Merge branch 'main' into perf/lowmem_sequential_guidance

docs/features/CLI.md

@@ -214,6 +214,8 @@ Here are the invoke> command that apply to txt2img:
 | `--variation <float>`                     | `-v<float>`                               | `0.0`                                    | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with `-S<seed>` and `-n<int>` to generate a series a riffs on a starting image. See [Variations](./VARIATIONS.md). |
 | `--with_variations <pattern>`             |                                           | `None`                                   | Combine two or more variations. See [Variations](./VARIATIONS.md) for now to use this.                                                                                                                                                           |
 | `--save_intermediates <n>`                |                                           | `None`                                   | Save the image from every nth step into an "intermediates" folder inside the output directory                                                                                                                                                    |
+| `--h_symmetry_time_pct <float>`           |                                           | `None`                                   | Create symmetry along the X axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.)                                   |
+| `--v_symmetry_time_pct <float>`           |                                           | `None`                                   | Create symmetry along the Y axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.)                                   |
 
 !!! note
 

docs/features/PROMPTS.md

@@ -40,7 +40,7 @@ for adj in adjectives:
             print(f'a {adj} day -A{samp} -C{cg}')
 ```
 
-It's output looks like this (abbreviated):
+Its output looks like this (abbreviated):
 
 ```bash
 a sunny day -Aklms -C7.5

ldm/generate.py

@@ -320,6 +320,8 @@ class Generate:
         variation_amount=0.0,
         threshold=0.0,
         perlin=0.0,
+        h_symmetry_time_pct = None,
+        v_symmetry_time_pct = None,
         karras_max=None,
         outdir=None,
         # these are specific to img2img and inpaint
@@ -390,6 +392,8 @@ class Generate:
            variation_amount                // optional 0-1 value to slerp from -S noise to random noise (allows variations on an image)
            threshold                       // optional value >=0 to add thresholding to latent values for k-diffusion samplers (0 disables)
            perlin                          // optional 0-1 value to add a percentage of perlin noise to the initial noise
+           h_symmetry_time_pct             // optional 0-1 value that indicates the time at which horizontal symmetry is applied
+           v_symmetry_time_pct             // optional 0-1 value that indicates the time at which vertical symmetry is applied
            embiggen                        // scale factor relative to the size of the --init_img (-I), followed by ESRGAN upscaling strength (0-1.0), followed by minimum amount of overlap between tiles as a decimal ratio (0 - 1.0) or number of pixels
            embiggen_tiles                  // list of tiles by number in order to process and replace onto the image e.g. `0 2 4`
            embiggen_strength               // strength for embiggen. 0.0 preserves image exactly, 1.0 replaces it completely
@@ -561,6 +565,8 @@ class Generate:
                 strength=strength,
                 threshold=threshold,
                 perlin=perlin,
+                h_symmetry_time_pct=h_symmetry_time_pct,
+                v_symmetry_time_pct=v_symmetry_time_pct,
                 embiggen=embiggen,
                 embiggen_tiles=embiggen_tiles,
                 embiggen_strength=embiggen_strength,

ldm/invoke/args.py

@@ -272,6 +272,10 @@ class Args(object):
             switches.append('--seamless')
         if a['hires_fix']:
             switches.append('--hires_fix')
+        if a['h_symmetry_time_pct']:
+            switches.append(f'--h_symmetry_time_pct {a["h_symmetry_time_pct"]}')
+        if a['v_symmetry_time_pct']:
+            switches.append(f'--v_symmetry_time_pct {a["v_symmetry_time_pct"]}')
 
         # img2img generations have parameters relevant only to them and have special handling
         if a['init_img'] and len(a['init_img'])>0:
@@ -852,6 +856,18 @@ class Args(object):
             type=float,
             help='Perlin noise scale (0.0 - 1.0) - add perlin noise to the initialization instead of the usual gaussian noise.',
         )
+        render_group.add_argument(
+            '--h_symmetry_time_pct',
+            default=None,
+            type=float,
+            help='Horizontal symmetry point (0.0 - 1.0) - apply horizontal symmetry at this point in image generation.',
+        )
+        render_group.add_argument(
+            '--v_symmetry_time_pct',
+            default=None,
+            type=float,
+            help='Vertical symmetry point (0.0 - 1.0) - apply vertical symmetry at this point in image generation.',
+        )
         render_group.add_argument(
             '--fnformat',
             default='{prefix}.{seed}.png',
@@ -1158,7 +1174,8 @@ def metadata_dumps(opt,
     # remove any image keys not mentioned in RFC #266
     rfc266_img_fields = ['type','postprocessing','sampler','prompt','seed','variations','steps',
                          'cfg_scale','threshold','perlin','step_number','width','height','extra','strength','seamless'
-                         'init_img','init_mask','facetool','facetool_strength','upscale']
+                         'init_img','init_mask','facetool','facetool_strength','upscale','h_symmetry_time_pct',
+                         'v_symmetry_time_pct']
     rfc_dict ={}
 
