Stable Diffusion 3.5

Generate high-fidelity images with accurate text rendering using Stable Diffusion 3.5 on Clore.ai GPUs.

Stable Diffusion 3.5 from Stability AI is a Multimodal Diffusion Transformer (MMDiT) that sets a new standard for open-weight image generation. It comes in three variants: Large (8B params), Medium (2.5B params), and Large Turbo (8B, distilled for 4-step inference). The standout feature is its accurate text rendering — SD 3.5 can reliably place readable text inside generated images, a capability most earlier models struggle with.

On Clore.ai you can rent the GPU power SD 3.5 needs for as little as $0.30/day and generate hundreds of images per hour.

Key Features

• Three variants — Large (8B, highest quality), Medium (2.5B, fast and light), Large Turbo (8B, 4-step distilled).

• Accurate text rendering — generates readable text, signs, labels, and typography within images.

• MMDiT architecture — joint image-text attention for superior prompt adherence.

• 1024×1024 native resolution — clean output without upscaling hacks.

• Flexible aspect ratios — handles non-square outputs (768×1344, 1344×768, etc.) without quality loss.

• Native diffusers support — StableDiffusion3Pipeline in diffusers >= 0.30.

• Open weights — Stability AI Community License; free for most commercial use.

Requirements

Component	Minimum	Recommended
GPU VRAM	12 GB (Medium)	24 GB (Large / Turbo)
System RAM	16 GB	32 GB
Disk	20 GB	40 GB
Python	3.10+	3.11
CUDA	12.1+	12.4
diffusers	0.30+	latest

Clore.ai GPU recommendation: An RTX 4090 (24 GB, ~$0.5–2/day) runs all three variants at full speed. For the Medium model, an RTX 3090 (24 GB, ~$0.3–1/day) or even a 16 GB card is sufficient and cheaper.

Quick Start

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124
pip install diffusers transformers accelerate sentencepiece protobuf

python -c "import torch; print(torch.cuda.get_device_name(0))"

Usage Examples

SD 3.5 Large — Maximum Quality

import torch
from diffusers import StableDiffusion3Pipeline

pipe = StableDiffusion3Pipeline.from_pretrained(
    "stabilityai/stable-diffusion-3.5-large",
    torch_dtype=torch.bfloat16,
)
pipe.to("cuda")

image = pipe(
    prompt=(
        "A weathered wooden sign reading 'OPEN 24 HOURS' hanging from "
        "a rusty chain outside a neon-lit diner, rainy night, reflections "
        "on wet asphalt, cinematic photography"
    ),
    negative_prompt="blurry, deformed text, low quality",
    guidance_scale=3.5,
    num_inference_steps=28,
    width=1024,
    height=1024,
    generator=torch.Generator("cuda").manual_seed(42),
).images[0]

image.save("diner_sign.png")
print("Saved diner_sign.png")

SD 3.5 Large Turbo — 4-Step Fast Generation

import torch
from diffusers import StableDiffusion3Pipeline

pipe = StableDiffusion3Pipeline.from_pretrained(
    "stabilityai/stable-diffusion-3.5-large-turbo",
    torch_dtype=torch.bfloat16,
).to("cuda")

# Turbo variant: only 4 steps needed, guidance_scale=0 (distilled)
image = pipe(
    prompt="Macro photo of a mechanical watch movement, intricate gears, golden light",
    guidance_scale=0.0,
    num_inference_steps=4,
    width=1024,
    height=1024,
).images[0]

image.save("watch_turbo.png")

SD 3.5 Medium — Lightweight Option

import torch
from diffusers import StableDiffusion3Pipeline

pipe = StableDiffusion3Pipeline.from_pretrained(
    "stabilityai/stable-diffusion-3.5-medium",
    torch_dtype=torch.float16,
).to("cuda")

image = pipe(
    prompt="Isometric view of a cozy coffee shop interior, pixel art style, warm lighting",
    guidance_scale=4.0,
    num_inference_steps=28,
    width=1024,
    height=1024,
).images[0]

image.save("coffee_shop_medium.png")

Batch Generation with Different Aspect Ratios

import torch
from diffusers import StableDiffusion3Pipeline

pipe = StableDiffusion3Pipeline.from_pretrained(
    "stabilityai/stable-diffusion-3.5-large",
    torch_dtype=torch.bfloat16,
).to("cuda")

jobs = [
    {"prompt": "Portrait of an astronaut in a field of sunflowers", "w": 768, "h": 1344},
    {"prompt": "Panoramic landscape of Icelandic highlands, moody skies", "w": 1344, "h": 768},
    {"prompt": "Product photo of a perfume bottle on marble surface", "w": 1024, "h": 1024},
]

for i, job in enumerate(jobs):
    img = pipe(
        prompt=job["prompt"],
        guidance_scale=3.5,
        num_inference_steps=28,
        width=job["w"],
        height=job["h"],
    ).images[0]
    img.save(f"batch_{i:03d}.png")
    print(f"[{i+1}/{len(jobs)}] {job['w']}x{job['h']} done")

Tips for Clore.ai Users

1. Turbo for iteration, Large for finals — use the 4-step Turbo variant to explore prompt ideas quickly, then switch to Large (28 steps) for the final render.

2. guidance_scale=3.5 — SD 3.5 Large works best at a lower CFG than older Stable Diffusion models. Going above 5.0 often causes oversaturation.

3. Turbo needs guidance_scale=0 — the distilled model already has guidance baked in; adding more degrades output.

4. Text in images — SD 3.5's text rendering is strong but not perfect. Use quotes around the exact text you want: 'OPEN 24 HOURS'. Keep it short (3–5 words max).

5. Cache weights — set HF_HOME=/workspace/hf_cache on persistent storage. Large is ~16 GB on disk.

6. bf16 for Large, fp16 for Medium — the 8B models were trained in bf16; the 2.5B Medium runs fine in fp16.

7. Batch efficiently — SD 3.5 Large generates one 1024×1024 image in ~3 seconds on an RTX 4090. Batch overnight for mass generation.

8. Accept HF license — you must accept the model license on the HuggingFace model page before downloading. Log in with huggingface-cli login.

Troubleshooting

Problem	Fix
OutOfMemoryError with Large	Use pipe.enable_model_cpu_offload(); or switch to Medium variant
Garbled text in image	Keep text short (3–5 words); put it in quotes in the prompt; increase num_inference_steps to 35
Oversaturated colors	Lower guidance_scale — try 2.5–3.5 for Large; use 0.0 for Turbo
403 error downloading model	Accept the license at https://huggingface.co/stabilityai/stable-diffusion-3.5-large and run huggingface-cli login
Slow first run	Initial download is ~16 GB for Large; subsequent runs use cache
KeyError: 'text_encoder_3'	Upgrade diffusers: pip install -U diffusers transformers
Black image output	Ensure torch_dtype=torch.bfloat16 for Large/Turbo; fp32 can cause silent failures on some cards