Merge branch 'development' of github.com:invoke-ai/InvokeAI into development

backend/invoke_ai_web_server.py

@@ -640,9 +640,11 @@ class InvokeAIWebServer:
                 if generation_parameters['progress_latents']:
                     image = self.generate.sample_to_lowres_estimated_image(sample)
                     (width, height) = image.size
+                    width *= 8
+                    height *= 8
                     buffered = io.BytesIO()
                     image.save(buffered, format="PNG")
-                    img_base64 = "data:image/jpeg;base64," + base64.b64encode(buffered.getvalue()).decode('UTF-8')
+                    img_base64 = "data:image/png;base64," + base64.b64encode(buffered.getvalue()).decode('UTF-8')
                     self.socketio.emit(
                         "intermediateResult",
                         {

docs/other/CONTRIBUTORS.md

@@ -60,6 +60,7 @@ We thank them for all of their time and hard work.
 - [Dmitry T.](https://github.com/ArDiouscuros)
 - [Kent Keirsey](https://github.com/hipsterusername)
 - [psychedelicious](https://github.com/psychedelicious)
+- [damian0815](https://github.com/damian0815)
 
 ## **Original CompVis Authors:**
 

ldm/invoke/generator/base.py

@@ -119,19 +119,19 @@ class Generator():
         # write an approximate RGB image from latent samples for a single step to PNG
 
     def sample_to_lowres_estimated_image(self,samples):
-        # adapted from code by @erucipe and @keturn here:
+        # origingally adapted from code by @erucipe and @keturn here:
         # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
 
-        # these numbers were determined empirically by @keturn
+        # these updated numbers for v1.5 are from @torridgristle
-        v1_4_latent_rgb_factors = torch.tensor([
+        v1_5_latent_rgb_factors = torch.tensor([
-                    # R        G        B
+            #    R        G        B
-                    [ 0.298, 0.207, 0.208],  # L1
+            [ 0.3444,  0.1385,  0.0670], # L1
-                    [ 0.187, 0.286, 0.173],  # L2
+            [ 0.1247,  0.4027,  0.1494], # L2
-                    [-0.158, 0.189, 0.264],  # L3
+            [-0.3192,  0.2513,  0.2103], # L3
-                    [-0.184, -0.271, -0.473],  # L4
+            [-0.1307, -0.1874, -0.7445]  # L4
         ], dtype=samples.dtype, device=samples.device)
 
-        latent_image = samples[0].permute(1, 2, 0) @ v1_4_latent_rgb_factors
+        latent_image = samples[0].permute(1, 2, 0) @ v1_5_latent_rgb_factors
         latents_ubyte = (((latent_image + 1) / 2)
                          .clamp(0, 1)  # change scale from -1..1 to 0..1
                          .mul(0xFF)  # to 0..255

ldm/models/diffusion/cross_attention_control.py

@@ -68,6 +68,8 @@ class CrossAttentionControl:
                     indices[b0:b1] = indices_target[a0:a1]
                     mask[b0:b1] = 1
 
+        cls.inject_attention_function(model)
+
         for m in cls.get_attention_modules(model, cls.CrossAttentionType.SELF):
             m.last_attn_slice_mask = None
             m.last_attn_slice_indices = None
@@ -76,8 +78,6 @@ class CrossAttentionControl:
             m.last_attn_slice_mask = mask.to(device)
             m.last_attn_slice_indices = indices.to(device)
 
-        cls.inject_attention_function(model)
-
 
     class CrossAttentionType(Enum):
         SELF = 1
@@ -151,72 +151,33 @@ class CrossAttentionControl:
             #else:
             #    print(f"in wrangler, whole, use_last_attn_slice is {self.use_last_attn_slice}, save_last_attn_slice is {self.save_last_attn_slice}")
 
