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README (7).md

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+---
+title: DreamDrop
+emoji: 🥏
+colorFrom: purple
+colorTo: purple
+sdk: gradio
+sdk_version: 3.47.1
+app_file: app.py
+pinned: true
+---
+

app (3).py

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+import numpy as np
+import gradio as gr
+import requests
+import time
+import json
+import base64
+import os
+from io import BytesIO
+import PIL
+from PIL.ExifTags import TAGS
+import html
+import re
+
+batch_count = 1
+batch_size = 1
+
+i2i_batch_count = 1
+i2i_batch_size = 1
+
+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.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("super_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, width, height):
+    result = prodia_client.generate({
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+        "model": model,
+        "steps": 30,
+        "sampler": "DPM++ SDE",
+        "cfg_scale": 7,
+        "width": width,
+        "height": height,
+        "seed": -1
+    })
+
+    job = prodia_client.wait(result)
+
+    return job["imageUrl"]
+
+def img2img(input_image, prompt, negative_prompt, model, width, height):
+    result = prodia_client.transform({
+        "imageData": image_to_base64(input_image),
+        "denoising_strength": 0.7,
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+        "model": i2i_model,
+        "steps": 30,
+        "sampler": "DPM++ SDE",
+        "cfg_scale": 7,
+        "width": width,
+        "height": height,
+        "seed": -1
+    })
+
+    job = prodia_client.wait(result)
+
+    return job["imageUrl"]
+
+
+css = """
+#generate {
+    height: 100%;
+}
+"""
+
+with gr.Blocks(css=css, theme="Base") as demo:
+    gr.HTML(value="<h1><center>🥏 DreamDrop</center></h1>")
+    with gr.Tabs() as tabs:
+        with gr.Tab("Text to Image", id='t2i'):
+            with gr.Row():
+                with gr.Column(scale=6, min_width=600):
+                    prompt = gr.Textbox(label="Prompt", placeholder="a cute cat, 8k", lines=2)
+                    negative_prompt = gr.Textbox(label="Negative Prompt", value="text, blurry, fuzziness", lines=1)
+                with gr.Column():
+                    text_button = gr.Button("Generate", variant='primary', elem_id="generate")
+                    
+            with gr.Row():
+                with gr.Column(scale=2):
+                    image_output = gr.Image(label="Result Image")
+            with gr.Row():
+                with gr.Accordion("⚙️ Settings", open=False):
+                    with gr.Column(scale=1):
+                        model = gr.Dropdown(interactive=True, value="absolutereality_v181.safetensors [3d9d4d2b]",
+                                            show_label=True, label="Model",
+                                            choices=prodia_client.list_models())
+                    width = gr.Slider(label="↔️ Width", maximum=1024, value=768, step=8)
+                    height = gr.Slider(label="↕️ Height", maximum=1024, value=768, step=8)
+            text_button.click(txt2img, inputs=[prompt, negative_prompt, model, width, height], outputs=image_output)
+        
+        with gr.Tab("Image to Image", id='i2i'):
+            with gr.Row():
+                with gr.Column(scale=6, min_width=600):
+                    i2i_prompt = gr.Textbox(label="Prompt", placeholder="a cute cat, 8k", lines=3)
+                    i2i_negative_prompt = gr.Textbox(label="Negative Prompt", lines=2, value="text, blurry, fuzziness")
+                with gr.Column():
+                    i2i_text_button = gr.Button("Generate", variant='primary', elem_id="generate")
+                    
+            with gr.Row():
+                with gr.Column(scale=3):
+                    i2i_image_input = gr.Image(label="Input Image", type="pil")
+                    
+                with gr.Column(scale=2):
+                    i2i_image_output = gr.Image(label="Result Image")
+            with gr.Row():
+                with gr.Accordion("⚙️ Settings", open=False):
+                    with gr.Column(scale=1):
+                        i2i_model = gr.Dropdown(interactive=True,
+                                                value="absolutereality_v181.safetensors [3d9d4d2b]",
+                                                show_label=True, label="Model",
+                                                choices=prodia_client.list_models())
+                    with gr.Column(scale=1):
+                        i2i_width = gr.Slider(label="↔️ Width", maximum=1024, value=768, step=8)
+                        i2i_height = gr.Slider(label="↕️ Height", maximum=1024, value=768, step=8)
+
+            i2i_text_button.click(img2img, inputs=[i2i_image_input, i2i_prompt, i2i_negative_prompt, model, i2i_width, i2i_height], outputs=i2i_image_output)
+
+
+demo.queue(concurrency_count=64, max_size=30, api_open=False).launch(max_threads=256, show_api=False)

requirements (3).txt

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+numpy
+gradio
+requests
+pillow
+pyexif