2img_from_embeddings(
|
|
init_image, strength, steps, conditioning_data,
|
|
noise_func=self.get_noise_like,
|
|
callback=step_callback
|
|
)
|
|
if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
|
|
attention_maps_callback(pipeline_output.attention_map_saver)
|
|
return pipeline.numpy_to_pil(pipeline_output.images)[0]
|
|
|
|
return make_image
|
|
|
|
def get_noise_like(self, like: torch.Tensor):
|
|
device = like.device
|
|
if device.type == 'mps':
|
|
x = torch.randn_like(like, device='cpu').to(device)
|
|
else:
|
|
x = torch.randn_like(like, device=device)
|
|
if self.perlin > 0.0:
|
|
shape = like.shape
|
|
x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
|
|
return x
|