fixed incorrect prompt assignment

README.md

@@ -108,8 +108,7 @@ recommended requirements allow to achieve more or less seamless experience.
 >Q: Can I run this UI on a 8GB GPU?
 
 A: The UI may run on a 8GB GPUs with 12GB of system RAM and the swap of the same size, but it may require constant restarts
-due to the insufficient memory. DeepFloyd IF is designed for 24GB of VRAM. 
-Some UI features, such as style transfer, are implemented only for GPUs with 24GB of video memory.
+due to the insufficient memory.
 
 >Q: My generations look like halftone prints that were shredded and glued back by the pieces. How can I improve them?
 

modules/iflab/const.py

@@ -1,6 +1,6 @@
 import os
 
-VERSION = "0.2.1"
+VERSION = "0.2.2"
 
 DEBUG = os.getenv("IFLAB_DEBUG", False)
 DEBUG = not not (DEBUG and DEBUG != "0")

modules/iflab/pipelines/pipeline.py

@@ -112,14 +112,14 @@ def compute_t5_embs(self, update_prompt=True, update_negative=True, update_style
         if update_negative:
             if self.negative_prompt is not None:
                 promptv = [self.negative_prompt] * self.count
-                self.t5_embs = self.stages.t5.get_text_embeddings(promptv)
+                self.negative_t5_embs = self.stages.t5.get_text_embeddings(promptv)
             else:
                 self.negative_t5_embs = None
 
         if update_style:
             if self.style_prompt is not None:
                 promptv = [self.style_prompt] * self.count
-                self.t5_embs = self.stages.t5.get_text_embeddings(promptv)
+                self.style_t5_embs = self.stages.t5.get_text_embeddings(promptv)
             else:
                 self.style_t5_embs = None
 

modules/iflab/pipelines/style_transfer.py

@@ -32,9 +32,3 @@ def modify_args(self, args):
         # TODO: move support images to the class level in all pipelines for optimization
         args["if_I_kwargs"].low_res = _prepare_pil_image(self.support_image, 64)
         args["if_I_kwargs"].mid_res = _prepare_pil_image(self.support_image, 256)
-
-    def upscale(self, **kwargs):
-        if self.stages.sequential_load != SEQ_LOAD_OFF:
-            raise ModelError("Upscale is implemented only for I+II+III")
-        else:
-            return super().upscale(**kwargs)