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import gradio as gr | |
import numpy as np | |
import random | |
from diffusers import DiffusionPipeline | |
import torch | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
model_repo_id = "black-forest-labs/FLUX.1-dev" | |
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 | |
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype).to(device) | |
pipe.load_lora_weights("pepper13/flux-anime") | |
MAX_SEED = np.iinfo(np.int32).max | |
MAX_IMAGE_SIZE = 1024 | |
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) | |
image = pipe( | |
prompt=prompt, | |
negative_prompt=negative_prompt, | |
guidance_scale=guidance_scale, | |
num_inference_steps=num_inference_steps, | |
width=width, | |
height=height, | |
generator=generator | |
).images[0] | |
return image, seed | |
with gr.Blocks() as demo: | |
with gr.Column(elem_id="col-container"): | |
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=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=256, maximum=MAX_IMAGE_SIZE, step=32, value=512) | |
height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512) | |
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=20) | |
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.launch() |