sharing feature and refactoring

app.py

@@ -22,31 +22,7 @@ from gradio import Interface
 from diffusers import AutoencoderKL
 import pandas as pd 
 import base64
-
-js_get_url_parameters = """console.log(window.location);
-        var urlParams = new URLSearchParams(window.location.hash.substr(1));
-        var decodedParams = {};
-        console.log(window.location);
-        urlParams.forEach(function(value, key) {
-            var decodedValue = atob(value);
-            decodedParams[key] = decodedValue;
-        });
-        console.log(JSON.stringify(decodedParams));
-    """
-
-def load_app_config():
-    global appConfig
-    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))
-
-load_app_config()
+from config import *
 
 # code output order
 code = {}
@@ -65,58 +41,9 @@ code_pos_inference_steps = '090_inference_steps'
 code_pos_guidance_scale = '095_guidance_scale'
 code_pos_run_inference = '100_run_inference'
 
-# model config
-model_configs = appConfig.get("models", {})
-models = list(model_configs.keys())
-model = None
-scheduler_configs = appConfig.get("schedulers", {})
-schedulers = list(scheduler_configs.keys())
-scheduler = None
-
-devices =  appConfig.get("devices", [])
-device = None
-
-variant = None
-allow_tensorfloat32 = False
-use_safetensors = False
-data_type = 'float16'
-safety_checker = False
-requires_safety_checker = False
-manual_seed = 42
-inference_steps = 10
-guidance_scale = 0.5
-prompt = 'A white rabbit'
-negative_prompt = 'lowres, cropped, worst quality, low quality, chat bubble, chat bubbles, ugly'
-
-# init device parameters
-if torch.cuda.is_available():
-    device = "cuda" 
-    data_type = 'bfloat16'
-    allow_tensorfloat32 = True
-elif torch.backends.mps.is_available():
-    device = "mps" 
-else:
-    device = "cpu" 
-
-# inference config
-current_config = {
-    "device": device,
-    "model": model,
-    "scheduler": scheduler,
-    "variant": variant,
-    "allow_tensorflow": allow_tensorfloat32,
-    "use_safetensors": use_safetensors,
-    "data_type": data_type,
-    "safety_checker": safety_checker,
-    "requires_safety_checker": requires_safety_checker,
-    "manual_seed": manual_seed,
-    "inference_steps": inference_steps,
-    "guidance_scale": guidance_scale,
-    "prompt": prompt,
-    "negative_prompt": negative_prompt,
-}
-
-config_history = [current_config]
+initial_config, devices, models, schedulers = get_inital_config()
+
+config_history = []
 
 def get_sorted_code():
     
@@ -131,11 +58,10 @@ def device_change(device):
 
 def models_change(model, scheduler):
         
-    print(model)
     use_safetensors = False
     
     # no model selected (because this is UI init run)
-    if type(model) != list:
+    if type(model) != list and model is not None:
         
         use_safetensors = str(model_configs[model]['use_safetensors'])
 
@@ -152,8 +78,7 @@ def models_change(model, scheduler):
 
     safety_checker_change(safety_checker)
     requires_safety_checker_change(requires_safety_checker)
-    
-    print(use_safetensors)
+
     return get_sorted_code(), use_safetensors, scheduler
 
 def data_type_change(selected_data_type):
@@ -210,7 +135,7 @@ def requires_safety_checker_change(requires_safety_checker):
 
 def schedulers_change(scheduler):
     
-    if type(scheduler) != list:
+    if type(scheduler) != list and scheduler is not None:
 
         code[code_pos_scheduler] = f'pipeline.scheduler = {scheduler}.from_config(pipeline.scheduler.config)'
     
@@ -320,30 +245,31 @@ code[code_pos_run_inference] = f'''image = pipeline(
     num_inference_steps=inference_steps, 
     guidance_scale=guidance_scale).images[0]'''
 
-def dict_list_to_markdown_table(data):
-    if not data:
+def dict_list_to_markdown_table(config_history):
+
+    if not config_history:
         return ""
 
-    headers = list(data[0].keys())
+    headers = list(config_history[0].keys())
     markdown_table = "| share | " + " | ".join(headers) + " |\n"
     markdown_table += "| ---  | " + " | ".join(["---"] * len(headers)) + " |\n"
 
-    for i, row in enumerate(data):
-        encoded_row = base64.b64encode(str(row).encode()).decode()
-        share_link = f'<a href="#share/{encoded_row}">📎</a>'
-        # Construct the row with links
-        markdown_table += f"| {share_link} | " + " | ".join(str(row.get(key, "")) for key in headers) + " |\n"
+    for index, config in enumerate(config_history):
+        encoded_config = base64.b64encode(str(config).encode()).decode()
+        share_link = f'<a target="_blank" href="?config={encoded_config}">📎</a>'
+        markdown_table += f"| {share_link} | " + " | ".join(str(config.get(key, "")) for key in headers) + " |\n"
 
