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Running
on
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Running
on
Zero
import os | |
import random | |
import base64 | |
import gradio as gr | |
import numpy as np | |
import PIL.Image | |
from PIL import ImageOps | |
import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
from transformers import BitsAndBytesConfig | |
import torchvision.transforms.functional as TF | |
from diffusers import ( | |
AutoencoderKL, | |
EulerAncestralDiscreteScheduler, | |
StableDiffusionXLAdapterPipeline, | |
T2IAdapter, | |
) | |
import urllib.parse | |
import requests | |
from io import BytesIO | |
import json | |
from pathlib import Path | |
import uuid | |
import os, uuid | |
from azure.identity import DefaultAzureCredential | |
from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient | |
from datetime import datetime | |
class DEFAULTS: | |
NEGATIVE_PROMPT = " extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured" | |
REWRITING_PROMPT = ( | |
"Rewrite the image caption by making it shorter (but retain all information about relative position), " | |
"remove information about style of objects or colors of background and foreground, and, most importantly, remove all details " | |
"that suggests it is a sketch. Write it as a Google image search query:" | |
) | |
MOONDREAM_PROMPT = "Describe this image." | |
NUM_STEPS = 25 | |
GUIDANCE_SCALE = 5 | |
ADAPTER_CONDITIONING_SCALE = 0.8 | |
ADAPTER_CONDITIONING_FACTOR = 0.8 | |
SEED = 1231245 | |
RANDOMIZE_SEED = True | |
DESCRIPTION = '''# Sketch to Image/Caption to Bing Search :) | |
This is a test space for the Sketch to Image/Caption to Bing Search model. You can draw a sketch on the left, provide a prompt, and select a style. The model will generate an image based on your sketch and prompt, and provide a Bing search query based on the generated image. | |
''' | |
if not torch.cuda.is_available(): | |
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" | |
style_list = [ | |
{ | |
"name": "(No style)", | |
"prompt": "{prompt}", | |
"negative_prompt": "", | |
}, | |
{ | |
"name": "Cinematic", | |
"prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", | |
"negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", | |
}, | |
{ | |
"name": "3D Model", | |
"prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting", | |
"negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting", | |
}, | |
{ | |
"name": "Anime", | |
"prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed", | |
"negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast", | |
}, | |
{ | |
"name": "Digital Art", | |
"prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed", | |
"negative_prompt": "photo, photorealistic, realism, ugly", | |
}, | |
{ | |
"name": "Photographic", | |
"prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed", | |
"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly", | |
}, | |
{ | |
"name": "Pixel art", | |
"prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics", | |
"negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic", | |
}, | |
{ | |
"name": "Fantasy art", | |
"prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy", | |
"negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white", | |
}, | |
{ | |
"name": "Neonpunk", | |
"prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional", | |
"negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured", | |
}, | |
{ | |
"name": "Manga", | |
"prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style", | |
"negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style", | |
}, | |
] | |
styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} | |
STYLE_NAMES = list(styles.keys()) | |
DEFAULT_STYLE_NAME = "Photographic" # "(No style)" | |
def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]: | |
p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) | |
return p.replace("{prompt}", positive), n + negative | |
if os.path.exists("azure_connection_string.txt"): | |
with open("azure_connection_string.txt", "r") as f: | |
CONNECTION_STRING = f.read().strip() | |
else: | |
CONNECTION_STRING = os.getenv("AZURE_CONNECTION_STRING") | |
def upload_pil_image_to_azure(image, connection_string=CONNECTION_STRING): | |
image_name = f"{uuid.uuid4()}.png" | |
image_bytes = BytesIO() | |
image.save(image_bytes, format="PNG") | |
image_bytes.seek(0) | |
try: | |
# Create the BlobServiceClient object | |
blob_service_client = BlobServiceClient.from_connection_string(connection_string) | |
# Create a blob client using the local file name as the name for the blob | |
blob_client = blob_service_client.get_blob_client(container="blob-image-hosting", blob=image_name) | |
# Upload the created file and retrieve the URL | |
blob_client.upload_blob(image_bytes) | |
file_url = blob_client.url | |
except Exception as ex: | |
print('Exception:') | |
print(ex) | |
file_url = None | |
# If this function did not fail, upload was successful | |
return file_url | |
if torch.cuda.is_available(): | |
if torch.cuda.device_count() > 1: | |
device_0, device_1 = torch.device("cuda:0"), torch.device("cuda:1") | |
else: | |
device_0, device_1 = torch.device("cuda:0"), torch.device("cuda:0") | |
else: | |
device_0, device_1 = torch.device("cpu"), torch.device("cpu") | |
# device_1 = 'cuda:0' | |
if torch.cuda.is_available(): | |
model_id = "stabilityai/stable-diffusion-xl-base-1.0" | |
adapter = T2IAdapter.from_pretrained( | |
"TencentARC/t2i-adapter-sketch-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" | |
) | |
scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") | |
pipe = StableDiffusionXLAdapterPipeline.from_pretrained( | |
model_id, | |
vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16), | |
adapter=adapter, | |
scheduler=scheduler, | |
torch_dtype=torch.float16, | |
variant="fp16", | |
) | |
pipe.to(device_0) | |
else: | |
pipe = None | |
MAX_SEED = np.iinfo(np.int32).max | |
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
return seed | |
nf4_config = BitsAndBytesConfig( | |
load_in_4bit=True, | |
bnb_4bit_quant_type="nf4", | |
bnb_4bit_use_double_quant=True, | |
bnb_4bit_compute_dtype=torch.bfloat16 | |
) | |
vlmodel_id = "vikhyatk/moondream2" | |
vlmodel_revision = "2024-07-23" | |
vlmodel = AutoModelForCausalLM.from_pretrained( | |
vlmodel_id, trust_remote_code=True, revision=vlmodel_revision, device_map={"": device_1}, | |
torch_dtype=torch.float16, | |
attn_implementation="flash_attention_2", | |
) | |
vltokenizer = AutoTokenizer.from_pretrained(vlmodel_id, revision=vlmodel_revision) | |
rewrite_model_name = "meta-llama/Meta-Llama-3.1-8B-Instruct" | |
rewrite_model = AutoModelForCausalLM.from_pretrained( | |
rewrite_model_name, | |
device_map={"": device_1}, | |
quantization_config=nf4_config, | |
# load_in_8bit=True, | |
torch_dtype=torch.bfloat16, | |
attn_implementation="flash_attention_2", | |
) | |
rewrite_tokenizer = AutoTokenizer.from_pretrained(rewrite_model_name) | |
