mirror of
https://github.com/invoke-ai/InvokeAI
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65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
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'''wrapper around part of Karen Crownson's k-duffsion library, making it call compatible with other Samplers'''
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import k_diffusion as K
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import torch.nn as nn
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class CFGDenoiser(nn.Module):
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def __init__(self, model):
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super().__init__()
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self.inner_model = model
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def forward(self, x, sigma, uncond, cond, cond_scale):
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x_in = torch.cat([x] * 2)
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sigma_in = torch.cat([sigma] * 2)
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cond_in = torch.cat([uncond, cond])
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uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
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return uncond + (cond - uncond) * cond_scale
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class KSampler(object):
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def __init__(self,model,schedule="lms", **kwargs):
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super().__init__()
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self.model = K.external.CompVisDenoiser(model)
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self.accelerator = accelerate.Accelerator()
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self.device = accelerator.device
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self.schedule = schedule
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# most of these arguments are ignored and are only present for compatibility with
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# other samples
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@torch.no_grad()
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def sample(self,
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S,
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batch_size,
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shape,
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conditioning=None,
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callback=None,
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normals_sequence=None,
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img_callback=None,
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quantize_x0=False,
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eta=0.,
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mask=None,
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x0=None,
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temperature=1.,
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noise_dropout=0.,
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score_corrector=None,
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corrector_kwargs=None,
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verbose=True,
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x_T=None,
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log_every_t=100,
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unconditional_guidance_scale=1.,
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unconditional_conditioning=None,
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# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
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**kwargs
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):
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sigmas = self.model.get_sigmas(S)
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if x_T:
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x = x_T
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else:
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x = torch.randn([batch_size, *shape], device=device) * sigmas[0] # for GPU draw
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model_wrap_cfg = CFGDenoiser(self.model)
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extra_args = {'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': unconditional_guidance_scale}
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return (K.sampling.sample_lms(model_wrap_cfg, x, sigmas, extra_args=extra_args, disable=not accelerator.is_main_process),
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None)
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def gather(samples_ddim):
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return self.accelerator.gather(samples_ddim)
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