Upload app.py

app.py

@@ -0,0 +1,263 @@
+import gradio as gr
+import numpy as np
+import random
+
+# import spaces #[uncomment to use ZeroGPU]
+from diffusers import DiffusionPipeline
+import torch
+from typing import Optional
+
+# кэш для пайплайнов (чтобы не перезагружать модель при каждом запросе)
+PIPE_CACHE: dict[str, DiffusionPipeline] = {}
+DEFAULT_MODEL = "CompVis/stable-diffusion-v1-4"
+BASE_MODEL_FOR_LORA = "CompVis/stable-diffusion-v1-4"  # Base model used for LoRA training
+LORA_MODEL_ID = "DiZH797/SberDiffusionModelsLora"  # Your uploaded LoRA model ID
+MODEL_OPTIONS = [
+    "CompVis/stable-diffusion-v1-4",
+    "stabilityai/stable-diffusion-2-1",
+    "stabilityai/sdxl-turbo",
+    LORA_MODEL_ID
+]
+
+
+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
+
+def get_pipe(model_id: str, lora_scale: float = 1.0):
+    """
+    Loads the pipeline for a given model ID.
+    If the selected model is the LoRA, it loads the base model and then merges the LoRA weights.
+    """
+    cache_key = f"{model_id}_{lora_scale}"
+
+    if cache_key in PIPE_CACHE:
+        return PIPE_CACHE[cache_key]
+
+    # Check if the selected model is the LoRA adapter
+    if model_id == LORA_MODEL_ID:
+        # Load the base model for LoRA
+        pipe = DiffusionPipeline.from_pretrained(
+            BASE_MODEL_FOR_LORA,
+            torch_dtype=torch_dtype
+        ).to(device)
+        # Load and merge the LoRA weights with the specified scale
+        pipe.load_lora_weights(LORA_MODEL_ID)
+        pipe.fuse_lora(lora_scale=lora_scale)
+    else:
+        # Load a standard model without LoRA
+        pipe = DiffusionPipeline.from_pretrained(
+            model_id,
+            torch_dtype=torch_dtype
+        ).to(device)
+
+    PIPE_CACHE[cache_key] = pipe
+    return pipe
+
+# @spaces.GPU #[uncomment to use ZeroGPU]
+def infer(
+    model_id: Optional[str] = DEFAULT_MODEL,
+    prompt: str = "",
+    negative_prompt: str = "",
+    seed: int = 42,
+    randomize_seed: bool = False,
+    width: int = 512,
+    height: int = 512,
+    guidance_scale: float = 7.0,
+    num_inference_steps: int = 20,
+    scheduler_name: Optional[str] = None,
+    progress=gr.Progress(track_tqdm=True),
+):
+    # получаем/загружаем нужный pipe
+    pipe = get_pipe(model_id)
+
+    # при желании можно подменить scheduler по имени (опционально)
+    if scheduler_name:
+        # примерная схема: словарь name->класс scheduler
+        # при необходимости добавить другие scheduler'ы — импортируйте их сверху и добавьте сюда
+        try:
+            from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler, PNDMScheduler
+            sched_map = {
+                "DDIM": DDIMScheduler,
+                "EulerAncestral": EulerAncestralDiscreteScheduler,
+                "PNDM": PNDMScheduler,
+            }
+            if scheduler_name in sched_map:
+                pipe.scheduler = sched_map[scheduler_name].from_config(pipe.scheduler.config)
+        except Exception:
+            # если что-то пошло не так — просто используем дефолтный scheduler
+            pass
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    generator = torch.Generator().manual_seed(int(seed))
+
+    image = pipe(
+        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")
+
+        # Model selector (выпадающий список)
+        model_select = gr.Dropdown(
+            label="Model",
+            choices=MODEL_OPTIONS,
+            value=DEFAULT_MODEL,
+            interactive=True,
+        )
+    
+        # опциональный селектор scheduler
+        scheduler_select = gr.Dropdown(
+            label="Scheduler (optional)",
+            choices=["", "DDIM", "EulerAncestral", "PNDM"],
+            value="",
+        )
+
+        # Add a new slider for LoRA scale
+        lora_scale_slider = gr.Slider(
+            label="LoRA Scale (Only for LoRA model)",
+            minimum=0.0,
+            maximum=2.0,
+            step=0.1,
+            value=1.0,
+            visible=False,  # Initially hidden
+        )
+
+        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=True,
+            )
+
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=42,
+            )
+
+            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=7.0,  # 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=20,  # Replace with defaults that work for your model
+                )
+
+        gr.Examples(examples=examples, inputs=[prompt])
+    
+    # Function to show/hide the LoRA scale slider based on model selection
+    def toggle_lora_scale_slider(model_id):
+        if model_id == LORA_MODEL_ID:
+            return gr.Slider(visible=True)
+        else:
+            return gr.Slider(visible=False)
+
+    model_select.change(
+        fn=toggle_lora_scale_slider,
+        inputs=model_select,
+        outputs=lora_scale_slider
+    )
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[
+            model_select,
+            prompt,
+            negative_prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            scheduler_select,
+            lora_scale_slider
+        ],
+        outputs=[result, seed],
+    )
+
+if __name__ == "__main__":
+    demo.launch()