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```python
import torch
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
# Model selection and device placement
model_id = "black-forest-labs/FLUX.1-schnell"
device = "mps" if torch.backends.mps.is_available() else "cpu" # Prioritize MPS
# Optimization parameters
torch.backends.mps.graph_mode = False # Disable graph mode for MPS for better debugging and potential performance in some cases
pipe = StableDiffusionPipeline.from_pretrained(
model_id,
torch_dtype=torch.float16, # Use float16 for better performance on MPS
scheduler=DPMSolverMultistepScheduler.from_config(model_id, algorithm_type="dpmsolver"), # Optimized scheduler for speed
)
pipe.to(device)
# Memory optimization
pipe.enable_attention_slicing() # Enable attention slicing for memory efficiency
pipe.enable_vae_slicing() # Enable VAE slicing for memory efficiency
# Speed optimization with torch.compile
pipe = torch.compile(pipe) # Compile the pipeline for better performance
# Inference parameters
prompt = "A cat holding a sign that says hello world"
height = 768
width = 1360
num_inference_steps = 4
# Inference
image = pipe(prompt, height=height, width=width, num_inference_steps=num_inference_steps).images[0]
# Save the image (optional)
image.save("cat_with_sign.png")
```
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