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import torch
from diffusers import StableDiffusionXLPipeline
import numpy as np
import gradio as gr
import random
from compel import Compel, ReturnedEmbeddingsType
device = "cuda" if torch.cuda.is_available() else "cpu"
if torch.cuda.is_available():
torch.cuda.max_memory_allocated(device=device)
pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
pipe = pipe.to(device)
else:
pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
pipe = pipe.to(device)
pipe.safety_checker = None
pipe.load_lora_weights("artificialguybr/ps1redmond-ps1-game-graphics-lora-for-sdxl", weight_name="PS1Redmond-PS1Game-Playstation1Graphics.safetensors")
lora_activation_words = "playstation 1 graphics, PS1 Game, "
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
def infer(conditioning, pooled, neg_conditioning, neg_pooled, height, width, num_inference_steps, guidance_scale, seed, randomize_seed, lora_weight):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
image = pipe(
prompt_embeds=conditioning,
pooled_prompt_embeds=pooled,
negative_prompt_embeds=neg_conditioning,
negative_pooled_prompt_embeds=neg_pooled,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
generator=generator,
cross_attention_kwargs={"scale": lora_weight}
).images[0]
return image
def get_embeds(prompt, negative_prompt, height, width, num_inference_steps, guidance_scale, seed, randomize_seed, lora_weight):
compel = Compel(
tokenizer=[pipe.tokenizer, pipe.tokenizer_2] ,
text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
requires_pooled=[False, True]
)
prompt = lora_activation_words + prompt
conditioning, pooled = compel(prompt)
neg_conditioning, neg_pooled = compel(negative_prompt)
image = infer(conditioning, pooled, neg_conditioning, neg_pooled, height, width, num_inference_steps, guidance_scale, seed, randomize_seed, lora_weight)
return image
css="""
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# Text-to-Image Gradio Template
Currently running on {device.upper()}.
""")
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)
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=True,
)
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=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=7.5,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=100,
step=1,
value=30,
)
with gr.Row():
lora_weight = gr.Slider(
label="LoRA weight",
minimum=0.0,
maximum=5.0,
step=0.01,
value=1,
)
run_button.click(
fn = get_embeds,
inputs = [prompt, negative_prompt, height, width, num_inference_steps, guidance_scale, seed, randomize_seed, lora_weight],
outputs = [result]
)
demo.launch(debug=True) |