PixArt

Fast image generation with PixArt-Alpha and PixArt-Sigma

Generate images quickly with PixArt-Alpha and PixArt-Sigma.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

Renting on CLORE.AI

1. Visit CLORE.AI Marketplace

2. Filter by GPU type, VRAM, and price

3. Choose On-Demand (fixed rate) or Spot (bid price)

4. Configure your order:

   • Select Docker image

   • Set ports (TCP for SSH, HTTP for web UIs)

   • Add environment variables if needed

   • Enter startup command

5. Select payment: CLORE, BTC, or USDT/USDC

6. Create order and wait for deployment

Access Your Server

• Find connection details in My Orders

• Web interfaces: Use the HTTP port URL

• SSH: ssh -p <port> root@<proxy-address>

What is PixArt?

PixArt models offer:

• 10x faster than SDXL

• High-quality 1024px images

• Strong text rendering

• Efficient training methods

Model Variants

Model	Quality	Speed	VRAM
PixArt-Alpha	Great	Fast	8GB
PixArt-Sigma	Best	Medium	12GB

Quick Deploy

Docker Image:

pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel

Ports:

22/tcp
7860/http

Command:

pip install diffusers transformers accelerate gradio && \
python -c "
import gradio as gr
from diffusers import PixArtAlphaPipeline
import torch

pipe = PixArtAlphaPipeline.from_pretrained('PixArt-alpha/PixArt-XL-2-1024-MS', torch_dtype=torch.float16)
pipe.to('cuda')

def generate(prompt, steps):
    image = pipe(prompt, num_inference_steps=steps).images[0]
    return image

demo = gr.Interface(fn=generate, inputs=[gr.Textbox(), gr.Slider(10, 50, 20)], outputs=gr.Image(), title='PixArt')
demo.launch(server_name='0.0.0.0', server_port=7860)
"

Accessing Your Service

After deployment, find your http_pub URL in My Orders:

1. Go to My Orders page

2. Click on your order

3. Find the http_pub URL (e.g., abc123.clorecloud.net)

Use https://YOUR_HTTP_PUB_URL instead of localhost in examples below.

Installation

pip install diffusers transformers accelerate

PixArt-Alpha

Basic Generation

from diffusers import PixArtAlphaPipeline
import torch

pipe = PixArtAlphaPipeline.from_pretrained(
    "PixArt-alpha/PixArt-XL-2-1024-MS",
    torch_dtype=torch.float16
)
pipe.to("cuda")

prompt = "A cat astronaut floating in space, Earth in background, photorealistic"

image = pipe(
    prompt=prompt,
    num_inference_steps=20,
    guidance_scale=4.5
).images[0]

image.save("output.png")

Generation Parameters

image = pipe(
    prompt="a beautiful sunset over mountains",
    negative_prompt="blurry, low quality",
    num_inference_steps=20,      # Quality (10-50)
    guidance_scale=4.5,          # Prompt adherence (3-7)
    height=1024,
    width=1024,
    generator=torch.Generator("cuda").manual_seed(42)
).images[0]

PixArt-Sigma

Higher quality version:

from diffusers import PixArtSigmaPipeline
import torch

pipe = PixArtSigmaPipeline.from_pretrained(
    "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS",
    torch_dtype=torch.float16
)
pipe.to("cuda")
pipe.enable_model_cpu_offload()

image = pipe(
    prompt="a professional photograph of a red sports car",
    num_inference_steps=30,
    guidance_scale=4.5
).images[0]

image.save("sigma_output.png")

Memory Optimization

For 8GB VRAM

pipe = PixArtAlphaPipeline.from_pretrained(
    "PixArt-alpha/PixArt-XL-2-1024-MS",
    torch_dtype=torch.float16
)

# CPU offload
pipe.enable_model_cpu_offload()

# Sequential CPU offload (more aggressive)

# pipe.enable_sequential_cpu_offload()

Enable VAE Slicing

pipe.enable_vae_slicing()
pipe.enable_vae_tiling()

Batch Generation

from diffusers import PixArtAlphaPipeline
import torch

pipe = PixArtAlphaPipeline.from_pretrained(
    "PixArt-alpha/PixArt-XL-2-1024-MS",
    torch_dtype=torch.float16
).to("cuda")

prompts = [
    "a cyberpunk city at night",
    "a peaceful japanese garden",
    "a fantasy castle on a cliff",
    "an underwater coral reef"
]

for i, prompt in enumerate(prompts):
    image = pipe(prompt, num_inference_steps=20).images[0]
    image.save(f"output_{i:03d}.png")
    print(f"Generated: {prompt[:50]}...")