-    # Wrap the Markdown table in a <div> tag with horizontal scrolling
     markdown_table = '<div style="overflow-x: auto;">\n\n' + markdown_table + '</div>'
 
     return markdown_table
 
 # interface
 with gr.Blocks() as demo:
-
+    
+    in_import_config = gr.Text()
+    
     gr.Markdown('''## Text-2-Image Playground
-                <small>by <a href="https://www.linkedin.com/in/nickyreinert/">Nicky Reinert</a> | 
+                <small>by <a target="_blank" href="https://www.linkedin.com/in/nickyreinert/">Nicky Reinert</a> | 
                 home base: https://huggingface.co/spaces/n42/pictero
                 </small>''')
     gr.Markdown("### Device specific settings")
@@ -389,7 +315,7 @@ with gr.Blocks() as demo:
         out_image = gr.Image()
         out_code = gr.Code(get_sorted_code(), label="Code")
     with gr.Row():
-        out_current_config = gr.Code(value=str(current_config), label="Current config")
+        out_current_config = gr.Code(value=str(initial_config), label="Current config")
     with gr.Row():
         out_config_history = gr.Markdown(dict_list_to_markdown_table(config_history))
     
@@ -416,5 +342,22 @@ with gr.Blocks() as demo:
         in_manual_seed,
         in_guidance_scale
         ], outputs=[out_image])
-        
+
+    demo.load(fn=init_config, inputs=out_current_config, 
+        outputs=[
+            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
+            ])
+
 demo.launch()

config.py

@@ -0,0 +1,113 @@
+import gradio as gr
+import base64
+import json
+import torch
+
+def load_app_config():
+    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
+
+appConfig = load_app_config()
+
+def get_inital_config():
+        
+    # model config
+    model_configs = appConfig.get("models", {})
+    models = list(model_configs.keys())
+    model = None
+    scheduler_configs = appConfig.get("schedulers", {})
+    schedulers = list(scheduler_configs.keys())
+    scheduler = None
+
+    devices =  appConfig.get("devices", [])
+    device = None
+
+    variant = None
+    allow_tensorfloat32 = False
+    use_safetensors = False
+    data_type = 'float16'
+    safety_checker = False
+    requires_safety_checker = False
+    manual_seed = 42
+    inference_steps = 10
+    guidance_scale = 0.5
+    prompt = 'A white rabbit'
+    negative_prompt = 'lowres, cropped, worst quality, low quality, chat bubble, chat bubbles, ugly'
+
+    # # init device parameters
+    if torch.cuda.is_available():
+        device = "cuda" 
+        data_type = 'bfloat16'
+        allow_tensorfloat32 = True
+    elif torch.backends.mps.is_available():
+        device = "mps" 
+    else:
+        device = "cpu"
+
+    # inference config
+    initial_config = {
+        "device": device,
+        "model": model,
+        "scheduler": scheduler,
+        "variant": variant,
+        "allow_tensorflow": allow_tensorfloat32,
+        "use_safetensors": use_safetensors,
+        "data_type": data_type,
+        "safety_checker": safety_checker,
+        "requires_safety_checker": requires_safety_checker,
+        "manual_seed": manual_seed,
+        "inference_steps": inference_steps,
+        "guidance_scale": guidance_scale,
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+    }
+
+
+    return initial_config, devices, models, schedulers
+
+def init_config(request: gr.Request, inital_config):
+    
+    encoded_params = request.request.query_params.get('config')
+    return_config = {}
+
+    # get configuration from URL if GET parameter `share` is set
+    if encoded_params is not None:
+        decoded_params = base64.b64decode(encoded_params)
+        decoded_params = decoded_params.decode('utf-8')
+        decoded_params = decoded_params.replace("'", '"').replace('None', 'null').replace('False', 'false')
+        dict_params = json.loads(decoded_params)
+        
+        return_config = dict_params
+            
+    # otherwise use default initial config
+    else:
+        
+        inital_config = inital_config.replace("'", '"').replace('None', 'null').replace('False', 'false')
+        dict_inital_config = json.loads(inital_config)
+        
+        return_config = dict_inital_config
+
+    return [return_config['model'],
+            return_config['device'],
+            return_config['use_safetensors'],
+            return_config['data_type'],
+            return_config['variant'],
+            return_config['safety_checker'],
+            return_config['requires_safety_checker'],
+            return_config['scheduler'],
+            return_config['prompt'],
+            return_config['negative_prompt'],
+            return_config['inference_steps'],
+            return_config['manual_seed'],
+            return_config['guidance_scale']
+            ]
+