report full size for fast latents and update conversion matrix for v1.5

backend/invoke_ai_web_server.py

@@ -637,6 +637,25 @@ class InvokeAIWebServer:
                             "height": height,
                         },
                     )
+                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/png;base64," + base64.b64encode(buffered.getvalue()).decode('UTF-8')
+                    self.socketio.emit(
+                        "intermediateResult",
+                        {
+                            "url": img_base64,
+                            "isBase64": True,
+                            "mtime": 0,
+                            "metadata": {},
+                            "width": width,
+                            "height": height,
+                        }
+                    )
                 self.socketio.emit("progressUpdate", progress.to_formatted_dict())
                 eventlet.sleep(0)
 

ldm/invoke/generator/base.py

@@ -116,6 +116,29 @@ class Generator():
         )
         return Image.fromarray(x_sample.astype(np.uint8))
 
+        # write an approximate RGB image from latent samples for a single step to PNG
+
+    def sample_to_lowres_estimated_image(self,samples):
+        # origingally adapted from code by @erucipe and @keturn here:
+        # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
+
+        # these updated numbers for v1.5 are from @torridgristle
+        v1_5_latent_rgb_factors = torch.tensor([
+            #    R        G        B
+            [ 0.3444,  0.1385,  0.0670], # L1
+            [ 0.1247,  0.4027,  0.1494], # L2
+            [-0.3192,  0.2513,  0.2103], # L3
+            [-0.1307, -0.1874, -0.7445]  # L4
+        ], dtype=samples.dtype, device=samples.device)
+
+        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
+                         .byte()).cpu()
+
+        return Image.fromarray(latents_ubyte.numpy())
+
     def generate_initial_noise(self, seed, width, height):
         initial_noise = None
         if self.variation_amount > 0 or len(self.with_variations) > 0: