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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=[] | |
def inti(): | |
device="cuda" | |
unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16) | |
unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda")) | |
pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda") | |
# Ensure sampler uses "trailing" timesteps. | |
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") | |
pipe_box.append(pipe) | |
def run(): | |
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() | |