Create app.py

app.py

@@ -0,0 +1,169 @@
+import spaces
+import gradio as gr
+import numpy as np
+import PIL.Image
+from PIL import Image
+import random
+from diffusers import StableDiffusionXLPipeline
+from diffusers import EulerAncestralDiscreteScheduler
+import torch
+import gradio_client.utils
+
+# もっと徹底的なモンキーパッチ
+old_get_type = gradio_client.utils.get_type
+def new_get_type(schema):
+    if isinstance(schema, bool):
+        return "bool"
+    return old_get_type(schema)
+
+gradio_client.utils.get_type = new_get_type
+
+# _json_schema_to_python_typeの修正も追加
+old_json_schema_to_python_type = gradio_client.utils._json_schema_to_python_type
+def new_json_schema_to_python_type(schema, defs=None):
+    if isinstance(schema, bool):
+        return "bool"
+    try:
+        return old_json_schema_to_python_type(schema, defs)
+    except Exception as e:
+        # エラーが発生した場合は汎用的な型を返す
+        return "any"
+
+gradio_client.utils._json_schema_to_python_type = new_json_schema_to_python_type
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Make sure to use torch.float16 consistently throughout the pipeline
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "votepurchase/novaAnimeXL_ilV60",
+    torch_dtype=torch.float16,
+    variant="fp16",  # Explicitly use fp16 variant
+    use_safetensors=True  # Use safetensors if available
+)
+
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe.to(device)
+
+# Force all components to use the same dtype
+pipe.text_encoder.to(torch.float16)
+pipe.text_encoder_2.to(torch.float16)
+pipe.vae.to(torch.float16)
+pipe.unet.to(torch.float16)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1216
+    
+@spaces.GPU
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+    # Check and truncate prompt if too long (CLIP can only handle 77 tokens)
+    if len(prompt.split()) > 60:  # Rough estimate to avoid exceeding token limit
+        print("Warning: Prompt may be too long and will be truncated by the model")
+        
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    generator = torch.Generator(device=device).manual_seed(seed)
+    
+    try:
+        output_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 output_image
+    except RuntimeError as e:
+        print(f"Error during generation: {e}")
+        # Return a blank image with error message
+        error_img = Image.new('RGB', (width, height), color=(0, 0, 0))
+        return error_img
+
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+
+    with gr.Column(elem_id="col-container"):
+
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt (keep it under 60 words for best results)",
+                container=False,
+            )
+
+            run_button = gr.Button("Run", scale=0)
+
+        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",
+                value="nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn"
+            )
+
+            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=0.0,
+                    maximum=20.0,
+                    step=0.1,
+                    value=7,
+                )
+
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=28,
+                    step=1,
+                    value=28,
+                )
+
+    run_button.click(
+        fn=infer,
+        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result]
+    )
+
+demo.queue().launch()