app.py

import gradio as gr
import jax
import numpy as np
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from PIL import Image
from diffusers import FlaxStableDiffusionPipeline

def create_key(seed=0):
    return jax.random.PRNGKey(seed)


pipe, params = FlaxStableDiffusionPipeline.from_pretrained(
    "MuhammadHanif/stable-diffusion-v1-5-high-res", 
    dtype=jnp.bfloat16, 
    use_memory_efficient_attention=True
)

def infer(prompts, negative_prompts):
    
    num_samples = 1 #jax.device_count()
    rng = create_key(0)
    rng = jax.random.split(rng, jax.device_count())
    
    prompt_ids = pipe.prepare_text_inputs([prompts] * num_samples)
    negative_prompt_ids = pipe.prepare_text_inputs([negative_prompts] * num_samples)
    
    p_params = replicate(params)
    prompt_ids = shard(prompt_ids)
    negative_prompt_ids = shard(negative_prompt_ids)
    
    output = pipe(
        prompt_ids=prompt_ids,
        params=p_params,
        height=1088,
        width=1088,
        prng_seed=rng,
        num_inference_steps=50,
        neg_prompt_ids=negative_prompt_ids,
        jit=True,
    ).images
    
    output_images = pipe.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))
    return output_images

gr.Interface(infer, inputs=["text", "text"], outputs="gallery").launch()