Fixed multiple settings and default values not working properly.

configs/webui/webui_streamlit.yaml

@@ -29,11 +29,11 @@ general:
     default_model_path: "models/ldm/stable-diffusion-v1/model.ckpt"
     use_sd_concepts_library: True
     sd_concepts_library_folder: "models/custom/sd-concepts-library"
-    GFPGAN_dir: "./src/gfpgan"
+    GFPGAN_dir: "./models/gfpgan"
     GFPGAN_model: "GFPGANv1.4"
     LDSR_dir: "./models/ldsr"
     LDSR_model: "model"
-    RealESRGAN_dir: "./src/realesrgan"
+    RealESRGAN_dir: "./models/realesrgan"
     RealESRGAN_model: "RealESRGAN_x4plus"
     upscaling_method: "RealESRGAN"
     outdir_txt2img: outputs/txt2img
@@ -91,6 +91,9 @@ txt2img:
 
     sampling_steps:
         value:      30
+        min_value:  10
+        max_value:  250
+        step:       10
 
     LDSR_config:
         sampling_steps: 50

scripts/Settings.py

@@ -282,14 +282,14 @@ def layout():
 				st.title("General Parameters")
 
 				# Batch Count
-				st.session_state["batch_count"] = st.text_input("Batch count.", value=st.session_state['defaults'].txt2img.batch_count.value,
-																help="How many iterations or batches of images to generate in total.")
+				st.session_state["defaults"].txt2img.batch_count.value = int(st.text_input("Batch count", value=st.session_state['defaults'].txt2img.batch_count.value,
+																					 help="How many iterations or batches of images to generate in total."))
 
-				st.session_state["batch_size"] = st.text_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
-																help="How many images are at once in a batch.\
-																It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
-																takes to finish generation as more images are generated at once.\
-																Default: 1") 					
+				st.session_state["defaults"].txt2img.batch_size.value = int(st.text_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
+																					 help="How many images are at once in a batch.\
+																					 It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
+																					 takes to finish generation as more images are generated at once.\
+																					 Default: 1"))				
 
 				default_sampler_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"]
 				st.session_state["defaults"].txt2img.default_sampler = st.selectbox("Default Sampler",
@@ -446,14 +446,14 @@ def layout():
 																								 help="Set the default value for the number of steps on the sampling steps slider. Default is: 10"))
 
 					# Batch Count
-					st.session_state["batch_count"] = st.text_input("Batch count.", value=st.session_state['defaults'].txt2img.batch_count.value,
-																	help="How many iterations or batches of images to generate in total.")
-
-					st.session_state["batch_size"] = st.text_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
-																   help="How many images are at once in a batch.\
-																   It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
-																   takes to finish generation as more images are generated at once.\
-																   Default: 1") 							
+					st.session_state["defaults"].img2img.batch_count.value = int(st.text_input("Img2img Batch count", value=st.session_state["defaults"].img2img.batch_count.value,
+																							   help="How many iterations or batches of images to generate in total."))
+
+					st.session_state["defaults"].img2img.batch_size.value = int(st.text_input("Img2img Batch size", value=st.session_state["defaults"].img2img.batch_size.value,
+																							  help="How many images are at once in a batch.\
+																							  It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
+																							  takes to finish generation as more images are generated at once.\
+																							  Default: 1")) 							
 					with col4:
 						# Inference Steps
 						st.session_state["defaults"].img2img.num_inference_steps.value = int(st.text_input("Default Inference Steps", 
@@ -635,14 +635,14 @@ def layout():
 																								 help="Set the default value for the number of steps on the sampling steps slider. Default is: 10"))
 
 					# Batch Count
-					st.session_state["batch_count"] = st.text_input("Batch count.", value=st.session_state['defaults'].txt2img.batch_count.value,
-																help="How many iterations or batches of images to generate in total.")
+					st.session_state["defaults"].txt2vid.batch_count.value = int(st.text_input("txt2vid Batch count", value=st.session_state['defaults'].txt2vid.batch_count.value,
+																							  help="How many iterations or batches of images to generate in total."))
 
-					st.session_state["batch_size"] = st.text_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
-																   help="How many images are at once in a batch.\
-																   It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
-																   takes to finish generation as more images are generated at once.\
-																   Default: 1") 
+					st.session_state["defaults"].txt2vid.batch_size.value = int(st.text_input("txt2vid Batch size", value=st.session_state.defaults.txt2vid.batch_size.value,
+																							  help="How many images are at once in a batch.\
+																							  It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it \
+																							  takes to finish generation as more images are generated at once.\
+																							  Default: 1") )
 
 					# Inference Steps
 					st.session_state["defaults"].txt2vid.num_inference_steps.value = int(st.text_input("Default Txt2Vid Inference Steps",

scripts/txt2img.py

@@ -214,9 +214,10 @@ def layout():
                                                                 help="How many iterations or batches of images to generate in total."))
 
                 st.session_state["batch_size"] = int(st.text_input("Batch size", value=st.session_state.defaults.txt2img.batch_size.value,
-                                            help="How many images are at once in a batch.\
-                                            It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
-                                            Default: 1") )
+                                                                   help="How many images are at once in a batch.\
+                                                                   It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes \
+                                                                   to finish generation as more images are generated at once.\
+                                                                   Default: 1") )
 
             with st.expander("Preview Settings"):
 
@@ -336,8 +337,9 @@ def layout():
                                 if st.session_state["LDSR_available"]:
                                     upscaling_method_list.append("LDSR")
 
+                                #print (st.session_state["RealESRGAN_available"])
                                 st.session_state["upscaling_method"] = st.selectbox("Upscaling Method", upscaling_method_list,
-                                                                                    index=upscaling_method_list.index(st.session_state['defaults'].general.upscaling_method))
+                                                                                    index=upscaling_method_list.index(str(st.session_state['defaults'].general.upscaling_method)))
 
                                 if st.session_state["RealESRGAN_available"]:
                                     with st.expander("RealESRGAN"):

scripts/txt2vid.py

@@ -867,13 +867,13 @@ def layout():
 		# run video generation
 		video, seed, info, stats = txt2vid(prompts=prompt, gpu=st.session_state["defaults"].general.gpu,
 	                                       num_steps=st.session_state.sampling_steps, max_frames=int(st.session_state.max_frames),
-	                       num_inference_steps=st.session_state.num_inference_steps,
-	                       cfg_scale=cfg_scale,do_loop=st.session_state["do_loop"],
-	                       seeds=seed, quality=100, eta=0.0, width=width,
-	                       height=height, weights_path=custom_model, scheduler=scheduler_name,
-	                       disable_tqdm=False, beta_start=st.session_state['defaults'].txt2vid.beta_start.value,
-	                       beta_end=st.session_state['defaults'].txt2vid.beta_end.value,
-	                       beta_schedule=beta_scheduler_type, starting_image=None)
+		                                   num_inference_steps=st.session_state.num_inference_steps,
+		                                   cfg_scale=cfg_scale,do_loop=st.session_state["do_loop"],
+		                                   seeds=seed, quality=100, eta=0.0, width=width,
+		                                   height=height, weights_path=custom_model, scheduler=scheduler_name,
+		                                   disable_tqdm=False, beta_start=st.session_state['defaults'].txt2vid.beta_start.value,
+		                                   beta_end=st.session_state['defaults'].txt2vid.beta_end.value,
+		                                   beta_schedule=beta_scheduler_type, starting_image=None)
 
 		#message.success('Done!', icon="✅")
 		message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")