AkashicPulse / app.py
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Update app.py
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import os
import spaces
import torch
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
import gradio as gr
import random
import tqdm
# Enable TQDM progress tracking
tqdm.monitor_interval = 0
#HF_TOKEN import
HF_TOKEN = os.getenv("HF_TOKEN")
# Load the diffusion pipeline
pipe = StableDiffusionXLPipeline.from_single_file(
"https://huggingface.co/kayfahaarukku/AkashicPulse-v1.0/blob/main/AkashicPulse-v1.0-ft-ft.safetensors",
torch_dtype=torch.float16,
custom_pipeline="lpw_stable_diffusion_xl",
use_safetensors=True,
use_auth_token=HF_TOKEN,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Function to generate an image
@spaces.GPU
def generate_image(prompt, negative_prompt, use_defaults, resolution, guidance_scale, num_inference_steps, seed, randomize_seed, progress=gr.Progress()):
try:
pipe.to('cuda')
if randomize_seed:
seed = random.randint(0, 99999999)
if use_defaults:
prompt = f"{prompt}, masterpiece, best quality"
negative_prompt = f"lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, signature, watermark, username, blurry, {negative_prompt}"
generator = torch.manual_seed(seed)
def callback(step, timestep, latents):
progress(step / num_inference_steps)
return
width, height = map(int, resolution.split('x'))
image = pipe(
prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
callback=callback,
callback_steps=1
).images[0]
torch.cuda.empty_cache()
metadata_text = f"{prompt}\nNegative prompt: {negative_prompt}\nSteps: {num_inference_steps}, Sampler: Euler a, Size: {width}x{height}, Seed: {seed}, CFG scale: {guidance_scale}"
return image, seed, metadata_text
except Exception as e:
return None, seed, f"Error during generation: {str(e)}"
# Define Gradio interface
def interface_fn(prompt, negative_prompt, use_defaults, resolution, guidance_scale, num_inference_steps, seed, randomize_seed, progress=gr.Progress()):
image, seed, metadata_text = generate_image(prompt, negative_prompt, use_defaults, resolution, guidance_scale, num_inference_steps, seed, randomize_seed, progress)
if image is None:
return gr.update(value=None), seed, gr.update(value=metadata_text)
return image, seed, gr.update(value=metadata_text)
def reset_inputs():
return gr.update(value=''), gr.update(value=''), gr.update(value=True), gr.update(value='832x1216'), gr.update(value=7), gr.update(value=28), gr.update(value=0), gr.update(value=True), gr.update(value='')
with gr.Blocks(title="AkashicPulse Demo", theme="NoCrypt/[email protected]") as demo:
gr.HTML(
"<h1>AkashicPulse Demo</h1>"
"<p>This demo is intended to showcase what the model is capable of and is not intended to be the main generation platform. "
"Results produced with Diffusers are not the best, and it's highly recommended for you to get the model running inside "
"Stable Diffusion WebUI or ComfyUI.</p>"
)
with gr.Row():
with gr.Column():
prompt_input = gr.Textbox(lines=2, placeholder="Enter prompt here", label="Prompt")
negative_prompt_input = gr.Textbox(lines=2, placeholder="Enter negative prompt here", label="Negative Prompt")
use_defaults_input = gr.Checkbox(label="Use Default Quality Tags and Negative Prompt", value=True)
resolution_input = gr.Radio(
choices=[
"1024x1024", "1152x896", "896x1152", "1216x832", "832x1216",
"1344x768", "768x1344", "1536x640", "640x1536"
],
label="Resolution",
value="832x1216"
)
guidance_scale_input = gr.Slider(minimum=1, maximum=20, step=0.5, label="Guidance Scale", value=7)
num_inference_steps_input = gr.Slider(minimum=1, maximum=100, step=1, label="Number of Inference Steps", value=28)
seed_input = gr.Slider(minimum=0, maximum=999999999, step=1, label="Seed", value=0, interactive=True)
randomize_seed_input = gr.Checkbox(label="Randomize Seed", value=True)
generate_button = gr.Button("Generate")
reset_button = gr.Button("Reset")
with gr.Column():
output_image = gr.Image(type="pil", label="Generated Image")
with gr.Accordion("Parameters", open=False):
gr.Markdown("This parameter is compatible with Stable Diffusion WebUI's parameter importer.")
metadata_textbox = gr.Textbox(lines=6, label="Image Parameters", interactive=False, max_lines=6)
gr.Markdown(
"""
### Recommended settings:
- Sampling: Euler a
- Steps: 20-30 (sweet spot: 28)
- CFG: 4-10 (sweet spot: 7)
- [Not mandatory] On reForge or ComfyUI, have MaHiRo CFG enabled
### Recommended prompt formatting:
- Prompt: [1girl/1boy], [character name], [series], by [artist name], [the rest of the prompt], masterpiece, best quality
- Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, signature, watermark, username, blurry, [the rest of the negative prompt]
"""
)
generate_button.click(
interface_fn,
inputs=[
prompt_input, negative_prompt_input, use_defaults_input, resolution_input,
guidance_scale_input, num_inference_steps_input, seed_input, randomize_seed_input
],
outputs=[output_image, seed_input, metadata_textbox]
)
reset_button.click(
reset_inputs,
inputs=[],
outputs=[
prompt_input, negative_prompt_input, use_defaults_input, resolution_input,
guidance_scale_input, num_inference_steps_input, seed_input, randomize_seed_input, metadata_textbox
]
)
demo.queue(max_size=20).launch(share=False)