     for item in image_dict.items():

ldm/invoke/generator/base.py

@@ -64,6 +64,7 @@ class Generator:
 
     def generate(self,prompt,init_image,width,height,sampler, iterations=1,seed=None,
                  image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
+                 h_symmetry_time_pct=None, v_symmetry_time_pct=None,
                  safety_checker:dict=None,
                  free_gpu_mem: bool=False,
                  **kwargs):
@@ -81,6 +82,8 @@ class Generator:
             step_callback = step_callback,
             threshold     = threshold,
             perlin        = perlin,
+            h_symmetry_time_pct     = h_symmetry_time_pct,
+            v_symmetry_time_pct     = v_symmetry_time_pct,
             attention_maps_callback = attention_maps_callback,
             **kwargs
         )

ldm/invoke/generator/img2img.py

@@ -16,8 +16,8 @@ class Img2Img(Generator):
         self.init_latent = None    # by get_noise()
 
     def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
-                       conditioning,init_image,strength,step_callback=None,threshold=0.0,perlin=0.0,
-                       attention_maps_callback=None,
+                       conditioning,init_image,strength,step_callback=None,threshold=0.0,warmup=0.2,perlin=0.0,
+                       h_symmetry_time_pct=None,v_symmetry_time_pct=None,attention_maps_callback=None,
                        **kwargs):
         """
         Returns a function returning an image derived from the prompt and the initial image
@@ -33,8 +33,13 @@ class Img2Img(Generator):
         conditioning_data = (
             ConditioningData(
                 uc, c, cfg_scale, extra_conditioning_info,
-                postprocessing_settings = PostprocessingSettings(threshold, warmup=0.2) if threshold else None)
-            .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
+                postprocessing_settings=PostprocessingSettings(
+                    threshold=threshold,
+                    warmup=warmup,
+                    h_symmetry_time_pct=h_symmetry_time_pct,
+                    v_symmetry_time_pct=v_symmetry_time_pct
+                )
+            ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
 
 
         def make_image(x_T):

ldm/invoke/generator/txt2img.py

@@ -15,8 +15,8 @@ class Txt2Img(Generator):
 
     @torch.no_grad()
     def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
-                       conditioning,width,height,step_callback=None,threshold=0.0,perlin=0.0,
-                       attention_maps_callback=None,
+                       conditioning,width,height,step_callback=None,threshold=0.0,warmup=0.2,perlin=0.0,
+                       h_symmetry_time_pct=None,v_symmetry_time_pct=None,attention_maps_callback=None,
                        **kwargs):
         """
         Returns a function returning an image derived from the prompt and the initial image
@@ -33,8 +33,13 @@ class Txt2Img(Generator):
         conditioning_data = (
             ConditioningData(
                 uc, c, cfg_scale, extra_conditioning_info,
-                postprocessing_settings = PostprocessingSettings(threshold, warmup=0.2) if threshold else None)
-            .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
+                postprocessing_settings=PostprocessingSettings(
+                    threshold=threshold,
+                    warmup=warmup,
+                    h_symmetry_time_pct=h_symmetry_time_pct,
+                    v_symmetry_time_pct=v_symmetry_time_pct
+                )
+            ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
 
         def make_image(x_T) -> PIL.Image.Image:
             pipeline_output = pipeline.image_from_embeddings(
@@ -44,8 +49,10 @@ class Txt2Img(Generator):
                 conditioning_data=conditioning_data,
                 callback=step_callback,
             )
+
             if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
                 attention_maps_callback(pipeline_output.attention_map_saver)
+
             return pipeline.numpy_to_pil(pipeline_output.images)[0]
 
         return make_image

ldm/invoke/generator/txt2img2img.py

@@ -21,12 +21,14 @@ class Txt2Img2Img(Generator):
 
     def get_make_image(self, prompt:str, sampler, steps:int, cfg_scale:float, ddim_eta,
                        conditioning, width:int, height:int, strength:float,
-                       step_callback:Optional[Callable]=None, threshold=0.0, **kwargs):
+                       step_callback:Optional[Callable]=None, threshold=0.0, warmup=0.2, perlin=0.0,
+                       h_symmetry_time_pct=None, v_symmetry_time_pct=None, attention_maps_callback=None, **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
 
