easydiffusion/ui/sd_internal/ddim_callback.patch

diff --git a/optimizedSD/ddpm.py b/optimizedSD/ddpm.py
index b967b55..10a7c32 100644
--- a/optimizedSD/ddpm.py
+++ b/optimizedSD/ddpm.py
@@ -22,7 +22,7 @@ from ldm.util import exists, default, instantiate_from_config
 from ldm.modules.diffusionmodules.util import make_beta_schedule
 from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
 from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
-from samplers import CompVisDenoiser, get_ancestral_step, to_d, append_dims,linear_multistep_coeff
+from .samplers import CompVisDenoiser, get_ancestral_step, to_d, append_dims,linear_multistep_coeff
 
 def disabled_train(self):
     """Overwrite model.train with this function to make sure train/eval mode
@@ -528,39 +528,46 @@ class UNet(DDPM):
         elif sampler == "ddim":
             samples = self.ddim_sampling(x_latent, conditioning, S, unconditional_guidance_scale=unconditional_guidance_scale,
                                          unconditional_conditioning=unconditional_conditioning,
-                                         mask = mask,init_latent=x_T,use_original_steps=False)
+                                         mask = mask,init_latent=x_T,use_original_steps=False,
+                                         callback=callback, img_callback=img_callback)
 
         elif sampler == "euler":
             self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
             samples = self.euler_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
-                                        unconditional_guidance_scale=unconditional_guidance_scale)
+                                        unconditional_guidance_scale=unconditional_guidance_scale,
+                                        img_callback=img_callback)
         elif sampler == "euler_a":
             self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
             samples = self.euler_ancestral_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
-                                        unconditional_guidance_scale=unconditional_guidance_scale)
+                                        unconditional_guidance_scale=unconditional_guidance_scale,
+                                        img_callback=img_callback)
 
         elif sampler == "dpm2":
             samples = self.dpm_2_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
-                                        unconditional_guidance_scale=unconditional_guidance_scale)
+                                        unconditional_guidance_scale=unconditional_guidance_scale,
+                                        img_callback=img_callback)
         elif sampler == "heun":
             samples = self.heun_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
-                                        unconditional_guidance_scale=unconditional_guidance_scale)
+                                        unconditional_guidance_scale=unconditional_guidance_scale,
+                                        img_callback=img_callback)
 
         elif sampler == "dpm2_a":
             samples = self.dpm_2_ancestral_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
-                                        unconditional_guidance_scale=unconditional_guidance_scale)
+                                        unconditional_guidance_scale=unconditional_guidance_scale,
+                                        img_callback=img_callback)
 
 
         elif sampler == "lms":
             samples = self.lms_sampling(self.alphas_cumprod,x_latent, S, conditioning, unconditional_conditioning=unconditional_conditioning,
-                                        unconditional_guidance_scale=unconditional_guidance_scale)
+                                        unconditional_guidance_scale=unconditional_guidance_scale,
+                                        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,
@@ -599,10 +606,10 @@ 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,
@@ -706,7 +713,8 @@ class UNet(DDPM):
 
     @torch.no_grad()
     def ddim_sampling(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
-               mask = None,init_latent=None,use_original_steps=False):
+               mask = None,init_latent=None,use_original_steps=False,
+               callback=None, img_callback=None):
 
         timesteps = self.ddim_timesteps
         timesteps = timesteps[:t_start]
@@ -730,10 +738,13 @@ class UNet(DDPM):
                                           unconditional_guidance_scale=unconditional_guidance_scale,
                                           unconditional_conditioning=unconditional_conditioning)
         
+            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()
@@ -779,13 +790,16 @@ class UNet(DDPM):
 
 
     @torch.no_grad()
-    def euler_sampling(self, ac, x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None,callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+    def euler_sampling(self, ac, x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None,callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.,
+                        img_callback=None):
         """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
         extra_args = {} if extra_args is None else extra_args
         cvd = CompVisDenoiser(ac)
         sigmas = cvd.get_sigmas(S)
         x = x*sigmas[0]
 
+        print(f"Running Euler Sampling with {len(sigmas) - 1} timesteps")
+
         s_in = x.new_ones([x.shape[0]]).half()
         for i in trange(len(sigmas) - 1, disable=disable):
             gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
@@ -807,13 +821,18 @@ class UNet(DDPM):
             d = to_d(x, sigma_hat, denoised)
             if callback is not None:
                 callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+
+            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):
+    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):
         """Ancestral sampling with Euler method steps."""
         extra_args = {} if extra_args is None else extra_args
 
