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import spaces
import os
import torch
import random
from huggingface_hub import snapshot_download
from diffusers import StableDiffusionXLPipeline, AutoencoderKL
from diffusers import (
EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler,
DPMSolverSDEScheduler,
HeunDiscreteScheduler,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UniPCMultistepScheduler,
)
from diffusers.models.attention_processor import AttnProcessor2_0
import gradio as gr
from PIL import Image
import numpy as np
from transformers import AutoProcessor, AutoModelForCausalLM, pipeline
import requests
from RealESRGAN import RealESRGAN
import subprocess
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
def download_file(url, folder_path, filename):
if not os.path.exists(folder_path):
os.makedirs(folder_path)
file_path = os.path.join(folder_path, filename)
if os.path.isfile(file_path):
print(f"File already exists: {file_path}")
else:
response = requests.get(url, stream=True)
if response.status_code == 200:
with open(file_path, 'wb') as file:
for chunk in response.iter_content(chunk_size=1024):
file.write(chunk)
print(f"File successfully downloaded and saved: {file_path}")
else:
print(f"Error downloading the file. Status code: {response.status_code}")
# Download ESRGAN models
download_file("https://huggingface.co/ai-forever/Real-ESRGAN/resolve/main/RealESRGAN_x2.pth?download=true", "models/upscalers/", "RealESRGAN_x2.pth")
download_file("https://huggingface.co/ai-forever/Real-ESRGAN/resolve/main/RealESRGAN_x4.pth?download=true", "models/upscalers/", "RealESRGAN_x4.pth")
# Download the model files
ckpt_dir_pony = snapshot_download(repo_id="Niggendar/autismmixSDXL_autismmixPony")
ckpt_dir_cyber = snapshot_download(repo_id="John6666/t-ponynai3-v6-sdxl")
ckpt_dir_stallion = snapshot_download(repo_id="John6666/prefect-pony-xl-v2-cleaned-style-sdxl")
# Load the models
vae_pony = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir_pony, "vae"), torch_dtype=torch.float16)
vae_cyber = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir_cyber, "vae"), torch_dtype=torch.float16)
vae_stallion = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir_stallion, "vae"), torch_dtype=torch.float16)
pipe_pony = StableDiffusionXLPipeline.from_pretrained(
ckpt_dir_pony,
vae=vae_pony,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16"
)
pipe_cyber = StableDiffusionXLPipeline.from_pretrained(
ckpt_dir_cyber,
vae=vae_cyber,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16"
)
pipe_stallion = StableDiffusionXLPipeline.from_pretrained(
ckpt_dir_stallion,
vae=vae_stallion,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16"
)
pipe_pony = pipe_pony.to("cuda")
pipe_cyber = pipe_cyber.to("cuda")
pipe_stallion = pipe_stallion.to("cuda")
pipe_pony.unet.set_attn_processor(AttnProcessor2_0())
pipe_cyber.unet.set_attn_processor(AttnProcessor2_0())
pipe_stallion.unet.set_attn_processor(AttnProcessor2_0())
# Define samplers
samplers = {
"Euler a": EulerAncestralDiscreteScheduler.from_config(pipe_pony.scheduler.config),
"DPM++ SDE Karras": DPMSolverSDEScheduler.from_config(pipe_pony.scheduler.config, use_karras_sigmas=True),
"Heun": HeunDiscreteScheduler.from_config(pipe_pony.scheduler.config),
# New samplers
"DPM++ 2M SDE Karras": DPMSolverMultistepScheduler.from_config(pipe_pony.scheduler.config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++"),
"DPM++ 2M": DPMSolverMultistepScheduler.from_config(pipe_pony.scheduler.config),
