Update app.py

app.py

@@ -1,107 +1,60 @@
 import gradio as gr
-
-from transformers import AutoModelForConditionalGeneration, AutoTokenizer
+import requests
 
 # Базовые настройки
-
-prompt = gr.inputs.Textbox(label="Промпт")
-model_list = [
-    "DALL-E 2",
-    "VQGAN+CLIP",
-    "BigGAN",
-    "StyleGAN2",
-    "VQGAN",
-    "CLIP",
-    "VQGAN+CLIP-Vanilla",
-    "VQGAN+CLIP-Cutout",
-    "VQGAN+CLIP-RandomizedCutout",
-]
-model = gr.widgets.ToggleButtons(options=model_list, label="Модель")
+prompt = gr.inputs.Textbox(label="Prompt")
+models = gr.inputs.RadioButtons(options=["Bard", "DALL-E 2", "VQGAN+CLIP", "VQGAN", "CLIP", "InceptionV3"])
 
 # Расширенные настройки
-
-negative_prompt = gr.inputs.Textbox(label="Отрицательный промпт")
-sampling_method = gr.inputs.RadioButtons(options=["greedy", "top-k", "nucleus"], label="Метод выборки")
-sampling_steps = gr.inputs.Number(min=1, max=100, label="Количество шагов выборки")
-cfg_scale = gr.inputs.Number(min=0.1, max=1.0, label="Масштаб CFG")
-seed = gr.inputs.Number(min=0, max=2**31 - 1, label="Случайное число")
+negative_prompt = gr.inputs.Textbox(label="Negative Prompt")
+sampling_method = gr.inputs.RadioButtons(options=["random", "greedy", "nucleus", "top_k", "top_p"])
+sampling_steps = gr.inputs.Number(label="Sampling Steps", min=1, max=1000, default=100)
+cfg_scale = gr.inputs.Number(label="CFG Scale", min=0.1, max=10.0, default=1.0)
+seed = gr.inputs.Number(label="Seed", min=0, max=2**31, default=0)
 
 # Улучшение качества
-
-upscale_algorithm = gr.inputs.RadioButtons(options=["bicubic", "lanczos"], label="Алгоритм увеличения")
+algorithm = gr.inputs.RadioButtons(options=["nearest", "bilinear", "bicubic", "lanczos", "cubic", "mitchell"])
 
 # Функция генерации изображения
-
 def generate_image(prompt, model, negative_prompt, sampling_method, sampling_steps, cfg_scale, seed):
-    if model == "DALL-E 2":
-        model = AutoModelForConditionalGeneration.from_pretrained("google/dalle-2-1024")
-        tokenizer = AutoTokenizer.from_pretrained("google/dalle-2-1024")
-    elif model == "VQGAN+CLIP":
-        model = AutoModelForConditionalGeneration.from_pretrained("openai/vqgan-clip")
-        tokenizer = AutoTokenizer.from_pretrained("openai/vqgan-clip")
-    elif model == "BigGAN":
-        model = AutoModelForConditionalGeneration.from_pretrained("karras2022/biggan-deep-256")
-        tokenizer = AutoTokenizer.from_pretrained("karras2022/biggan-deep-256")
-    elif model == "StyleGAN2":
-        model = AutoModelForConditionalGeneration.from_pretrained("NVlabs/stylegan2-ada")
-        tokenizer = AutoTokenizer.from_pretrained("NVlabs/stylegan2-ada")
-    elif model == "VQGAN":
-        model = AutoModelForConditionalGeneration.from_pretrained("vqgan/vqgan_imagenet_f16_1024")
-        tokenizer = AutoTokenizer.from_pretrained("vqgan/vqgan_imagenet_f16_1024")
-    elif model == "CLIP":
-        model = AutoModelForConditionalGeneration.from_pretrained("openai/clip")
-        tokenizer = AutoTokenizer.from_pretrained("openai/clip")
-    elif model == "VQGAN+CLIP-Vanilla":
-        model = AutoModelForConditionalGeneration.from_pretrained("vqgan/vqgan_imagenet_f16_1024_clip_vanilla")
-        tokenizer = AutoTokenizer.from_pretrained("vqgan/vqgan_imagenet_f16_1024_clip_vanilla")
-    elif model == "VQGAN+CLIP-Cutout":
-        model = AutoModelForConditionalGeneration.from_pretrained("vqgan/vqgan_imagenet_f16_1024_clip_cutout")
-        tokenizer = AutoTokenizer.from_pretrained("vqgan/vqgan_imagenet_f16_1024_clip_cutout")
-    elif model == "VQGAN+CLIP-RandomizedCutout":
-        model = AutoModelForConditionalGeneration.from_pretrained("vqgan/vqgan_imagenet_f16_1024_clip_randomized_cutout")
-        tokenizer = AutoTokenizer.from_pretrained("vqgan/vqgan_imagenet_f16_1024_clip_randomized_cutout")
-
-    prompt = f"{prompt} {negative_prompt}"
-
-    image = model.generate(
-        text=prompt,
-        sampling_method=sampling_method,
-        sampling_steps=sampling_steps,
-        cfg_scale=cfg_scale,
-        seed=seed,
-    )
-
+    url = "https://api.huggingface.co/models/text-to-image/v1/generate"
+    data = {
+        "prompt": prompt,
+        "model": model,
+        "negative_prompt": negative_prompt,
+        "sampling_method": sampling_method,
+        "sampling_steps": sampling_steps,
+        "cfg_scale": cfg_scale,
+        "seed": seed,
+    }
+    response = requests.post(url, json=data)
+    image = response.json()["image"]
     return image
 
 # Функция улучшения качества изображения
-
-def upscale_image(image, upscale_algorithm):
-    if upscale_algorithm == "bicubic":
-        image = cv2.resize(image, dsize=(image.shape[1] * 2, image.shape[0] * 2), interpolation=cv2.INTER_CUBIC)
-    elif upscale_algorithm == "lanczos":
-        image = cv2.resize(image, dsize=(image.shape[1] * 2, image.shape[0] * 2), interpolation=cv2.INTER_LANCZOS4)
+def improve_quality(image, algorithm):
+    url = "https://api.huggingface.co/models/text-to-image/v1/improve-quality"
+    data = {
+        "image": image,
+        "algorithm": algorithm,
+    }
+    response = requests.post(url, json=data)
+    image = response.json()["image"]
     return image
 
-# Функция отображения изображения
-
-def show_image(image):
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    cv2.imshow("Image", image)
-    cv2.waitKey(0)
-
 # Основная функция
-
 def main():
-    image = generate_image(prompt.value, model.value, negative_prompt.value, sampling_method.value, sampling_steps.value, cfg_scale.value, seed.value)
-
-    # Если выбрано улучшение качества изображения
-
-    if upscale_algorithm.value != "none":
-        image = upscale_image(image, upscale_algorithm.value)
-
-    # Отображение изображения
-
-    show_image(image)
+    gr.Interface(
+        generate_image,
+        inputs=[prompt, models, negative_prompt, sampling_method, sampling_steps, cfg_scale, seed],
+        outputs=gr.outputs.Image(),
+        title="Gradio Image Generator",
+        tabs=[
+            gr.Tab("Базовые настройки", [prompt, models]),
+            gr.Tab("Расширенные настройки", [negative_prompt, sampling_method, sampling_steps, cfg_scale, seed]),
+            gr.Tab("Улучшение качества", [algorithm]),
+        ],
+    ).launch()
 
 if __name__ == "__main__":
     main()