Update app.py

app.py

@@ -1,27 +1,100 @@
+# app.py
 import gradio as gr
 import numpy as np
 import random
-
-# import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
+from diffusers import (
+    DDIMScheduler,
+    PNDMScheduler,
+    LMSDiscreteScheduler,
+    EulerDiscreteScheduler,
+    DPMSolverMultistepScheduler,
+)
 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
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+DEFAULT_MODEL = "CompVis/stable-diffusion-v1-4"
+MODEL_OPTIONS = [
+    "CompVis/stable-diffusion-v1-4",
+    "stabilityai/sdxl-turbo",
+    # add other model ids you want to expose here
+]
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+SCHEDULER_MAP = {
+    "default": None,
+    "DDIM": DDIMScheduler,
+    "PNDM": PNDMScheduler,
+    "LMS": LMSDiscreteScheduler,
+    "Euler": EulerDiscreteScheduler,
+    "DPMSolver": DPMSolverMultistepScheduler,
+}
+
+
+def get_torch_dtype():
+    return torch.float16 if torch.cuda.is_available() else torch.float32
+
+
+def load_pipeline(model_id: str, scheduler_name: str = "default"):
+    """Load pipeline from pretrained model_id and optionally replace scheduler."""
+    torch_dtype = get_torch_dtype()
+    pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
+    # try to replace scheduler if requested
+    sched_cls = SCHEDULER_MAP.get(scheduler_name)
+    if sched_cls is not None:
+        try:
+            pipe.scheduler = sched_cls.from_config(pipe.scheduler.config)
+        except Exception:
+            # fallback to default if replacement failed
+            pass
+    pipe = pipe.to(device)
+    return pipe
+
+
+# preload default pipeline (may take time on startup)
+print(f"Loading default model {DEFAULT_MODEL} ...")
+try:
+    default_pipe = load_pipeline(DEFAULT_MODEL, "default")
+    print("Loaded default model.")
+except Exception as e:
+    default_pipe = None
+    print("Failed to preload default model:", e)
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 880px;
+}
+"""
+
+examples = [
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a green horse",
+    "A delicious ceviche cheesecake slice",
+]
+
+def load_model_and_update(model_id, scheduler_name):
+    """Called when user selects a model or scheduler: load and return new pipeline + status."""
+    try:
+        pipe = load_pipeline(model_id, scheduler_name)
+        return pipe, f"Loaded `{model_id}` (scheduler: {scheduler_name})"
+    except Exception as e:
+        return None, f"Error loading `{model_id}`: {e}"
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+
+def size_to_dims(size_str):
+    try:
+        w, h = map(int, size_str.split("x"))
+        # clamp to limits
+        w = min(max(256, w), MAX_IMAGE_SIZE)
+        h = min(max(256, h), MAX_IMAGE_SIZE)
+        return gr.Slider.update(value=w), gr.Slider.update(value=h)
+    except Exception:
+        return gr.Slider.update(value=512), gr.Slider.update(value=512)
 
 
-# @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
     prompt,
     negative_prompt,
@@ -31,42 +104,58 @@ def infer(
     height,
     guidance_scale,
     num_inference_steps,
+    pipe_state,  # gr.State containing pipeline
     progress=gr.Progress(track_tqdm=True),
 ):
+    if pipe_state is None:
+        return None, seed, "Model not loaded."
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    generator = torch.Generator().manual_seed(seed)
+    # create generator on proper device
+    if device.startswith("cuda"):
+        generator = torch.Generator(device=device).manual_seed(seed)
+    else:
+        generator = torch.Generator().manual_seed(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]
+    try:
+        out = pipe_state(
+            prompt=prompt,
+            negative_prompt=negative_prompt if negative_prompt else None,
+            guidance_scale=float(guidance_scale),
+            num_inference_steps=int(num_inference_steps),
+            width=int(width),
+            height=int(height),
+            generator=generator,
+        )
+        image = out.images[0]
+        return image, seed, "OK"
+    except Exception as e:
+        return None, seed, f"Inference error: {e}"
 
-    return image, seed
 
+with gr.Blocks(css=css, title="Text-to-Image") as demo:
+    pipe_state = gr.State(value=default_pipe)
 
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("# Text-to-Image — demo")
 
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
+        with gr.Row():
+            model_selector = gr.Dropdown(
+                label="Model ID",
+                choices=MODEL_OPTIONS,
+                value=DEFAULT_MODEL,
+                interactive=True,
+            )
+            scheduler_selector = gr.Dropdown(
+                label="Scheduler",
+                choices=list(SCHEDULER_MAP.keys()),
+                value="default",
+                interactive=True,
+            )
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+        status = gr.Markdown("Model status: ready" if default_pipe else "Model status: not loaded")
 
         with gr.Row():
             prompt = gr.Text(
@@ -76,8 +165,7 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
+            run_button = gr.Button("Run", variant="primary")
 
         result = gr.Image(label="Result", show_label=False)
 
@@ -86,7 +174,7 @@ with gr.Blocks(css=css) as demo:
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                visible=False,
+                visible=True,
             )
 
             seed = gr.Slider(
@@ -94,18 +182,24 @@ with gr.Blocks(css=css) as demo:
                 minimum=0,
                 maximum=MAX_SEED,
                 step=1,
-                value=0,
+                value=42,
             )
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
 
             with gr.Row():
+                size_preset = gr.Dropdown(
+                    label="Size preset",
+                    choices=["512x512", "768x512", "1024x1024"],
+                    value="512x512",
+                )
+
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=512,
                 )
 
                 height = gr.Slider(
@@ -113,29 +207,45 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=512,
                 )
 
             with gr.Row():
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
-                    maximum=10.0,
+                    maximum=20.0,
                     step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                    value=7.0,
                 )
 
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=50,
+                    maximum=150,
                     step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    value=20,
                 )
 
         gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
+
+    # Events
+    model_selector.change(
+        fn=load_model_and_update,
+        inputs=[model_selector, scheduler_selector],
+        outputs=[pipe_state, status],
+        queue=True,
+    )
+    scheduler_selector.change(
+        fn=load_model_and_update,
+        inputs=[model_selector, scheduler_selector],
+        outputs=[pipe_state, status],
+        queue=True,
+    )
+
+    size_preset.change(fn=size_to_dims, inputs=size_preset, outputs=[width, height])
+
+    run_button.click(
         fn=infer,
         inputs=[
             prompt,
@@ -146,8 +256,10 @@ with gr.Blocks(css=css) as demo:
             height,
             guidance_scale,
             num_inference_steps,
+            pipe_state,
         ],
-        outputs=[result, seed],
+        outputs=[result, seed, status],
+        queue=True,
     )
 
 if __name__ == "__main__":