Create app.py

app.py

@@ -0,0 +1,186 @@
+import os
+import random
+import gradio as gr
+import torch
+from diffusers import (
+    DiffusionPipeline,
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UniPCMultistepScheduler,
+)
+from diffusers.utils import make_image_grid
+
+ACCESS_TOKEN = os.environ["ACCESS_TOKEN"]
+pipeline = DiffusionPipeline.from_pretrained(
+    "stabilityai/japanese-stable-diffusion-xl",
+    trust_remote_code=True,
+    torch_dtype=torch.float16,
+    use_auth_token=ACCESS_TOKEN
+)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipeline.to(device)
+SCHEDULER_MAPPING = {
+    "ddim": DDIMScheduler,
+    "plms": PNDMScheduler,
+    "lms": LMSDiscreteScheduler,
+    "euler": EulerDiscreteScheduler,
+    "euler_ancestral": EulerAncestralDiscreteScheduler,
+    "dpm_solver++": DPMSolverMultistepScheduler,
+    "unipc": UniPCMultistepScheduler,
+}
+noise_scheduler_name = "euler"
+SD_XL_BASE_RATIOS = {
+    "0.5": (704, 1408),
+    "0.52": (704, 1344),
+    "0.57": (768, 1344),
+    "0.6": (768, 1280),
+    "0.68": (832, 1216),
+    "0.72": (832, 1152),
+    "0.78": (896, 1152),
+    "0.82": (896, 1088),
+    "0.88": (960, 1088),
+    "0.94": (960, 1024),
+    "1.0": (1024, 1024),
+    "1.07": (1024, 960),
+    "1.13": (1088, 960),
+    "1.21": (1088, 896),
+    "1.29": (1152, 896),
+    "1.38": (1152, 832),
+    "1.46": (1216, 832),
+    "1.67": (1280, 768),
+    "1.75": (1344, 768),
+    "1.91": (1344, 704),
+    "2.0": (1408, 704),
+    "2.09": (1472, 704),
+    "2.4": (1536, 640),
+    "2.5": (1600, 640),
+    "2.89": (1664, 576),
+    "3.0": (1728, 576),
+    # "small": (512, 512),  # for testing
+}
+
+
+def set_noise_scheduler(name) -> None:
+    pipeline.scheduler = SCHEDULER_MAPPING[name].from_config(pipeline.scheduler.config)
+
+
+def infer(
+    prompt,
+    scale=7.5,
+    steps=40,
+    ratio="1.0",
+    n_samples=1,
+    seed="random",
+    negative_prompt="",
+    scheduler_name="euler",
+):
+    global noise_scheduler_name
+    if noise_scheduler_name != scheduler_name:
+        set_noise_scheduler(scheduler_name)
+        noise_scheduler_name = scheduler_name
+    scale = float(scale)
+    steps = int(steps)
+    W, H = SD_XL_BASE_RATIOS[ratio]
+    n_samples = int(n_samples)
+    if seed == "random":
+        seed = random.randint(0, 2**32)
+    seed = int(seed)
+
+    images = pipeline(
+        prompt=prompt,
+        negative_prompt=negative_prompt if len(negative_prompt) > 0 else None,
+        guidance_scale=scale,
+        generator=torch.Generator(device=device).manual_seed(seed),
+        num_images_per_prompt=n_samples,
+        num_inference_steps=steps,
+        height=H,
+        width=W,
+    ).images
+    # grid = make_image_grid(images, 1, len(images))
+    return (
+        images,
+        {
+            "seed": seed,
+        },
+    )
+
+
+examples = [
+    ["柴犬、カラフルアート"],
+    ["満面の笑みのお爺さん、スケッチ"],
+    ["星空の中の1匹の鹿、アート"],
+    ["ジャングルに立っている日本男性のポートレート"],
+    ["茶色の猫のイラスト、アニメ"],
+    ["舞妓さんのポートレート、デジタルアート"],
+]
+with gr.Blocks() as demo:
+    gr.Markdown("# Japanese Stable Diffusion XL Demo")
+    gr.Markdown(
+        """[Japanese Stable Diffusion XL](https://huggingface.co/stabilityai/japanese-stable-diffusion-xl) is a Japanese-version SDXL by [Stability AI](https://ja.stability.ai/).
+                - Blog: https://ja.stability.ai/blog/japanese-stable-diffusion-xl
+                - Twitter: https://twitter.com/StabilityAI_JP
+                - Discord: https://discord.com/invite/StableJP"""
+    )
+    gr.Markdown(
+        "### You can also try JSDXL on Google Colab [here](https://colab.research.google.com/github/Stability-AI/model-demo-notebooks/blob/main/japanese_stable_diffusion_xl.ipynb). "
+    )
+    with gr.Group():
+        with gr.Row():
+            prompt = gr.Textbox(
+                label="prompt",
+                max_lines=1,
+                show_label=False,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            btn = gr.Button("Run", scale=0)
+        gallery = gr.Gallery(label="Generated images", show_label=False)
+        with gr.Accordion(label="sampling info", open=False):
+            info = gr.JSON(label="sampling_info")
+    with gr.Accordion(open=False, label="Advanced options"):
+        scale = gr.Number(value=7.5, label="cfg_scale")
+        steps = gr.Number(value=25, label="steps", visible=False)
+        size_ratio = gr.Dropdown(
+            choices=list(SD_XL_BASE_RATIOS.keys()),
+            value="1.0",
+            label="size ratio",
+            multiselect=False,
+        )
+        n_samples = gr.Slider(
+            minimum=1,
+            maximum=3,
+            value=2,
+            label="n_samples",
+        )
+        seed = gr.Text(
+            value="random",
+            label="seed (integer or 'random')",
+        )
+        negative_prompt = gr.Textbox(
+            label="negative prompt",
+            value="",
+        )
+        noise_scheduler = gr.Dropdown(
+            list(SCHEDULER_MAPPING.keys()), value="euler", visible=False
+        )
+
+    inputs = [
+        prompt,
+        scale,
+        steps,
+        size_ratio,
+        n_samples,
+        seed,
+        negative_prompt,
+        noise_scheduler,
+    ]
+    outputs = [gallery, info]
+    prompt.submit(infer, inputs=inputs, outputs=outputs)
+    btn.click(infer, inputs=inputs, outputs=outputs)
+    gr.Examples(examples=examples, inputs=inputs, outputs=outputs, fn=infer)
+
+demo.queue().launch(debug=True, share=True, show_error=True)