def caption_image_with_recaption(pil_image, moondream_prompt, rewriting_prompt, user_prompt=""): | |
enc_image = vlmodel.encode_image(pil_image) | |
img_caption = vlmodel.answer_question(enc_image, moondream_prompt, vltokenizer) | |
rewritten_caption = rewrite_prompt(img_caption, rewriting_prompt, user_prompt=user_prompt) | |
rewritten_caption = rewritten_caption.strip('"').replace("\n", " ") | |
return img_caption, rewritten_caption | |
def rewrite_prompt(image_cap: str, guide: str, user_prompt: str = "") -> str: | |
prompt = f"{guide}\n{image_cap}" | |
messages = [ | |
{"role": "system", "content": "You are a helpful assistant."}, | |
{"role": "user", "content": prompt} | |
] | |
text = rewrite_tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) | |
model_inputs = rewrite_tokenizer([text], return_tensors="pt").to(device_1) | |
generated_ids = rewrite_model.generate(model_inputs.input_ids, max_new_tokens=128) | |
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)] | |
response = rewrite_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
return response | |
def run_full( | |
image, | |
user_prompt: str, | |
negative_prompt: str, | |
rewriting_prompt: str, | |
moondream_prompt: str, | |
style_name: str = DEFAULT_STYLE_NAME, | |
num_steps: int = 25, | |
guidance_scale: float = 5, | |
adapter_conditioning_scale: float = 0.8, | |
adapter_conditioning_factor: float = 0.8, | |
seed: int = 0, | |
progress=None, | |
) -> PIL.Image.Image: | |
# image is a white background with black sketch | |
image = ImageOps.invert(image) | |
# resize to 1024x1024 | |
image = image.resize((1024, 1024)) | |
# Threshold the image to get a binary sketch | |
image = TF.to_tensor(image) > 0.5 | |
image = TF.to_pil_image(image.to(torch.float32)) | |
full_log = [] | |
if user_prompt == "": | |
pre_caption = True | |
start_time = datetime.now() | |
img_caption, rewritten_caption = caption_image_with_recaption( | |
pil_image=image, rewriting_prompt=rewriting_prompt, moondream_prompt=moondream_prompt) | |
full_log.append(f"Combined captioning time: {datetime.now() - start_time}") | |
full_log.append(f"img_caption (pre): {img_caption}") | |
full_log.append(f"rewritten_caption (pre): {rewritten_caption}") | |
drawing_prompt = rewritten_caption | |
else: | |
pre_caption = False | |
drawing_prompt = user_prompt | |
full_log.append(f"Pre-caption: {pre_caption}") | |
# Generate image | |
start_time = datetime.now() | |
drawing_prompt, negative_prompt = apply_style(style_name, drawing_prompt, negative_prompt) | |
generator = torch.Generator(device=device_0).manual_seed(seed) | |
out_img = pipe( | |
prompt=drawing_prompt, | |
negative_prompt=negative_prompt, | |
image=image, | |
num_inference_steps=num_steps, | |
generator=generator, | |
guidance_scale=guidance_scale, | |
adapter_conditioning_scale=adapter_conditioning_scale, | |
adapter_conditioning_factor=adapter_conditioning_factor, | |
).images[0] | |
full_log.append(f"Image generation time: {datetime.now() - start_time}") | |
if not pre_caption: | |
start_time = datetime.now() | |
img_caption, rewritten_caption = caption_image_with_recaption( | |
pil_image=out_img, | |
rewriting_prompt=rewriting_prompt, | |
moondream_prompt=moondream_prompt, | |
user_prompt=user_prompt) | |
full_log.append(f"Combined captioning time: {datetime.now() - start_time}") | |
full_log.append(f"img_caption (post): {img_caption}") | |
full_log.append(f"rewritten_caption (post): {rewritten_caption}") | |
