Create app.py

app.py

@@ -0,0 +1,326 @@
+import gradio as gr
+import requests
+import time
+import json
+import base64
+import os
+from io import BytesIO
+import html
+import re
+
+class Prodia:
+    def __init__(self, api_key, base=None):
+        self.base = base or "https://api.prodia.com/v1"
+        self.headers = {
+            "X-Prodia-Key": api_key
+        }
+    
+    def generate(self, params):
+        response = self._post(f"{self.base}/sd/generate", params)
+        return response.json()
+    
+    def transform(self, params):
+        response = self._post(f"{self.base}/sd/transform", params)
+        return response.json()
+    
+    def controlnet(self, params):
+        response = self._post(f"{self.base}/sd/controlnet", params)
+        return response.json()
+    
+    def get_job(self, job_id):
+        response = self._get(f"{self.base}/job/{job_id}")
+        return response.json()
+
+    def wait(self, job):
+        job_result = job
+
+        while job_result['status'] not in ['succeeded', 'failed']:
+            time.sleep(0.25)
+            job_result = self.get_job(job['job'])
+
+        return job_result
+
+    def list_models(self):
+        response = self._get(f"{self.base}/sd/models")
+        return response.json()
+
+    def list_samplers(self):
+        response = self._get(f"{self.base}/sd/samplers")
+        return response.json()
+
+    def _post(self, url, params):
+        headers = {
+            **self.headers,
+            "Content-Type": "application/json"
+        }
+        response = requests.post(url, headers=headers, data=json.dumps(params))
+
+        if response.status_code != 200:
+            raise Exception(f"Bad Prodia Response: {response.status_code}")
+
+        return response
+
+    def _get(self, url):
+        response = requests.get(url, headers=self.headers)
+
+        if response.status_code != 200:
+            raise Exception(f"Bad Prodia Response: {response.status_code}")
+
+        return response
+
+
+def image_to_base64(image):
+    # Convert the image to bytes
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")  # You can change format to PNG if needed
+    
+    # Encode the bytes to base64
+    img_str = base64.b64encode(buffered.getvalue())
+
+    return img_str.decode('utf-8')  # Convert bytes to string
+
+
+def remove_id_and_ext(text):
+    text = re.sub(r'\[.*\]$', '', text)
+    extension = text[-12:].strip()
+    if extension == "safetensors":
+        text = text[:-13]
+    elif extension == "ckpt":
+        text = text[:-4]
+    return text
+
+
+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.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)
+
+        return result
+
+
+prodia_client = Prodia(api_key="7b736a45-069e-483c-8e7f-098067fb32b2")
+model_list = prodia_client.list_models()
+model_names = {}
+
+for model_name in model_list:
+    name_without_ext = remove_id_and_ext(model_name)
+    model_names[name_without_ext] = model_name
+
+
+def txt2img(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
+    result = prodia_client.generate({
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+        "model": model,
+        "steps": steps,
+        "sampler": sampler,
+        "cfg_scale": cfg_scale,
+        "width": width,
+        "height": height,
+        "seed": seed
+    })
+
+    job = prodia_client.wait(result)
+
+    return job["imageUrl"]
+
+
+def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
+    result = prodia_client.transform({
+        "imageData": image_to_base64(input_image),
+        "denoising_strength": denoising,
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+        "model": model,
+        "steps": steps,
+        "sampler": sampler,
+        "cfg_scale": cfg_scale,
+        "width": width,
+        "height": height,
+        "seed": seed
+    })
+
+    job = prodia_client.wait(result)
+
+    return job["imageUrl"]
+
+
+css = """
+#generate {
+    height: 100%;
+}
+"""
+
+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())
+  
+        with gr.Column(scale=1):
+            gr.Markdown(elem_id="powered-by-prodia", value="AUTOMATIC1111 Stable Diffusion Web UI.<br>Powered by [Arifzyn.](https://api.arifzyn.biz.id).")
+
+    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="DPM++ 2M Karras", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers())
+                                
+                            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(txt2img, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height,
+                                               seed], outputs=image_output, concurrency_limit=64)
+        
+        with gr.Tab("img2img", id='i2i'):
+            with gr.Row():
+                with gr.Column(scale=6, min_width=600):
+                    i2i_prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", placeholder="Prompt", show_label=False, lines=3)
+                    i2i_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():
+                    i2i_text_button = gr.Button("Generate", variant='primary', elem_id="generate")
+                    
+            with gr.Row():
+                with gr.Column(scale=3):
+                    with gr.Tab("Generation"):
+                        i2i_image_input = gr.Image(type="pil")
+
+                        with gr.Row():
+                            with gr.Column(scale=1):
+                                i2i_sampler = gr.Dropdown(value="Euler a", show_label=True, label="Sampling Method", choices=prodia_client.list_samplers())
+                                
+                            with gr.Column(scale=1):
+                                i2i_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=30, value=25, step=1)
+
+                        with gr.Row():
+                            with gr.Column(scale=1):
+                                i2i_width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
+                                i2i_height = gr.Slider(label="Height", maximum=1024, value=512, step=8)
+                            
+                            with gr.Column(scale=1):
+                                i2i_batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
+                                i2i_batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
+    
+                        i2i_cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
+                        i2i_denoising = gr.Slider(label="Denoising Strength", minimum=0, maximum=1, value=0.7, step=0.1)
+                        i2i_seed = gr.Number(label="Seed", value=-1)
+
+                with gr.Column(scale=2):
+                    i2i_image_output = gr.Image(value="https://images.prodia.xyz/8ede1a7c-c0ee-4ded-987d-6ffed35fc477.png")
+    
+            i2i_text_button.click(img2img, inputs=[i2i_image_input, i2i_denoising, i2i_prompt, i2i_negative_prompt,
+                                                   model, i2i_steps, i2i_sampler, i2i_cfg_scale, i2i_width, i2i_height,
+                                                   i2i_seed], outputs=i2i_image_output, concurrency_limit=64)
+        
+        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],
+                                      concurrency_limit=64)
+
+demo.queue(max_size=80, api_open=False).launch(show_error=True)
+