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https://github.com/invoke-ai/InvokeAI
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Merge branch 'development' into development
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commit
5d911b43c0
@ -32,7 +32,7 @@ class Txt2Img(Generator):
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if self.free_gpu_mem and self.model.model.device != self.model.device:
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self.model.model.to(self.model.device)
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sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=True)
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sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
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samples, _ = sampler.sample(
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batch_size = 1,
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@ -79,17 +79,9 @@ class KSampler(Sampler):
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ddim_eta=0.0,
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verbose=False,
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)
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self.model = outer_model
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self.model = outer_model
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self.ddim_num_steps = ddim_num_steps
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sigmas = K.sampling.get_sigmas_karras(
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n=ddim_num_steps,
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sigma_min=self.model.sigmas[0].item(),
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sigma_max=self.model.sigmas[-1].item(),
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rho=7.,
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device=self.device,
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# Birch-san recommends this, but it doesn't match the call signature in his branch of k-diffusion
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# concat_zero=False
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)
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sigmas = self.model.get_sigmas(ddim_num_steps)
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self.sigmas = sigmas
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# ALERT: We are completely overriding the sample() method in the base class, which
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@ -133,7 +125,8 @@ class KSampler(Sampler):
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# sigmas = self.model.get_sigmas(S)
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# sigmas are now set up in make_schedule - we take the last steps items
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sigmas = self.sigmas[-S:]
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sigmas = self.sigmas[-S-1:]
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if x_T is not None:
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x = x_T * sigmas[0]
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else:
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@ -147,7 +140,7 @@ class KSampler(Sampler):
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'uncond': unconditional_conditioning,
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'cond_scale': unconditional_guidance_scale,
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}
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print(f'>> Sampling with k__{self.schedule}')
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print(f'>> Sampling with k_{self.schedule}')
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return (
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K.sampling.__dict__[f'sample_{self.schedule}'](
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model_wrap_cfg, x, sigmas, extra_args=extra_args,
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