|
--- |
|
library_name: diffusers |
|
--- |
|
|
|
<img src="demo.png" alt="a cute robot digital illustration, full pose"/> |
|
|
|
|
|
Quick start: |
|
|
|
```python |
|
from diffusers.models import AutoencoderKL |
|
from diffusers import StableDiffusionPipeline |
|
from diffusers.schedulers.scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler |
|
from PIL import Image |
|
import torch |
|
|
|
DPM_SOLVER_MULTI_STEP_SCHEDULER_CONFIG = { |
|
"algorithm_type": "dpmsolver++", |
|
"beta_end": 0.012, |
|
"beta_schedule": "scaled_linear", |
|
"beta_start": 0.00085, |
|
"clip_sample": False, |
|
"dynamic_thresholding_ratio": 0.995, |
|
"euler_at_final": False, |
|
"final_sigmas_type": "zero", |
|
"lambda_min_clipped": float("-inf"), |
|
"lower_order_final": True, |
|
"num_train_timesteps": 1000, |
|
"prediction_type": "epsilon", |
|
"sample_max_value": 1.0, |
|
"set_alpha_to_one": False, |
|
"solver_order": 2, |
|
"solver_type": "midpoint", |
|
"steps_offset": 1, |
|
"thresholding": False, |
|
"timestep_spacing": "linspace", |
|
"trained_betas": None, |
|
"use_karras_sigmas": True, |
|
"use_lu_lambdas": False, |
|
"variance_type": None, |
|
} |
|
|
|
if __name__ == "__main__": |
|
width = 512 |
|
height = int((width * 1.25 // 8) * 8) |
|
|
|
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse") |
|
pipe = StableDiffusionPipeline.from_pretrained( |
|
"runwayml/stable-diffusion-v1-5", |
|
use_safetensors=True, |
|
safety_checker=None, |
|
vae=vae |
|
).to("cuda") |
|
pipe.scheduler = DPMSolverMultistepScheduler.from_config( |
|
DPM_SOLVER_MULTI_STEP_SCHEDULER_CONFIG, |
|
) |
|
|
|
prompt = "a cute robot digital illustration, full pose" |
|
seed = 2544574284 |
|
|
|
images = [] |
|
scales = [-1, 0, 1, 1.5] |
|
|
|
for scale in scales: |
|
generator = torch.Generator(device="cpu").manual_seed(seed) |
|
pipe.load_lora_weights("scenario-labs/more_details", weight_name="more_details.safetensors") |
|
pipe.fuse_lora(lora_scale=scale) |
|
image = pipe( |
|
prompt, |
|
generator=generator, |
|
num_inference_steps=25, |
|
num_samples=1, |
|
width=width, |
|
height=height |
|
).images[0] |
|
pipe.unfuse_lora() |
|
images.append(image) |
|
|
|
# Combine images into a single row |
|
combined_image = Image.new('RGB', (width * len(images), height)) |
|
x_offset = 0 |
|
for image in images: |
|
combined_image.paste(image, (x_offset, 0)) |
|
x_offset += width |
|
|
|
# Display the combined image |
|
combined_image.save("demo.png") |
|
|
|
``` |
