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import gradio as gr
import numpy as np
import random
import torch
from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
import modal
import random
import io
from config.config import prompts, models # Indirect import
import os
import sentencepiece
from huggingface_hub import login
from transformers import AutoTokenizer
from datetime import datetime
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
CACHE_DIR = "/model_cache"
image = (
modal.Image.from_registry("nvidia/cuda:12.2.0-devel-ubuntu22.04", add_python="3.9")
.pip_install_from_requirements("requirements.txt")
.env({
"HF_HUB_ENABLE_HF_TRANSFER": "1", "HF_HOME": "HF_HOME", "HF_HUB_CACHE": CACHE_DIR
})
)
app = modal.App("img-gen-modal-live", image=image)
with image.imports():
import os
flux_model_vol = modal.Volume.from_name("flux-model-vol", create_if_missing=True)
@app.function(volumes={"/data": flux_model_vol},
secrets=[modal.Secret.from_name("huggingface-token")],
gpu="L40S",
timeout=300)
def infer(prompt, seed=42, randomize_seed=False, width=640, height=360, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
taef1 = AutoencoderTiny.from_pretrained("/data/taef1", torch_dtype=dtype).to(device)
good_vae = AutoencoderKL.from_pretrained("/data/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
pipe = DiffusionPipeline.from_pretrained("/data/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device)
torch.cuda.empty_cache()
pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
prompt=prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
output_type="pil",
good_vae=good_vae,
):
yield img, seed
examples = [
"a tiny astronaut hatching from an egg on the moon",
"a cat holding a sign that says hello world",
"an anime illustration of a wiener schnitzel",
]
css="""
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
hf_token = os.environ["HF_TOKEN"]
print("Initializing HF TOKEN")
print(hf_token)
print("HF TOKEN:")
login(token=hf_token)
with gr.Blocks(css=css) as demo:
f = modal.Function.from_name("img-gen-modal-live", "infer")
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""# FLUX.1 [dev]
12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)
[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
""")
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):
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():
width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=640)
height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=360)
with gr.Row():
guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=15, step=0.1, value=3.5)
num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=28)
gr.Examples(
examples=examples,
fn=f.remote,
inputs=[prompt],
outputs=[result, seed],
cache_examples="lazy"
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=lambda *args: [next(f.remote_gen(*args)), seed], # Adjusted to process generator
inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs=[result, seed]
)
demo.launch()
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