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https://github.com/easydiffusion/easydiffusion.git
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Remove unused patch files
This commit is contained in:
cmdr2
parent
accfec9007
commit
aa59575df3
@ -1,162 +0,0 @@
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diff --git a/optimizedSD/ddpm.py b/optimizedSD/ddpm.py
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index 79058bc..a473411 100644
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--- a/optimizedSD/ddpm.py
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+++ b/optimizedSD/ddpm.py
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@@ -564,12 +564,12 @@ class UNet(DDPM):
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unconditional_guidance_scale=unconditional_guidance_scale,
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callback=callback, img_callback=img_callback)
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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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-
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@torch.no_grad()
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def plms_sampling(self, cond,b, img,
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ddim_use_original_steps=False,
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@@ -608,10 +608,10 @@ 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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+ if callback: yield from callback(i)
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+ if img_callback: yield from img_callback(pred_x0, i)
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- return img
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+ yield from img_callback(img, 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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@@ -740,13 +740,13 @@ class UNet(DDPM):
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unconditional_guidance_scale=unconditional_guidance_scale,
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unconditional_conditioning=unconditional_conditioning)
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- if callback: callback(i)
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- if img_callback: img_callback(x_dec, i)
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+ if callback: yield from callback(i)
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+ if img_callback: yield from 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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+ yield from img_callback(x_dec, len(iterator)-1)
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@torch.no_grad()
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@@ -820,12 +820,12 @@ class UNet(DDPM):
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d = to_d(x, sigma_hat, denoised)
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- if callback: callback(i)
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- if img_callback: img_callback(x, i)
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+ if callback: yield from callback(i)
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+ if img_callback: yield from img_callback(x, i)
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dt = sigmas[i + 1] - sigma_hat
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# Euler method
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x = x + d * dt
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- return x
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+ yield from img_callback(x, len(sigmas)-1)
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@torch.no_grad()
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def euler_ancestral_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None, callback=None, disable=None, img_callback=None):
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@@ -852,14 +852,14 @@ class UNet(DDPM):
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denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
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sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
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- if callback: callback(i)
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- if img_callback: img_callback(x, i)
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+ if callback: yield from callback(i)
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+ if img_callback: yield from img_callback(x, i)
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d = to_d(x, sigmas[i], denoised)
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# Euler method
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dt = sigma_down - sigmas[i]
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x = x + d * dt
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x = x + torch.randn_like(x) * sigma_up
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- return x
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+ yield from img_callback(x, len(sigmas)-1)
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@@ -892,8 +892,8 @@ class UNet(DDPM):
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denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
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d = to_d(x, sigma_hat, denoised)
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- if callback: callback(i)
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- if img_callback: img_callback(x, i)
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+ if callback: yield from callback(i)
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+ if img_callback: yield from img_callback(x, i)
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dt = sigmas[i + 1] - sigma_hat
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if sigmas[i + 1] == 0:
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# Euler method
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@@ -913,7 +913,7 @@ class UNet(DDPM):
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d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
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d_prime = (d + d_2) / 2
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x = x + d_prime * dt
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- return x
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+ yield from img_callback(x, len(sigmas)-1)
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@torch.no_grad()
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@@ -944,8 +944,8 @@ class UNet(DDPM):
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e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
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denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
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- if callback: callback(i)
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- if img_callback: img_callback(x, i)
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+ if callback: yield from callback(i)
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+ if img_callback: yield from img_callback(x, i)
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d = to_d(x, sigma_hat, denoised)
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# Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
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@@ -966,7 +966,7 @@ class UNet(DDPM):
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d_2 = to_d(x_2, sigma_mid, denoised_2)
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x = x + d_2 * dt_2
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- return x
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+ yield from img_callback(x, len(sigmas)-1)
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@torch.no_grad()
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@@ -994,8 +994,8 @@ class UNet(DDPM):
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sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
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- if callback: callback(i)
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- if img_callback: img_callback(x, i)
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+ if callback: yield from callback(i)
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+ if img_callback: yield from img_callback(x, i)
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d = to_d(x, sigmas[i], denoised)
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# Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule
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sigma_mid = ((sigmas[i] ** (1 / 3) + sigma_down ** (1 / 3)) / 2) ** 3
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@@ -1016,7 +1016,7 @@ class UNet(DDPM):
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d_2 = to_d(x_2, sigma_mid, denoised_2)
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x = x + d_2 * dt_2
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x = x + torch.randn_like(x) * sigma_up
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- return x
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+ yield from img_callback(x, len(sigmas)-1)
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@torch.no_grad()
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@@ -1042,8 +1042,8 @@ class UNet(DDPM):
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e_t_uncond, e_t = (x_in + eps * c_out).chunk(2)
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denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
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- if callback: callback(i)
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- if img_callback: img_callback(x, i)
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+ if callback: yield from callback(i)
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+ if img_callback: yield from img_callback(x, i)
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d = to_d(x, sigmas[i], denoised)
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ds.append(d)
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@@ -1054,4 +1054,4 @@ class UNet(DDPM):
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cur_order = min(i + 1, order)
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coeffs = [linear_multistep_coeff(cur_order, sigmas.cpu(), i, j) for j in range(cur_order)]
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x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
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- return x
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+ yield from img_callback(x, len(sigmas)-1)
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@ -1,84 +0,0 @@
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diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
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index 27ead0e..6215939 100644
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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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