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import gradio as gr
import requests
import time
import random
import json
import base64
import os
from transformers import pipeline, set_seed
from io import BytesIO
import io
import html
import PIL
from PIL import Image
import re
def send_prompt(prompt):
return prompt
def query(payload, model):
HF_TOKEN = os.getenv("HF_TOKEN")
headers = {"Authorization": f"Bearer {HF_TOKEN}"}
url = "https://api-inference.huggingface.co/models/"
API_URL = f"{url}{model}"
response = requests.post(API_URL, headers=headers, json=payload)
return response.content
def hf_inference(prompt, negative, model, steps, sampler, guidance, width, height, seed, progress=gr.Progress()):
try:
progress(0, desc="Starting")
images=[]
time.sleep(2.5)
progress(0.05)
progress(0.25, desc="Generating")
image_bytes = query(payload={
"inputs": f"{prompt}",
"parameters": {
"negative_prompt": f"{negative}",
"num_inference_steps": steps,
"guidance_scale": guidance,
"width": width, "height": height,
"seed": seed,
},
}, model=model)
progress(0.75, desc="Opening image")
time.sleep(1)
image = Image.open(io.BytesIO(image_bytes))
images.append(image)
progress(0.99, desc="Sending image")
time.sleep(0.5)
return images
except PIL.UnidentifiedImageError:
gr.Warning("This model is not loaded now. Try others models.")
class Prodia:
def __init__(self, api_key, base=None):
self.base = base or "https://api.prodia.com/v1"
self.headers = {
"X-Prodia-Key": api_key
}
def generate(self, params):
response = self._post(f"{self.base}/sd/generate", params)
return response.json()
def transform(self, params):
response = self._post(f"{self.base}/sd/transform", params)
return response.json()
def controlnet(self, params):
response = self._post(f"{self.base}/sd/controlnet", params)
return response.json()
def get_job(self, job_id):
response = self._get(f"{self.base}/job/{job_id}")
return response.json()
def wait(self, job):
job_result = job
while job_result['status'] not in ['succeeded', 'failed']:
time.sleep(0.25)
job_result = self.get_job(job['job'])
return job_result
def list_models(self):
response = self._get(f"{self.base}/sd/models")
return response.json()
def list_samplers(self):
response = self._get(f"{self.base}/sd/samplers")
return response.json()
def _post(self, url, params):
headers = {
**self.headers,
"Content-Type": "application/json"
}
response = requests.post(url, headers=headers, data=json.dumps(params))
if response.status_code != 200:
raise Exception(f"Bad Prodia Response: {response.status_code}")
return response
def _get(self, url):
response = requests.get(url, headers=self.headers)
if response.status_code != 200:
raise Exception(f"Bad Prodia Response: {response.status_code}")
return response
def image_to_base64(image):
# Convert the image to bytes
buffered = BytesIO()
image.save(buffered, format="PNG") # You can change format to PNG if needed
# Encode the bytes to base64
img_str = base64.b64encode(buffered.getvalue())
return img_str.decode('utf-8') # Convert bytes to string
def remove_id_and_ext(text):
text = re.sub(r'\[.*\]$', '', text)
extension = text[-12:].strip()
if extension == "safetensors":
text = text[:-13]
elif extension == "ckpt":
text = text[:-4]
return text
def get_data(text):
results = {}
patterns = {
'prompt': r'(.*)',
'negative_prompt': r'Negative prompt: (.*)',
'steps': r'Steps: (\d+),',
'seed': r'Seed: (\d+),',
'sampler': r'Sampler:\s*([^\s,]+(?:\s+[^\s,]+)*)',
'model': r'Model:\s*([^\s,]+)',
'cfg_scale': r'CFG scale:\s*([\d\.]+)',
'size': r'Size:\s*([0-9]+x[0-9]+)'
}
for key in ['prompt', 'negative_prompt', 'steps', 'seed', 'sampler', 'model', 'cfg_scale', 'size']:
match = re.search(patterns[key], text)
if match:
results[key] = match.group(1)
else:
results[key] = None
if results['size'] is not None:
w, h = results['size'].split("x")
results['w'] = w
results['h'] = h
else:
results['w'] = None
results['h'] = None
return results
def send_to_img2img_def(images):
return images
def send_to_txt2img(image):
result = {tabs: gr.update(selected="t2i")}
try:
text = image.info['parameters']
data = get_data(text)
result[prompt] = gr.update(value=data['prompt'])
result[negative_prompt] = gr.update(value=data['negative_prompt']) if data[
'negative_prompt'] is not None else gr.update()
result[steps] = gr.update(value=int(data['steps'])) if data['steps'] is not None else gr.update()
result[seed] = gr.update(value=int(data['seed'])) if data['seed'] is not None else gr.update()
result[cfg_scale] = gr.update(value=float(data['cfg_scale'])) if data['cfg_scale'] is not None else gr.update()
result[width] = gr.update(value=int(data['w'])) if data['w'] is not None else gr.update()
result[height] = gr.update(value=int(data['h'])) if data['h'] is not None else gr.update()
result[sampler] = gr.update(value=data['sampler']) if data['sampler'] is not None else gr.update()
if model in model_names:
result[model] = gr.update(value=model_names[model])
else:
result[model] = gr.update()
return result
except Exception as e:
print(e)
return result
prodia_client = Prodia(api_key=os.getenv("PRODIA_API_KEY"))
model_list = prodia_client.list_models()
model_names = {}
for model_name in model_list:
name_without_ext = remove_id_and_ext(model_name)
model_names[name_without_ext] = model_name
