fixing wrong history address

app.py

@@ -104,24 +104,24 @@ def get_tensorfloat32(allow_tensorfloat32):
         
     return True if str(allow_tensorfloat32).lower() == 'true' else False
 
-def get_scheduler(scheduler, config):
+def get_scheduler(scheduler, pipeline_config):
     
     if scheduler == "DDPMScheduler":
-        return DDPMScheduler.from_config(config)
+        return DDPMScheduler.from_config(pipeline_config)
     elif scheduler == "DDIMScheduler":
-        return DDIMScheduler.from_config(config)
+        return DDIMScheduler.from_config(pipeline_config)
     elif scheduler == "PNDMScheduler":
-        return PNDMScheduler.from_config(config)
+        return PNDMScheduler.from_config(pipeline_config)
     elif scheduler == "LMSDiscreteScheduler":
-        return LMSDiscreteScheduler.from_config(config)
+        return LMSDiscreteScheduler.from_config(pipeline_config)
     elif scheduler == "EulerAncestralDiscreteScheduler":
-        return EulerAncestralDiscreteScheduler.from_config(config)
+        return EulerAncestralDiscreteScheduler.from_config(pipeline_config)
     elif scheduler == "EulerDiscreteScheduler":
-        return EulerDiscreteScheduler.from_config(config)
+        return EulerDiscreteScheduler.from_config(pipeline_config)
     elif scheduler == "DPMSolverMultistepScheduler":
-        return DPMSolverMultistepScheduler.from_config(config)
+        return DPMSolverMultistepScheduler.from_config(pipeline_config)
     else:
-        return DPMSolverMultistepScheduler.from_config(config)
+        return DPMSolverMultistepScheduler.from_config(pipeline_config)
 
 # get 
 # - initial configuration, 
@@ -129,41 +129,9 @@ def get_scheduler(scheduler, config):
 # - a list of available models from the config file
 # - a list of available schedulers from the config file
 # - a dict that contains code to for reproduction
-config.set_inital_config()
-# config.current, devices, model_configs, scheduler_configs, code = config.get_inital_config()
-
-# device                  = config.current["device"]
-# model                   = config.current["model"]
-# scheduler               = config.current["scheduler"]
-# variant                 = config.current["variant"]
-# allow_tensorfloat32     = config.current["allow_tensorfloat32"]
-# use_safetensors         = config.current["use_safetensors"]
-# data_type               = config.current["data_type"]
-# safety_checker          = config.current["safety_checker"]
-# requires_safety_checker = config.current["requires_safety_checker"]
-# manual_seed             = config.current["manual_seed"]
-# inference_steps         = config.current["inference_steps"]
-# guidance_scale          = config.current["guidance_scale"]
-# prompt                  = config.current["prompt"]
-# negative_prompt         = config.current["negative_prompt"]
-
-config_history = []
 
 # pipeline
-def run_inference(model,
-                    device, 
-                    use_safetensors, 
-                    data_type, 
-                    variant, 
-                    safety_checker,
-                    requires_safety_checker,
-                    scheduler,
-                    prompt,
-                    negative_prompt,
-                    inference_steps,
-                    manual_seed,
-                    guidance_scale,
-                    progress=gr.Progress(track_tqdm=True)):
+def run_inference(progress=gr.Progress(track_tqdm=True)):
     
     if config.current["model"] != None and config.current["scheduler"] != None:
         
@@ -175,35 +143,34 @@ def run_inference(model,
         
         pipeline = DiffusionPipeline.from_pretrained(
             config.current["model"], 
-            use_safetensors=config.current["use_safetensors"], 
-            torch_dtype=get_data_type(config.current["data_type"]), 
-            variant=variant).to(config.current["device"])
+            use_safetensors = config.current["use_safetensors"], 
+            torch_dtype = get_data_type(config.current["data_type"]), 
+            variant = config.current["variant"]).to(config.current["device"])
         
         if config.current["safety_checker"] is None or str(config.current["safety_checker"]).lower == 'false':
             pipeline.safety_checker = None 
 
         pipeline.requires_safety_checker = config.current["requires_safety_checker"]
                 
