Create app-api.py

app-api.py

@@ -0,0 +1,112 @@
+from flask import Flask, request, jsonify
+from PIL import Image
+import base64
+import io 
+import random
+import uuid
+import numpy as np
+import spaces
+import torch
+from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+
+
+# Créer une instance FastAPI
+app = Flask(__name__)
+
+def save_image(img):
+    unique_name = str(uuid.uuid4()) + ".png"
+    img.save(unique_name)
+    return unique_name
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+MAX_SEED = np.iinfo(np.int32).max
+
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
+
+MAX_SEED = np.iinfo(np.int32).max
+
+USE_TORCH_COMPILE = 0
+ENABLE_CPU_OFFLOAD = 0
+
+
+if torch.cuda.is_available():
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        "fluently/Fluently-XL-v2",
+        torch_dtype=torch.float16,
+        use_safetensors=True,
+    )
+    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+    
+    
+    pipe.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle")
+    pipe.set_adapters("dalle")
+
+    pipe.to("cuda")
+    
+    
+
+@spaces.GPU(enable_queue=True)
+def generate(
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 3,
+    randomize_seed: bool = False,
+):
+
+    
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+
+    if not use_negative_prompt:
+        negative_prompt = ""  # type: ignore
+
+    images = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=25,
+        num_images_per_prompt=1,
+        cross_attention_kwargs={"scale": 0.65},
+        output_type="pil",
+    ).images
+    image_paths = [save_image(img) for img in images]
+    print(image_paths)
+    return image_paths, seed
+
+@app.get("/")
+def root():
+    return "Welcome to the Fashion Outfit "
+
+# Route pour l'API REST
+@app.route('/api/run', methods=['POST'])
+def run():
+    data = request.json
+    print(data)
+    text = data['prompt']
+    negative_prompt = data['negative_prompt']
+    use_negative_prompt = data['use_negative_prompt']
+    guidance_scale = data['guidance_scale']
+    randomize_seed = data['randomize_seed']
+    result = generate(
+    prompt,
+    negative_prompt,
+    use_negative_prompt,
+    0,
+    1024,
+    1024,
+    guidance_scale,
+    randomize_seed)
+    return jsonify({'out': result})
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)