"DDIM": DDIMScheduler.from_config(pipe_pony.scheduler.config),
"LMS": LMSDiscreteScheduler.from_config(pipe_pony.scheduler.config),
"PNDM": PNDMScheduler.from_config(pipe_pony.scheduler.config),
"UniPC": UniPCMultistepScheduler.from_config(pipe_pony.scheduler.config),
}
DEFAULT_POSITIVE_PREFIX = "Score_9 score_8_up score_7_up BREAK"
DEFAULT_POSITIVE_SUFFIX = "(masterpiece) very_aesthetic hi_res absurd_res superabsurd_res"
DEFAULT_NEGATIVE_PREFIX = "Score_1 score_2 score _3 text low_res"
DEFAULT_NEGATIVE_SUFFIX = "Nsfw oversaturated crappy_art low_quality blurry bad_anatomy extra_digits fewer_digits simple_background very_displeasing watermark signature"
# Initialize Florence model
device = "cuda" if torch.cuda.is_available() else "cpu"
florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval()
florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True)
# Prompt Enhancer
enhancer_medium = pipeline("summarization", model="gokaygokay/Lamini-Prompt-Enchance", device=device)
enhancer_long = pipeline("summarization", model="gokaygokay/Lamini-Prompt-Enchance-Long", device=device)
class LazyRealESRGAN:
def __init__(self, device, scale):
self.device = device
self.scale = scale
self.model = None
def load_model(self):
if self.model is None:
self.model = RealESRGAN(self.device, scale=self.scale)
self.model.load_weights(f'models/upscalers/RealESRGAN_x{self.scale}.pth', download=False)
def predict(self, img):
self.load_model()
return self.model.predict(img)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
lazy_realesrgan_x2 = LazyRealESRGAN(device, scale=2)
lazy_realesrgan_x4 = LazyRealESRGAN(device, scale=4)
# Florence caption function
def florence_caption(image):
# Convert image to PIL if it's not already
if not isinstance(image, Image.Image):
image = Image.fromarray(image)
inputs = florence_processor(text="<DETAILED_CAPTION>", images=image, return_tensors="pt").to(device)
generated_ids = florence_model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
early_stopping=False,
do_sample=False,
num_beams=3,
)
generated_text = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = florence_processor.post_process_generation(
generated_text,
task="<DETAILED_CAPTION>",
image_size=(image.width, image.height)
)
return parsed_answer["<DETAILED_CAPTION>"]
# Prompt Enhancer function
def enhance_prompt(input_prompt, model_choice):
if model_choice == "Medium":
result = enhancer_medium("Enhance the description: " + input_prompt)
enhanced_text = result[0]['summary_text']
else: # Long
result = enhancer_long("Enhance the description: " + input_prompt)
enhanced_text = result[0]['summary_text']
return enhanced_text
def upscale_image(image, scale):
# Ensure image is a PIL Image object
if not isinstance(image, Image.Image):
if isinstance(image, np.ndarray):
image = Image.fromarray(image)
else:
raise ValueError("Input must be a PIL Image or a numpy array")
if scale == 2:
return lazy_realesrgan_x2.predict(image)
elif scale == 4:
return lazy_realesrgan_x4.predict(image)
else:
return image
@spaces.GPU(duration=120)
def generate_image(model_choice, additional_positive_prompt, additional_negative_prompt, height, width, num_inference_steps,
guidance_scale, num_images_per_prompt, use_random_seed, seed, sampler, clip_skip,
use_florence2, use_medium_enhancer, use_long_enhancer,
use_positive_prefix, use_positive_suffix, use_negative_prefix, use_negative_suffix,
use_upscaler, upscale_factor,
input_image=None, progress=gr.Progress(track_tqdm=True)):
