Spaces:
Runtime error
Runtime error
File size: 2,787 Bytes
64e1ee8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
import gradio as gr
from PIL import Image
from pathlib import Path
from transformers import CLIPTokenizer
import torch
from nanograd.models.stable_diffusion import model_loader, pipeline
DEVICE = "cpu"
ALLOW_CUDA = False
ALLOW_MPS = False
if torch.cuda.is_available() and ALLOW_CUDA:
DEVICE = "cuda"
elif torch.backends.mps.is_available() and ALLOW_MPS:
DEVICE = "mps"
print(f"Using device: {DEVICE}")
tokenizer_vocab_path = Path("C:\\Users\\Esmail\\Desktop\\nanograd\\nanograd\\models\\stable_diffusion\\sd_data\\tokenizer_vocab.json")
tokenizer_merges_path = Path("C:\\Users\\Esmail\\Desktop\\nanograd\\nanograd\\models\\stable_diffusion\\sd_data\\tokenizer_merges.txt")
model_file = Path("C:\\Users\\Esmail\\Desktop\\nanograd\\nanograd\\models\\stable_diffusion\\sd_data\\v1-5-pruned-emaonly.ckpt")
tokenizer = CLIPTokenizer(str(tokenizer_vocab_path), merges_file=str(tokenizer_merges_path))
models = model_loader.preload_models_from_standard_weights(str(model_file), DEVICE)
def generate_image(prompt, cfg_scale, num_inference_steps, sampler):
uncond_prompt = ""
do_cfg = True
input_image = None
strength = 0.9
seed = 42
output_image = pipeline.generate(
prompt=prompt,
uncond_prompt=uncond_prompt,
input_image=input_image,
strength=strength,
do_cfg=do_cfg,
cfg_scale=cfg_scale,
sampler_name=sampler,
n_inference_steps=num_inference_steps,
seed=seed,
models=models,
device=DEVICE,
idle_device="cpu",
tokenizer=tokenizer,
)
output_image = Image.fromarray(output_image)
return output_image
# Gradio interface
def gradio_interface():
with gr.Blocks() as demo:
with gr.Row():
with gr.Column(scale=2):
prompt_input = gr.Textbox(label="Prompt", placeholder="A cat stretching on the floor, highly detailed, ultra sharp, cinematic, 100mm lens, 8k resolution")
cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
num_inference_steps = gr.Slider(label="Sampling Steps", minimum=10, maximum=100, value=20, step=5)
sampler = gr.Radio(label="Sampling Method", choices=["ddpm", "Euler a", "Euler", "LMS", "Heun", "DPM2 a", "PLMS"], value="ddpm")
generate_btn = gr.Button("Generate", variant="primary")
with gr.Column(scale=2):
output_image = gr.Image(label="Output", show_label=False, height=512, width=512)
generate_btn.click(fn=generate_image, inputs=[prompt_input, cfg_scale, num_inference_steps, sampler], outputs=output_image)
demo.launch()
if __name__ == "__main__":
gradio_interface()
|