def txt2img(prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, progress=gr.Progress()):
progress(0, desc="Starting")
time.sleep(2.5)
progress(0.25, desc="Generating")
result = prodia_client.generate({
"prompt": prompt,
"negative_prompt": negative_prompt,
"model": model,
"steps": steps,
"sampler": sampler,
"cfg_scale": cfg_scale,
"width": width,
"height": height,
"seed": seed
})
progress(0.75, desc="Opening image")
job = prodia_client.wait(result)
progress(0.99, desc="Sending image")
return [job["imageUrl"]], job["imageUrl"]
def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed, progress=gr.Progress()):
progress(0, desc="Starting")
time.sleep(1.5)
progress(0.10, desc="Uploading input image")
time.sleep(1.5)
progress(0.25, desc="Generating")
result = prodia_client.transform({
"imageData": image_to_base64(input_image),
"denoising_strength": denoising,
"prompt": prompt,
"negative_prompt": negative_prompt,
"model": model,
"steps": steps,
"sampler": sampler,
"cfg_scale": cfg_scale,
"width": width,
"height": height,
"seed": seed
})
progress(0.75, desc="Opening image")
job = prodia_client.wait(result)
progress(0.99, desc="Sending image")
time.sleep(0.5)
return [job["imageUrl"]], job["imageUrl"]
css = """
#generate {
height: 100%;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Row():
with gr.Column(scale=6):
model = gr.Dropdown(interactive=True, value="absolutereality_v181.safetensors [3d9d4d2b]", show_label=True,
label="Stable Diffusion Checkpoint", choices=prodia_client.list_models())
with gr.Tabs() as tabs:
with gr.Tab("txt2img", id='t2i'):
with gr.Row():
with gr.Column(scale=6, min_width=600):
prompt = gr.Textbox(placeholder="Prompt", show_label=False, lines=3)
negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3,
value="(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")
with gr.Column():
text_button = gr.Button("Generate", variant='primary', elem_id="generate")
with gr.Row():
with gr.Column(scale=3):
with gr.Tab("Generation"):
with gr.Row():
with gr.Column(scale=1):
sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method",
choices=prodia_client.list_samplers())
with gr.Column(scale=1):
steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1)
with gr.Row():
with gr.Column(scale=1):
width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
height = gr.Slider(label="Height", maximum=1024, value=512, step=8)
with gr.Column(scale=1):
batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=8, step=1)
seed = gr.Number(label="Seed", value=-1)
with gr.Column(scale=2):
image_output = gr.Gallery(show_label=False, rows=2)
send_to_img2img = gr.Button(value="Send OUTPUT IMAGE to img2img")
send_to_png = gr.Button(value="Send OUTPUT IMAGE to PNG Info")
past_url = gr.Textbox(visible=False, interactive=False)
text_button.click(txt2img, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height,
seed], outputs=[image_output, past_url], concurrency_limit=64)
with gr.Tab("img2img", id='i2i'):
with gr.Row():
with gr.Column(scale=6, min_width=600):
i2i_prompt = gr.Textbox(placeholder="Prompt", show_label=False, lines=3)
i2i_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3,
value="(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")
with gr.Column():
i2i_text_button = gr.Button("Generate", variant='primary', elem_id="generate")
with gr.Row():
with gr.Column(scale=3):
with gr.Tab("Generation"):
i2i_image_input = gr.Image(type="pil", interactive=True)
with gr.Row():
with gr.Column(scale=1):
i2i_sampler = gr.Dropdown(value="Euler a", show_label=True, label="Sampling Method",
choices=prodia_client.list_samplers())
with gr.Column(scale=1):
i2i_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1)
with gr.Row():
with gr.Column(scale=1):
i2i_width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
i2i_height = gr.Slider(label="Height", maximum=1024, value=512, step=8)
with gr.Column(scale=1):
i2i_batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
i2i_batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
i2i_cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7, step=1)
i2i_denoising = gr.Slider(label="Denoising Strength", minimum=0, maximum=1, value=0.7, step=0.1)
i2i_seed = gr.Number(label="Seed", value=-1)
with gr.Column(scale=2):
i2i_image_output = gr.Gallery(show_label=False, rows=2)
send_to_png_i2i = gr.Button(value="Send INPUT IMAGE to PNG Info")
i2i_past_url = gr.Textbox(visible=False, interactive=False)
i2i_text_button.click(img2img, inputs=[i2i_image_input, i2i_denoising, i2i_prompt, i2i_negative_prompt,
model, i2i_steps, i2i_sampler, i2i_cfg_scale, i2i_width, i2i_height,
i2i_seed], outputs=[i2i_image_output, i2i_past_url], concurrency_limit=64)
send_to_img2img.click(send_to_img2img_def, inputs=past_url, outputs=i2i_image_input)
with gr.Tab("PNG Info"):
def plaintext_to_html(text, classname=None):
content = "<br>\n".join(html.escape(x) for x in text.split('\n'))
return f"<p class='{classname}'>{content}</p>" if classname else f"<p>{content}</p>"
def get_exif_data(path):
image = Image.open(path)
items = image.info
info = ''
for key, text in items.items():
info += f"""
<div>
<p><b>{plaintext_to_html(str(key))}</b></p>
<p>{plaintext_to_html(str(text))}</p>
</div>
""".strip() + "\n"
if len(info) == 0:
message = "Nothing found in the image."