-        pipeline.scheduler = get_scheduler(scheduler, pipeline.scheduler.config)
+        pipeline.scheduler = get_scheduler(config.current["scheduler"], pipeline.scheduler.config)
         
-        manual_seed = int(manual_seed)
-        if manual_seed < 0 or manual_seed is None or manual_seed == '':
-            generator = torch.Generator(device)
+        if config.current["manual_seed"] < 0 or config.current["manual_seed"] is None or config.current["manual_seed"] == '':
+            generator = torch.Generator(config.current["device"])
         else:
             generator = torch.manual_seed(42) 
         
         progress((3,3), desc="Creating the result...")
 
         image = pipeline(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            generator=generator,
-            num_inference_steps=int(inference_steps), 
-            guidance_scale=float(guidance_scale)).images[0]
+            prompt = config.current["prompt"],
+            negative_prompt = config.current["negative_prompt"],
+            generator = generator,
+            num_inference_steps = config.current["inference_steps"], 
+            guidance_scale = config.current["guidance_scale"]).images[0]
         
-        config_history.append(config.current.copy())
+        config.history.append(config.current.copy())
 
-        return image, dict_list_to_markdown_table(config_history)
+        return image, dict_list_to_markdown_table(config.history)
     
     else:
     
@@ -297,24 +264,10 @@ with gr.Blocks() as demo:
     in_guidance_scale.change(guidance_scale_change, inputs=[in_guidance_scale], outputs=[out_current_config, out_code])
     in_prompt.change(prompt_change, inputs=[in_prompt], outputs=[out_current_config, out_code])
     in_negative_prompt.change(negative_prompt_change, inputs=[in_negative_prompt], outputs=[out_current_config, out_code])
-    btn_start_pipeline.click(run_inference, inputs=[
-        in_models, 
-        in_devices,
-        in_use_safetensors,
-        in_data_type,
-        in_variant,
-        in_safety_checker,
-        in_requires_safety_checker,
-        in_schedulers,
-        in_prompt,
-        in_negative_prompt,
-        in_inference_steps,
-        in_manual_seed,
-        in_guidance_scale
-        ], outputs=[
-            out_image,
-            out_config_history])
+    btn_start_pipeline.click(run_inference, inputs=[], outputs=[out_image, out_config_history])
 
+    # send current respect initial config to init_config to populate parameters to all relevant input fields
+    # if GET parameter is set, it will overwrite initial config parameters
     demo.load(fn=init_config, inputs=out_current_config, 
         outputs=[
             in_models,

config.py

@@ -49,21 +49,6 @@ class Config:
         self.history = []
         self.devices = []
 
-    def load_app_config(self):
-        try:
-            with open('appConfig.json', 'r') as f:
-                appConfig = json.load(f)
-        except FileNotFoundError:
-            print("App config file not found.")
-        except json.JSONDecodeError:
-            print("Error decoding JSON in app config file.")
-        except Exception as e:
-            print("An error occurred while loading app config:", str(e))
-            
-        return appConfig
-
-    def set_inital_config(self):
-
         appConfig = self.load_app_config()
 
         self.model_configs = appConfig.get("models", {})
@@ -102,6 +87,19 @@ class Config:
 
         self.assemble_code()
 
+    def load_app_config(self):
+        try:
+            with open('appConfig.json', 'r') as f:
+                appConfig = json.load(f)
+        except FileNotFoundError:
+            print("App config file not found.")
+        except json.JSONDecodeError:
+            print("Error decoding JSON in app config file.")
+        except Exception as e:
+            print("An error occurred while loading app config:", str(e))
+            
+        return appConfig
+
     def set_config(self, key, value):
         
         self.current[key] = value