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llm-rest-a
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rdna
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099f2160c3 | ||
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9d2d62dedf | ||
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15ed05b221 | ||
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7c825fc288 |
540
web/index.py
540
web/index.py
@@ -1,299 +1,241 @@
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# from models.resnet50 import resnet_inf
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# from models.albert_maskfill import albert_maskfill_inf
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from models.stable_diffusion.main import stable_diff_inf
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# from models.diffusion.v_diffusion import vdiff_inf
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import gradio as gr
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from PIL import Image
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import json
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import os
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||||
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def debug_event(debug):
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return gr.Textbox.update(visible=debug)
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|
||||
|
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with gr.Blocks() as shark_web:
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with gr.Row():
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with gr.Group():
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with gr.Column(scale=1):
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nod_logo = Image.open("./logos/Nod_logo.png")
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gr.Image(
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value=nod_logo, show_label=False, interactive=False
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||||
).style(height=80, width=150)
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with gr.Column(scale=1):
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logo2 = Image.open("./logos/other_logo.png")
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gr.Image(
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value=logo2,
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show_label=False,
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interactive=False,
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visible=False,
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).style(height=80, width=150)
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with gr.Column(scale=1):
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gr.Label(value="Ultra fast Stable Diffusion")
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||||
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with gr.Tabs():
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# with gr.TabItem("ResNet50"):
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# image = device = debug = resnet = output = std_output = None
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||||
# with gr.Row():
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# with gr.Column(scale=1, min_width=600):
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# image = gr.Image(label="Image")
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# device = gr.Radio(
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||||
# label="Device",
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# value="cpu",
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# choices=["cpu", "cuda", "vulkan"],
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# )
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# debug = gr.Checkbox(label="DEBUG", value=False)
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||||
# resnet = gr.Button("Recognize Image").style(
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||||
# full_width=True
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||||
# )
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||||
# with gr.Column(scale=1, min_width=600):
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||||
# output = gr.Label(label="Output")
|
||||
# std_output = gr.Textbox(
|
||||
# label="Std Output",
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||||
# value="Nothing to show.",
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||||
# visible=False,
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||||
# )
|
||||
# debug.change(
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||||
# debug_event,
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# inputs=[debug],
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# outputs=[std_output],
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# show_progress=False,
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# )
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# resnet.click(
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# resnet_inf,
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# inputs=[image, device],
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# outputs=[output, std_output],
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# )
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#
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# with gr.TabItem("Albert MaskFill"):
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# masked_text = (
|
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# device
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# ) = debug = albert_mask = decoded_res = std_output = None
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# with gr.Row():
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# with gr.Column(scale=1, min_width=600):
|
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# masked_text = gr.Textbox(
|
||||
# label="Masked Text",
|
||||
# placeholder="Give me a sentence with [MASK] to fill",
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# )
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||||
# device = gr.Radio(
|
||||
# label="Device",
|
||||
# value="cpu",
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# choices=["cpu", "cuda", "vulkan"],
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# )
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||||
# debug = gr.Checkbox(label="DEBUG", value=False)
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# albert_mask = gr.Button("Decode Mask")
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# with gr.Column(scale=1, min_width=600):
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# decoded_res = gr.Label(label="Decoded Results")
|
||||
# std_output = gr.Textbox(
|
||||
# label="Std Output",
|
||||
# value="Nothing to show.",
|
||||
# visible=False,
|
||||
# )
|
||||
# debug.change(
|
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# debug_event,
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# inputs=[debug],
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# outputs=[std_output],
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# show_progress=False,
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# )
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# albert_mask.click(
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# albert_maskfill_inf,
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# inputs=[masked_text, device],
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# outputs=[decoded_res, std_output],
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# )
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|
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# with gr.TabItem("V-Diffusion"):
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# prompt = sample_count = batch_size = iters = device = v_diffusion = generated_img = None
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# with gr.Row():
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# with gr.Column(scale=1, min_width=600):
|
||||
# prompt = gr.Textbox(
|
||||
# label="Prompt", value="New York City, oil on canvas:5"
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||||
# )
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||||
# sample_count = gr.Number(label="Sample Count", value=1)
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||||
# batch_size = gr.Number(label="Batch Size", value=1)
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||||
# iters = gr.Number(label="Steps", value=2)
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# device = gr.Textbox(label="Device", value="gpu")
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||||
# v_diffusion = gr.Button("Generate image from prompt")
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# with gr.Column(scale=1, min_width=600):
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# generated_img = gr.Image(type="pil", shape=(100, 100))
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# std_output = gr.Textbox(label="Std Output", value="Nothing.")
