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import spaces
import gradio as gr
import torch
#import transformers
#from transformers import AutoTokenizer
#from transformers import pipeline
from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
base = "stabilityai/stable-diffusion-xl-base-1.0"
repo = "ByteDance/SDXL-Lightning"
ckpt = "sdxl_lightning_4step_unet.safetensors" # Use the correct ckpt for your step setting!
# Load model.
pipe_box=[]
@spaces.GPU()
def main():
def init():
device="cuda:0"
#unet = UNet2DConditionModel.from_config(base, subfolder="unet").to(device, torch.float16)
#unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device))
#pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to(device)
pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to(device)
# Ensure sampler uses "trailing" timesteps.
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
pipe_box.append(pipe)
#init()
@spaces.GPU()
def run():
init()
pipe=pipe_box[0]
# Ensure using the same inference steps as the loaded model and CFG set to 0.
return pipe("A cat", num_inference_steps=4, guidance_scale=0).images[0].save("output.png")
'''
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
model = transformers.AutoModelForCausalLM.from_pretrained(
'mosaicml/mpt-7b-instruct',
trust_remote_code=True
)
pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0')
INSTRUCTION_KEY = "### Instruction:"
RESPONSE_KEY = "### Response:"
INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
PROMPT_FOR_GENERATION_FORMAT = """{intro}
{instruction_key}
{instruction}
{response_key}
""".format(
intro=INTRO_BLURB,
instruction_key=INSTRUCTION_KEY,
instruction="{instruction}",
response_key=RESPONSE_KEY,
)
example = "James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week? Explain before answering."
fmt_ex = PROMPT_FOR_GENERATION_FORMAT.format(instruction=example)
@spaces.GPU
def run():
with torch.autocast('cuda', dtype=torch.bfloat16):
return(
pipe('Here is a recipe for vegan banana bread:\n',
max_new_tokens=100,
do_sample=True,
use_cache=True))
'''
with gr.Blocks() as app:
btn = gr.Button()
#outp=gr.Textbox()
outp=gr.Image()
btn.click(run,None,outp)
app.launch()
if __name__ == "__main__":
main() |