Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,545 Bytes
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import torch
from diffusers import PixArtSigmaPipeline
import gradio as gr
import spaces
# Load the pre-trained diffusion model
pipe = PixArtSigmaPipeline.from_pretrained('ptx0/pixart-900m-1024-ft', torch_dtype=torch.bfloat16)
pipe.to('cuda')
import re
def extract_resolution(resolution_str):
match = re.match(r'(\d+)x(\d+)', resolution_str)
if match:
width = int(match.group(1))
height = int(match.group(2))
return (width, height)
else:
return None
# Define the image generation function with adjustable parameters and a progress bar
@spaces.GPU
def generate(prompt, guidance_scale, num_inference_steps, resolution, negative_prompt):
width, height = extract_resolution(resolution) or (1024, 1024)
return pipe(
prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width, height=height
).images
# Example prompts to demonstrate the model's capabilities
example_prompts = [
["A futuristic cityscape at night under a starry sky", 7.5, 25, "blurry, overexposed"],
["A serene landscape with a flowing river and autumn trees", 8.0, 20, "crowded, noisy"],
["An abstract painting of joy and energy in bright colors", 9.0, 30, "dark, dull"]
]
# Create a Gradio interface, 1024x1024,1152x960,896x1152
iface = gr.Interface(
fn=generate,
inputs=[
gr.Text(label="Enter your prompt"),
gr.Slider(1, 20, step=0.1, label="Guidance Scale", value=3.4),
gr.Slider(1, 50, step=1, label="Number of Inference Steps", value=28),
gr.Radio(["1024x1024", "1152x960", "896x1152"], label="Resolution", value="1152x960"),
gr.Text(value="underexposed, blurry, ugly, washed-out", label="Negative Prompt")
],
outputs=gr.Gallery(height=1024, min_width=1024, columns=2),
examples=example_prompts,
title="PixArt 900M",
description=(
"This is a 900M parameter model expanded from PixArt Sigma 1024px (600M) by adding 14 layers to deepen the transformer."
"<br />This model is being <strong>actively trained</strong> on 3.5M samples across a wide distribution of photos, synthetic data, cinema, anime, and safe-for-work furry art."
"<br />"
"<br /> The datasets been filtered for extremist and illegal content, but it is possible to produce toxic outputs. <strong>This model has not been safety-aligned or fine-tuned</strong>."
" You may receive non-aesthetic results, or prompts might be partially or wholly ignored."
"<br />Although celebrity names and artist styles haven't been scrubbed from the datasets, the low volume of these samples in the training set result in a lack of representation for public figures."
"<br />"
"<br />Be mindful when using this demo space that you do not inadvertently share images without adequate preparation and informing the receivers that these images are AI generated."
"<br />"
"<br />This model is being trained by <strong>Terminus Research Group</strong> with support from <a href='https://fal.ai'>Fal.ai</a>."
" See https://fal.ai/grants for more information on how Fal.ai can help your team."
"<br />"
"<br />"
"<ul>"
"<li>Lead trainer: @pseudoterminalx (bghira@GitHub)</li>"
"<li>Architecture: @jimmycarter (AmericanPresidentJimmyCarter@GitHub)</li>"
"<li>Datasets: @ProGamerGov, @jimmycarter, @pseudoterminalx</li>"
"</ul>"
)
).launch()
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