* Update to resolve conflicts.

ldm/dream/generator/base.py

@@ -10,6 +10,7 @@ from PIL               import Image
 from einops import rearrange, repeat
 from pytorch_lightning import seed_everything
 from ldm.dream.devices import choose_autocast_device
+from ldm.util import rand_perlin_2d
 
 downsampling = 8
 
@@ -36,7 +37,7 @@ class Generator():
         self.with_variations  = with_variations
 
     def generate(self,prompt,init_image,width,height,iterations=1,seed=None,
-                 image_callback=None, step_callback=None,
+                 image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
                  **kwargs):
         device_type,scope   = choose_autocast_device(self.model.device)
         make_image          = self.get_make_image(
@@ -45,6 +46,8 @@ class Generator():
             width         = width,
             height        = height,
             step_callback = step_callback,
+            threshold     = threshold,
+            perlin        = perlin,
             **kwargs
         )
 
@@ -63,10 +66,8 @@ class Generator():
                     x_T = initial_noise
                 else:
                     seed_everything(seed)
-                    if self.model.device.type == 'mps':
+                    x_T = self.get_noise(width,height)
-                        x_T = self.get_noise(width,height)
 
-                # make_image will do the equivalent of get_noise itself
                 image = make_image(x_T)
                 results.append([image, seed])
                 if image_callback is not None:
@@ -115,6 +116,10 @@ class Generator():
         """
         raise NotImplementedError("get_noise() must be implemented in a descendent class")
     
+    def get_perlin_noise(self,width,height):
+        return torch.stack([rand_perlin_2d((height, width), (8, 8)).to(self.model.device) for _ in range(self.latent_channels)], dim=0)
+
+    
     def new_seed(self):
         self.seed = random.randrange(0, np.iinfo(np.uint32).max)
         return self.seed

ldm/dream/generator/img2img.py

@@ -15,12 +15,12 @@ class Img2Img(Generator):
     
     @torch.no_grad()
     def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
-                       conditioning,init_image,strength,step_callback=None,**kwargs):
+                       conditioning,init_image,strength,step_callback=None,threshold=0.0,perlin=0.0,**kwargs):
         """
         Returns a function returning an image derived from the prompt and the initial image
         Return value depends on the seed at the time you call it.
         """
-
+        self.perlin = perlin
         # PLMS sampler not supported yet, so ignore previous sampler
         if not isinstance(sampler,DDIMSampler):
             print(
@@ -67,6 +67,10 @@ class Img2Img(Generator):
         init_latent = self.init_latent
         assert init_latent is not None,'call to get_noise() when init_latent not set'
         if device.type == 'mps':
-            return torch.randn_like(init_latent, device='cpu').to(device)
+            x = torch.randn_like(init_latent, device='cpu').to(device)
         else:
-            return torch.randn_like(init_latent, device=device)
+            x = torch.randn_like(init_latent, device=device)
+        if self.perlin > 0.0:
+            shape = init_latent.shape
+            x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
+        return x

ldm/dream/generator/txt2img.py

@@ -12,12 +12,13 @@ class Txt2Img(Generator):
     
     @torch.no_grad()
     def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
-                       conditioning,width,height,step_callback=None,**kwargs):
+                       conditioning,width,height,step_callback=None,threshold=0.0,perlin=0.0,**kwargs):
         """
         Returns a function returning an image derived from the prompt and the initial image
         Return value depends on the seed at the time you call it
         kwargs are 'width' and 'height'
         """
+        self.perlin = perlin
         uc, c   = conditioning
 
         @torch.no_grad()
@@ -37,7 +38,8 @@ class Txt2Img(Generator):
                 unconditional_guidance_scale = cfg_scale,
                 unconditional_conditioning   = uc,
                 eta                          = ddim_eta,
-                img_callback                 = step_callback
+                img_callback                 = step_callback,
+                threshold                    = threshold,
             )
             return self.sample_to_image(samples)
 
@@ -48,14 +50,18 @@ class Txt2Img(Generator):
     def get_noise(self,width,height):
         device         = self.model.device
         if device.type == 'mps':
-            return torch.randn([1,
+            x = torch.randn([1,
                                 self.latent_channels,
                                 height // self.downsampling_factor,
                                 width  // self.downsampling_factor],
                                device='cpu').to(device)
         else:
-            return torch.randn([1,
+            x = torch.randn([1,
                                 self.latent_channels,
                                 height // self.downsampling_factor,
                                 width  // self.downsampling_factor],
                                device=device)
+        print(self.perlin)
+        if self.perlin > 0.0:
+            x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width  // self.downsampling_factor, height // self.downsampling_factor)
+        return x

ldm/generate.py

@@ -193,6 +193,8 @@ class Generate:
             log_tokenization=  False,
             with_variations =   None,
             variation_amount =  0.0,
+            threshold       =   0.0,
+            perlin          =   0.0,
             # these are specific to img2img
             init_img       =    None,
             mask           =    None,
@@ -335,7 +337,9 @@ class Generate:
                 height         = height,
                 init_image     = init_image,   # notice that init_image is different from init_img
                 init_mask      = init_mask_image,
-                strength       = strength
+                strength       = strength,
+                threshold      = threshold,
+                perlin         = perlin,
             )
 
             if upscale is not None or gfpgan_strength > 0: