squaadai / app.py
amazonaws-la's picture
Update app.py
41548b6 verified
raw
history blame
9.81 kB
#!/usr/bin/env python
from __future__ import annotations
import os
import random
import gradio as gr
import numpy as np
import PIL.Image
import spaces
import torch
from diffusers import AutoencoderKL, DiffusionPipeline
DESCRIPTION = "# SDXL"
if not torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1824"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
ENABLE_REFINER = os.getenv("ENABLE_REFINER", "1") == "1"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
@spaces.GPU
def generate(
prompt: str,
negative_prompt: str = "",
prompt_2: str = "",
negative_prompt_2: str = "",
use_negative_prompt: bool = False,
use_prompt_2: bool = False,
use_negative_prompt_2: bool = False,
seed: int = 0,
width: int = 1024,
height: int = 1024,
guidance_scale_base: float = 5.0,
guidance_scale_refiner: float = 5.0,
num_inference_steps_base: int = 25,
num_inference_steps_refiner: int = 25,
apply_refiner: bool = False,
model = 'stabilityai/stable-diffusion-xl-base-1.0',
vaecall = 'madebyollin/sdxl-vae-fp16-fix',
lora = 'pierroromeu/lora-trained-xl-folder',
) -> PIL.Image.Image:
if torch.cuda.is_available():
vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16)
# Substitua o valor dinâmico antes de chamar from_pretrained
pipe_model_name = model
pipe = DiffusionPipeline.from_pretrained(
pipe_model_name,
vae=vae,
torch_dtype=torch.float16,
use_safetensors=True,
)
if ENABLE_REFINER:
refiner_model_name = model
refiner = DiffusionPipeline.from_pretrained(
refiner_model_name,
vae=vae,
torch_dtype=torch.float16,
use_safetensors=True,
)
pipe.load_lora_weights(lora)
if ENABLE_CPU_OFFLOAD:
pipe.enable_model_cpu_offload()
if ENABLE_REFINER:
refiner.enable_model_cpu_offload()
else:
pipe.to(device)
if ENABLE_REFINER:
refiner.to(device)
if USE_TORCH_COMPILE:
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
if ENABLE_REFINER:
refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
generator = torch.Generator().manual_seed(seed)
if not use_negative_prompt:
negative_prompt = None # type: ignore
if not use_prompt_2:
prompt_2 = None # type: ignore
if not use_negative_prompt_2:
negative_prompt_2 = None # type: ignore
if not apply_refiner:
return pipe(
prompt=prompt,
negative_prompt=negative_prompt,
prompt_2=prompt_2,
negative_prompt_2=negative_prompt_2,
width=width,
height=height,
guidance_scale=guidance_scale_base,
num_inference_steps=num_inference_steps_base,
generator=generator,
output_type="pil",
).images[0]
else:
latents = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
prompt_2=prompt_2,
negative_prompt_2=negative_prompt_2,
width=width,
height=height,
guidance_scale=guidance_scale_base,
num_inference_steps=num_inference_steps_base,
generator=generator,
output_type="latent",
).images
image = refiner(
prompt=prompt,
negative_prompt=negative_prompt,
prompt_2=prompt_2,
negative_prompt_2=negative_prompt_2,
guidance_scale=guidance_scale_refiner,
num_inference_steps=num_inference_steps_refiner,
image=latents,
generator=generator,
).images[0]
return image
examples = [
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"An astronaut riding a green horse",
]
with gr.Blocks(css="style.css") as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(
value="Duplicate Space for private use",
elem_id="duplicate-button",
visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
)
with gr.Group():
model = gr.Text(label='Modelo')
vaecall = gr.Text(label='VAE')
lora = gr.Text(label='LoRA')
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 options", open=False):
with gr.Row():
use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False)
use_negative_prompt_2 = gr.Checkbox(label="Use negative prompt 2", value=False)
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
visible=False,
)
prompt_2 = gr.Text(
label="Prompt 2",
max_lines=1,
placeholder="Enter your prompt",
visible=False,
)
negative_prompt_2 = gr.Text(
label="Negative prompt 2",
max_lines=1,
placeholder="Enter a negative prompt",
visible=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=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
apply_refiner = gr.Checkbox(label="Apply refiner", value=False, visible=ENABLE_REFINER)
with gr.Row():
guidance_scale_base = gr.Slider(
label="Guidance scale for base",
minimum=1,
maximum=20,
step=0.1,
value=5.0,
)
num_inference_steps_base = gr.Slider(
label="Number of inference steps for base",
minimum=10,
maximum=100,
step=1,
value=25,
)
with gr.Row(visible=False) as refiner_params:
guidance_scale_refiner = gr.Slider(
label="Guidance scale for refiner",
minimum=1,
maximum=20,
step=0.1,
value=5.0,
)
num_inference_steps_refiner = gr.Slider(
label="Number of inference steps for refiner",
minimum=10,
maximum=100,
step=1,
value=25,
)
gr.Examples(
examples=examples,
inputs=prompt,
outputs=result,
fn=generate,
cache_examples=CACHE_EXAMPLES,
)
use_negative_prompt.change(
fn=lambda x: gr.update(visible=x),
inputs=use_negative_prompt,
outputs=negative_prompt,
queue=False,
api_name=False,
)
use_prompt_2.change(
fn=lambda x: gr.update(visible=x),
inputs=use_prompt_2,
outputs=prompt_2,
queue=False,
api_name=False,
)
use_negative_prompt_2.change(
fn=lambda x: gr.update(visible=x),
inputs=use_negative_prompt_2,
outputs=negative_prompt_2,
queue=False,
api_name=False,
)
apply_refiner.change(
fn=lambda x: gr.update(visible=x),
inputs=apply_refiner,
outputs=refiner_params,
queue=False,
api_name=False,
)
gr.on(
triggers=[
prompt.submit,
negative_prompt.submit,
prompt_2.submit,
negative_prompt_2.submit,
run_button.click,
],
fn=randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=generate,
inputs=[
prompt,
negative_prompt,
prompt_2,
negative_prompt_2,
use_negative_prompt,
use_prompt_2,
use_negative_prompt_2,
seed,
width,
height,
guidance_scale_base,
guidance_scale_refiner,
num_inference_steps_base,
num_inference_steps_refiner,
apply_refiner,
model,
vaecall,
lora,
],
outputs=result,
api_name="run",
)
if __name__ == "__main__":
demo.queue(max_size=20).launch()