app.py

import random
import spaces

import gradio as gr
import numpy as np
import torch
from diffusers import StableDiffusion3Pipeline, SD3Transformer2DModel, FlashFlowMatchEulerDiscreteScheduler
from peft import PeftModel
import os
from huggingface_hub import snapshot_download

huggingface_token = os.getenv("HUGGINFACE_TOKEN")

model_path = snapshot_download(
    repo_id="stabilityai/stable-diffusion-3-medium", 
    revision="refs/pr/26",
    repo_type="model", 
    ignore_patterns=["*.md", "*..gitattributes"],
    local_dir="stable-diffusion-3-medium",
    token=huggingface_token, # type a new token-id.
    )

device = "cuda" if torch.cuda.is_available() else "cpu"
IS_SPACE = os.environ.get("SPACE_ID", None) is not None

transformer = SD3Transformer2DModel.from_pretrained(
    model_path,
    subfolder="transformer",
    torch_dtype=torch.float16,
)
transformer = PeftModel.from_pretrained(transformer, "jasperai/flash-sd3")


if torch.cuda.is_available():
    torch.cuda.max_memory_allocated(device=device)
    pipe = StableDiffusion3Pipeline.from_pretrained(
        model_path,
        transformer=transformer,
        torch_dtype=torch.float16,
        text_encoder_3=None,
        tokenizer_3=None,
    )

    pipe = pipe.to(device)
else:
    pipe = StableDiffusion3Pipeline.from_pretrained(
        model_path,
        transformer=transformer,
        torch_dtype=torch.float16,
        text_encoder_3=None,
        tokenizer_3=None,
    )
    pipe = pipe.to(device)


pipe.scheduler = FlashFlowMatchEulerDiscreteScheduler.from_pretrained(
  model_path,
  subfolder="scheduler",
)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
NUM_INFERENCE_STEPS = 4


@spaces.GPU
def infer(prompt, seed, randomize_seed, guidance_scale, num_inference_steps, negative_prompt, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    image = pipe(
        prompt=prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=generator,
        negative_prompt=negative_prompt
    ).images[0]

    return image


examples = [
    "The image showcases a freshly baked bread, possibly focaccia, with rosemary sprigs and red pepper flakes sprinkled on top. It's sliced and placed on a wire cooling rack, with a bowl of mixed peppercorns beside it.",
    'a 3D render of a wizard raccoon holding a sign saying "SD 3" with a magic wand.',
    "A panda reading a book in a lush forest.",
    "A raccoon trapped inside a glass jar full of colorful candies, the background is steamy with vivid colors",
    "Pirate ship sailing on a sea with the milky way galaxy in the sky and purple glow lights",
    "a cute cartoon fluffy rabbit pilot walking on a military aircraft carrier, 8k, cinematic",
    "A 3d render of a futuristic city with a giant robot in the middle full of neon lights, pink and blue colors",
    "A close up of an old elderly man with green eyes looking straight at the camera",
    "photo of a huge red cat with green eyes sitting on a cloud in the sky, looking at the camera"
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 512px;
}
"""

if torch.cuda.is_available():
    power_device = "GPU"
else:
    power_device = "CPU"

with gr.Blocks(css=css, theme="Nymbo/Alyx_Theme") as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(
            f"""
        # ⚡ SD3 Flash ⚡
        """
        )

        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.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="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, bad text"
            )
            
            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():
                
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=3.0,
                    step=0.1,
                    value=1.0,
                )
                
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=4,
                    maximum=8,
                    step=1,
                    value=4,
                )

        examples = gr.Examples(examples=examples, inputs=[prompt], cache_examples=False)

        gr.Markdown("")
    gr.on(
        [run_button.click, seed.change, randomize_seed.change, prompt.submit],
        fn=infer,
        inputs=[prompt, seed, randomize_seed, guidance_scale, num_inference_steps, negative_prompt],
        outputs=[result],
        # show_progress="minimal",
        #show_api=False,
        #trigger_mode="always_last",
    )

demo.queue().launch(show_api=True)