         # noinspection PyTypeChecker
         pipeline: StableDiffusionGeneratorPipeline = self.model
@@ -36,8 +38,13 @@ class Txt2Img2Img(Generator):
         conditioning_data = (
             ConditioningData(
                 uc, c, cfg_scale, extra_conditioning_info,
-                postprocessing_settings = PostprocessingSettings(threshold=threshold, warmup=0.2) if threshold else None)
-            .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
+                postprocessing_settings = PostprocessingSettings(
+                    threshold=threshold,
+                    warmup=0.2,
+                    h_symmetry_time_pct=h_symmetry_time_pct,
+                    v_symmetry_time_pct=v_symmetry_time_pct
+                )
+            ).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
 
         def make_image(x_T):
 
@@ -69,19 +76,28 @@ class Txt2Img2Img(Generator):
             if clear_cuda_cache is not None:
                 clear_cuda_cache()
 
-            second_pass_noise = self.get_noise_like(resized_latents)
+            second_pass_noise = self.get_noise_like(resized_latents, override_perlin=True)
+
+            # Clear symmetry for the second pass
+            from dataclasses import replace
+            new_postprocessing_settings = replace(conditioning_data.postprocessing_settings, h_symmetry_time_pct=None)
+            new_postprocessing_settings = replace(new_postprocessing_settings, v_symmetry_time_pct=None)
+            new_conditioning_data = replace(conditioning_data, postprocessing_settings=new_postprocessing_settings)
 
             verbosity = get_verbosity()
             set_verbosity_error()
             pipeline_output = pipeline.img2img_from_latents_and_embeddings(
                 resized_latents,
                 num_inference_steps=steps,
-                conditioning_data=conditioning_data,
+                conditioning_data=new_conditioning_data,
                 strength=strength,
                 noise=second_pass_noise,
                 callback=step_callback)
             set_verbosity(verbosity)
 
+            if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
+                attention_maps_callback(pipeline_output.attention_map_saver)
+
             return pipeline.numpy_to_pil(pipeline_output.images)[0]
 
 
@@ -95,13 +111,13 @@ class Txt2Img2Img(Generator):
 
         return make_image
 
-    def get_noise_like(self, like: torch.Tensor):
+    def get_noise_like(self, like: torch.Tensor, override_perlin: bool=False):
         device = like.device
         if device.type == 'mps':
             x = torch.randn_like(like, device='cpu', dtype=self.torch_dtype()).to(device)
         else:
             x = torch.randn_like(like, device=device, dtype=self.torch_dtype())
-        if self.perlin > 0.0:
+        if self.perlin > 0.0 and override_perlin == False:
             shape = like.shape
             x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
         return x
@@ -139,6 +155,9 @@ class Txt2Img2Img(Generator):
         shape = (1, channels,
                  scaled_height // self.downsampling_factor, scaled_width // self.downsampling_factor)
         if self.use_mps_noise or device.type == 'mps':
-            return torch.randn(shape, dtype=self.torch_dtype(), device='cpu').to(device)
+            tensor = torch.empty(size=shape, device='cpu')
+            tensor = self.get_noise_like(like=tensor).to(device)
         else:
-            return torch.randn(shape, dtype=self.torch_dtype(), device=device)
+            tensor = torch.empty(size=shape, device=device)
+            tensor = self.get_noise_like(like=tensor)
+        return tensor

ldm/invoke/readline.py

@@ -58,6 +58,8 @@ COMMANDS = (
     '--inpaint_replace','-r',
     '--png_compression','-z',
     '--text_mask','-tm',
+    '--h_symmetry_time_pct',
+    '--v_symmetry_time_pct',
     '!fix','!fetch','!replay','!history','!search','!clear',
     '!models','!switch','!import_model','!optimize_model','!convert_model','!edit_model','!del_model',
     '!mask','!triggers',
@@ -138,7 +140,7 @@ class Completer(object):
             elif re.match('^'+'|'.join(MODEL_COMMANDS),buffer):
                 self.matches= self._model_completions(text, state)
 
