add tensor to samples method

modules/esrgan_model.py

@@ -220,7 +220,7 @@ def esrgan_upscale(model, img):
 
     with Progress(TextColumn('[cyan]{task.description}'), BarColumn(), TaskProgressColumn(), TimeRemainingColumn(), TimeElapsedColumn(), console=console) as progress:
         total = 0
-        for y, h, row in grid.tiles:
+        for _y, _h, row in grid.tiles:
             total += len(row)
         task = progress.add_task(description="Upscaling", total=total)
         for y, h, row in grid.tiles:

modules/realesrgan_model_arch.py

@@ -155,7 +155,7 @@ class RealESRGANer():
                         # input tile dimensions
                         input_tile_width = input_end_x - input_start_x
                         input_tile_height = input_end_y - input_start_y
-                        tile_idx = y * tiles_x + x + 1
+                        tile_idx = y * tiles_x + x + 1 # noqa
                         input_tile = self.img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]
 
                         # upscale tile
@@ -315,7 +315,7 @@ class IOConsumer(threading.Thread):
 
 from basicsr.utils.registry import ARCH_REGISTRY
 from torch import nn as nn
-from torch.nn import functional as F
+from torch.nn import functional as F # noqa
 
 
 class SRVGGNetCompact(nn.Module):
@@ -381,4 +381,3 @@ class SRVGGNetCompact(nn.Module):
         base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
         out += base
         return out
-    

modules/sd_samplers_common.py

@@ -55,6 +55,29 @@ def samples_to_image_grid(samples, approximation=None):
     return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
 
 
+def images_tensor_to_samples(image, approximation=None, model=None):
+    '''image[0, 1] -> latent'''
+    if approximation is None:
+        approximation = approximation_indexes.get(shared.opts.show_progress_type, 0)
+    if approximation == 3:
+        image = image.to(devices.device, devices.dtype)
+        x_latent = sd_vae_taesd.encode(image)
+    else:
+        if model is None:
+            model = shared.sd_model
+        model.first_stage_model.to(devices.dtype_vae)
+        image = image.to(shared.device, dtype=devices.dtype_vae)
+        image = image * 2 - 1
+        if len(image) > 1:
+            x_latent = torch.stack([
+                model.get_first_stage_encoding(model.encode_first_stage(torch.unsqueeze(img, 0)))[0]
+                for img in image
+            ])
+        else:
+            x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
+    return x_latent
+
+
 def store_latent(decoded):
     shared.state.current_latent = decoded
     if shared.opts.live_previews_enable and shared.opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % shared.opts.show_progress_every_n_steps == 0: