From GANs to Diffusion Transformers: Master State-of-the-Art Image Generation
- 🚀 2024-2025 State of the Art
- 🎯 Model Comparison Matrix
- 🏗️ Architecture Evolution
- ⚡ Modern Diffusion Models
- 🎨 GANs Legacy
- 🛠️ Implementation Guides
- 🔧 Production Deployment
- 📊 Performance Benchmarks
- 🌐 Resources & Community
timeline
title Generative AI Evolution 2014-2025
2014 : GANs Introduced
: DCGAN
2020 : DDPM
: Denoising Diffusion
2022 : Stable Diffusion 1.5
: DALL-E 2
2023 : SDXL
: Midjourney v5
2024 : Stable Diffusion 3
: SDXL Turbo
: Flux
2025 : SD 3.5
: Flux Pro
: DiT Models
| Model | Developer | Release | Key Feature | Speed | Quality |
|---|---|---|---|---|---|
| Stable Diffusion 3.5 | Stability AI | Oct 2024 | Multimodal DiT | ⚡⚡⚡ | ⭐⭐⭐⭐⭐ |
| Flux.1 Pro | Black Forest Labs | Aug 2024 | 12B params, Flow Matching | ⚡⚡⚡⚡ | ⭐⭐⭐⭐⭐ |
| SDXL Turbo | Stability AI | Nov 2023 | 1-step generation | ⚡⚡⚡⚡⚡ | ⭐⭐⭐⭐ |
| SDXL Lightning | ByteDance | Feb 2024 | 2-4 step generation | ⚡⚡⚡⚡⚡ | ⭐⭐⭐⭐ |
| Playground v2.5 | Playground AI | Feb 2024 | Aesthetic quality | ⚡⚡⚡ | ⭐⭐⭐⭐⭐ |
| PixArt-Σ | PixArt Team | Mar 2024 | Efficient DiT | ⚡⚡⚡⚡ | ⭐⭐⭐⭐ |
graph TD
A[Image Generation Models 2024-2025] --> B[Diffusion Transformers]
A --> C[Latent Diffusion]
A --> D[Flow Matching]
B --> B1[SD 3.5 - Multimodal DiT]
B --> B2[PixArt-Σ - Efficient DiT]
C --> C1[SDXL - High Resolution]
C --> C2[SDXL Turbo - Real-time]
C --> C3[SDXL Lightning - Fast]
D --> D1[Flux.1 Pro - Premium Quality]
D --> D2[Flux.1 Dev - Developer]
D --> D3[Flux.1 Schnell - Speed]
style B1 fill:#a855f7,stroke:#7e22ce,stroke-width:3px,color:#fff
style D1 fill:#ec4899,stroke:#be185d,stroke-width:3px,color:#fff
style C2 fill:#f59e0b,stroke:#d97706,stroke-width:3px,color:#fff
| Feature | SD 3.5 | SDXL Turbo | Flux.1 Pro | DALL-E 3 | Midjourney v6 |
|---|---|---|---|---|---|
| Parameters | 8B | 3.5B | 12B | Unknown | Unknown |
| Resolution | Up to 2K | 1024x1024 | Up to 2K | 1024x1024 | Variable |
| Steps (typical) | 25-40 | 1-4 | 20-30 | N/A | N/A |
| Speed | Moderate | ⚡⚡⚡⚡⚡ | Moderate | Slow | Slow |
| Text Accuracy | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| Prompt Following | Excellent | Good | Excellent | Excellent | Good |
| Open Source | ✅ | ✅ | ❌ | ❌ | |
| Commercial Use | ✅ | ✅ | 💰 | 💰 | 💰 |
| Fine-tuning | ✅ | ✅ | Limited | ❌ | ❌ |
| ControlNet Support | ✅ | ✅ | In Progress | ❌ | ❌ |
| Best For | Quality+Control | Real-time Apps | Premium Quality | API Integration | Creative Work |
flowchart LR
A[Text Prompt] --> B[Text Encoder<br/>CLIP/T5]
B --> C[Conditioning]
D[Random Noise] --> E[Latent Space<br/>VAE Encoder]
E --> F[Denoising U-Net/<br/>DiT Transformer]
C --> F
F --> G[Step 1]
G --> H[Step 2]
H --> I[...]
I --> J[Step N]
J --> K[VAE Decoder]
K --> L[Final Image]
style A fill:#3b82f6,stroke:#1e40af,stroke-width:2px,color:#fff
style L fill:#10b981,stroke:#047857,stroke-width:2px,color:#fff
style F fill:#a855f7,stroke:#7e22ce,stroke-width:2px,color:#fff
graph TB
subgraph SD35["Stable Diffusion 3.5 (DiT)"]
SD35_A[Text Encoder: T5-XXL] --> SD35_B[Multimodal DiT]
SD35_B --> SD35_C[Flow Matching]
SD35_C --> SD35_D[VAE Decoder]
end
subgraph FLUX["Flux.1 (Flow Matching)"]
FLUX_A[Dual Text Encoders] --> FLUX_B[Rectified Flow]
FLUX_B --> FLUX_C[Guidance Distillation]
FLUX_C --> FLUX_D[12B Transformer]
end
subgraph SDXL["SDXL (Latent Diffusion)"]
SDXL_A[Dual CLIP Encoders] --> SDXL_B[U-Net 3.5B]
SDXL_B --> SDXL_C[Refiner Model]
SDXL_C --> SDXL_D[VAE Decoder]
end
style SD35 fill:#e0e7ff,stroke:#4338ca,stroke-width:2px
style FLUX fill:#fce7f3,stroke:#9f1239,stroke-width:2px
style SDXL fill:#dbeafe,stroke:#1e3a8a,stroke-width:2px
Revolutionary Features:
- 🎯 Multimodal Diffusion Transformer (MMDiT)
- 📝 Superior text rendering capabilities
- 🎨 Enhanced prompt understanding with T5-XXL
- ⚡ Efficient inference with flow matching
- 🔧 Better fine-tuning capabilities
Implementation:
import torch
from diffusers import StableDiffusion3Pipeline
# Load SD 3.5 model
pipe = StableDiffusion3Pipeline.from_pretrained(
"stabilityai/stable-diffusion-3.5-large",
torch_dtype=torch.float16,
variant="fp16"
)
pipe.to("cuda")
# Enable optimizations
pipe.enable_model_cpu_offload()
pipe.enable_vae_slicing()
# Generate with advanced prompting
prompt = """
A hyperrealistic photograph of a futuristic cityscape at sunset,
neon lights reflecting on wet streets, flying cars,
cyberpunk aesthetic, 8k ultra detailed, professional photography
"""
negative_prompt = "blurry, low quality, distorted, deformed"
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=28,
guidance_scale=7.0,
height=1024,
width=1024,
).images[0]
image.save("sd35_output.png")Performance Optimization:
# Use SDXL Turbo scheduler for faster generation
from diffusers import LCMScheduler
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
# 4-step generation with quality
image = pipe(
prompt=prompt,
num_inference_steps=4,
guidance_scale=1.0,
).images[0]
Speed Comparison:
gantt
title Generation Speed Comparison (1024x1024 image)
dateFormat X
axisFormat %S steps
section SD 1.5
25-50 steps :0, 40
section SDXL
25-40 steps :0, 30
section SDXL Lightning
2-4 steps :0, 4
section SDXL Turbo
1 step :0, 1
SDXL Turbo Implementation:
from diffusers import AutoPipelineForText2Image
import torch
# Load SDXL Turbo
pipe = AutoPipelineForText2Image.from_pretrained(
"stabilityai/sdxl-turbo",
torch_dtype=torch.float16,
variant="fp16"
)
pipe.to("cuda")
# Real-time generation (1 step!)
image = pipe(
prompt="a beautiful landscape with mountains and lake, sunset",
num_inference_steps=1,
guidance_scale=0.0, # Turbo doesn't use guidance
).images[0]SDXL Lightning (2-4 steps):
from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_pretrained(
"ByteDance/SDXL-Lightning",
torch_dtype=torch.float16,
variant="fp16"
)
pipe.scheduler = EulerDiscreteScheduler.from_config(
pipe.scheduler.config,
timestep_spacing="trailing"
)
pipe.to("cuda")
# 4-step high quality generation
image = pipe(
prompt="cinematic photo of a lone warrior in the desert",
num_inference_steps=4,
guidance_scale=0.0,
).images[0]
Three Variants:
| Model | Purpose | Speed | Quality | License |
|---|---|---|---|---|
| Flux.1 Pro | Premium quality | Moderate | ⭐⭐⭐⭐⭐ | Commercial API |
| Flux.1 Dev | Development | Fast | ⭐⭐⭐⭐ | Non-commercial |
| Flux.1 Schnell | Real-time | ⚡⚡⚡⚡⚡ | ⭐⭐⭐ | Apache 2.0 |
Key Innovations:
- 🔄 Rectified flow matching
- 🎯 Rotary positional embeddings
- 🧠 12B parameter transformer
- 📊 Guidance distillation
- 🎨 Exceptional prompt adherence
Flux.1 Schnell (Fast):
from diffusers import FluxPipeline
import torch
pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-schnell",
torch_dtype=torch.bfloat16
)
pipe.to("cuda")
# Ultra-fast 4-step generation
image = pipe(
prompt="a photo of a forest with a river, autumn colors",
num_inference_steps=4,
guidance_scale=0.0,
).images[0]
Available Controls 2024-2025:
mindmap
root((ControlNet v2))
Edge Detection
Canny
HED
Lineart
Scribble
Depth Maps
MiDaS
ZoeDepth
Depth Anything
Pose Detection
OpenPose
DWPose
Animal Pose
Segmentation
Seg
OneFormer
SAM
Style Transfer
Reference
Shuffle
Color
New 2024
QR Code
Tile
IP2P
Recolor
Multi-ControlNet Example:
from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel
import torch
from PIL import Image
# Load multiple ControlNets
controlnet_canny = ControlNetModel.from_pretrained(
"diffusers/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16
)
controlnet_depth = ControlNetModel.from_pretrained(
"diffusers/controlnet-depth-sdxl-1.0",
torch_dtype=torch.float16
)
# Create pipeline with multiple controls
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=[controlnet_canny, controlnet_depth],
torch_dtype=torch.float16,
)
pipe.to("cuda")
# Load control images
canny_image = Image.open("canny_map.png")
depth_image = Image.open("depth_map.png")
# Generate with dual control
image = pipe(
prompt="a modern architectural marvel, glass and steel",
image=[canny_image, depth_image],
controlnet_conditioning_scale=[0.5, 0.8],
num_inference_steps=30,
).images[0]Style Transfer Workflow:
flowchart LR
A[Style Reference Image] --> B[CLIP Image Encoder]
B --> C[Image Embeddings]
C --> D[Cross-Attention Layers]
E[Text Prompt] --> F[Text Embeddings]
F --> D
D --> G[Diffusion Model]
G --> H[Styled Output]
style A fill:#fbbf24,stroke:#d97706,stroke-width:2px,color:#000
style H fill:#10b981,stroke:#047857,stroke-width:2px,color:#fff
Implementation:
from diffusers import StableDiffusionXLPipeline
from ip_adapter import IPAdapterXL
import torch
from PIL import Image
# Load base model
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
)
pipe.to("cuda")
# Load IP-Adapter
ip_adapter = IPAdapterXL(pipe, "ip-adapter_sdxl.bin")
# Style reference image
style_image = Image.open("style_reference.jpg")
# Generate with style transfer
image = ip_adapter.generate(
prompt="a portrait of a person",
image=style_image,
scale=0.7, # Style strength
num_inference_steps=30,
)[0]IP-Adapter FaceID:
from ip_adapter.ip_adapter_faceid import IPAdapterFaceID
# Face-focused IP-Adapter
ip_adapter_faceid = IPAdapterFaceID(pipe, "ip-adapter-faceid_sdxl.bin")
# Generate maintaining face identity
face_image = Image.open("face_reference.jpg")
image = ip_adapter_faceid.generate(
prompt="professional headshot, studio lighting",
faceid_embeds=face_image,
scale=0.8,
num_inference_steps=30,
)[0]Ultra-Fast Generation:
from diffusers import DiffusionPipeline, LCMScheduler
import torch
# Load any SDXL model
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
variant="fp16",
)
# Add LCM-LoRA for 4-step generation
pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
pipe.fuse_lora()
# Use LCM scheduler
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")
# Generate in 4 steps
image = pipe(
prompt="vibrant alien landscape with bioluminescent plants",
num_inference_steps=4,
guidance_scale=1.0,
).images[0]graph TD
A[GANs 2014] --> B[DCGAN 2015]
B --> C[Progressive GAN 2017]
C --> D[StyleGAN 2018]
D --> E[StyleGAN2 2019]
E --> F[StyleGAN3 2021]
F --> G[StyleGAN-XL 2022]
style A fill:#fef3c7,stroke:#f59e0b,stroke-width:2px
style G fill:#dbeafe,stroke:#3b82f6,stroke-width:2px
Use Cases:
- 👤 High-quality face generation
- 🎭 Latent space manipulation
- 🔄 Style transfer
- 📊 Dataset augmentation
import torch
import legacy
import dnnlib
# Load StyleGAN3
with dnnlib.util.open_url('https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-ffhq-1024x1024.pkl') as f:
G = legacy.load_network_pkl(f)['G_ema'].to('cuda')
# Generate random face
z = torch.randn([1, G.z_dim]).cuda()
w = G.mapping(z, None)
img = G.synthesis(w, noise_mode='const')
# Save image
from PIL import Image
img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
Image.fromarray(img[0].cpu().numpy(), 'RGB').save('stylegan3_output.png')Advanced SD 3.5 + ControlNet + IP-Adapter Workflow:
{
"workflow": {
"nodes": [
{
"id": 1,
"type": "CheckpointLoaderSimple",
"values": {
"ckpt_name": "sd35_large.safetensors"
}
},
{
"id": 2,
"type": "CLIPTextEncode",
"values": {
"text": "masterpiece, best quality, cinematic lighting"
}
},
{
"id": 3,
"type": "ControlNetLoader",
"values": {
"control_net_name": "controlnet_canny_sd35.safetensors"
}
},
{
"id": 4,
"type": "IPAdapterModelLoader",
"values": {
"preset": "PLUS (high strength)"
}
},
{
"id": 5,
"type": "KSampler",
"values": {
"seed": 42,
"steps": 28,
"cfg": 7.0,
"sampler_name": "dpmpp_2m",
"scheduler": "karras"
}
}
]
}
}import torch
from diffusers import (
StableDiffusion3Pipeline,
ControlNetModel,
StableDiffusion3ControlNetPipeline,
)
from diffusers.utils import load_image
import numpy as np
import cv2
class ModernDiffusionPipeline:
def __init__(self, model_id="stabilityai/stable-diffusion-3.5-large"):
"""Initialize complete SD 3.5 pipeline with all features"""
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.dtype = torch.float16 if self.device == "cuda" else torch.float32
# Load base pipeline
self.pipe = StableDiffusion3Pipeline.from_pretrained(
model_id,
torch_dtype=self.dtype,
variant="fp16" if self.device == "cuda" else None
)
self.pipe.to(self.device)
