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import random
import requests
import torch
import time
import gradio as gr
from io import BytesIO
from PIL import Image
import imageio
from dotenv import load_dotenv
import os
load_dotenv("config.txt")
path_to_base_model = "models/checkpoint/gpu-model/base/dreamdrop-v1.safetensors"
path_to_inpaint_model = "models/checkpoint/gpu-model/inpaint/dreamdrop-inpainting.safetensors"
xl = os.getenv("xl")
if xl == "True":
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline, StableDiffusionXLInpaintPipeline
pipe_t2i = StableDiffusionXLPipeline.from_single_file(path_to_base_model, torch_dtype=torch.float16, use_safetensors=True)
pipe_t2i = pipe_t2i.to("cuda")
pipe_i2i = StableDiffusionXLImg2ImgPipeline.from_single_file(path_to_base_model, torch_dtype=torch.float16, use_safetensors=True)
pipe_i2i = pipe_i2i.to("cuda")
pipe_inpaint = StableDiffusionXLInpaintPipeline.from_single_file(path_to_inpaint_model, torch_dtype=torch.float16, use_safetensors=True)
pipe_inpaint = pipe_inpaint.to("cuda")
else:
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipeline
pipe_t2i = StableDiffusionPipeline.from_single_file(path_to_base_model, torch_dtype=torch.float16, use_safetensors=True)
pipe_t2i = pipe_t2i.to("cuda")
pipe_i2i = StableDiffusionImg2ImgPipeline.from_single_file(path_to_base_model, torch_dtype=torch.float16, use_safetensors=True)
pipe_i2i = pipe_i2i.to("cuda")
pipe_inpaint = StableDiffusionInpaintPipeline.from_single_file(path_to_inpaint_model, torch_dtype=torch.float16, use_safetensors=True)
pipe_inpaint = pipe_inpaint.to("cuda")
pipe_t2i.load_lora_weights(pretrained_model_name_or_path_or_dict="models/lora", weight_name="epic_noiseoffset.safetensors")
pipe_t2i.fuse_lora(lora_scale=0.1)
pipe_i2i.load_lora_weights(pretrained_model_name_or_path_or_dict="models/lora", weight_name="epic_noiseoffset.safetensors")
pipe_i2i.fuse_lora(lora_scale=0.1)
pipe_inpaint.load_lora_weights(pretrained_model_name_or_path_or_dict="models/lora", weight_name="epic_noiseoffset.safetensors")
pipe_inpaint.fuse_lora(lora_scale=0.1)
def gpugen(prompt, mode, guidance, width, height, num_images, i2i_strength, inpaint_strength, i2i_change, inpaint_change, init=None, inpaint_image=None, progress = gr.Progress(track_tqdm=True)):
if mode == "Fast":
steps = 30
elif mode == "High Quality":
steps = 45
else:
steps = 20
results = []
seed = random.randint(1, 9999999)
if not i2i_change and not inpaint_change:
num = random.randint(100, 99999)
start_time = time.time()
for _ in range(num_images):
image = pipe_t2i(
prompt=prompt,
negative_prompt="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
num_inference_steps=steps,
guidance_scale=guidance,
width=width, height=height,
seed=seed,
).images
image[0].save(f"outputs/{num}_txt2img_gpu{_}.jpg")
results.append(image[0])
end_time = time.time()
execution_time = end_time - start_time
return results, f"Time taken: {execution_time} sec."
elif inpaint_change and not i2i_change:
imageio.imwrite("output_image.png", inpaint_image["mask"])
num = random.randint(100, 99999)
start_time = time.time()
for _ in range(num_images):
image = pipe_inpaint(
prompt=prompt,
image=inpaint_image["image"],
mask_image=inpaint_image["mask"],
negative_prompt="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
num_inference_steps=steps,
guidance_scale=guidance,
strength=inpaint_strength,
width=width, height=height,
seed=seed,
).images
image[0].save(f"outputs/{num}_inpaint_gpu{_}.jpg")
results.append(image[0])
end_time = time.time()
execution_time = end_time - start_time
return results, f"Time taken: {execution_time} sec."
else:
num = random.randint(100, 99999)
start_time = time.time()
for _ in range(num_images):
image = pipe_i2i(
prompt=prompt,
negative_prompt="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
image=init,
num_inference_steps=steps,
guidance_scale=guidance,
width=width, height=height,
strength=i2i_strength,
seed=seed,
).images
image[0].save(f"outputs/{num}_img2img_gpu{_}.jpg")
results.append(image[0])
end_time = time.time()
execution_time = end_time - start_time
return results, f"Time taken: {execution_time} sec."
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