Initial Files

app.py

@@ -1,27 +1,19 @@
+import os
+from dotenv import load_dotenv
+import requests
+import json
 import gradio as gr
 import numpy as np
 import random
+import io
+from PIL import Image
 
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+# Load environment variables
+load_dotenv()
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
     prompt,
     negative_prompt,
@@ -36,20 +28,31 @@ def infer(
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
+    url = "https://inference.prodia.com/v2/job"
+    headers = {
+        'accept': 'image/jpeg',
+        'content-type': 'application/json',
+        'authorization': f'Bearer {os.getenv("PRODIA_KEY")}'
+    }
+    data = {
+        "type": "inference.flux.dev.txt2img.v1",
+        "config": {
+            "prompt": prompt,
+            "guidance_scale": guidance_scale,
+            "steps": num_inference_steps,
+            "width": width,
+            "height": height
+        }
+    }
+
+    response = requests.post(url, headers=headers, data=json.dumps(data))
+    
+    if response.status_code == 200:
+        image_bytes = io.BytesIO(response.content)
+        image = Image.open(image_bytes)
+        return image, seed
+    else:
+        return f"Error: {response.status_code}, {response.text}", seed
 
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
@@ -130,7 +133,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    value=25,  # Replace with defaults that work for your model
                 )
 
         gr.Examples(examples=examples, inputs=[prompt])

requirements.txt

@@ -3,4 +3,5 @@ diffusers
 invisible_watermark
 torch
 transformers
-xformers
+xformers
+python-dotenv