@@ -822,6 +841,8 @@ class UNet(DDPM):
         sigmas = cvd.get_sigmas(S)
         x = x*sigmas[0]
 
+        print(f"Running Euler Ancestral Sampling with {len(sigmas) - 1} timesteps")
+
         s_in = x.new_ones([x.shape[0]]).half()
         for i in trange(len(sigmas) - 1, disable=disable):
 
@@ -837,17 +858,22 @@ class UNet(DDPM):
             sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
             if callback is not None:
                 callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+            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)
 
 
 
     @torch.no_grad()
-    def heun_sampling(self, ac, x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+    def heun_sampling(self, ac, x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.,
+                        img_callback=None):
         """Implements Algorithm 2 (Heun steps) from Karras et al. (2022)."""
         extra_args = {} if extra_args is None else extra_args
 
@@ -855,6 +881,8 @@ class UNet(DDPM):
         sigmas = cvd.get_sigmas(S)
         x = x*sigmas[0]
 
+        print(f"Running Heun Sampling with {len(sigmas) - 1} timesteps")
+
 
         s_in = x.new_ones([x.shape[0]]).half()
         for i in trange(len(sigmas) - 1, disable=disable):
@@ -876,6 +904,9 @@ class UNet(DDPM):
             d = to_d(x, sigma_hat, denoised)
             if callback is not None:
                 callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+
+            if img_callback: yield from img_callback(x, i)
+
             dt = sigmas[i + 1] - sigma_hat
             if sigmas[i + 1] == 0:
                 # Euler method
@@ -895,11 +926,13 @@ 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()
-    def dpm_2_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+    def dpm_2_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1,extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.,
+                        img_callback=None):
         """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022)."""
         extra_args = {} if extra_args is None else extra_args
 
@@ -907,6 +940,8 @@ class UNet(DDPM):
         sigmas = cvd.get_sigmas(S)
         x = x*sigmas[0]
 
+        print(f"Running DPM2 Sampling with {len(sigmas) - 1} timesteps")
+
         s_in = x.new_ones([x.shape[0]]).half()
         for i in trange(len(sigmas) - 1, disable=disable):
             gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
@@ -924,7 +959,7 @@ 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 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
@@ -945,11 +980,13 @@ 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()
-    def dpm_2_ancestral_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None):
+    def dpm_2_ancestral_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None,
+                        img_callback=None):
         """Ancestral sampling with DPM-Solver inspired second-order steps."""
         extra_args = {} if extra_args is None else extra_args
 
@@ -957,6 +994,8 @@ class UNet(DDPM):
         sigmas = cvd.get_sigmas(S)
         x = x*sigmas[0]
 
+        print(f"Running DPM2 Ancestral Sampling with {len(sigmas) - 1} timesteps")
+
         s_in = x.new_ones([x.shape[0]]).half()
         for i in trange(len(sigmas) - 1, disable=disable):
 
@@ -973,6 +1012,9 @@ class UNet(DDPM):
             sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1])
             if callback is not None:
                 callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+            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
@@ -993,11 +1035,13 @@ 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()
-    def lms_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None, order=4):
+    def lms_sampling(self,ac,x, S, cond, unconditional_conditioning = None, unconditional_guidance_scale = 1, extra_args=None, callback=None, disable=None, order=4,
+                        img_callback=None):
         extra_args = {} if extra_args is None else extra_args
         s_in = x.new_ones([x.shape[0]])
 
@@ -1005,6 +1049,8 @@ class UNet(DDPM):
         sigmas = cvd.get_sigmas(S)
         x = x*sigmas[0]
 
+        print(f"Running LMS Sampling with {len(sigmas) - 1} timesteps")
+
         ds = []
         for i in trange(len(sigmas) - 1, disable=disable):
 
@@ -1017,6 +1063,7 @@ 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 img_callback: yield from img_callback(x, i)
 
             d = to_d(x, sigmas[i], denoised)
             ds.append(d)
@@ -1027,4 +1074,5 @@ 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/optimizedSD/openaimodelSplit.py b/optimizedSD/openaimodelSplit.py
index abc3098..7a32ffe 100644
--- a/optimizedSD/openaimodelSplit.py
+++ b/optimizedSD/openaimodelSplit.py
@@ -13,7 +13,7 @@ from ldm.modules.diffusionmodules.util import (
     normalization,
     timestep_embedding,
 )
-from splitAttention import SpatialTransformer
+from .splitAttention import SpatialTransformer
 
 
 class AttentionPool2d(nn.Module):