Update space

app.py

@@ -1,154 +1,59 @@
 import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+from diffusers import StableDiffusionPipeline
 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)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
+# Загрузка модели
+def load_model(model_id):
+    pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+    pipe.to("cuda")
+    return pipe
+
+# Генерация изображения
+def generate_image(
+    model_id: str,
+    prompt: str,
+    negative_prompt: str,
+    seed: int,
+    guidance_scale: float,
+    num_inference_steps: int,
 ):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
+    # Установка начального состояния (seed)
+    generator = torch.manual_seed(seed)
+    
+    # Загрузка модели
+    pipe = load_model(model_id)
 
+    # Генерация
     image = pipe(
-        prompt=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
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        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, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=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,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
+    return image
+
+# Интерфейс Gradio
+with gr.Blocks() as demo:
+    gr.Markdown("## Stable Diffusion Generator")
+    
+    model_id = gr.Textbox(label="Model ID", value="CompVis/stable-diffusion-v1-4")
+    prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
+    negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here")
+    seed = gr.Number(label="Seed", value=42, precision=0)
+    guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=20, value=7)
+    num_inference_steps = gr.Slider(label="Number of Inference Steps", minimum=1, maximum=50, value=20)
+    
+    output = gr.Image(label="Generated Image")
+    submit = gr.Button("Generate")
+    
+    submit.click(
+        fn=generate_image,
+        inputs=[model_id, prompt, negative_prompt, seed, guidance_scale, num_inference_steps],
+        outputs=output,
     )
 
+# Запуск
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()

requirements.txt

@@ -3,4 +3,6 @@ diffusers
 invisible_watermark
 torch
 transformers
-xformers
+xformers
+gradio
+torch