# SERP query | |
bing_serp_query = f"https://www.bing.com/images/search?q={urllib.parse.quote(rewritten_caption)}" | |
md_text = f"### Bing search query\n[{bing_serp_query}]({bing_serp_query})\n" | |
# Visual Search query | |
out_img_imgur_url = upload_pil_image_to_azure(out_img) | |
if out_img_imgur_url is None: | |
md_text += "### Bing Visual Search\n**Error:** Failed to upload image to Azure Blob Storage\n" | |
bing_image_search_url = "https://www.bing.com/images" | |
else: | |
imgur_url_quote = urllib.parse.quote(out_img_imgur_url) | |
bing_image_search_url = f"https://www.bing.com/images/search?view=detailv2&iss=SBI&form=SBIIRP&q=imgurl:{imgur_url_quote}" | |
md_text += f"### Bing Visual Search\n[{bing_image_search_url}]({bing_image_search_url})\n" | |
# Debug info | |
md_text += f"### Debug: sketch caption\n{img_caption}\n\n### Debug: rewritten caption\n{rewritten_caption}\n" | |
# Full log dump | |
md_text += f"### Debug: full log\n{'<br>'.join(full_log)}" | |
# return dict | |
return { | |
"image": out_img, | |
"text_search_url": bing_serp_query, | |
"visual_search_url": bing_image_search_url, | |
"logs": md_text, | |
} | |
def run_full_gradio( | |
image, | |
user_prompt: str, | |
negative_prompt: str, | |
rewriting_prompt: str, | |
moondream_prompt: str, | |
style_name: str = DEFAULT_STYLE_NAME, | |
num_steps: int = 25, | |
guidance_scale: float = 5, | |
adapter_conditioning_scale: float = 0.8, | |
adapter_conditioning_factor: float = 0.8, | |
seed: int = 0, | |
progress=gr.Progress(track_tqdm=True), | |
) -> PIL.Image.Image: | |
image = image['composite'] | |
background = PIL.Image.new('RGBA', image.size, (255, 255, 255)) | |
alpha_composite = PIL.Image.alpha_composite(background, image) | |
image = alpha_composite.convert("RGB") | |
results = run_full( | |
image=image, | |
user_prompt=user_prompt, | |
negative_prompt=negative_prompt, | |
rewriting_prompt=rewriting_prompt, | |
moondream_prompt=moondream_prompt, | |
style_name=style_name, | |
num_steps=num_steps, | |
guidance_scale=guidance_scale, | |
adapter_conditioning_scale=adapter_conditioning_scale, | |
adapter_conditioning_factor=adapter_conditioning_factor, | |
seed=seed, | |
progress=progress, | |
) | |
# construct markdown output | |
return results["image"], results["logs"] | |
def run_full_api( | |
image_url: str, | |
image_bytes: str, | |
user_prompt: str, | |
progress=gr.Progress(track_tqdm=True), | |
) -> str: | |
seed = randomize_seed_fn(0, True) | |
if image_url: | |
image = PIL.Image.open(BytesIO(requests.get(image_url).content)) | |
elif image_bytes: | |
decoded_image = base64.b64decode(image_bytes) | |
image = PIL.Image.open(BytesIO(decoded_image)) | |
# if image is RGBA, convert to RGB | |
if image.mode == "RGBA": | |
background = PIL.Image.new('RGBA', image.size, (255, 255, 255)) | |
alpha_composite = PIL.Image.alpha_composite(background, image) | |
image = alpha_composite.convert("RGB") | |
results = run_full( | |
image=image, user_prompt=user_prompt, | |
negative_prompt=DEFAULTS.NEGATIVE_PROMPT, | |
rewriting_prompt=DEFAULTS.REWRITING_PROMPT, | |
moondream_prompt=DEFAULTS.MOONDREAM_PROMPT, | |
style_name=DEFAULT_STYLE_NAME, | |
num_steps=DEFAULTS.NUM_STEPS, | |
guidance_scale=DEFAULTS.GUIDANCE_SCALE, | |
adapter_conditioning_scale=DEFAULTS.ADAPTER_CONDITIONING_SCALE, | |
adapter_conditioning_factor=DEFAULTS.ADAPTER_CONDITIONING_FACTOR, | |
seed=seed) | |
return results["text_search_url"], results["visual_search_url"], results["logs"] | |
def run_caponly( | |
image, | |
