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720e5cd651
* start refactoring -not yet functional * first phase of refactor done - not sure weighted prompts working * Second phase of refactoring. Everything mostly working. * The refactoring has moved all the hard-core inference work into ldm.dream.generator.*, where there are submodules for txt2img and img2img. inpaint will go in there as well. * Some additional refactoring will be done soon, but relatively minor work. * fix -save_orig flag to actually work * add @neonsecret attention.py memory optimization * remove unneeded imports * move token logging into conditioning.py * add placeholder version of inpaint; porting in progress * fix crash in img2img * inpainting working; not tested on variations * fix crashes in img2img * ported attention.py memory optimization #117 from basujindal branch * added @torch_no_grad() decorators to img2img, txt2img, inpaint closures * Final commit prior to PR against development * fixup crash when generating intermediate images in web UI * rename ldm.simplet2i to ldm.generate * add backward-compatibility simplet2i shell with deprecation warning * add back in mps exception, addresses @vargol comment in #354 * replaced Conditioning class with exported functions * fix wrong type of with_variations attribute during intialization * changed "image_iterator()" to "get_make_image()" * raise NotImplementedError for calling get_make_image() in parent class * Update ldm/generate.py better error message Co-authored-by: Kevin Gibbons <bakkot@gmail.com> * minor stylistic fixes and assertion checks from code review * moved get_noise() method into img2img class * break get_noise() into two methods, one for txt2img and the other for img2img * inpainting works on non-square images now * make get_noise() an abstract method in base class * much improved inpainting Co-authored-by: Kevin Gibbons <bakkot@gmail.com>
62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
'''
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ldm.dream.generator.txt2img inherits 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.generator.base import Generator
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class Txt2Img(Generator):
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def __init__(self,model):
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super().__init__(model)
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@torch.no_grad()
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def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
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conditioning,width,height,step_callback=None,**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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kwargs are 'width' and 'height'
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"""
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uc, c = conditioning
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@torch.no_grad()
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def make_image(x_T):
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shape = [
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self.latent_channels,
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height // self.downsampling_factor,
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width // self.downsampling_factor,
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]
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samples, _ = sampler.sample(
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batch_size = 1,
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S = steps,
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x_T = x_T,
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conditioning = c,
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shape = shape,
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verbose = False,
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unconditional_guidance_scale = cfg_scale,
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unconditional_conditioning = uc,
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eta = ddim_eta,
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img_callback = step_callback
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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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# returns a tensor filled with random numbers from a normal distribution
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def get_noise(self,width,height):
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device = self.model.device
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if device.type == 'mps':
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return torch.randn([1,
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self.latent_channels,
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height // self.downsampling_factor,
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width // self.downsampling_factor],
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device='cpu').to(device)
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else:
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return torch.randn([1,
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self.latent_channels,
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height // self.downsampling_factor,
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width // self.downsampling_factor],
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device=device)
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