Different Resolutions

# Supported resolutions
resolutions = [
    (512, 512),
    (768, 768),
    (1024, 1024),
    (1024, 512),   # Landscape
    (512, 1024),   # Portrait
    (768, 1024),
    (1024, 768),
]

for w, h in resolutions:
    image = pipe(
        prompt="a beautiful landscape",
        width=w,
        height=h,
        num_inference_steps=20
    ).images[0]

    image.save(f"output_{w}x{h}.png")

Text Rendering

PixArt excels at text in images:

prompt = """
A vintage movie poster with the title "COSMIC ADVENTURE" in bold letters,
featuring a spaceship and planets, retro 1950s style
"""

image = pipe(
    prompt=prompt,
    num_inference_steps=30,
    guidance_scale=5.0
).images[0]

Gradio Interface

import gradio as gr
from diffusers import PixArtAlphaPipeline
import torch

pipe = PixArtAlphaPipeline.from_pretrained(
    "PixArt-alpha/PixArt-XL-2-1024-MS",
    torch_dtype=torch.float16
).to("cuda")

def generate(prompt, negative_prompt, steps, guidance, width, height, seed):
    generator = torch.Generator("cuda").manual_seed(seed) if seed > 0 else None

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        num_inference_steps=steps,
        guidance_scale=guidance,
        width=width,
        height=height,
        generator=generator
    ).images[0]

    return image

demo = gr.Interface(
    fn=generate,
    inputs=[
        gr.Textbox(label="Prompt", lines=3),
        gr.Textbox(label="Negative Prompt"),
        gr.Slider(10, 50, value=20, step=1, label="Steps"),
        gr.Slider(1, 10, value=4.5, step=0.5, label="Guidance"),
        gr.Slider(512, 1024, value=1024, step=64, label="Width"),
        gr.Slider(512, 1024, value=1024, step=64, label="Height"),
        gr.Number(value=-1, label="Seed (-1 for random)")
    ],
    outputs=gr.Image(label="Generated Image"),
    title="PixArt Image Generator"
)

demo.launch(server_name="0.0.0.0", server_port=7860)

API Server

from fastapi import FastAPI
from fastapi.responses import Response
from diffusers import PixArtAlphaPipeline
import torch
import io

app = FastAPI()

pipe = PixArtAlphaPipeline.from_pretrained(
    "PixArt-alpha/PixArt-XL-2-1024-MS",
    torch_dtype=torch.float16
).to("cuda")

@app.post("/generate")
async def generate(
    prompt: str,
    negative_prompt: str = "",
    steps: int = 20,
    guidance: float = 4.5,
    width: int = 1024,
    height: int = 1024
):
    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        num_inference_steps=steps,
        guidance_scale=guidance,
        width=width,
        height=height
    ).images[0]

    buffer = io.BytesIO()
    image.save(buffer, format="PNG")
    return Response(content=buffer.getvalue(), media_type="image/png")

# Run: uvicorn server:app --host 0.0.0.0 --port 8000

Performance Comparison

Model	GPU	1024x1024 Time
PixArt-Alpha	RTX 3090	~3s
PixArt-Sigma	RTX 3090	~5s
SDXL	RTX 3090	~15s
PixArt-Alpha	RTX 4090	~2s
PixArt-Sigma	RTX 4090	~3s

Quality Settings

Use Case	Steps	Guidance
Preview	10-15	4.0
Standard	20	4.5
High Quality	30-40	5.0

Troubleshooting

Out of Memory

# Enable offloading
pipe.enable_model_cpu_offload()

# Or use smaller resolution
width, height = 768, 768

Poor Quality

• Increase steps (25-40)

• Adjust guidance scale

• More detailed prompts

Slow Generation

• Use PixArt-Alpha (faster)

• Reduce steps

• Lower resolution

Cost Estimate

Typical CLORE.AI marketplace rates (as of 2024):

GPU	Hourly Rate	Daily Rate	4-Hour Session
RTX 3060	~$0.03	~$0.70	~$0.12
RTX 3090	~$0.06	~$1.50	~$0.25
RTX 4090	~$0.10	~$2.30	~$0.40
A100 40GB	~$0.17	~$4.00	~$0.70
A100 80GB	~$0.25	~$6.00	~$1.00

Prices vary by provider and demand. Check CLORE.AI Marketplace for current rates.

Save money:

• Use the Spot market for interruptible work — about a third of servers price spot below on-demand (median ~13% off), the rest match it

• Pay with CLORE tokens

• Compare prices across different providers

Next Steps

• FLUX Generation - Best quality

• Stable Diffusion WebUI - More features

• ControlNet Guide - Add control