rewriting_prompt: str, | |
moondream_prompt: str, | |
seed: int = 0, | |
progress=None, | |
) -> PIL.Image.Image: | |
# image is a white background with black sketch | |
image = ImageOps.invert(image) | |
# resize to 1024x1024 | |
image = image.resize((1024, 1024)) | |
# Threshold the image to get a binary sketch | |
image = TF.to_tensor(image) > 0.5 | |
image = TF.to_pil_image(image.to(torch.float32)) | |
full_log = [] | |
start_time = datetime.now() | |
img_caption, rewritten_caption = caption_image_with_recaption( | |
pil_image=image, rewriting_prompt=rewriting_prompt, moondream_prompt=moondream_prompt) | |
full_log.append(f"Combined captioning time: {datetime.now() - start_time}") | |
full_log.append(f"img_caption (pre): {img_caption}") | |
full_log.append(f"rewritten_caption (pre): {rewritten_caption}") | |
final_prompt = rewritten_caption | |
# SERP query | |
bing_serp_query = f"https://www.bing.com/images/search?q={urllib.parse.quote(rewritten_caption)}" | |
md_text = f"### Bing search query\n[{bing_serp_query}]({bing_serp_query})\n" | |
# Debug info | |
md_text += f"### Debug: sketch caption\n{img_caption}\n\n### Debug: rewritten caption\n{rewritten_caption}\n" | |
# Full log dump | |
md_text += f"### Debug: full log\n{'<br>'.join(full_log)}" | |
# return dict | |
return { | |
"text_search_url": bing_serp_query, | |
"logs": md_text, | |
} | |
def run_caponly_api( | |
image_url: str, | |
image_bytes: str, | |
progress=gr.Progress(track_tqdm=True), | |
) -> str: | |
seed = randomize_seed_fn(0, True) | |
if image_url: | |
image = PIL.Image.open(BytesIO(requests.get(image_url).content)) | |
elif image_bytes: | |
decoded_image = base64.b64decode(image_bytes) | |
image = PIL.Image.open(BytesIO(decoded_image)) | |
# if image is RGBA, convert to RGB | |
if image.mode == "RGBA": | |
background = PIL.Image.new('RGBA', image.size, (255, 255, 255)) | |
alpha_composite = PIL.Image.alpha_composite(background, image) | |
image = alpha_composite.convert("RGB") | |
results = run_caponly( | |
image=image, | |
rewriting_prompt=DEFAULTS.REWRITING_PROMPT, | |
moondream_prompt=DEFAULTS.MOONDREAM_PROMPT, | |
seed=seed) | |
return results["text_search_url"], results["logs"] | |
with gr.Blocks(css="style.css") as demo: | |
gr.Markdown(DESCRIPTION, elem_id="description") | |
gr.DuplicateButton( | |
value="Duplicate Space for private use", | |
elem_id="duplicate-button", | |
visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", | |
) | |
with gr.Row(): | |
with gr.Column(): | |
with gr.Group(): | |
image = gr.Sketchpad( | |
# sources=["canvas"], | |
# tool="sketch", | |
type="pil", | |
image_mode="RGBA", | |
# invert_colors=True, | |
layers=False, | |
canvas_size=(1024, 1024), | |
brush=gr.Brush( | |
default_color="black", | |
colors=None, | |
default_size=4, | |
color_mode="fixed", | |
), | |
eraser=gr.Eraser(), | |
height=440, | |
) | |
prompt = gr.Textbox(label="Prompt") | |
style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME) | |
run_button = gr.Button("Run") | |
with gr.Accordion("Advanced options", open=False): | |
negative_prompt = gr.Textbox( | |
label="Negative prompt", | |
value=DEFAULTS.NEGATIVE_PROMPT, | |
) | |
rewriting_prompt = gr.Textbox( | |
label="Rewriting prompt", | |
value=DEFAULTS.REWRITING_PROMPT, | |
) | |
moondream_prompt = gr.Textbox( | |
label="Moondream prompt", | |
value=DEFAULTS.MOONDREAM_PROMPT, | |
) | |
num_steps = gr.Slider( | |
label="Number of steps", | |
minimum=1, | |
maximum=50, | |
step=1, | |
