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from __future__ import annotations
import math
import random
import gradio as gr
import numpy as np
import torch
from PIL import Image
from diffusers import StableDiffusionXLImg2ImgPipeline, EDMEulerScheduler, AutoencoderKL
from huggingface_hub import hf_hub_download
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe_edit = StableDiffusionXLImg2ImgPipeline.from_single_file(
hf_hub_download(repo_id="stabilityai/cosxl", filename="cosxl_edit.safetensors"),
num_in_channels=8,
is_cosxl_edit=True,
vae=vae,
torch_dtype=torch.float16,
)
pipe_edit.scheduler = EDMEulerScheduler(sigma_min=0.002, sigma_max=120.0, sigma_data=1.0, prediction_type="v_prediction")
pipe_edit.to("cuda")
refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0",
vae=vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16"
)
refiner.to("cuda")
def set_timesteps_patched(self, num_inference_steps: int, device=None):
self.num_inference_steps = num_inference_steps
ramp = np.linspace(0, 1, self.num_inference_steps)
sigmas = torch.linspace(math.log(self.config.sigma_min), math.log(self.config.sigma_max), len(ramp)).exp().flip(0)
sigmas = sigmas.to(dtype=torch.float32, device=device)
self.timesteps = self.precondition_noise(sigmas)
self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
self._step_index = None
self._begin_index = None
self.sigmas = self.sigmas.to("cpu")
EDMEulerScheduler.set_timesteps = set_timesteps_patched
def king(input_image, instruction: str, negative_prompt: str = "", steps: int = 25, randomize_seed: bool = True, seed: int = 2404, guidance_scale: float = 6, progress=gr.Progress(track_tqdm=True)):
input_image = Image.open(input_image).convert('RGB')
if randomize_seed:
seed = random.randint(0, 999999)
generator = torch.manual_seed(seed)
output_image = pipe_edit(
instruction,
negative_prompt=negative_prompt,
image=input_image,
guidance_scale=guidance_scale,
image_guidance_scale=1.5,
width=input_image.width,
height=input_image.height,
num_inference_steps=steps,
generator=generator,
output_type="latent",
).images
refine = refiner(
prompt=f"{instruction}, 4k, hd, high quality, masterpiece",
negative_prompt=negative_prompt,
guidance_scale=7.5,
num_inference_steps=steps,
image=output_image,
generator=generator,
).images[0]
return seed, refine
css = '''
.gradio-container{max-width: 700px !important}
h1{text-align:center}
footer {
visibility: hidden
}
'''
examples = [
["./supercar.png", "make it red"],
["./red_car.png", "add some snow"],
]
with gr.Blocks(css=css) as demo:
gr.Markdown("# Image Editing\n### Note: First image generation takes time")
with gr.Row():
instruction = gr.Textbox(lines=1, label="Instruction", interactive=True)
generate_button = gr.Button("Run", scale=0)
with gr.Row():
input_image = gr.Image(label="Image", type='filepath', interactive=True)
with gr.Row():
guidance_scale = gr.Number(value=6.0, step=0.1, label="Guidance Scale", interactive=True)
steps = gr.Number(value=25, step=1, label="Steps", interactive=True)
with gr.Accordion("Advanced options", open=False):
with gr.Row():
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, ugly, disgusting, blurry, amputation,(face asymmetry, eyes asymmetry, deformed eyes, open mouth)",
visible=True
)
with gr.Row():
randomize_seed = gr.Checkbox(label="Randomize Seed", value=True, interactive=True)
seed = gr.Number(value=2404, step=1, label="Seed", interactive=True)
gr.Examples(
examples=examples,
inputs=[input_image, instruction],
outputs=[input_image],
cache_examples=False,
)
generate_button.click(
king,
inputs=[input_image, instruction, negative_prompt, steps, randomize_seed, seed, guidance_scale],
outputs=[seed, input_image],
)
demo.queue(max_size=500).launch() |