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8816192 | 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 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 | import json
import os.path
import random
import time
import gradio as gr
import numpy as np
import spaces
import torch
from diffusers import FluxPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
PRE_TRAINED_MODEL = "black-forest-labs/FLUX.1-dev"
FINE_TUNED_MODEL = "tryonlabs/FLUX.1-dev-LoRA-Outfit-Generator"
RESULTS_DIR = "~/results"
os.makedirs(RESULTS_DIR, exist_ok=True)
if torch.cuda.is_available():
torch_dtype = torch.bfloat16
else:
torch_dtype = torch.float32
# Load Flux
pipe = FluxPipeline.from_pretrained(PRE_TRAINED_MODEL, torch_dtype=torch.float16).to("cuda")
# Load your fine-tuned model
pipe.load_lora_weights(FINE_TUNED_MODEL, adapter_name="default", weight_name="outfit-generator.safetensors")
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
@spaces.GPU(duration=65)
def infer(
prompt,
seed=42,
randomize_seed=False,
width=1024,
height=1024,
guidance_scale=4.5,
num_inference_steps=40,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
image = pipe(prompt, height=width, width=height, num_images_per_prompt=1, generator=generator,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps).images[0]
try:
# save image
current_time = int(time.time() * 1000)
image.save(os.path.join(RESULTS_DIR, f"gen_img_{current_time}.png"))
with open(os.path.join(RESULTS_DIR, f"gen_img_{current_time}.json"), "w") as f:
json.dump({"prompt": prompt, "height": height, "width": width, "guidance_scale": guidance_scale,
"num_inference_steps": num_inference_steps, "seed": seed}, f)
except Exception as e:
print(str(e))
return image, seed
examples = [
"stripe red striped jersey top in a soft cotton and modal blend with short sleeves a chest pocket and rounded hem",
"A dress with Color: Orange, Department: Dresses, Detail: Split Thigh, Fabric-Elasticity: No Sretch, Fit: Fitted, Hemline: Slit, Material: Gabardine, Neckline: Gathered, Pattern: Tropical, Sleeve-Length: Sleeveless, Style: Boho, Type: A Line Skirt, Waistline: High",
"treatment dark pink knee-length skirt in crocodile-patterned imitation leather high waist with belt loops and press-studs a zip fly diagonal side pockets and a slit at the front the polyester content of the skirt is partly recycled",
"A dress with Color: Maroon, Department: Dresses, Detail: Ruched Bust, Fabric-Elasticity: Slight Stretch, Fit: Fitted, Hemline: Slit, Material: Gabardine, Neckline: Spaghetti Straps, Pattern: Floral, Sleeve-Length: Sleeveless, Style: Boho, Type: Cami Top, Waistline: Regular",
"denim dark blue 5-pocket ankle-length jeans in washed stretch denim slightly looser fit with a wide waist panel for best fit over the tummy and tapered legs with raw-edge frayed hems"
]
css = """
#col-container {
margin: 0 auto;
max-width: 768px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# FLUX.1-dev LoRA Outfit Generator
## by TryOn Labs (https://www.tryonlabs.ai)
Generate an outfit by describing the color, pattern, fit, style, material, type, etc.
""")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0, variant="primary")
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=512,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=512,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=7.5,
step=0.1,
value=4.5,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=40,
)
gr.Examples(examples=examples, inputs=[prompt], outputs=[result, seed], fn=infer, cache_examples=True,
cache_mode="lazy")
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch(share=True)
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