Spaces:
Running
Running
File size: 6,641 Bytes
c0aebfb |
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 |
import gradio as gr
from PIL import Image
import diffusers
from diffusers.models import AutoencoderKL
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse")
def read_content(file_path: str) -> str:
"""read the content of target file
"""
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
return content
def predict(prompt, negative_prompt, guidance_scale, num_inference_steps,model, scheduler, lora, lora_weight):
pipeline = diffusers.DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", vae=vae).to("cuda")
pipeline.safety_checker = lambda images, **kwargs: (images, [False] * len(images))
if model == "Realistic_V5.1":
pipeline = diffusers.DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", vae=vae).to("cuda")
if model == "Realistic_V5.0":
pipeline = diffusers.DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.0_noVAE", vae=vae).to("cuda")
if model == "EpicRealism":
pipeline = diffusers.DiffusionPipeline.from_pretrained("emilianJR/epiCRealism", vae=vae).to("cuda")
pipeline.safety_checker = lambda images, **kwargs: (images, [False] * len(images))
scheduler_class_name = scheduler.split("-")[0]
add_kwargs = {}
if len(scheduler.split("-")) > 1:
add_kwargs["use_karras_sigmas"] = True
if len(scheduler.split("-")) > 2:
add_kwargs["algorithm_type"] = "sde-dpmsolver++"
scheduler = getattr(diffusers, scheduler_class_name)
pipeline.scheduler = scheduler.from_pretrained("emilianJR/epiCRealism", subfolder="scheduler", **add_kwargs)
if lora == "nayanthara":
lora = "profaker/Naya_lora"
if lora == "saipallavi":
lora = "profaker/saipallavi_lora"
if lora == "shobita":
lora = "profaker/Shobita_lora"
if lora == "surya":
lora = "profaker/Surya_lora"
if lora == "vijay":
lora = "profaker/Vijay_lora"
if lora == "None":
images = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=int(num_inference_steps),
guidance_scale=guidance_scale,
clip_skip=1
).images[0]
print("Prompt", prompt)
print("Negative", negative_prompt)
print("Steps", num_inference_steps)
print("Scale", guidance_scale)
print("Scheduler", scheduler)
return images
pipeline.load_lora_weights(lora)
images = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=int(num_inference_steps),
guidance_scale=guidance_scale,
cross_attention_kwargs={"scale": lora_weight}
).images[0]
print("Prompt", prompt)
print("Negative", negative_prompt)
print("Steps", num_inference_steps)
print("Scale", guidance_scale)
print("Scheduler", scheduler)
return images
css = '''
.gradio-container{max-width: 1100px !important}
#image_upload{min-height:400px}
#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
#mask_radio .gr-form{background:transparent; border: none}
#word_mask{margin-top: .75em !important}
#word_mask textarea:disabled{opacity: 0.3}
.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
.dark .footer {border-color: #303030}
.dark .footer>p {background: #0b0f19}
.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
#image_upload .touch-none{display: flex}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
#prompt-container{margin-top:-18px;}
#prompt-container .form{border-top-left-radius: 0;border-top-right-radius: 0}
'''
image_blocks = gr.Blocks(css=css, elem_id="total-container")
with image_blocks as demo:
gr.HTML(read_content("header.html"))
with gr.Row():
with gr.Column():
with gr.Row(elem_id="prompt-container", equal_height=True):
with gr.Row():
prompt = gr.Textbox(placeholder="Your prompt", show_label=False, elem_id="prompt", lines=5)
with gr.Accordion(label="Advanced Settings", open=False):
with gr.Row(equal_height=True):
guidance_scale = gr.Number(value=7.5, minimum=1.0, maximum=20.0, step=0.1, label="guidance_scale")
steps = gr.Number(value=40, minimum=0, maximum=100, step=1, label="steps")
with gr.Row(equal_height=True):
negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt",
info="what you don't want to see in the image")
with gr.Row(equal_height=True):
models = ['Realistic_V6.0','Realistic_V5.1','Realistic_V5.0','EpicRealism']
model = gr.Dropdown(label="Models",choices=models,value="Realistic_V6.0")
with gr.Row(equal_height=True):
schedulers = ["DEISMultistepScheduler", "HeunDiscreteScheduler", "EulerDiscreteScheduler",
"DPMSolverMultistepScheduler", "DPMSolverMultistepScheduler-Karras",
"DPMSolverMultistepScheduler-Karras-SDE"]
scheduler = gr.Dropdown(label="Schedulers", choices=schedulers,
value="DPMSolverMultistepScheduler-Karras")
with gr.Row(equal_height=True):
loras = ['None','add_detail','nayanthara','shobita','surya','vijay','saipallavi']
lora = gr.Dropdown(label='Lora', choices=loras, value="None")
lora_weight = gr.Number(value=0.5, minimum=0, maximum=1, step=0.01, label="Lora Weights")
with gr.Row(equal_height=True):
btn = gr.Button("Generate", elem_id="run_button")
with gr.Column():
image_out = gr.Image(label="Output", elem_id="output-img", height=1024, width=512)
btn.click(fn=predict, inputs=[prompt, negative_prompt, guidance_scale, steps, model,scheduler, lora, lora_weight],
outputs=[image_out], api_name='run')
prompt.submit(fn=predict, inputs=[prompt, negative_prompt, guidance_scale, steps, model,scheduler, lora, lora_weight],
outputs=[image_out])
image_blocks.queue(max_size=25, api_open=True).launch(show_api=True)
|