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# v_diffusion.click(
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# vdiff_inf,
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# inputs=[prompt, sample_count, batch_size, iters, device],
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# outputs=[generated_img, std_output]
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# )
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with gr.TabItem("Stable-Diffusion"):
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prompt = (
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scheduler
|
||||
) = (
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||||
iters_count
|
||||
) = (
|
||||
batch_size
|
||||
) = (
|
||||
steps
|
||||
) = (
|
||||
guidance
|
||||
) = (
|
||||
height
|
||||
) = (
|
||||
width
|
||||
) = (
|
||||
seed
|
||||
) = (
|
||||
precision
|
||||
) = (
|
||||
device
|
||||
) = (
|
||||
cache
|
||||
) = (
|
||||
iree_vulkan_target_triple
|
||||
) = (
|
||||
live_preview
|
||||
) = (
|
||||
debug
|
||||
) = save_img = stable_diffusion = generated_img = std_output = None
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# load prompts.
|
||||
prompt_examples = []
|
||||
prompt_loc = "./prompts.json"
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||||
if os.path.exists(prompt_loc):
|
||||
fopen = open("./prompts.json")
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||||
prompt_examples = json.load(fopen)
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=1, min_width=600):
|
||||
with gr.Group():
|
||||
prompt = gr.Textbox(
|
||||
label="Prompt",
|
||||
value="a photograph of an astronaut riding a horse",
|
||||
lines=5,
|
||||
)
|
||||
ex = gr.Examples(
|
||||
examples=prompt_examples,
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||||
inputs=prompt,
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||||
cache_examples=False,
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||||
)
|
||||
with gr.Row():
|
||||
iters_count = gr.Slider(
|
||||
1,
|
||||
24,
|
||||
value=1,
|
||||
step=1,
|
||||
label="Iteration Count",
|
||||
visible=False,
|
||||
)
|
||||
batch_size = gr.Slider(
|
||||
1,
|
||||
4,
|
||||
value=1,
|
||||
step=1,
|
||||
label="Batch Size",
|
||||
visible=False,
|
||||
)
|
||||
with gr.Row():
|
||||
steps = gr.Slider(
|
||||
1, 100, value=50, step=1, label="Steps"
|
||||
)
|
||||
guidance = gr.Slider(
|
||||
0,
|
||||
50,
|
||||
value=7.5,
|
||||
step=0.1,
|
||||
label="Guidance Scale",
|
||||
interactive=False,
|
||||
)
|
||||
with gr.Row():
|
||||
height = gr.Slider(
|
||||
384,
|
||||
768,
|
||||
value=512,
|
||||
step=64,
|
||||
label="Height",
|
||||
interactive=False,
|
||||
)
|
||||
width = gr.Slider(
|
||||
384,
|
||||
768,
|
||||
value=512,
|
||||
step=64,
|
||||
label="Width",
|
||||
interactive=False,
|
||||
)
|
||||
with gr.Row():
|
||||
scheduler = gr.Radio(
|
||||
label="Scheduler",
|
||||
value="LMS",
|
||||
choices=["PNDM", "LMS", "DDIM"],
|
||||
interactive=False,
|
||||
visible=False,
|
||||
)
|
||||
device = gr.Radio(
|
||||
label="Device",
|
||||
value="vulkan",
|
||||
choices=["cpu", "cuda", "vulkan"],
|
||||
interactive=False,
|
||||
visible=False,
|
||||
)
|
||||
with gr.Row():
|
||||
precision = gr.Radio(
|
||||
label="Precision",
|
||||
value="fp16",
|
||||
