Add "send to txt2img" button to PNGInfo

app.py

@@ -10,6 +10,54 @@ import PIL
 from PIL.ExifTags import TAGS
 import html
 
+model_names = {
+    'absolutereality_V16': 'absolutereality_V16.safetensors [37db0fc3]',
+    'absolutereality_v181': 'absolutereality_v181.safetensors [3d9d4d2b]',
+    'analog-diffusion-1.0': 'analog-diffusion-1.0.ckpt [9ca13f02]',
+    'anythingv3_0-pruned': 'anythingv3_0-pruned.ckpt [2700c435]',
+    'anything-v4.5-pruned': 'anything-v4.5-pruned.ckpt [65745d25]',
+    'anythingV5_PrtRE': 'anythingV5_PrtRE.safetensors [893e49b9]',
+    'AOM3A3_orangemixs': 'AOM3A3_orangemixs.safetensors [9600da17]',
+    'childrensStories_v13D': 'childrensStories_v13D.safetensors [9dfaabcb]',
+    'childrensStories_v1SemiReal': 'childrensStories_v1SemiReal.safetensors [a1c56dbb]',
+    'childrensStories_v1ToonAnime': 'childrensStories_v1ToonAnime.safetensors [2ec7b88b]',
+    'cyberrealistic_v33': 'cyberrealistic_v33.safetensors [82b0d085]',
+    'deliberate_v2': 'deliberate_v2.safetensors [10ec4b29]',
+    'deliberate_v3': 'deliberate_v3.safetensors [afd9d2d4]',
+    'dreamlike-anime-1.0': 'dreamlike-anime-1.0.safetensors [4520e090]',
+    'dreamlike-diffusion-1.0': 'dreamlike-diffusion-1.0.safetensors [5c9fd6e0]',
+    'dreamlike-photoreal-2.0': 'dreamlike-photoreal-2.0.safetensors [fdcf65e7]',
+    'dreamshaper_6BakedVae': 'dreamshaper_6BakedVae.safetensors [114c8abb]',
+    'dreamshaper_7': 'dreamshaper_7.safetensors [5cf5ae06]',
+    'dreamshaper_8': 'dreamshaper_8.safetensors [9d40847d]',
+    'edgeOfRealism_eorV20': 'edgeOfRealism_eorV20.safetensors [3ed5de15]',
+    'EimisAnimeDiffusion_V1': 'EimisAnimeDiffusion_V1.ckpt [4f828a15]',
+    'elldreths-vivid-mix': 'elldreths-vivid-mix.safetensors [342d9d26]',
+    'epicrealism_naturalSinRC1VAE': 'epicrealism_naturalSinRC1VAE.safetensors [90a4c676]',
+    'ICantBelieveItsNotPhotography_seco': 'ICantBelieveItsNotPhotography_seco.safetensors [4e7a3dfd]',
+    'juggernaut_aftermath': 'juggernaut_aftermath.safetensors [5e20c455]',
+    'lyriel_v16': 'lyriel_v16.safetensors [68fceea2]',
+    'mechamix_v10': 'mechamix_v10.safetensors [ee685731]',
+    'meinamix_meinaV9': 'meinamix_meinaV9.safetensors [2ec66ab0]',
+    'meinamix_meinaV11': 'meinamix_meinaV11.safetensors [b56ce717]',
+    'openjourney_V4': 'openjourney_V4.ckpt [ca2f377f]',
+    'portraitplus_V1.0': 'portraitplus_V1.0.safetensors [1400e684]',
+    'Realistic_Vision_V1.4-pruned-fp16': 'Realistic_Vision_V1.4-pruned-fp16.safetensors [8d21810b]',
+    'Realistic_Vision_V2.0': 'Realistic_Vision_V2.0.safetensors [79587710]',
+    'Realistic_Vision_V4.0': 'Realistic_Vision_V4.0.safetensors [29a7afaa]',
+    'Realistic_Vision_V5.0': 'Realistic_Vision_V5.0.safetensors [614d1063]',
+    'redshift_diffusion-V10': 'redshift_diffusion-V10.safetensors [1400e684]',
+    'revAnimated_v122': 'revAnimated_v122.safetensors [3f4fefd9]',
+    'rundiffusionFX25D_v10': 'rundiffusionFX25D_v10.safetensors [cd12b0ee]',
+    'rundiffusionFX_v10': 'rundiffusionFX_v10.safetensors [cd4e694d]',
+    'sdv1_4': 'sdv1_4.ckpt [7460a6fa]',
+    'v1-5-pruned-emaonly': 'v1-5-pruned-emaonly.safetensors [d7049739]',
+    'shoninsBeautiful_v10': 'shoninsBeautiful_v10.safetensors [25d8c546]',
+    'theallys-mix-ii-churned': 'theallys-mix-ii-churned.safetensors [5d9225a4]',
+    'timeless-1.0': 'timeless-1.0.ckpt [7c4971d4]',
+    'toonyou_beta6': 'toonyou_beta6.safetensors [980f6b15]'
+}
+
 