# Enable optimizations
self.pipe.enable_model_cpu_offload()
self.pipe.enable_vae_slicing()
self.pipe.enable_vae_tiling()
# Optional: Enable xFormers for memory efficiency
try:
self.pipe.enable_xformers_memory_efficient_attention()
except:
print("xFormers not available")
def generate(
self,
prompt: str,
negative_prompt: str = "",
num_inference_steps: int = 28,
guidance_scale: float = 7.0,
height: int = 1024,
width: int = 1024,
num_images: int = 1,
):
"""Generate images with advanced parameters"""
with torch.inference_mode():
images = self.pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
height=height,
width=width,
num_images_per_prompt=num_images,
).images
return images
def generate_with_controlnet(
self,
prompt: str,
control_image,
controlnet_type: str = "canny",
conditioning_scale: float = 0.5,
):
"""Generate with ControlNet guidance"""
# Load ControlNet
controlnet = ControlNetModel.from_pretrained(
f"diffusers/controlnet-{controlnet_type}-sdxl-1.0",
torch_dtype=self.dtype
)
# Create ControlNet pipeline
controlnet_pipe = StableDiffusion3ControlNetPipeline(
**self.pipe.components,
controlnet=controlnet
)
# Preprocess control image if needed
if controlnet_type == "canny":
control_image = self._canny_preprocess(control_image)
# Generate
with torch.inference_mode():
image = controlnet_pipe(
prompt=prompt,
image=control_image,
controlnet_conditioning_scale=conditioning_scale,
num_inference_steps=28,
).images[0]
return image
def _canny_preprocess(self, image):
"""Preprocess image for Canny ControlNet"""
image = np.array(image)
low_threshold = 100
high_threshold = 200
image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
return Image.fromarray(image)
# Usage example
pipeline = ModernDiffusionPipeline()
# Simple generation
images = pipeline.generate(
prompt="a futuristic city at night, neon lights, cyberpunk, 8k",
negative_prompt="blurry, low quality",
num_images=4
)
# Save images
for idx, img in enumerate(images):
img.save(f"output_{idx}.png")import modal
from modal import Image, Stub, web_endpoint
# Create container with dependencies
image = Image.debian_slim().pip_install(
"diffusers>=0.24.0",
"transformers",
"accelerate",
"torch",
"safetensors",
)
stub = Stub("sd35-api", image=image)
@stub.cls(gpu="A100", container_idle_timeout=300)
class StableDiffusion35:
def __enter__(self):
import torch
from diffusers import StableDiffusion3Pipeline
self.pipe = StableDiffusion3Pipeline.from_pretrained(
"stabilityai/stable-diffusion-3.5-large",
torch_dtype=torch.float16,
variant="fp16"
)
self.pipe.to("cuda")
self.pipe.enable_model_cpu_offload()
@web_endpoint(method="POST")
def generate(self, request: dict):
import io
import base64
# Generate image
image = self.pipe(
prompt=request["prompt"],
negative_prompt=request.get("negative_prompt", ""),
num_inference_steps=request.get("steps", 28),
guidance_scale=request.get("guidance", 7.0),
).images[0]
# Convert to base64
buffered = io.BytesIO()
image.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode()
return {"image": img_str}FROM nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04
# Install Python
RUN apt-get update && apt-get install -y \
python3.10 \
python3-pip \
git \
&& rm -rf /var/lib/apt/lists/*
# Install dependencies
WORKDIR /app
COPY requirements.txt .
RUN pip3 install --no-cache-dir -r requirements.txt
# Copy application
COPY . .