-            # looking for a ckpt model 
+            # looking for a ckpt model
             elif re.match('^'+'|'.join(CKPT_MODEL_COMMANDS),buffer):
                 self.matches= self._model_completions(text, state, ckpt_only=True)
 
@@ -255,7 +257,7 @@ class Completer(object):
         update our list of models
         '''
         self.models = models
-        
+
     def _seed_completions(self, text, state):
         m = re.search('(-S\s?|--seed[=\s]?)(\d*)',text)
         if m:

ldm/models/diffusion/shared_invokeai_diffusion.py

@@ -24,6 +24,8 @@ ModelForwardCallback: TypeAlias = Union[
 class PostprocessingSettings:
     threshold: float
     warmup: float
+    h_symmetry_time_pct: Optional[float]
+    v_symmetry_time_pct: Optional[float]
 
 
 class InvokeAIDiffuserComponent:
@@ -179,6 +181,7 @@ class InvokeAIDiffuserComponent:
         if postprocessing_settings is not None:
             percent_through = self.calculate_percent_through(sigma, step_index, total_step_count)
             latents = self.apply_threshold(postprocessing_settings, latents, percent_through)
+            latents = self.apply_symmetry(postprocessing_settings, latents, percent_through)
         return latents
 
     def calculate_percent_through(self, sigma, step_index, total_step_count):
@@ -320,8 +323,12 @@ class InvokeAIDiffuserComponent:
         self,
         postprocessing_settings: PostprocessingSettings,
         latents: torch.Tensor,
-        percent_through
+        percent_through: float
     ) -> torch.Tensor:
+
+        if postprocessing_settings.threshold is None or postprocessing_settings.threshold == 0.0:
+            return latents
+
         threshold = postprocessing_settings.threshold
         warmup = postprocessing_settings.warmup
 
@@ -370,6 +377,56 @@ class InvokeAIDiffuserComponent:
 
         return latents
 
+    def apply_symmetry(
+        self,
+        postprocessing_settings: PostprocessingSettings,
+        latents: torch.Tensor,
+        percent_through: float
+    ) -> torch.Tensor:
+
+        # Reset our last percent through if this is our first step.
+        if percent_through == 0.0:
+            self.last_percent_through = 0.0
+
+        if postprocessing_settings is None:
+            return latents
+
+        # Check for out of bounds
+        h_symmetry_time_pct = postprocessing_settings.h_symmetry_time_pct
+        if (h_symmetry_time_pct is not None and (h_symmetry_time_pct <= 0.0 or h_symmetry_time_pct > 1.0)):
+            h_symmetry_time_pct = None
+
+        v_symmetry_time_pct = postprocessing_settings.v_symmetry_time_pct
+        if (v_symmetry_time_pct is not None and (v_symmetry_time_pct <= 0.0 or v_symmetry_time_pct > 1.0)):
+            v_symmetry_time_pct = None
+
+        dev = latents.device.type
+
+        latents.to(device='cpu')
+
+        if (
+            h_symmetry_time_pct != None and
+            self.last_percent_through < h_symmetry_time_pct and
+            percent_through >= h_symmetry_time_pct
+        ):
+            # Horizontal symmetry occurs on the 3rd dimension of the latent
+            width = latents.shape[3]
+            x_flipped = torch.flip(latents, dims=[3])
+            latents = torch.cat([latents[:, :, :, 0:int(width/2)], x_flipped[:, :, :, int(width/2):int(width)]], dim=3)
+
+        if (
+            v_symmetry_time_pct != None and
+            self.last_percent_through < v_symmetry_time_pct and
+            percent_through >= v_symmetry_time_pct
+        ):
+            # Vertical symmetry occurs on the 2nd dimension of the latent
+            height = latents.shape[2]
+            y_flipped = torch.flip(latents, dims=[2])
+            latents = torch.cat([latents[:, :, 0:int(height / 2)], y_flipped[:, :, int(height / 2):int(height)]], dim=2)
+
+        self.last_percent_through = percent_through
+        return latents.to(device=dev)
+
     def estimate_percent_through(self, step_index, sigma):
         if step_index is not None and self.cross_attention_control_context is not None:
             # percent_through will never reach 1.0 (but this is intended)