 class Prodia:
     def __init__(self, api_key, base=None):
@@ -80,6 +128,68 @@ def image_to_base64(image_path):
 
     return img_str.decode('utf-8')  # Convert bytes to string
 
+def get_data(text):
+    results = {}
+    patterns = {
+        'prompt': r'(.*)',
+        'negative_prompt': r'Negative prompt: (.*)',
+        'steps': r'Steps: (\d+),',
+        'seed': r'Seed: (\d+),',
+        'sampler': r'Sampler:\s*([^\s,]+(?:\s+[^\s,]+)*)', 
+        'model': r'Model:\s*([^\s,]+)',
+        'cfg_scale': r'CFG scale:\s*([\d\.]+)',
+        'size': r'Size:\s*([0-9]+x[0-9]+)'
+        }
+    for key in ['prompt', 'negative_prompt', 'steps', 'seed', 'sampler', 'model', 'cfg_scale', 'size']:
+        match = re.search(patterns[key], text)
+        if match:
+            results[key] = match.group(1)
+        else:
+            results[key] = None
+    if results['size'] is not None:
+        w, h = results['size'].split("x")
+        results['w'] = w
+        results['h'] = h
+    else:
+        results['w'] = None
+        results['h'] = None
+    return results
+
+def send_to_txt2img(image):
+    
+    result = {tabs: gr.Tabs.update(selected="t2i")}
+
+    try:
+        text = image.info['parameters']
+        data = get_data(text)
+        result[prompt] = gr.update(value=data['prompt'])
+        result[negative_prompt] = gr.update(value=data['negative_prompt']) if data['negative_prompt'] is not None else gr.update()
+        result[steps] = gr.update(value=int(data['steps'])) if data['steps'] is not None else gr.update()
+        result[seed] = gr.update(value=int(data['seed'])) if data['seed'] is not None else gr.update()
+        result[cfg_scale] = gr.update(value=float(data['cfg_scale'])) if data['cfg_scale'] is not None else gr.update()
+        result[width] = gr.update(value=int(data['w'])) if data['w'] is not None else gr.update()
+        result[height] = gr.update(value=int(data['h'])) if data['h'] is not None else gr.update()
+        result[sampler] = gr.update(value=data['sampler']) if data['sampler'] is not None else gr.update()
+        if model in model_names:
+            result[model] = gr.update(value=model_names[model])
+        else:
+            result[model] = gr.update()
+        return result
+
+    except Exception as e:
+        print(e)
+        result[prompt] = gr.update()
+        result[negative_prompt] = gr.update()
+        result[steps] = gr.update()
+        result[seed] = gr.update()
+        result[cfg_scale] = gr.update()
+        result[width] = gr.update()
+        result[height] = gr.update()
+        result[sampler] = gr.update()
+        result[model] = gr.update()
+
+        return result
+
 
 
 prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY"))
@@ -110,7 +220,7 @@ css = """
 
 with gr.Blocks(css=css) as demo:
 
-
+    
     with gr.Row():
         with gr.Column(scale=6):
             model = gr.Dropdown(interactive=True,value="absolutereality_v181.safetensors [3d9d4d2b]", show_label=True, label="Stable Diffusion Checkpoint", choices=prodia_client.list_models())
@@ -118,96 +228,99 @@ with gr.Blocks(css=css) as demo:
         with gr.Column(scale=1):
             gr.Markdown(elem_id="powered-by-prodia", value="AUTOMATIC1111 Stable Diffusion Web UI.<br>Powered by [Prodia](https://prodia.com).")
 
