Create app.py

app.py

@@ -0,0 +1,176 @@
+# Thanks: https://huggingface.co/spaces/markmagic/Stable-Diffusion-3/blob/main/app.py
+
+import os
+import random
+import uuid
+
+import gradio as gr
+import numpy as np
+from PIL import Image
+import spaces
+import torch
+from diffusers import StableDiffusion3Pipeline, DPMSolverMultistepScheduler, AutoencoderKL
+
+huggingface_token = os.getenv("TOKEN")
+
+DESCRIPTION = """# Stable Diffusion 3 with Japanese"""
+
+pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+@spaces.GPU()
+def generate(
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 7,
+    randomize_seed: bool = False,
+    num_inference_steps=30,
+    use_resolution_binning: bool = True,
+    progress=gr.Progress(track_tqdm=True),
+):
+    pipe.to(device)
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+    generator = torch.Generator().manual_seed(seed)
+    
+    #pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+    
+    if not use_negative_prompt:
+        negative_prompt = None  # type: ignore
+    
+    output = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        output_type="pil",
+    ).images
+
+    return output
+
+
+examples = [
+    "A red sofa on top of a white building.",
+    "A cardboard which is large and sits on a theater stage.",
+    "A painting of an astronaut riding a pig wearing a tutu holding a pink umbrella.",
+    "Studio photograph closeup of a chameleon over a black background.",
+    "Closeup portrait photo of beautiful goth woman, makeup.",
+    "A living room, bright modern Scandinavian style house, large windows.",
+    "Portrait photograph of an anthropomorphic tortoise seated on a New York City subway train.",
+    "Batman, cute modern Disney style, Pixar 3d portrait, ultra detailed, gorgeous, 3d zbrush, trending on dribbble, 8k render.",
+    "Cinnamon bun on the plate, watercolor painting, detailed, brush strokes, light palette, light, cozy.",
+    "A lion, colorful, low-poly, cyan and orange eyes, poly-hd, 3d, low-poly game art, polygon mesh, jagged, blocky, wireframe edges, centered composition.",
+    "Long exposure photo of Tokyo street, blurred motion, streaks of light, surreal, dreamy, ghosting effect, highly detailed.",
+    "A glamorous digital magazine photoshoot, a fashionable model wearing avant-garde clothing, set in a futuristic cyberpunk roof-top environment, with a neon-lit city background, intricate high fashion details, backlit by vibrant city glow, Vogue fashion photography.",
+    "Masterpiece, best quality, girl, collarbone, wavy hair, looking at viewer, blurry foreground, upper body, necklace, contemporary, plain pants, intricate, print, pattern, ponytail, freckles, red hair, dappled sunlight, smile, happy."
+
+]
+
+css = '''
+.gradio-container{max-width: 1000px !important}
+h1{text-align:center}
+'''
+with gr.Blocks(css=css) as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.HTML(
+            """
+            <h1 style='text-align: center'>
+            Stable Diffusion 3 Medium with Japanese
+            </h1>
+            """
+        )
+            gr.HTML(
+                """
+               
+                """
+        )
+    with gr.Group():
+        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)
+        result = gr.Gallery(label="Result", elem_id="gallery", show_label=False)
+    with gr.Accordion("Advanced options", open=False):
+        with gr.Row():
+            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=1,
+                value = "deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW",
+                visible=True,
+            )
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=0,
+        )
+
+        steps = gr.Slider(
+            label="Steps",
+            minimum=0,
+            maximum=60,
+            step=1,
+            value=30,
+        )
+        
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        with gr.Row():
+            guidance_scale = gr.Slider(
+                label="Guidance Scale",
+                minimum=0.1,
+                maximum=10,
+                step=0.1,
+                value=7.0,
+            )
+
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=[result],
+        fn=generate,
+        cache_examples=CACHE_EXAMPLES,
+    )
+
+    use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt,
+        outputs=negative_prompt,
+        api_name=False,
+    )
+
+    gr.on(
+        triggers=[
+            prompt.submit,
+            negative_prompt.submit,
+            run_button.click,
+        ],
+        fn=generate,
+        inputs=[
+            prompt,
+            negative_prompt,
+            use_negative_prompt,
+            seed,
+            guidance_scale,
+            randomize_seed,
+            steps,
+        ],
+        outputs=[result],
+        api_name="run",
+    )
+
+if __name__ == "__main__":
+    demo.queue().launch()