app.py

import os
import gradio as gr
import numpy as np
import random
import spaces
from diffusers import DiffusionPipeline
import torch
import json
import logging
from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
from huggingface_hub import login
from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
import copy
import random
import time
import boto3
from io import BytesIO
from datetime import datetime

from diffusers import UNet2DConditionModel



HF_TOKEN = os.environ.get("HF_TOKEN")

login(token=HF_TOKEN)

# init
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
base_model = "black-forest-labs/FLUX.1-dev"

# unet = UNet2DConditionModel.from_pretrained(
#     base_model,
#     torch_dtype=torch.float16,
#     use_safetensors=True,
#     variant="fp16",
#     subfolder="unet",
# ).to("cuda")


pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype).to(device)



MAX_SEED = 2**32-1

class calculateDuration:
    def __init__(self, activity_name=""):
        self.activity_name = activity_name

    def __enter__(self):
        self.start_time = time.time()
        return self
    
    def __exit__(self, exc_type, exc_value, traceback):
        self.end_time = time.time()
        self.elapsed_time = self.end_time - self.start_time
        if self.activity_name:
            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
        else:
            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")


def upload_image_to_r2(image, account_id, access_key, secret_key, bucket_name):
    print("upload_image_to_r2", account_id, access_key, secret_key, bucket_name)
    connectionUrl = f"https://{account_id}.r2.cloudflarestorage.com"

    s3 = boto3.client(
        's3',
        endpoint_url=connectionUrl,
        region_name='auto',
        aws_access_key_id=access_key,
        aws_secret_access_key=secret_key
    )

    current_time = datetime.now().strftime("%Y/%m/%d/%H%M%S")
    image_file = f"generated_images/{current_time}_{random.randint(0, MAX_SEED)}.png"
    buffer = BytesIO()
    image.save(buffer, "PNG")
    buffer.seek(0)
    s3.upload_fileobj(buffer, bucket_name, image_file)
    print("upload finish", image_file)
    return image_file


@spaces.GPU
def generate_image(prompt, steps, seed, cfg_scale, width, height, progress):
    pipe.to("cuda")
    generator = torch.Generator(device="cuda").manual_seed(seed)
    with calculateDuration("Generating image"):
        # Generate image
        generate_image = pipe(
            prompt=prompt,
            num_inference_steps=steps,
            guidance_scale=cfg_scale,
            width=width,
            height=height,
            generator=generator,
            joint_attention_kwargs={"scale": 1}
        ).images[0]
        
    progress(99, "Generate success!")
    return generate_image


def run_lora(prompt, cfg_scale, steps, lora_strings, randomize_seed, seed, width, height, lora_scale, upload_to_r2, account_id, access_key, secret_key, bucket, progress=gr.Progress(track_tqdm=True)):
    

    # Load LoRA weights
    if lora_strings:
        with calculateDuration(f"Loading LoRA weights for {lora_strings}"):
            pipe.unload_lora_weights()
            lora_array = lora_strings.split(',')
            adapter_names = []
            for lora_string in lora_array:
                parts = lora_string.split(':')
                if len(parts) == 3:
                    lora_repo, weights, adapter_name = parts
                    # 调用 pipe.load_lora_weights() 方法加载权重
                    pipe.load_lora_weights(lora_repo, weight_name=weights, adapter_name=adapter_name)
                    adapter_names.append(adapter_name)
                else:
                    print(f"Invalid format for lora_string: {lora_string}")
            
            adapter_weights = [lora_scale] * len(adapter_names)
            # 调用 pipeline.set_adapters 方法设置 adapter 和对应权重
            pipe.set_adapters(adapter_names, adapter_weights=adapter_weights)
        
    # Set random seed for reproducibility
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    final_image = generate_image(prompt, steps, seed, cfg_scale, width, height, progress)
    
    if upload_to_r2:
        with calculateDuration("upload r2"):
            url = upload_image_to_r2(final_image, account_id, access_key, secret_key, bucket)
            result = {"status": "success", "url": url}
    else:
        result = {"status": "success", "message": "Image generated but not uploaded"}
    
    progress(100, "Completed!")

    yield final_image, seed, json.dumps(result)


css="""
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown("Flux with lora")
    with gr.Row():
        
        with gr.Column():
            prompt = gr.Text(label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False)
            lora_strings = gr.Text( label="lora_strings", max_lines=1, placeholder="Enter a lora strings", visible=True)        
            run_button = gr.Button("Run", scale=0)

            with gr.Accordion("Advanced Settings", open=False):

                with gr.Row():
                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                    lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3, step=0.01, value=0.95)

                with gr.Row():
                    width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
                    height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)

                with gr.Row():
                    cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28) 

                upload_to_r2 = gr.Checkbox(label="Upload to R2", value=False)
                account_id = gr.Textbox(label="Account Id", placeholder="Enter R2 account id")
                access_key = gr.Textbox(label="Access Key", placeholder="Enter R2 access key here")
                secret_key = gr.Textbox(label="Secret Key", placeholder="Enter R2 secret key here")
                bucket = gr.Textbox(label="Bucket Name", placeholder="Enter R2 bucket name here")
        

        with gr.Column():
            result = gr.Image(label="Result", show_label=False)
            json_text = gr.Text()

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn = run_lora,
        inputs = [prompt, cfg_scale, steps, lora_strings, randomize_seed, seed, width, height, lora_scale, upload_to_r2, account_id, access_key, secret_key, bucket],
        outputs=[result, seed, json_text]
    )

demo.queue().launch()