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onnew commited on Nov 30, 2024

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1 Parent(s): d8e57d4

Update app.py

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app.py +44 -110

app.py CHANGED Viewed

@@ -1,112 +1,46 @@
-from flask import Flask, request, jsonify
-import requests
-import base64
-app = Flask(__name__)
-# URL do modelo Hugging Face e seu token de API
-HUGGINGFACE_API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2"
-HUGGINGFACE_API_TOKEN = "SEU_TOKEN_AQUI"
-headers = {
-    "Authorization": f"Bearer {HUGGINGFACE_API_TOKEN}"
-}
-@app.route("/")
-def home():
-    return '''
-    <!DOCTYPE html>
-    <html>
-    <head>
-        <title>Gerador de Imagens</title>
-    </head>
-    <body>
-        <h1>Gerador de Imagens com Texto</h1>
-        <form action="/generate" method="post">
-            <label for="prompt">Descrição da imagem:</label><br>
-            <input type="text" id="prompt" name="prompt" required><br><br>
-            <h3>Configurações Avançadas</h3>
-            <label for="seed">Seed:</label>
-            <input type="number" id="seed" name="seed" value="0"><br><br>
-            <label for="randomize_seed">Randomizar Seed:</label>
-            <input type="checkbox" id="randomize_seed" name="randomize_seed" checked><br><br>
-            <label for="width">Largura (Width):</label>
-            <input type="number" id="width" name="width" value="1024"><br><br>
-            <label for="height">Altura (Height):</label>
-            <input type="number" id="height" name="height" value="1024"><br><br>
-            <label for="guidance_scale">Escala de Orientação (Guidance Scale):</label>
-            <input type="number" id="guidance_scale" name="guidance_scale" step="0.1" value="7.5"><br><br>
-            <label for="steps">Número de Etapas (Inference Steps):</label>
-            <input type="number" id="steps" name="steps" value="50"><br><br>
-            <button type="submit">Gerar Imagem</button>
-        </form>
-    </body>
-    </html>
-    '''
-@app.route("/generate", methods=["POST"])
-def generate_image():
-    # Obter os parâmetros enviados pelo formulário
-    prompt = request.form.get("prompt")
-    seed = int(request.form.get("seed", 0))
-    randomize_seed = "randomize_seed" in request.form
-    width = int(request.form.get("width", 1024))
-    height = int(request.form.get("height", 1024))
-    guidance_scale = float(request.form.get("guidance_scale", 7.5))
-    steps = int(request.form.get("steps", 50))
-    # Ajustar o seed se for para randomizar
-    if randomize_seed:
-        import random
-        seed = random.randint(0, 999999)
-    # Requisição para o modelo Hugging Face
-    payload = {
-        "inputs": prompt,
-        "parameters": {
-            "width": width,
-            "height": height,
-            "guidance_scale": guidance_scale,
-            "num_inference_steps": steps,
-            "seed": seed
-        }
-    }
-    response = requests.post(
-        HUGGINGFACE_API_URL,
-        headers=headers,
-        json=payload
-    )
-    if response.status_code != 200:
-        return jsonify({"error": "Erro ao gerar a imagem", "details": response.text}), 500
-    # Converte a resposta para Base64
-    image_data = response.content
-    image_base64 = base64.b64encode(image_data).decode("utf-8")
-    # Retorna a imagem gerada
-    return f'''
-    <!DOCTYPE html>
-    <html>
-    <head>
-        <title>Imagem Gerada</title>
-    </head>
-    <body>
-        <h1>Imagem Gerada</h1>
-        <img src="data:image/png;base64,{image_base64}" alt="Generated Image">
-        <br><br>
-        <a href="/">Voltar</a>
-    </body>
-    </html>
-    '''
 if __name__ == "__main__":
-    from waitress import serve
-    serve(app, host="0.0.0.0", port=8080)

+model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+pipe = StableDiffusionXLPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16, revision="fp16")
+pipe = pipe.to("cuda")
+def generate_image(prompt: str, negative_prompt: str = "", height: int = 512, width: int = 512, num_inference_steps: int = 50, guidance_scale: float = 7.5, num_images_per_prompt: int = 1) -> Tuple[Image.Image, str]:
+    generator = torch.Generator(device="cuda").manual_seed(random.randint(0, 2**32 - 1))
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        height=height,
+        width=width,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        num_images_per_prompt=num_images_per_prompt,
+        generator=generator,
+    ).images[0]
+    image_id = str(uuid.uuid4())
+    image_path = f"/tmp/{image_id}.png"
+    image.save(image_path)
+    return image, image_path
+def gradio_interface():
+    with gr.Blocks(css=css) as demo:
+        gr.Markdown("## Gere imagens usando Stable Diffusion XL")
+        with gr.Row():
+            with gr.Column():
+                prompt = gr.Textbox(label="Prompt", placeholder="Digite o prompt aqui...")
+                negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Digite o negative prompt aqui...")
+                run_button = gr.Button("Gerar Imagem")
+            with gr.Column():
+                result = gr.Image(label="Imagem Gerada")
+        run_button.click(
+            fn=lambda p, np: generate_image(p, np)[0],
+            inputs=[prompt, negative_prompt],
+            outputs=result,
+        )
+    return demo
 if __name__ == "__main__":
+    demo = gradio_interface()
+    demo.launch()