-
             if self.use_last_attn_slice:
-                this_attn_slice = attn_slice
+                if dim is None:
-                if self.last_attn_slice_mask is not None:
+                    last_attn_slice = self.last_attn_slice
-                    # indices and mask operate on dim=2, no need to slice
+                    # print("took whole slice of shape", attn_slice.shape, "from complete shape", self.last_attn_slice.shape)
-                    base_attn_slice_full = torch.index_select(self.last_attn_slice, -1, self.last_attn_slice_indices)
-                    base_attn_slice_mask = self.last_attn_slice_mask
-                    if dim is None:
-                        base_attn_slice = base_attn_slice_full
-                        #print("using whole base slice of shape", base_attn_slice.shape, "from complete shape", base_attn_slice_full.shape)
-                    elif dim == 0:
-                        base_attn_slice = base_attn_slice_full[start:end]
-                        #print("using base dim 0 slice of shape", base_attn_slice.shape, "from complete shape", base_attn_slice_full.shape)
-                    elif dim == 1:
-                        base_attn_slice = base_attn_slice_full[:, start:end]
-                        #print("using base dim 1 slice of shape", base_attn_slice.shape, "from complete shape", base_attn_slice_full.shape)
-
-                    attn_slice = this_attn_slice * (1 - base_attn_slice_mask) + \
-                                 base_attn_slice * base_attn_slice_mask
                 else:
-                    if dim is None:
+                    last_attn_slice = self.last_attn_slice[offset]
-                        attn_slice = self.last_attn_slice
+
-                        #print("took whole slice of shape", attn_slice.shape, "from complete shape", self.last_attn_slice.shape)
+                if self.last_attn_slice_mask is None:
-                    elif dim == 0:
+                    # just use everything
-                        attn_slice = self.last_attn_slice[start:end]
+                    attn_slice = last_attn_slice
-                        #print("took dim 0 slice of shape", attn_slice.shape, "from complete shape", self.last_attn_slice.shape)
+                else:
-                    elif dim == 1:
+                    last_attn_slice_mask = self.last_attn_slice_mask
-                        attn_slice = self.last_attn_slice[:, start:end]
+                    remapped_last_attn_slice = torch.index_select(last_attn_slice, -1, self.last_attn_slice_indices)
-                        #print("took dim 1 slice of shape", attn_slice.shape, "from complete shape", self.last_attn_slice.shape)
+
+                    this_attn_slice = attn_slice
+                    this_attn_slice_mask = 1 - last_attn_slice_mask
+                    attn_slice = this_attn_slice * this_attn_slice_mask + \
+                                 remapped_last_attn_slice * last_attn_slice_mask
 
             if self.save_last_attn_slice:
                 if dim is None:
                     self.last_attn_slice = attn_slice
-                elif dim == 0:
+                else:
-                    # dynamically grow last_attn_slice if needed
                     if self.last_attn_slice is None:
-                        self.last_attn_slice = attn_slice
+                        self.last_attn_slice = { offset: attn_slice }
-                        #print("no last_attn_slice: shape now", self.last_attn_slice.shape)
-                    elif self.last_attn_slice.shape[0] == start:
-                        self.last_attn_slice = torch.cat([self.last_attn_slice, attn_slice], dim=0)
-                        assert(self.last_attn_slice.shape[0] == end)
-                        #print("last_attn_slice too small, appended dim 0 shape", attn_slice.shape, ", shape now", self.last_attn_slice.shape)
                     else:
-                        # no need to grow
+                        self.last_attn_slice[offset] = attn_slice
-                        self.last_attn_slice[start:end] = attn_slice
-                        #print("last_attn_slice shape is fine, setting dim 0 shape", attn_slice.shape, ", shape now", self.last_attn_slice.shape)
-
-                elif dim == 1:
-                    # dynamically grow last_attn_slice if needed
-                    if self.last_attn_slice is None:
-                        self.last_attn_slice = attn_slice
-                    elif self.last_attn_slice.shape[1] == start:
-                        self.last_attn_slice = torch.cat([self.last_attn_slice, attn_slice], dim=1)
-                        assert(self.last_attn_slice.shape[1] == end)
-                    else:
-                        # no need to grow
-                        self.last_attn_slice[:, start:end] = attn_slice
-
-            if self.use_last_attn_weights and self.last_attn_slice_weights is not None:
-                if dim is None:
-                    weights = self.last_attn_slice_weights
-                elif dim == 0:
-                    weights = self.last_attn_slice_weights[start:end]
-                elif dim == 1:
-                    weights = self.last_attn_slice_weights[:, start:end]
-                attn_slice = attn_slice * weights
 
             return attn_slice