# Select the appropriate pipe based on the model choice
if model_choice == "AutismMix SDXL":
pipe = pipe_pony
elif model_choice == "T-ponynai3":
pipe = pipe_cyber
else: # "Stallion Dreams Pony Realistic v1"
pipe = pipe_stallion
if use_random_seed:
seed = random.randint(0, 2**32 - 1)
else:
seed = int(seed) # Ensure seed is an integer
# Set the scheduler based on the selected sampler
pipe.scheduler = samplers[sampler]
# Set clip skip
pipe.text_encoder.config.num_hidden_layers -= (clip_skip - 1)
# Start with the default positive prompt prefix if enabled
full_positive_prompt = DEFAULT_POSITIVE_PREFIX + ", " if use_positive_prefix else ""
# Add Florence-2 caption if enabled and image is provided
if use_florence2 and input_image is not None:
florence2_caption = florence_caption(input_image)
florence2_caption = florence2_caption.lower().replace('.', ',')
additional_positive_prompt = f"{florence2_caption}, {additional_positive_prompt}" if additional_positive_prompt else florence2_caption
# Enhance only the additional positive prompt if enhancers are enabled
if additional_positive_prompt:
enhanced_prompt = additional_positive_prompt
if use_medium_enhancer:
medium_enhanced = enhance_prompt(enhanced_prompt, "Medium")
medium_enhanced = medium_enhanced.lower().replace('.', ',')
enhanced_prompt = f"{enhanced_prompt}, {medium_enhanced}"
if use_long_enhancer:
long_enhanced = enhance_prompt(enhanced_prompt, "Long")
long_enhanced = long_enhanced.lower().replace('.', ',')
enhanced_prompt = f"{enhanced_prompt}, {long_enhanced}"
full_positive_prompt += enhanced_prompt
# Add the default positive suffix if enabled
if use_positive_suffix:
full_positive_prompt += f", {DEFAULT_POSITIVE_SUFFIX}"
# Combine default negative prompt with additional negative prompt
full_negative_prompt = ""
if use_negative_prefix:
full_negative_prompt += f"{DEFAULT_NEGATIVE_PREFIX}, "
full_negative_prompt += additional_negative_prompt if additional_negative_prompt else ""
if use_negative_suffix:
full_negative_prompt += f", {DEFAULT_NEGATIVE_SUFFIX}"
try:
images = pipe(
prompt=full_positive_prompt,
negative_prompt=full_negative_prompt,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
num_images_per_prompt=num_images_per_prompt,
generator=torch.Generator(pipe.device).manual_seed(seed)
).images
if use_upscaler:
print("Upscaling images")
upscaled_images = []
for i, img in enumerate(images):
print(f"Upscaling image {i+1}")
if not isinstance(img, Image.Image):
print(f"Converting image {i+1} to PIL Image")
img = Image.fromarray(np.uint8(img))
upscaled_img = upscale_image(img, upscale_factor)
upscaled_images.append(upscaled_img)
images = upscaled_images
print("Returning results")
return images, seed, full_positive_prompt, full_negative_prompt
except Exception as e:
print(f"Error during image generation: {str(e)}")
import traceback
traceback.print_exc()
return None, seed, full_positive_prompt, full_negative_prompt
# Gradio interface
with gr.Blocks(theme='bethecloud/storj_theme') as demo:
gr.HTML("""
<h1 align="center">AutismMix SDXL / T-ponynai3 / Prefect Pony XL</h1>
<p align="center">
<a href="https://huggingface.co/Niggendar/autismmixSDXL_autismmixPony/" target="_blank">[AutismMix SDXL]</a>
<a href="https://huggingface.co/John6666/t-ponynai3-v6-sdxl" target="_blank">[T-ponynai3]</a>
<a href="https://huggingface.co/John6666/prefect-pony-xl-v2-cleaned-style-sdxl" target="_blank">[Prefect Pony XL]</a><br>
<a href="https://civitai.com/models/288584/autismmix-sdxl" target="_blank">[AutismMix SDXL civitai]</a>