choices=["fp16", "fp32"],
|
||||
)
|
||||
seed = gr.Textbox(
|
||||
value="42", max_lines=1, label="Seed"
|
||||
)
|
||||
with gr.Row():
|
||||
cache = gr.Checkbox(label="Cache", value=True)
|
||||
live_preview = gr.Checkbox(
|
||||
label="Live Preview", value=False
|
||||
)
|
||||
debug = gr.Checkbox(label="DEBUG", value=False)
|
||||
save_img = gr.Checkbox(label="Save Image", value=False)
|
||||
iree_vulkan_target_triple = gr.Textbox(
|
||||
value="",
|
||||
max_lines=1,
|
||||
label="IREE VULKAN TARGET TRIPLE",
|
||||
visible=False,
|
||||
)
|
||||
stable_diffusion = gr.Button("Generate Image")
|
||||
with gr.Column(scale=1, min_width=600):
|
||||
generated_img = gr.Image(type="pil", shape=(100, 100))
|
||||
std_output = gr.Textbox(
|
||||
label="Std Output",
|
||||
value="Nothing.",
|
||||
lines=5,
|
||||
visible=False,
|
||||
)
|
||||
|
||||
debug.change(
|
||||
debug_event,
|
||||
inputs=[debug],
|
||||
outputs=[std_output],
|
||||
show_progress=False,
|
||||
)
|
||||
stable_diffusion.click(
|
||||
stable_diff_inf,
|
||||
inputs=[
|
||||
prompt,
|
||||
scheduler,
|
||||
iters_count,
|
||||
batch_size,
|
||||
steps,
|
||||
guidance,
|
||||
height,
|
||||
width,
|
||||
seed,
|
||||
precision,
|
||||
device,
|
||||
cache,
|
||||
iree_vulkan_target_triple,
|
||||
live_preview,
|
||||
save_img,
|
||||
],
|
||||
outputs=[generated_img, std_output],
|
||||
)
|
||||
|
||||
shark_web.queue()
|
||||
shark_web.launch(server_name="0.0.0.0", server_port=8080, enable_queue=True)
|
||||
# from models.resnet50 import resnet_inf
|
||||
# from models.albert_maskfill import albert_maskfill_inf
|
||||
from models.stable_diffusion.main import stable_diff_inf
|
||||
|
||||
# from models.diffusion.v_diffusion import vdiff_inf
|
||||
import gradio as gr
|
||||
from PIL import Image
|
||||
import json
|
||||
import os
|
||||
|
||||
|
||||
def debug_event(debug):
|
||||
return gr.Textbox.update(visible=debug)
|
||||
|
||||
|
||||
prompt_examples = []
|
||||
prompt_loc = "./prompts.json"
|
||||
if os.path.exists(prompt_loc):
|
||||
with open("./prompts.json", encoding="utf-8") as fopen:
|
||||
prompt_examples = json.load(fopen)
|
||||
|
||||
|
||||
demo_css = """
|
||||
.gradio-container {background-color: black}
|
||||
.container {background-color: black !important; padding-top:20px !important; }
|
||||
#ui_title {padding: 10px !important; }
|
||||
#top_logo {background-color: transparent; border-radius: 0 !important; border: 0; }
|
||||
#demo_title {background-color: black; border-radius: 0 !important; border: 0; padding-top: 50px; padding-bottom: 0px; width: 460px !important;}
|
||||
|
||||
#demo_title_outer {border-radius: 0; }
|
||||
#prompt_box_outer div:first-child {border-radius: 0 !important}
|
||||
#prompt_box textarea {background-color:#1d1d1d !important}
|
||||
#prompt_examples {margin:0 !important}
|
||||
#prompt_examples svg {display: none !important;}
|
||||
|
||||
.gr-sample-textbox { border-radius: 1rem !important; border-color: rgb(31,41,55) !important; border-width:2px !important; }
|
||||
#ui_body {background-color: #111111 !important; padding: 10px !important; border-radius: 0.5em !important;}
|
||||
|
||||
#img_result+div {display: none !important;}
|
||||
|
||||
footer {display: none !important;}
|
||||
"""
|
||||
|
||||
with gr.Blocks(css=demo_css) as shark_web:
