Create app.py

app.py

@@ -0,0 +1,187 @@
+import numpy as np
+import gradio as gr
+import requests
+import time
+import json
+import base64
+import os
+from PIL import Image
+from io import BytesIO
+
+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}/sdxl/generate", 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}/sdxl/models")
+        return response.json()
+
+    def list_samplers(self):
+        response = self._get(f"{self.base}/sdxl/samplers")
+        return response.json()
+
+
+    def generate_v2(self, config):
+        response = self._post("https://inference.prodia.com/v2/job", {"type": "v2.job.sdxl.txt2img", "config": config}, v2=True)
+        return Image.open(BytesIO(response.content)).convert("RGBA")
+        
+
+    def _post(self, url, params, v2=False):
+        headers = {
+            **self.headers,
+            "Content-Type": "application/json"
+        }
+        if v2:
+            headers['Authorization'] = f"Bearer {os.getenv('API_KEY')}"
+            
+        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_path):
+    # Open the image with PIL
+    with Image.open(image_path) as 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
+
+
+
+prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY"))
+
+def flip_text(prompt, negative_prompt, model, steps, sampler, cfg_scale, resolution, seed):
+
+    width, height = resolution.split("x")
+
+    config_without_model_and_sampler = {
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+        "steps": steps,
+        "cfg_scale": cfg_scale,
+        "width": int(width),
+        "height": int(height),
+        "seed": seed
+    }
+
+# 本条注释替换成下面那条，避免sd_xl_base_1.0.safetensors [be9edd61]使用API_KEY导致的无法使用
+#    if model == "sd_xl_base_1.0.safetensors [be9edd61]":
+
+    if model == "xxxxx":
+        return prodia_client.generate_v2(config_without_model_and_sampler)
+    
+    result = prodia_client.generate({
+        **config_without_model_and_sampler,
+        "model": model,
+        "sampler": sampler
+    })
+
+    job = prodia_client.wait(result)
+
+    return job["imageUrl"]
+
+css = """
+#generate {
+    height: 100%;
+}
+"""
+
+list_resolutions = [
+    "512x512",
+    "640x960",
+    "800x1200",
+    "1280x720",
+    "1368x768",
+    "1024x1024",
+    "1216x832",
+    "1344x768",
+    "1536x640",
+    "640x1536",
+    "768x1344",
+    "832x1216"
+]
+
+with gr.Blocks(css=css) as demo:
+
+
+    with gr.Row():
+        with gr.Column(scale=6):
+            model = gr.Dropdown(interactive=True,value="animagineXLV3_v30.safetensors [75f2f05b]", 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 for SDXL V1.0.<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("(masterpiece,highres,best quality,8k),1girl,solo,space warrior, ultrarealistic, soft lighting", placeholder="Prompt", show_label=False, lines=3)
+                negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, value="(nsfw:1.2),lowres,[bad anatomy,bad hands,missing fingers,long neck],text,error")
+            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=50, value=25, step=1)
+
+                    with gr.Row():
+                        with gr.Column(scale=1):
+                            resolution = gr.Dropdown(value="800x1200", show_label=True, label="Resolution", choices=list_resolutions)
+
+                        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=10, step=1)
+                    seed = gr.Number(label="Seed", value=-1)
+
+            with gr.Column(scale=2):
+#                image_output = gr.Image(value="https://cdn-uploads.huggingface.co/production/uploads/noauth/XWJyh9DhMGXrzyRJk7SfP.png")
+                image_output = gr.Image(value="./image.png")
+
+        text_button.click(flip_text, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, resolution, seed], outputs=image_output)
+
+demo.queue(default_concurrency_limit=1, max_size=32, api_open=False).launch(max_threads=128, auth=(os.getenv("USERNAME"), os.getenv("PASSWORD")))