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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) | |
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() | |