<a href="https://civitai.com/models/317902/t-ponynai3" target="_blank">[T-ponynai3 civitai]</a>
<a href="https://civitai.com/models/439889/prefect-pony-xl" target="_blank">[Prefect Pony XL civitai]</a>
<a href="https://huggingface.co/microsoft/Florence-2-base" target="_blank">[Florence-2 Model]</a>
<a href="https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance-Long" target="_blank">[Prompt Enhancer Long]</a>
<a href="https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance" target="_blank">[Prompt Enhancer Medium]</a>
</p>
""")
with gr.Row():
with gr.Column(scale=1):
model_choice = gr.Dropdown(
["AutismMix SDXL", "T-ponynai3", "Prefect Pony XL"],
label="Model Choice",
value="AutismMix SDXL")
positive_prompt = gr.Textbox(label="Positive Prompt", placeholder="Add your positive prompt here")
negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Add your negative prompt here")
with gr.Accordion("Advanced settings", open=False):
height = gr.Slider(512, 2048, 1024, step=64, label="Height")
width = gr.Slider(512, 2048, 1024, step=64, label="Width")
num_inference_steps = gr.Slider(20, 100, 30, step=1, label="Number of Inference Steps")
guidance_scale = gr.Slider(1, 20, 6, step=0.1, label="Guidance Scale")
num_images_per_prompt = gr.Slider(1, 4, 1, step=1, label="Number of images per prompt")
use_random_seed = gr.Checkbox(label="Use Random Seed", value=True)
seed = gr.Number(label="Seed", value=0, precision=0)
sampler = gr.Dropdown(label="Sampler", choices=list(samplers.keys()), value="Euler a")
clip_skip = gr.Slider(1, 4, 2, step=1, label="Clip skip")
with gr.Accordion("Captioner and Enhancers", open=False):
input_image = gr.Image(label="Input Image for Florence-2 Captioner")
use_florence2 = gr.Checkbox(label="Use Florence-2 Captioner", value=False)
use_medium_enhancer = gr.Checkbox(label="Use Medium Prompt Enhancer", value=False)
use_long_enhancer = gr.Checkbox(label="Use Long Prompt Enhancer", value=False)
with gr.Accordion("Upscaler Settings", open=False):
use_upscaler = gr.Checkbox(label="Use Upscaler", value=False)
upscale_factor = gr.Radio(label="Upscale Factor", choices=[2, 4], value=2)
generate_btn = gr.Button("Generate Image")
with gr.Accordion("Prefix and Suffix Settings", open=True):
use_positive_prefix = gr.Checkbox(
label="Use Positive Prefix",
value=True,
info=f"Prefix: {DEFAULT_POSITIVE_PREFIX}"
)
use_positive_suffix = gr.Checkbox(
label="Use Positive Suffix",
value=True,
info=f"Suffix: {DEFAULT_POSITIVE_SUFFIX}"
)
use_negative_prefix = gr.Checkbox(
label="Use Negative Prefix",
value=True,
info=f"Prefix: {DEFAULT_NEGATIVE_PREFIX}"
)
use_negative_suffix = gr.Checkbox(
label="Use Negative Suffix",
value=True,
info=f"Suffix: {DEFAULT_NEGATIVE_SUFFIX}"
)
with gr.Column(scale=1):
output_gallery = gr.Gallery(label="Result", elem_id="gallery", show_label=False)
seed_used = gr.Number(label="Seed Used")
full_positive_prompt_used = gr.Textbox(label="Full Positive Prompt Used")
full_negative_prompt_used = gr.Textbox(label="Full Negative Prompt Used")
generate_btn.click(
fn=generate_image,
inputs=[
model_choice, # Add this new input
positive_prompt, negative_prompt, height, width, num_inference_steps,
guidance_scale, num_images_per_prompt, use_random_seed, seed, sampler,
clip_skip, use_florence2, use_medium_enhancer, use_long_enhancer,
use_positive_prefix, use_positive_suffix, use_negative_prefix, use_negative_suffix,
use_upscaler, upscale_factor,
input_image
],
outputs=[output_gallery, seed_used, full_positive_prompt_used, full_negative_prompt_used]
)
demo.launch(debug=True) |