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import os
import time
from io import BytesIO
import uuid
import torch
import gradio as gr
import spaces
import numpy as np
from einops import rearrange
from PIL import Image, ExifTags
from dataclasses import dataclass
from flux.sampling import denoise, get_noise, get_schedule, prepare, unpack, prepare_tokens
from flux.util import configs, embed_watermark, load_ae, load_clip, load_flow_model, load_t5
import jax
import jax.numpy as jnp
from flax import nnx
from jax import Array as Tensor
from einops import repeat
@dataclass
class SamplingOptions:
prompt: str
width: int
height: int
num_steps: int
guidance: float
seed: int | None
NSFW_THRESHOLD = 0.85
@spaces.GPU
def get_models(name: str, device: torch.device, offload: bool, is_schnell: bool):
t5 = load_t5(device, max_length=256 if is_schnell else 512)
clip = load_clip(device)
model = load_flow_model(name, device="cpu" if offload else device)
ae = load_ae(name, device="cpu" if offload else device)
# nsfw_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection", device=device)
# return model, ae, t5, clip, nsfw_classifier
return nnx.split(model), nnx.split(ae), nnx.split(t5), t5.tokenizer, nnx.split(clip), clip.tokenizer, None
@jax.jit
def encode(ae,x):
ae=nnx.merge(*ae)
return ae.encode(x)
def _generate(model, ae, t5, clip, x, t5_tokens, clip_tokens, num_steps, guidance,
#init_image=None,
#image2image_strength=0.0,
shift=True):
b,h,w,c=x.shape
model=nnx.merge(*model)
ae=nnx.merge(*ae)
t5=nnx.merge(*t5)
clip=nnx.merge(*clip)
timesteps = get_schedule(
num_steps,
x.shape[-1] * x.shape[-2] // 4,
shift=shift,
)
# if init_image is not None:
# t_idx = int((1 - image2image_strength) * num_steps)
# t = timesteps[t_idx]
# timesteps = timesteps[t_idx:]
# x = t * x + (1.0 - t) * init_image.astype(x.dtype)
inp = prepare(t5, clip, x, t5_tokens, clip_tokens)
x = denoise(model, **inp, timesteps=timesteps, guidance=guidance)
x = unpack(x.astype(jnp.float32), h*8, w*8)
x = ae.decode(x)
return x
generate=jax.jit(_generate, static_argnames=("num_steps","shift"))
def prepare_tokens(t5_tokenizer, clip_tokenizer, prompt: str | list[str]) -> tuple[Tensor, Tensor]:
if isinstance(prompt, str):
prompt = [prompt]
t5_tokens = t5_tokenizer(
prompt,
truncation=True,
max_length=512,
return_length=False,
return_overflowing_tokens=False,
padding="max_length",
return_tensors="jax",
)["input_ids"]
clip_tokens = clip_tokenizer(
prompt,
truncation=True,
max_length=77,
return_length=False,
return_overflowing_tokens=False,
padding="max_length",
return_tensors="jax",
)["input_ids"]
return t5_tokens, clip_tokens
class FluxGenerator:
def __init__(self, model_name: str, device: str, offload: bool):
self.device = None
self.offload = offload
self.model_name = model_name
self.is_schnell = model_name == "flux-schnell"
self.model, self.ae, self.t5, self.t5_tokenizer, self.clip, self.clip_tokenizer, self.nsfw_classifier = get_models(
model_name,
device=self.device,
offload=self.offload,
is_schnell=self.is_schnell,
)
self.key = jax.random.key(0)
@spaces.GPU(duration=180)
def generate_image(
self,
img_size,
num_steps,
guidance,
seed,
prompt,
# init_image=None,
# image2image_strength=0.0,
add_sampling_metadata=True,
):
seed = int(seed)
if seed == -1:
seed = None
if img_size == "1,024x1,024":
width, height = 1024, 1024
else:
width, height = 512, 512
opts = SamplingOptions(
prompt=prompt,
width=width,
height=height,
num_steps=num_steps,
guidance=guidance,
seed=seed,
)
if opts.seed is None:
# opts.seed = torch.Generator(device="cpu").seed()
key,self.key=jax.random.split(self.key,2)
opts.seed=jax.random.randint(key,(),0,2**30)
print(f"Generating '{opts.prompt}' with seed {opts.seed}")
t0 = time.perf_counter()
# if init_image is not None:
# if isinstance(init_image, np.ndarray):
# init_image = jnp.asarray(init_image).astype(jnp.float32) / 255.0
# init_image = init_image[None]
# # init_image = torch.nn.functional.interpolate(init_image, (opts.height, opts.width))
# init_image = jax.image.resize(init_image, (opts.height, opts.width), method="lanczos5")
# # if self.offload:
# # self.ae.encoder.to(self.device)
# # init_image = self.ae.encode(init_image)
# init_image = encode(self.ae, init_image)
# prepare input
t5_tokens, clip_tokens = prepare_tokens(self.t5_tokenizer, self.clip_tokenizer, prompt=opts.prompt)
x = get_noise(
1,
opts.height,
opts.width,
device=None,
dtype=jnp.bfloat16,
seed=opts.seed,
)
x = generate(self.model, self.ae, self.t5, self.clip, x, t5_tokens, clip_tokens, opts.num_steps, opts.guidance, shift=(not self.is_schnell))
t1 = time.perf_counter()