|
||||
# load prompt examples.
|
||||
|
||||
with gr.Row(elem_id="ui_title"):
|
||||
with gr.Column(scale=1, elem_id="demo_title_outer"):
|
||||
logo2 = Image.open("./logos/sd-demo-logo.png")
|
||||
gr.Image(
|
||||
value=logo2,
|
||||
show_label=False,
|
||||
interactive=False,
|
||||
elem_id="demo_title",
|
||||
).style(width=230)
|
||||
# with gr.Column(scale=1):
|
||||
# gr.Label(value="Ultra fast Stable Diffusion")
|
||||
|
||||
with gr.Row(elem_id="ui_body"):
|
||||
prompt = (
|
||||
scheduler
|
||||
) = (
|
||||
iters_count
|
||||
) = (
|
||||
batch_size
|
||||
) = (
|
||||
steps
|
||||
) = (
|
||||
guidance
|
||||
) = (
|
||||
height
|
||||
) = (
|
||||
width
|
||||
) = (
|
||||
seed
|
||||
) = (
|
||||
precision
|
||||
) = (
|
||||
device
|
||||
) = (
|
||||
cache
|
||||
) = (
|
||||
iree_vulkan_target_triple
|
||||
) = (
|
||||
live_preview
|
||||
) = (
|
||||
debug
|
||||
) = save_img = stable_diffusion = generated_img = std_output = None
|
||||
# load prompts.
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=1, min_width=600):
|
||||
with gr.Group(elem_id="prompt_box_outer"):
|
||||
prompt = gr.Textbox(
|
||||
label="Prompt",
|
||||
value="A photograph of an astronaut riding a horse",
|
||||
lines=1,
|
||||
elem_id="prompt_box",
|
||||
)
|
||||
with gr.Group():
|
||||
ex = gr.Examples(
|
||||
label="Examples",
|
||||
examples=prompt_examples,
|
||||
inputs=prompt,
|
||||
cache_examples=False,
|
||||
elem_id="prompt_examples",
|
||||
)
|
||||
with gr.Row():
|
||||
steps = gr.Slider(1, 100, value=50, step=1, label="Steps")
|
||||
guidance = gr.Slider(
|
||||
0,
|
||||
50,
|
||||
value=7.5,
|
||||
step=0.1,
|
||||
label="Guidance Scale",
|
||||
interactive=False,
|
||||
)
|
||||
with gr.Row():
|
||||
height = gr.Slider(
|
||||
384,
|
||||
768,
|
||||
value=512,
|
||||
step=64,
|
||||
label="Height",
|
||||
interactive=False,
|
||||
)
|
||||
width = gr.Slider(
|
||||
384,
|
||||
768,
|
||||
value=512,
|
||||
step=64,
|
||||
label="Width",
|
||||
interactive=False,
|
||||
)
|
||||
with gr.Row():
|
||||
precision = gr.Radio(
|
||||
label="Precision",
|
||||
value="fp16",
|
||||
choices=["fp16", "fp32"],
|
||||
)
|
||||
seed = gr.Textbox(value="42", max_lines=1, label="Seed")
|
||||
with gr.Row():
|
||||
cache = gr.Checkbox(label="Cache", value=True)
|
||||
# debug = gr.Checkbox(label="DEBUG", value=False)
|
||||
save_img = gr.Checkbox(label="Save Image", value=False)
|
||||
live_preview = gr.Checkbox(
|
||||
label="Live Preview", value=False
|
||||
)