value=DEFAULTS.NUM_STEPS, | |
) | |
guidance_scale = gr.Slider( | |
label="Guidance scale", | |
minimum=0.1, | |
maximum=10.0, | |
step=0.1, | |
value=DEFAULTS.GUIDANCE_SCALE, | |
) | |
adapter_conditioning_scale = gr.Slider( | |
label="Adapter conditioning scale", | |
minimum=0.5, | |
maximum=1, | |
step=0.1, | |
value=DEFAULTS.ADAPTER_CONDITIONING_SCALE, | |
) | |
adapter_conditioning_factor = gr.Slider( | |
label="Adapter conditioning factor", | |
info="Fraction of timesteps for which adapter should be applied", | |
minimum=0.5, | |
maximum=1, | |
step=0.1, | |
value=DEFAULTS.ADAPTER_CONDITIONING_FACTOR, | |
) | |
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.Column(): | |
result_img = gr.Image(label="Result", height=400, interactive=False) | |
result_caption = gr.Markdown(label="Image caption") | |
result = [result_img, result_caption] | |
with gr.Row(): | |
gr.Markdown("# API endpoints\nThe fields below are only used to test the served API endpoints of this space.", elem_id="description") | |
with gr.Row(): | |
with gr.Column(): | |
with gr.Accordion("Full Experience API", open=False): | |
api_fullexp_image_url = gr.Textbox(label="Image URL") | |
api_fullexp_image_bytes = gr.Textbox(label="Image Base64 bytes") | |
api_fullexp_user_prompt = gr.Textbox(label="User prompt") | |
api_fullexp_run_button = gr.Button("Run API") | |
api_fullexp_text_search_url = gr.Textbox(label="Text search URL") | |
api_fullexp_visual_search_url = gr.Textbox(label="Visual search URL") | |
api_fullexp_logs = gr.Markdown(label="Logs") | |
with gr.Column(): | |
with gr.Accordion("Caption Only API", open=False): | |
api_caponly_image_url = gr.Textbox(label="Image URL") | |
api_caponly_image_bytes = gr.Textbox(label="Image Base64 bytes") | |
api_caponly_run_button = gr.Button("Run API") | |
api_caponly_text_search_url = gr.Textbox(label="Text search URL") | |
api_caponly_logs = gr.Markdown(label="Logs") | |
# Gradio components interconnections | |
inputs = [ | |
image, | |
prompt, | |
negative_prompt, | |
rewriting_prompt, | |
moondream_prompt, | |
style, | |
num_steps, | |
guidance_scale, | |
adapter_conditioning_scale, | |
adapter_conditioning_factor, | |
seed, | |
] | |
prompt.submit( | |
fn=randomize_seed_fn, | |
inputs=[seed, randomize_seed], | |
outputs=seed, | |
queue=False, | |
api_name=False, | |
).then( | |
fn=run_full_gradio, | |
inputs=inputs, | |
outputs=result, | |
api_name=False, | |
) | |
negative_prompt.submit( | |
fn=randomize_seed_fn, | |
inputs=[seed, randomize_seed], | |
outputs=seed, | |
queue=False, | |
api_name=False, | |
).then( | |
fn=run_full_gradio, | |
inputs=inputs, | |
outputs=result, | |
api_name=False, | |
) | |
run_button.click( | |
fn=randomize_seed_fn, | |
inputs=[seed, randomize_seed], | |
outputs=seed, | |
queue=False, | |
api_name=False, | |
).then( | |
fn=run_full_gradio, | |
inputs=inputs, | |
outputs=result, | |
api_name=False, | |
) | |
# API interconnections | |
api_fullexp_run_button.click( | |
fn=run_full_api, | |
inputs=[api_fullexp_image_url, api_fullexp_image_bytes, api_fullexp_user_prompt], | |
outputs=[api_fullexp_text_search_url, api_fullexp_visual_search_url, api_fullexp_logs], | |
api_name="full_experience", | |
) | |
api_caponly_run_button.click( | |
fn=run_caponly_api, | |
inputs=[api_caponly_image_url, api_caponly_image_bytes], | |
outputs=[api_caponly_text_search_url, api_caponly_logs], | |
api_name="caption_only", | |
) | |
if __name__ == "__main__": | |
demo.queue(max_size=20).launch() |