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import gradio as gr | |
import numpy as np | |
import random | |
import os | |
from pathlib import Path | |
# import spaces #[uncomment to use ZeroGPU] | |
from diffusers import DiffusionPipeline, StableDiffusionPipeline, schedulers | |
import torch | |
MODEL_REPO_ID = os.environ.get('MODEL_REPO_ID', 'myxlmynx/cyberrealistic_classic40') | |
MODEL_REPO_LOCAL = os.environ.get('MODEL_REPO_LOCAL', '') | |
MODEL_REPO_NAME = os.environ.get('MODEL_REPO_NAME', 'CyberRealistic Classic 4.0') | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
print("Running on " + device) | |
if torch.cuda.is_available(): | |
torch_dtype = torch.float16 | |
else: | |
torch_dtype = torch.float32 | |
print("Loading " + MODEL_REPO_ID) | |
if MODEL_REPO_LOCAL and Path(MODEL_REPO_LOCAL).is_file(): | |
pipe = StableDiffusionPipeline.from_single_file(MODEL_REPO_LOCAL, torch_dtype=torch_dtype) | |
else: | |
pipe = DiffusionPipeline.from_pretrained(MODEL_REPO_ID, torch_dtype=torch_dtype) | |
extra_inference_parameters = {} | |
# add accel LoRA to boost generation speed | |
pipe.load_lora_weights("wangfuyun/PCM_Weights", | |
subfolder='sd15', weight_name='pcm_sd15_smallcfg_2step_converted.safetensors', | |
adapter_name='pcm_smallcfg_2step') | |
pipe.set_adapters(['pcm_smallcfg_2step'], adapter_weights=[1.0]) | |
pipe.fuse_lora() | |
# for very low step counts with PCM | |
#pipe.scheduler = schedulers.DDIMScheduler(timestep_spacing='trailing', | |
# clip_sample=False, set_alpha_to_one=False) | |
pipe.scheduler = schedulers.TCDScheduler() | |
extra_inference_parameters['eta'] = 0.3 | |
#pipe.scheduler = schedulers.LCMScheduler() | |
#pipe.scheduler = schedulers.EulerAncestralDiscreteScheduler() | |
# lib default will fry the image | |
default_guidance_scale = 1 | |
pipe = pipe.to(device) | |
MAX_SEED = np.iinfo(np.int32).max | |
MIN_IMAGE_SIZE = 128 | |
MAX_IMAGE_SIZE = 1024 | |
# @spaces.GPU #[uncomment to use ZeroGPU] | |
def infer( | |
prompt, | |
negative_prompt, | |
seed, | |
randomize_seed, | |
width, | |
height, | |
guidance_scale, | |
num_inference_steps, | |
progress=gr.Progress(track_tqdm=True), | |
): | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
generator = torch.Generator().manual_seed(seed) | |
if guidance_scale == 0: | |
guidance_scale = default_guidance_scale | |
image = pipe( | |
prompt=prompt, | |
negative_prompt=negative_prompt, | |
guidance_scale=guidance_scale, | |
num_inference_steps=num_inference_steps, | |
width=width, | |
height=height, | |
generator=generator, | |
**extra_inference_parameters | |
).images[0] | |
return image, seed | |
examples = [ | |
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", | |
"An astronaut riding a green horse", | |
"A delicious ceviche cheesecake slice", | |
] | |
css = """ | |
#col-container { | |
margin: 0 auto; | |
max-width: 640px; | |
} | |
""" | |
with gr.Blocks(css=css) as demo_device: | |
with gr.Column(elem_id="col-container"): | |
gr.Markdown("# " + MODEL_REPO_NAME + " - on " + device.upper()) | |
if device == 'cpu': | |
gr.Markdown("Note: running on CPU, generation will be very slow. Expect at least" + | |
" a minute for minimal parameters (512x512 image, guidance <= 1, <=4 steps).\n" + | |
"It's also on a single queue, so clone this space for experimenting with it.") | |
with gr.Row(): | |
prompt = gr.Text( | |
label="Prompt", | |
show_label=False, | |
max_lines=1, | |
placeholder="Enter your prompt", | |
container=False, | |
) | |
run_button = gr.Button("Run", scale=0, variant="primary") | |
result = gr.Image(label="Result", show_label=False) | |
with gr.Accordion("Advanced Settings", open=False): | |
negative_prompt = gr.Text( | |
label="Negative prompt", | |
max_lines=1, | |
placeholder="Enter a negative prompt", | |
visible=False, | |
) | |
seed = gr.Slider( | |
label="Seed", | |
minimum=0, | |
maximum=MAX_SEED, | |
step=1, | |
value=0, | |
) | |
randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
with gr.Row(): | |
width = gr.Slider( | |
label="Width", | |
minimum=MIN_IMAGE_SIZE, | |
maximum=MAX_IMAGE_SIZE, | |
step=32, | |
value=512, | |
) | |
height = gr.Slider( | |
label="Height", | |
minimum=MIN_IMAGE_SIZE, | |
maximum=MAX_IMAGE_SIZE, | |
step=32, | |
value=768, | |
) | |
with gr.Row(): | |
guidance_scale = gr.Slider( | |
label="Guidance scale", | |
minimum=0.0, | |
maximum=10.0, | |
step=0.1, | |
value=0.0, | |
) | |
num_inference_steps = gr.Slider( | |
label="Number of inference steps", | |
minimum=1, | |
maximum=50, | |
step=1, | |
value=3, | |
) | |
gr.Examples(examples=examples, inputs=[prompt]) | |
gr.on( | |
triggers=[run_button.click, prompt.submit], | |
fn=infer, | |
inputs=[ | |
prompt, | |
negative_prompt, | |
seed, | |
randomize_seed, | |
width, | |
height, | |
guidance_scale, | |
num_inference_steps, | |
], | |
outputs=[result, seed], | |
) | |
demo_inference = gr.load(MODEL_REPO_ID, title=MODEL_REPO_NAME, src='models') | |
demo = gr.TabbedInterface([demo_inference, demo_device], ["Inference API", device.upper()]) | |
if __name__ == "__main__": | |
demo.launch() | |