|
||||
# Hidden Items.
|
||||
scheduler = gr.Radio(
|
||||
label="Scheduler",
|
||||
value="LMS",
|
||||
choices=["PNDM", "LMS", "DDIM"],
|
||||
interactive=False,
|
||||
visible=False,
|
||||
)
|
||||
device = gr.Radio(
|
||||
label="Device",
|
||||
value="vulkan",
|
||||
choices=["cpu", "cuda", "vulkan"],
|
||||
interactive=False,
|
||||
visible=False,
|
||||
elem_id="ugly_line",
|
||||
)
|
||||
iters_count = gr.Slider(
|
||||
1,
|
||||
24,
|
||||
value=1,
|
||||
step=1,
|
||||
label="Iteration Count",
|
||||
visible=False,
|
||||
)
|
||||
batch_size = gr.Slider(
|
||||
1,
|
||||
4,
|
||||
value=1,
|
||||
step=1,
|
||||
label="Batch Size",
|
||||
visible=False,
|
||||
)
|
||||
iree_vulkan_target_triple = gr.Textbox(
|
||||
value="",
|
||||
max_lines=1,
|
||||
label="IREE VULKAN TARGET TRIPLE",
|
||||
visible=False,
|
||||
elem_id="ugly_line",
|
||||
)
|
||||
stable_diffusion = gr.Button("Generate Image")
|
||||
# logo
|
||||
nod_logo = Image.open("./logos/amd-nod-logo.png")
|
||||
gr.Image(
|
||||
value=nod_logo,
|
||||
show_label=False,
|
||||
interactive=False,
|
||||
elem_id="top_logo",
|
||||
).style(width=230)
|
||||
with gr.Column(scale=1, min_width=600):
|
||||
generated_img = gr.Image(
|
||||
type="pil", elem_id="img_result", interactive=False
|
||||
).style(height=768, width=768)
|
||||
std_output = gr.Textbox(
|
||||
label="Std Output",
|
||||
value="Nothing.",
|
||||
lines=5,
|
||||
visible=False,
|
||||
elem_id="ugly_line",
|
||||
)
|
||||
"""
|
||||
debug.change(
|
||||
debug_event,
|
||||
inputs=[debug],
|
||||
outputs=[std_output],
|
||||
show_progress=False,
|
||||
)
|
||||
"""
|
||||
|
||||
stable_diffusion.click(
|
||||
stable_diff_inf,
|
||||
inputs=[
|
||||
prompt,
|
||||
scheduler,
|
||||
iters_count,
|
||||
batch_size,
|
||||
steps,
|
||||
guidance,
|
||||
height,
|
||||
width,
|
||||
seed,
|
||||
precision,
|
||||
device,
|
||||
cache,
|
||||
iree_vulkan_target_triple,
|
||||
live_preview,
|
||||
save_img,
|
||||
],
|
||||
outputs=[generated_img, std_output],
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
shark_web.queue()
|
||||
shark_web.launch(server_name="0.0.0.0", server_port=8080, enable_queue=True)
|
||||
|
||||
BIN
web/logos/amd-nod-logo.png
Normal file
BIN
web/logos/amd-nod-logo.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 38 KiB |
BIN
web/logos/sd-demo-logo.png
Normal file
BIN
web/logos/sd-demo-logo.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 5.0 KiB |
@@ -183,7 +183,7 @@ args = Arguments(
|
||||
seed=42,
|
||||
precision="fp16",
|
||||
device="vulkan",
|
||||
cache=False,
|
||||
cache=True,
|
||||
iree_vulkan_target_triple="",
|
||||
live_preview=False,
|
||||
save_img=False,
|
||||
|
||||
@@ -4,6 +4,5 @@
|
||||
["an insect robot preparing a delicious meal"],
|
||||
["A digital Illustration of the Babel tower, 4k, detailed, trending in artstation, fantasy vivid colors"],
|
||||
["Cluttered house in the woods, anime, oil painting, high resolution, cottagecore, ghibli inspired, 4k"],
|
||||
["A beautiful castle beside a waterfall in the woods, by Josef Thoma, matte painting, trending on artstation HQ"],
|
||||
["A beautiful mansion beside a waterfall in the woods, by josef thoma, matte painting, trending on artstation HQ"],
|
||||
["A small cabin on top of a snowy mountain in the style of Disney, artstation"]]
|
||||
["portrait photo of a asia old warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes"]]
|
||||
|
||||
Reference in New Issue
Block a user