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import torch
import os
import torch.distributed as dist
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel
from accelerate import PartialState
from diffusers import StableDiffusionPipeline
from diffusers import DiffusionPipeline
#model_path = "/home/gomishra/diffusers.old/examples/text_to_image/caleb_training_2"
#model_path ="/home/gomishra/Reliance/shareddata/reliance-model-lora-sdxl/"
model_path ="/shared/prerelease/home/gomishra/diffusers/examples/text_to_image/caleb_training"
#pipe = DiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16, variant="fp16",
#use_safetensors=True,)
pipe =DiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
distributed_state = PartialState()
pipe.to(distributed_state.device)
#pipe.to("cuda")
refiner = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0",
text_encoder_2=pipe.text_encoder_2,
vae=pipe.vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16")
refiner.to("cuda")
prompts = {
"amitabh bachchan":"amitabh bachchan in black suit with blue background and KBC as logo",
"Prabhas":"prabhas with green background ",
"Shah Rukh Khan":"Shah Rukh Khan on night market street",
"Hritik Roshan":"Hritik Roshan singing on a stage at night "
}
folder_name = model_path.split("/")[-2]
#outDir = f"/data3/harshita_output/{folder_name}"
#outDir = f"/home/aac/sdxl_node2/output/try/{folder_name}"
outDir =f"/shared/prerelease/home/gomishra/diffusers/examples/text_to_image/outputdir"
if not os.path.exists(outDir):
os.makedirs(outDir)
for key in list(prompts.keys()):
print(key)
prompt=prompts[key]
image = pipe(
prompt=prompt,
num_inference_steps=50,
denoising_end=0.8,
guidance_scale=7.5,
output_type="latent",
).images
image = refiner(
prompt=prompt,
num_inference_steps=50,
denoising_start=0.8,
image=image,
).images[0]
image.save(f"{outDir}/{key}.png")
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