# Download models
RUN python3 -c "from diffusers import StableDiffusion3Pipeline; \
StableDiffusion3Pipeline.from_pretrained('stabilityai/stable-diffusion-3.5-large')"
# Expose port
EXPOSE 8000
# Run server
CMD ["python3", "server.py"]requirements.txt:
torch>=2.1.0
diffusers>=0.24.0
transformers>=4.35.0
accelerate>=0.25.0
fastapi>=0.104.0
uvicorn>=0.24.0
pillow>=10.0.0
| Model | Resolution | Steps | Time | Images/min |
|---|---|---|---|---|
| SD 3.5 | 1024x1024 | 28 | 3.2s | 18.75 |
| SDXL | 1024x1024 | 30 | 2.8s | 21.43 |
| SDXL Turbo | 1024x1024 | 1 | 0.8s | 75 |
| SDXL Lightning | 1024x1024 | 4 | 1.2s | 50 |
| Flux.1 Dev | 1024x1024 | 20 | 4.5s | 13.33 |
| Flux.1 Schnell | 1024x1024 | 4 | 1.5s | 40 |
| SD 1.5 + LCM | 512x512 | 4 | 0.5s | 120 |
graph LR
A[Model VRAM Usage] --> B[SD 3.5 Large: 18GB]
A --> C[SDXL: 12GB]
A --> D[SDXL Turbo: 12GB]
A --> E[Flux.1 Pro: 24GB]
A --> F[Flux.1 Dev: 20GB]
A --> G[SD 1.5: 6GB]
style B fill:#dc2626,stroke:#991b1b,stroke-width:2px,color:#fff
style F fill:#dc2626,stroke:#991b1b,stroke-width:2px,color:#fff
style C fill:#f59e0b,stroke:#d97706,stroke-width:2px,color:#fff
style G fill:#10b981,stroke:#047857,stroke-width:2px,color:#fff
Aesthetic Score (0-10):
| Model | Average Score | Prompt Adherence | Text Accuracy | Details |
|---|---|---|---|---|
| Flux.1 Pro | 9.2 | 9.5 | 9.7 | 9.0 |
| SD 3.5 Large | 8.8 | 9.2 | 9.5 | 8.5 |
| Midjourney v6 | 9.0 | 8.5 | 8.0 | 9.5 |
| DALL-E 3 | 8.7 | 9.0 | 9.3 | 8.3 |
| SDXL | 8.3 | 8.5 | 7.5 | 8.0 |
| SDXL Turbo | 7.5 | 7.8 | 6.5 | 7.2 |
| Repository | Stars | Description |
|---|---|---|
| CompVis/stable-diffusion | |
Original SD repository |
| Stability-AI/generative-models |  |
Official SDXL & SD 3 |
| comfyanonymous/ComfyUI |  |
Node-based UI |
| AUTOMATIC1111/stable-diffusion-webui |  |
Popular WebUI |
| huggingface/diffusers |  |
Diffusers library |
| lllyasviel/ControlNet |  |
ControlNet models |
| tencent-ailab/IP-Adapter |  |
Image prompting |
-
Papers:
- Stable Diffusion 3 - Latest architecture
- SDXL - SDXL technical report
- Flow Matching - Flux's core technique
- ControlNet - Spatial conditioning
- IP-Adapter - Image prompting
-
Tutorials:
-
Discord:
-
Reddit:
- r/StableDiffusion - 500K+ members
- r/comfyui - Active workflow sharing
- r/ArtificialIntelligence
-
Forums:
- Civitai - Model sharing
- Hugging Face Discussions
- Hugging Face - Open-source models
- Civitai - Community fine-tunes & LoRAs
- Replicate - API access
- Stability AI - Official API
# Install dependencies
# pip install diffusers transformers accelerate torch
import torch
from diffusers import StableDiffusion3Pipeline
# Load model (auto-downloads)
pipe = StableDiffusion3Pipeline.from_pretrained(
"stabilityai/stable-diffusion-3.5-large",
torch_dtype=torch.float16
)
pipe.to("cuda")
# Generate your first image
image = pipe("a majestic lion in the savanna, golden hour").images[0]
image.save("my_first_generation.png")# Enable all optimizations
pipe.enable_model_cpu_offload()
pipe.enable_vae_slicing()
pipe.enable_vae_tiling()
# Use flash attention if available
try:
pipe.enable_xformers_memory_efficient_attention()
except:
pass
# Compile for 40% speedup (PyTorch 2.0+)
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)# Multi-GPU inference
pipe = StableDiffusion3Pipeline.from_pretrained(
"stabilityai/stable-diffusion-3.5-large",
torch_dtype=torch.float16,
variant="fp16",
device_map="balanced" # Auto-distribute across GPUs
)
# Batch generation
prompts = ["prompt1", "prompt2", "prompt3", "prompt4"]
images = pipe(prompts, num_inference_steps=28).imagesWe welcome contributions! Check out these areas:
This resource collection is available under the MIT License.
Model Licenses:
- Stable Diffusion 3.5: Stability AI License
- SDXL: Open RAIL++-M License
- Flux.1: Various (check specific model)
If this resource helped you, please star the repository!
Last Updated: January 2025 | Next Update: Monthly
Stay tuned for updates on new models and techniques!