2022-11-25 09:59:24 +01:00
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diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
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2022-11-25 20:37:55 +01:00
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index 27ead0e..6215939 100644
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2022-11-25 09:59:24 +01:00
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--- a/ldm/models/diffusion/ddim.py
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+++ b/ldm/models/diffusion/ddim.py
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@@ -100,7 +100,7 @@ class DDIMSampler(object):
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size = (batch_size, C, H, W)
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print(f'Data shape for DDIM sampling is {size}, eta {eta}')
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- samples, intermediates = self.ddim_sampling(conditioning, size,
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+ samples = self.ddim_sampling(conditioning, size,
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callback=callback,
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img_callback=img_callback,
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quantize_denoised=quantize_x0,
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@@ -117,7 +117,8 @@ class DDIMSampler(object):
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dynamic_threshold=dynamic_threshold,
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ucg_schedule=ucg_schedule
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)
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- return samples, intermediates
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+ # return samples, intermediates
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+ yield from samples
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@torch.no_grad()
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def ddim_sampling(self, cond, shape,
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@@ -168,14 +169,15 @@ class DDIMSampler(object):
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unconditional_conditioning=unconditional_conditioning,
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dynamic_threshold=dynamic_threshold)
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img, pred_x0 = outs
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- if callback: callback(i)
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- if img_callback: img_callback(pred_x0, i)
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+ if callback: yield from callback(i)
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+ if img_callback: yield from img_callback(pred_x0, i)
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if index % log_every_t == 0 or index == total_steps - 1:
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intermediates['x_inter'].append(img)
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intermediates['pred_x0'].append(pred_x0)
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- return img, intermediates
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+ # return img, intermediates
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+ yield from img_callback(pred_x0, len(iterator)-1)
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@torch.no_grad()
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def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
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diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py
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index 7002a36..0951f39 100644
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--- a/ldm/models/diffusion/plms.py
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+++ b/ldm/models/diffusion/plms.py
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@@ -96,7 +96,7 @@ class PLMSSampler(object):
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size = (batch_size, C, H, W)
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print(f'Data shape for PLMS sampling is {size}')
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- samples, intermediates = self.plms_sampling(conditioning, size,
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+ samples = self.plms_sampling(conditioning, size,
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callback=callback,
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img_callback=img_callback,
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quantize_denoised=quantize_x0,
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@@ -112,7 +112,8 @@ class PLMSSampler(object):
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unconditional_conditioning=unconditional_conditioning,
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dynamic_threshold=dynamic_threshold,
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)
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- return samples, intermediates
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+ #return samples, intermediates
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+ yield from samples
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@torch.no_grad()
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def plms_sampling(self, cond, shape,
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@@ -165,14 +166,15 @@ class PLMSSampler(object):
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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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+ if callback: yield from callback(i)
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+ if img_callback: yield from img_callback(pred_x0, i)
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if index % log_every_t == 0 or index == total_steps - 1:
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intermediates['x_inter'].append(img)
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intermediates['pred_x0'].append(pred_x0)
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- return img, intermediates
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+ # return img, intermediates
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+ yield from img_callback(pred_x0, len(iterator)-1)
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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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