Delete app (1).py

app (1).py

@@ -1,338 +0,0 @@
-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:
-            print(params)
-            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=os.getenv("PRODIA_API_KEY"))
-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)
-
-    if job['status'] != "succeeded":
-        raise gr.Error("job failed")
-
-    return job["imageUrl"]
-
-
-def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed):
-    if input_image is None:
-        raise gr.Error("Please add an image to run img2img")
-        
-    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)
-
-    if job['status'] != "succeeded":
-        raise gr.Error("job failed")
-
-    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 [Prodia](https://prodia.com).<br>For more features and faster generation times check out our [API Docs](https://docs.prodia.com/reference/getting-started-guide).")
-
-    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=25, value=20, step=1)
-    
-                        with gr.Row():
-                            with gr.Column(scale=1):
-                                width = gr.Slider(label="Width", maximum=1024, minimum=128, value=512, step=8)
-                                height = gr.Slider(label="Height", maximum=1024, minimum=128, 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=25, value=20, 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=32)
-
-demo.queue(max_size=20, api_open=False).launch(max_threads=40, show_api=False)