add option to read generation params for learning previews from txt2img

modules/hypernetworks/hypernetwork.py

@@ -180,7 +180,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
     return self.to_out(out)
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
+def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
@@ -265,20 +265,31 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
 
-            preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
-
             optimizer.zero_grad()
             shared.sd_model.cond_stage_model.to(devices.device)
             shared.sd_model.first_stage_model.to(devices.device)
 
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
-                prompt=preview_text,
-                steps=20,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
 
+            if preview_from_txt2img:
+                p.prompt = preview_prompt
+                p.negative_prompt = preview_negative_prompt
+                p.steps = preview_steps
+                p.sampler_index = preview_sampler_index
+                p.cfg_scale = preview_cfg_scale
+                p.seed = preview_seed
+                p.width = preview_width
+                p.height = preview_height
+            else:
+                p.prompt = entry.cond_text
+                p.steps = 20
+
+            preview_text = p.prompt
+
             processed = processing.process_images(p)
             image = processed.images[0] if len(processed.images)>0 else None
 

modules/textual_inversion/textual_inversion.py

@@ -172,7 +172,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -259,18 +259,29 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
 
-            preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
-
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
-                prompt=preview_text,
-                steps=20,
-                height=training_height,
-                width=training_width,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
 
+            if preview_from_txt2img:
+                p.prompt = preview_prompt
+                p.negative_prompt = preview_negative_prompt
+                p.steps = preview_steps
+                p.sampler_index = preview_sampler_index
+                p.cfg_scale = preview_cfg_scale
+                p.seed = preview_seed
+                p.width = preview_width
+                p.height = preview_height
+            else:
+                p.prompt = entry.cond_text
+                p.steps = 20
+                p.width = training_width
+                p.height = training_height
+
+            preview_text = p.prompt
+
             processed = processing.process_images(p)
             image = processed.images[0]
 

modules/ui.py

@@ -711,6 +711,18 @@ def create_ui(wrap_gradio_gpu_call):
                 (firstphase_width, "First pass size-1"),
                 (firstphase_height, "First pass size-2"),
             ]
+
+            txt2img_preview_params = [
+                txt2img_prompt,
+                txt2img_negative_prompt,
+                steps,
+                sampler_index,
+                cfg_scale,
+                seed,
+                width,
+                height,
+            ]
+
             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
 
     with gr.Blocks(analytics_enabled=False) as img2img_interface:
@@ -1162,7 +1174,7 @@ def create_ui(wrap_gradio_gpu_call):
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
-                    preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
+                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
 
                     with gr.Row():
                         interrupt_training = gr.Button(value="Interrupt")
@@ -1240,7 +1252,8 @@ def create_ui(wrap_gradio_gpu_call):
                 save_embedding_every,
                 template_file,
                 save_image_with_stored_embedding,
-                preview_image_prompt,
+                preview_from_txt2img,
+                *txt2img_preview_params,
             ],
             outputs=[
                 ti_output,
@@ -1260,7 +1273,8 @@ def create_ui(wrap_gradio_gpu_call):
                 create_image_every,
                 save_embedding_every,
                 template_file,
-                preview_image_prompt,
+                preview_from_txt2img,
+                *txt2img_preview_params,
             ],
             outputs=[
                 ti_output,