From aa59575df39b8fec43b2a09bddec573961c0d3dd Mon Sep 17 00:00:00 2001 From: cmdr2 Date: Fri, 9 Dec 2022 15:24:55 +0530 Subject: [PATCH] Remove unused patch files --- ui/sd_internal/ddim_callback.patch | 162 ------------------------- ui/sd_internal/ddim_callback_sd2.patch | 84 ------------- 2 files changed, 246 deletions(-) delete mode 100644 ui/sd_internal/ddim_callback.patch delete mode 100644 ui/sd_internal/ddim_callback_sd2.patch diff --git a/ui/sd_internal/ddim_callback.patch b/ui/sd_internal/ddim_callback.patch deleted file mode 100644 index e4dd69e0..00000000 --- a/ui/sd_internal/ddim_callback.patch +++ /dev/null @@ -1,162 +0,0 @@ -diff --git a/optimizedSD/ddpm.py b/optimizedSD/ddpm.py -index 79058bc..a473411 100644 ---- a/optimizedSD/ddpm.py -+++ b/optimizedSD/ddpm.py -@@ -564,12 +564,12 @@ class UNet(DDPM): - unconditional_guidance_scale=unconditional_guidance_scale, - callback=callback, img_callback=img_callback) - -+ yield from samples -+ - if(self.turbo): - self.model1.to("cpu") - self.model2.to("cpu") - -- return samples -- - @torch.no_grad() - def plms_sampling(self, cond,b, img, - ddim_use_original_steps=False, -@@ -608,10 +608,10 @@ class UNet(DDPM): - old_eps.append(e_t) - if len(old_eps) >= 4: - old_eps.pop(0) -- if callback: callback(i) -- if img_callback: img_callback(pred_x0, i) -+ if callback: yield from callback(i) -+ if img_callback: yield from img_callback(pred_x0, i) - -- return img -+ yield from img_callback(img, len(iterator)-1) - - @torch.no_grad() - def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, -@@ -740,13 +740,13 @@ class UNet(DDPM): - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning) - -- if callback: callback(i) -- if img_callback: img_callback(x_dec, i) -+ if callback: yield from callback(i) -+ if img_callback: yield from img_callback(x_dec, i) - - if mask is not None: -- return x0 * mask + (1. - mask) * x_dec -+ x_dec = x0 * mask + (1. - mask) * x_dec - -- return x_dec -+ yield from img_callback(x_dec, len(iterator)-1) - - - @torch.no_grad() -@@ -820,12 +820,12 @@ class UNet(DDPM): - - - d = to_d(x, sigma_hat, denoised) -- if callback: callback(i) -- if img_callback: img_callback(x, i) -+ if callback: yield from callback(i) -+ if img_callback: yield from img_callback(x, i) - dt = sigmas[i + 1] - sigma_hat - # Euler method - x = x + d * dt -- return x -+ yield from img_callback(x, len(sigmas)-1) - - @torch.no_grad() - 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): -@@ -852,14 +852,14 @@ class UNet(DDPM): - denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) - - sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1]) -- if callback: callback(i) -- if img_callback: img_callback(x, i) -+ if callback: yield from callback(i) -+ if img_callback: yield from img_callback(x, i) - d = to_d(x, sigmas[i], denoised) - # Euler method - dt = sigma_down - sigmas[i] - x = x + d * dt - x = x + torch.randn_like(x) * sigma_up -- return x -+ yield from img_callback(x, len(sigmas)-1) - - - -@@ -892,8 +892,8 @@ class UNet(DDPM): - denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) - - d = to_d(x, sigma_hat, denoised) -- if callback: callback(i) -- if img_callback: img_callback(x, i) -+ if callback: yield from callback(i) -+ if img_callback: yield from img_callback(x, i) - dt = sigmas[i + 1] - sigma_hat - if sigmas[i + 1] == 0: - # Euler method -@@ -913,7 +913,7 @@ class UNet(DDPM): - d_2 = to_d(x_2, sigmas[i + 1], denoised_2) - d_prime = (d + d_2) / 2 - x = x + d_prime * dt -- return x -+ yield from img_callback(x, len(sigmas)-1) - - - @torch.no_grad() -@@ -944,8 +944,8 @@ class UNet(DDPM): - e_t_uncond, e_t = (x_in + eps * c_out).chunk(2) - denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) - -- if callback: callback(i) -- if img_callback: img_callback(x, i) -+ if callback: yield from callback(i) -+ if img_callback: yield from img_callback(x, i) - - d = to_d(x, sigma_hat, denoised) - # Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule -@@ -966,7 +966,7 @@ class UNet(DDPM): - - d_2 = to_d(x_2, sigma_mid, denoised_2) - x = x + d_2 * dt_2 -- return x -+ yield from img_callback(x, len(sigmas)-1) - - - @torch.no_grad() -@@ -994,8 +994,8 @@ class UNet(DDPM): - - - sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1]) -- if callback: