Update app.py

app.py

@@ -1,139 +1,112 @@
-import gradio as gr
-import numpy as np
-import random
-import spaces
-import torch
-from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
-from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
-from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
-
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
-good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
-pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device)
-torch.cuda.empty_cache()
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 2048
-
-pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
-
-@spaces.GPU(duration=75)
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator().manual_seed(seed)
-    
-    for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
-            prompt=prompt,
-            guidance_scale=guidance_scale,
-            num_inference_steps=num_inference_steps,
-            width=width,
-            height=height,
-            generator=generator,
-            output_type="pil",
-            good_vae=good_vae,
-        ):
-            yield img, seed
-    
-examples = [
-    "a tiny astronaut hatching from an egg on the moon",
-    "a cat holding a sign that says hello world",
-    "an anime illustration of a wiener schnitzel",
-]
-
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
+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}"
 }
-"""
-
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [dev]
-12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
-[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
-        
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
-            
-            with gr.Row():
-
-                guidance_scale = gr.Slider(
-                    label="Guidance Scale",
-                    minimum=1,
-                    maximum=15,
-                    step=0.1,
-                    value=3.5,
-                )
-  
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=28,
-                )
-        
-        gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
-            cache_examples="lazy"
-        )
-
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
+
+@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
     )
 
-demo.launch()
+    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)