# print(f"Done in {t1 - t0:.1f}s.")
runtime = t1 - t0
# print(f"Done in {t1 - t0:.1f}s.")
# bring into PIL format
x= jnp.clip(x, -1, 1)
# x = embed_watermark(x.astype(jnp.float32))
# x = rearrange(x[0], "c h w -> h w c")
img = Image.fromarray(np.asarray((127.5 * (x[0] + 1.0))).astype(np.uint8))
# img = Image.fromarray((127.5 * (x + 1.0)).cpu().byte().numpy())
# nsfw_score = [x["score"] for x in self.nsfw_classifier(img) if x["label"] == "nsfw"][0]
if True:
filename = f"output/gradio/{uuid.uuid4()}.jpg"
os.makedirs(os.path.dirname(filename), exist_ok=True)
exif_data = Image.Exif()
# if init_image is None:
exif_data[ExifTags.Base.Software] = "AI generated;txt2img;flux"
# else:
# exif_data[ExifTags.Base.Software] = "AI generated;img2img;flux"
exif_data[ExifTags.Base.Make] = "Black Forest Labs"
exif_data[ExifTags.Base.Model] = self.model_name
if add_sampling_metadata:
exif_data[ExifTags.Base.ImageDescription] = prompt
img.save(filename, format="jpeg", exif=exif_data, quality=95, subsampling=0)
return img, runtime, str(opts.seed), filename, None
else:
return None, str(opts.seed), None, "Your generated image may contain NSFW content."
@spaces.GPU(duration=180)
def create_demo(model_name: str, device: str = "cuda", offload: bool = False):
generator = FluxGenerator(model_name, device, offload)
is_schnell = model_name == "flux-schnell"
with open("./assets/banner.html") as f:
banner = f.read()
with gr.Blocks() as demo:
with gr.Column(elem_id="app-container"):
gr.HTML(f"""<iframe scrolling="no" style="width: 100%; height: 125px; border: 0" srcdoc='{banner}'>""")
gr.Markdown(f"""🚀 [Flux-Flax](https://github.com/lkwq007/flux-flax) is a JAX implementation of Flux models. 1-step time statistics for `FLUX.1-schnell`: `0.4s` for 1024x1024, `0.1s` for 512x512; 2-step: `0.6s` for 1024x1024, `0.2s` for 512x512; 4-step: `2.4s` for 1024x1024, `0.8s` for 512x512.
""")
with gr.Row():
with gr.Column(scale=3):
output_image = gr.Image(label="Generated Image")
warning_text = gr.Textbox(label="Warning", visible=False)
download_btn = gr.File(label="Download full-resolution")
gr.Markdown("""
💡 Note: More resolutions are supports, but here this demo limits to 1024x1024 and 512x512 to avoid jit recompilation (which takes 130s). Flux-Flax also support `FLUX.1-dev`, 50-step time statistics: `18s` for 1024x1024, `6s` for 512x512""")
with gr.Column(scale=1):
prompt = gr.Textbox(label="Prompt", value="a photo of a forest with mist swirling around the tree trunks. The word \"FLUX\" is painted over it in big, red brush strokes with visible texture")
generate_btn = gr.Button("Generate")
with gr.Row():
seed_output = gr.Number(label="Used Seed")
runtime = gr.Number(label="Inference Time", precision=3)
with gr.Row():
seed = gr.Textbox(-1, label="Seed (-1 for random)")
img_size = gr.Radio(["1,024x1,024", "512x512"], label="Image Resolution", value="1,024x1,024")
num_steps = gr.Slider(1, 50, 4 if is_schnell else 50, step=1, label="Number of steps")
guidance = gr.Slider(1.0, 10.0, 3.5, step=0.1, label="Guidance", interactive=not is_schnell)
add_sampling_metadata = gr.Checkbox(label="Add sampling parameters to metadata?", value=True)
# guidance = gr.Slider(1.0, 10.0, 3.5, step=0.1, label="Guidance", interactive=not is_schnell, visible=False)
# def update_img2img(do_img2img):
# return {
# init_image: gr.update(visible=do_img2img),
# image2image_strength: gr.update(visible=do_img2img),
# }
# do_img2img.change(update_img2img, do_img2img, [init_image, image2image_strength])
generate_btn.click(
fn=generator.generate_image,
inputs=[img_size, num_steps, guidance, seed, prompt, add_sampling_metadata],
outputs=[output_image, runtime, seed_output, download_btn, warning_text],
)
return demo
# if __name__ == "__main__":
# import argparse
# parser = argparse.ArgumentParser(description="Flux")
# parser.add_argument("--name", type=str, default="flux-schnell", choices=list(configs.keys()), help="Model name")
# parser.add_argument("--device", type=str, default="cpu", help="Device to use")
# parser.add_argument("--offload", action="store_true", help="Offload model to CPU when not in use")
# parser.add_argument("--share", action="store_true", help="Create a public link to your demo")
# args = parser.parse_args()
demo = create_demo("flux-schnell", None, False)
demo.launch() |