forked from extern/easydiffusion
122 lines
5.1 KiB
Diff
122 lines
5.1 KiB
Diff
diff --git a/optimizedSD/ddpm.py b/optimizedSD/ddpm.py
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index dcf7901..4028a70 100644
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--- a/optimizedSD/ddpm.py
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+++ b/optimizedSD/ddpm.py
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@@ -485,6 +485,7 @@ class UNet(DDPM):
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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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+ streaming_callbacks = False,
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):
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@@ -523,12 +524,15 @@ class UNet(DDPM):
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log_every_t=log_every_t,
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unconditional_guidance_scale=unconditional_guidance_scale,
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unconditional_conditioning=unconditional_conditioning,
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+ streaming_callbacks=streaming_callbacks
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)
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elif sampler == "ddim":
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samples = self.ddim_sampling(x_latent, conditioning, S, unconditional_guidance_scale=unconditional_guidance_scale,
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unconditional_conditioning=unconditional_conditioning,
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- mask = mask,init_latent=x_T,use_original_steps=False)
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+ mask = mask,init_latent=x_T,use_original_steps=False,
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+ callback=callback, img_callback=img_callback,
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+ streaming_callbacks=streaming_callbacks)
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# elif sampler == "euler":
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# cvd = CompVisDenoiser(self.alphas_cumprod)
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@@ -536,11 +540,15 @@ class UNet(DDPM):
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# samples = self.heun_sampling(noise, sig, conditioning, unconditional_conditioning=unconditional_conditioning,
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# unconditional_guidance_scale=unconditional_guidance_scale)
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+ if streaming_callbacks: # this line needs to be right after the sampling() call
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+ yield from samples
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+
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if(self.turbo):
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self.model1.to("cpu")
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self.model2.to("cpu")
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- return samples
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+ if not streaming_callbacks:
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+ return samples
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@torch.no_grad()
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def plms_sampling(self, cond,b, img,
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@@ -548,7 +556,8 @@ class UNet(DDPM):
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callback=None, quantize_denoised=False,
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mask=None, x0=None, img_callback=None, log_every_t=100,
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temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
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- unconditional_guidance_scale=1., unconditional_conditioning=None,):
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+ unconditional_guidance_scale=1., unconditional_conditioning=None,
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+ streaming_callbacks=False):
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device = self.betas.device
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timesteps = self.ddim_timesteps
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@@ -580,10 +589,22 @@ class UNet(DDPM):
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old_eps.append(e_t)
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if len(old_eps) >= 4:
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old_eps.pop(0)
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- if callback: callback(i)
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- if img_callback: img_callback(pred_x0, i)
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- return img
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+ if callback:
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+ if streaming_callbacks:
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+ yield from callback(i)
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+ else:
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+ callback(i)
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+ if img_callback:
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+ if streaming_callbacks:
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+ yield from img_callback(pred_x0, i)
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+ else:
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+ img_callback(pred_x0, i)
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+
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+ if streaming_callbacks and img_callback:
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+ yield from img_callback(img, len(iterator)-1)
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+ else:
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+ return img
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@torch.no_grad()
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def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
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@@ -687,7 +708,9 @@ class UNet(DDPM):
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@torch.no_grad()
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def ddim_sampling(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
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- mask = None,init_latent=None,use_original_steps=False):
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+ mask = None,init_latent=None,use_original_steps=False,
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+ callback=None, img_callback=None,
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+ streaming_callbacks=False):
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timesteps = self.ddim_timesteps
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timesteps = timesteps[:t_start]
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@@ -710,11 +733,25 @@ class UNet(DDPM):
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x_dec = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
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unconditional_guidance_scale=unconditional_guidance_scale,
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unconditional_conditioning=unconditional_conditioning)
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+
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+ if callback:
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+ if streaming_callbacks:
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+ yield from callback(i)
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+ else:
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+ callback(i)
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+ if img_callback:
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+ if streaming_callbacks:
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+ yield from img_callback(x_dec, i)
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+ else:
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+ img_callback(x_dec, i)
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if mask is not None:
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- return x0 * mask + (1. - mask) * x_dec
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+ x_dec = x0 * mask + (1. - mask) * x_dec
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- return x_dec
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+ if streaming_callbacks and img_callback:
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+ yield from img_callback(x_dec, len(iterator)-1)
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+ else:
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+ return x_dec
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@torch.no_grad()
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