callback(i) -- if img_callback: img_callback(x, i) -+ if callback: yield from callback(i) -+ if img_callback: yield from img_callback(x, i) - d = to_d(x, sigmas[i], denoised) - # Midpoint method, where the midpoint is chosen according to a rho=3 Karras schedule - sigma_mid = ((sigmas[i] ** (1 / 3) + sigma_down ** (1 / 3)) / 2) ** 3 -@@ -1016,7 +1016,7 @@ class UNet(DDPM): - d_2 = to_d(x_2, sigma_mid, denoised_2) - x = x + d_2 * dt_2 - x = x + torch.randn_like(x) * sigma_up -- return x -+ yield from img_callback(x, len(sigmas)-1) - - - @torch.no_grad() -@@ -1042,8 +1042,8 @@ class UNet(DDPM): - e_t_uncond, e_t = (x_in + eps * c_out).chunk(2) - denoised = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) - -- if callback: callback(i) -- if img_callback: img_callback(x, i) -+ if callback: yield from callback(i) -+ if img_callback: yield from img_callback(x, i) - - d = to_d(x, sigmas[i], denoised) - ds.append(d) -@@ -1054,4 +1054,4 @@ class UNet(DDPM): - cur_order = min(i + 1, order) - coeffs = [linear_multistep_coeff(cur_order, sigmas.cpu(), i, j) for j in range(cur_order)] - x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) -- return x -+ yield from img_callback(x, len(sigmas)-1) diff --git a/ui/sd_internal/ddim_callback_sd2.patch b/ui/sd_internal/ddim_callback_sd2.patch deleted file mode 100644 index cadf81ca..00000000 --- a/ui/sd_internal/ddim_callback_sd2.patch +++ /dev/null @@ -1,84 +0,0 @@ -diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py -index 27ead0e..6215939 100644 ---- a/ldm/models/diffusion/ddim.py -+++ b/ldm/models/diffusion/ddim.py -@@ -100,7 +100,7 @@ class DDIMSampler(object): - size = (batch_size, C, H, W) - print(f'Data shape for DDIM sampling is {size}, eta {eta}') - -- samples, intermediates = self.ddim_sampling(conditioning, size, -+ samples = self.ddim_sampling(conditioning, size, - callback=callback, - img_callback=img_callback, - quantize_denoised=quantize_x0, -@@ -117,7 +117,8 @@ class DDIMSampler(object): - dynamic_threshold=dynamic_threshold, - ucg_schedule=ucg_schedule - ) -- return samples, intermediates -+ # return samples, intermediates -+ yield from samples - - @torch.no_grad() - def ddim_sampling(self, cond, shape, -@@ -168,14 +169,15 @@ class DDIMSampler(object): - unconditional_conditioning=unconditional_conditioning, - dynamic_threshold=dynamic_threshold) - img, pred_x0 = outs -- if callback: callback(i) -- if img_callback: img_callback(pred_x0, i) -+ if callback: yield from callback(i) -+ if img_callback: yield from img_callback(pred_x0, i) - - if index % log_every_t == 0 or index == total_steps - 1: - intermediates['x_inter'].append(img) - intermediates['pred_x0'].append(pred_x0) - -- return img, intermediates -+ # return img, intermediates -+ yield from img_callback(pred_x0, len(iterator)-1) - - @torch.no_grad() - def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, -diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py -index 7002a36..0951f39 100644 ---- a/ldm/models/diffusion/plms.py -+++ b/ldm/models/diffusion/plms.py -@@ -96,7 +96,7 @@ class PLMSSampler(object): - size = (batch_size, C, H, W) - print(f'Data shape for PLMS sampling is {size}') - -- samples, intermediates = self.plms_sampling(conditioning, size, -+ samples = self.plms_sampling(conditioning, size, - callback=callback, - img_callback=img_callback, - quantize_denoised=quantize_x0, -@@ -112,7 +112,8 @@ class PLMSSampler(object): - unconditional_conditioning=unconditional_conditioning, - dynamic_threshold=dynamic_threshold, - ) -- return samples, intermediates -+ #return samples, intermediates -+ yield from samples - - @torch.no_grad() - def plms_sampling(self, cond, shape, -@@ -165,14 +166,15 @@ class PLMSSampler(object): - old_eps.append(e_t) - if len(old_eps) >= 4: - old_eps.pop(0) -- if callback: callback(i) -- if img_callback: img_callback(pred_x0, i) -+ if callback: yield from callback(i) -+ if img_callback: yield from img_callback(pred_x0, i) - - if index % log_every_t == 0 or index == total_steps - 1: - intermediates['x_inter'].append(img) - intermediates['pred_x0'].append(pred_x0) - -- return img, intermediates -+ # return img, intermediates -+ yield from img_callback(pred_x0, len(iterator)-1) - - @torch.no_grad() - def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,