-    with gr.Tab("txt2img"):
-        with gr.Row():
-            with gr.Column(scale=6, min_width=600):
-                prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3)
-                negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly")
-            with gr.Column():
-                text_button = gr.Button("Generate", variant='primary', elem_id="generate")
-                
-        with gr.Row():
-            with gr.Column(scale=3):
-                with gr.Tab("Generation"):
-                    with gr.Row():
-                        with gr.Column(scale=1):
-                            sampler = gr.Dropdown(value="Euler a", show_label=True, label="Sampling Method", choices=[
-                                "Euler",
-                                "Euler a",
-                                "LMS",
-                                "Heun",
-                                "DPM2",
-                                "DPM2 a",
-                                "DPM++ 2S a",
-                                "DPM++ 2M",
-                                "DPM++ SDE",
-                                "DPM fast",
-                                "DPM adaptive",
-                                "LMS Karras",
-                                "DPM2 Karras",
-                                "DPM2 a Karras",
-                                "DPM++ 2S a Karras",
-                                "DPM++ 2M Karras",
-                                "DPM++ SDE Karras",
-                                "DDIM",
-                                "PLMS",
-                            ])
+    with gr.Tabs() as tabs:
+        with gr.Tab("txt2img", id='t2i'):
+            with gr.Row():
+                with gr.Column(scale=6, min_width=600):
+                    prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3)
+                    negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly")
+                with gr.Column():
+                    text_button = gr.Button("Generate", variant='primary', elem_id="generate")
+                    
+            with gr.Row():
+                with gr.Column(scale=3):
+                    with gr.Tab("Generation"):
+                        with gr.Row():
+                            with gr.Column(scale=1):
+                                sampler = gr.Dropdown(value="Euler a", show_label=True, label="Sampling Method", choices=[
+                                    "Euler",
+                                    "Euler a",
+                                    "LMS",
+                                    "Heun",
+                                    "DPM2",
+                                    "DPM2 a",
+                                    "DPM++ 2S a",
+                                    "DPM++ 2M",
+                                    "DPM++ SDE",
+                                    "DPM fast",
+                                    "DPM adaptive",
+                                    "LMS Karras",
+                                    "DPM2 Karras",
+                                    "DPM2 a Karras",
+                                    "DPM++ 2S a Karras",
+                                    "DPM++ 2M Karras",
+                                    "DPM++ SDE Karras",
+                                    "DDIM",
+                                    "PLMS",
+                                ])
+                                
+                            with gr.Column(scale=1):
+                                steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=30, value=25, step=1)
+    
+                        with gr.Row():
+                            with gr.Column(scale=1):
+                                width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
+                                height = gr.Slider(label="Height", maximum=1024, value=512, step=8)
                             
-                        with gr.Column(scale=1):
-                            steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=30, value=25, step=1)
-
-                    with gr.Row():
-                        with gr.Column(scale=1):
-                            width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
-                            height = gr.Slider(label="Height", maximum=1024, value=512, step=8)
-                        
-                        with gr.Column(scale=1):
-                            batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
-                            batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
-
-                    cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
-                    seed = gr.Number(label="Seed", value=-1)
-
-                
-            with gr.Column(scale=2):
-                image_output = gr.Image(value="https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png")
-
-        text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output)
+                            with gr.Column(scale=1):
+                                batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
+                                batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
     
-    with gr.Tab("PNG Info"):
-        def plaintext_to_html(text, classname=None):
-            content = "<br>\n".join(html.escape(x) for x in text.split('\n'))
-
-            return f"<p class='{classname}'>{content}</p>" if classname else f"<p>{content}</p>"
-
-
-        def get_exif_data(image):
-            items = image.info
-
-            info = ''
-            for key, text in items.items():
-                info += f"""
-                <div>
-                <p><b>{plaintext_to_html(str(key))}</b></p>
-                <p>{plaintext_to_html(str(text))}</p>
-                </div>
-                """.strip()+"\n"
-
-            if len(info) == 0:
-                message = "Nothing found in the image."
-                info = f"<div><p>{message}<p></div>"
-
-            return info
-
-        with gr.Row():
-            with gr.Column():
-                image_input = gr.Image(type="pil")
-                
-            with gr.Column():
-                exif_output = gr.HTML(label="EXIF Data")
-
-        image_input.upload(get_exif_data, inputs=[image_input], outputs=exif_output)
-
+                        cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
+                        seed = gr.Number(label="Seed", value=-1)
+    
+                    
+                with gr.Column(scale=2):
+                    image_output = gr.Image(value="https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png")
+    
+            text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed], outputs=image_output)
+        
+        with gr.Tab("PNG Info"):
+            def plaintext_to_html(text, classname=None):
+                content = "<br>\n".join(html.escape(x) for x in text.split('\n'))
+    
+                return f"<p class='{classname}'>{content}</p>" if classname else f"<p>{content}</p>"
+    
+    
+            def get_exif_data(image):
+                items = image.info
+    
+                info = ''
+                for key, text in items.items():
+                    info += f"""
+                    <div>
+                    <p><b>{plaintext_to_html(str(key))}</b></p>
+                    <p>{plaintext_to_html(str(text))}</p>
+                    </div>
+                    """.strip()+"\n"
+    
+                if len(info) == 0:
+                    message = "Nothing found in the image."
+                    info = f"<div><p>{message}<p></div>"
+    
+                return info
+    
+            with gr.Row():
+                with gr.Column():
+                    image_input = gr.Image(type="pil")
+                    
+                with gr.Column():
+                    exif_output = gr.HTML(label="EXIF Data")
+                    send_to_txt2img_btn = gr.Button("Send to txt2img")
+    
+            image_input.upload(get_exif_data, inputs=[image_input], outputs=exif_output)
+            send_to_txt2img_btn.click(send_to_txt2img, inputs=[image_input], outputs=[tabs, prompt, negative_prompt, steps, seed,
+                                                                                          model, sampler, width, height, cfg_scale])
 
 demo.queue(concurrency_count=32)
 demo.launch()