mirror of
https://github.com/invoke-ai/InvokeAI
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71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
'''
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ldm.dream.generator.img2img descends from ldm.dream.generator
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'''
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import torch
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import numpy as np
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from ldm.dream.devices import choose_autocast
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from ldm.dream.generator.base import Generator
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from ldm.models.diffusion.ddim import DDIMSampler
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class Img2Img(Generator):
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def __init__(self, model, precision):
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super().__init__(model, precision)
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self.init_latent = None # by get_noise()
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def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
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conditioning,init_image,strength,step_callback=None,threshold=0.0,perlin=0.0,**kwargs):
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"""
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Returns a function returning an image derived from the prompt and the initial image
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Return value depends on the seed at the time you call it.
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"""
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self.perlin = perlin
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sampler.make_schedule(
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ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
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)
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scope = choose_autocast(self.precision)
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with scope(self.model.device.type):
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self.init_latent = self.model.get_first_stage_encoding(
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self.model.encode_first_stage(init_image)
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) # move to latent space
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t_enc = int(strength * steps)
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uc, c = conditioning
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def make_image(x_T):
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# encode (scaled latent)
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z_enc = sampler.stochastic_encode(
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self.init_latent,
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torch.tensor([t_enc]).to(self.model.device),
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noise=x_T
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)
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# decode it
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samples = sampler.decode(
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z_enc,
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c,
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t_enc,
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img_callback = step_callback,
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unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=uc,
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init_latent = self.init_latent, # changes how noising is performed in ksampler
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)
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return self.sample_to_image(samples)
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return make_image
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def get_noise(self,width,height):
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device = self.model.device
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init_latent = self.init_latent
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assert init_latent is not None,'call to get_noise() when init_latent not set'
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if device.type == 'mps':
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x = torch.randn_like(init_latent, device='cpu').to(device)
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else:
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x = torch.randn_like(init_latent, device=device)
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if self.perlin > 0.0:
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shape = init_latent.shape
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x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
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return x
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