info = f"<div><p>{message}<p></div>"
return info
with gr.Row():
with gr.Column():
image_input = gr.Image(type="filepath", interactive=True)
png_button = gr.Button("Get Info", variant="primary")
with gr.Row():
with gr.Column():
exif_output = gr.HTML(label="EXIF Data")
send_to_txt2img_btn = gr.Button("Send PARAMETRS to txt2img")
send_to_img2img_png = gr.Button("Send IMAGE to img2img")
image_input.upload(get_exif_data, inputs=[image_input], outputs=exif_output)
png_button.click(get_exif_data, inputs=[image_input], outputs=exif_output)
send_to_txt2img_btn.click(send_to_txt2img, inputs=[image_input], outputs=[tabs, prompt, negative_prompt,
steps, seed, model, sampler,
width, height, cfg_scale],
concurrency_limit=64)
send_to_png.click(send_to_img2img_def, inputs=past_url, outputs=image_input)
send_to_img2img_png.click(send_to_img2img_def, inputs=past_url, outputs=i2i_image_input)
send_to_png_i2i.click(send_to_img2img_def, inputs=i2i_past_url, outputs=image_input)
with gr.Tab("HuggingFace Inference"):
with gr.Row():
gr.Markdown("Add your model from HF.co, enter model ID.")
hf_model = gr.Dropdown(label="HuggingFace checkpoint", choices=["runwayml/stable-diffusion-v1-5", "stabilityai/stable-diffusion-2-1", "dataautogpt3/OpenDalleV1.1", "CompVis/stable-diffusion-v1-4", "playgroundai/playground-v2-1024px-aesthetic", "prompthero/openjourney", "openskyml/dreamdrop-v1", "SG161222/Realistic_Vision_V1.4", "digiplay/AbsoluteReality_v1.8.1", "openskyml/dalle-3-xl", "Lykon/dreamshaper-7", "Pclanglais/Mickey-1928"], value="runwayml/stable-diffusion-v1-5", allow_custom_value=True, interactive=True)
with gr.Row():
with gr.Column(scale=6, min_width=600):
hf_prompt = gr.Textbox(placeholder="Prompt", show_label=False, lines=3)
hf_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3,
value="(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")
with gr.Column():
hf_text_button = gr.Button("Generate with HF", variant='primary', elem_id="generate")
with gr.Row():
with gr.Column(scale=3):
with gr.Tab("Generation"):
with gr.Row():
with gr.Column(scale=1):
hf_steps = gr.Slider(label="Sampling Steps", minimum=1, maximum=25, value=20, step=1)
with gr.Row():
with gr.Column(scale=1):
hf_width = gr.Slider(label="Width", maximum=1024, value=512, step=8)
hf_height = gr.Slider(label="Height", maximum=1024, value=512, step=8)
with gr.Column(scale=1):
hf_batch_size = gr.Slider(label="Batch Size", maximum=1, value=1)
hf_batch_count = gr.Slider(label="Batch Count", maximum=1, value=1)
hf_cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=8, step=1)
hf_seed = gr.Number(label="Seed", value=-1)
with gr.Column(scale=2):
hf_image_output = gr.Gallery(show_label=False, preview=True, columns=4, allow_preview=True)
#hf_send_to_img2img = gr.Button(value="Send to img2img")
hf_text_button.click(hf_inference, inputs=[hf_prompt, hf_negative_prompt, hf_model, hf_steps, sampler, hf_cfg_scale, hf_width, hf_height,
hf_seed], outputs=hf_image_output, concurrency_limit=64)
with gr.Tab("Prompt Generator"):
gpt2_pipe = pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2')
with open("ideas.txt", "r") as f:
line = f.readlines()
def prompt_gen(starting_text):
seed = random.randint(100, 1000000)
set_seed(seed)
if starting_text == "":
starting_text: str = line[random.randrange(0, len(line))].replace("\n", "").lower().capitalize()
starting_text: str = re.sub(r"[,:\-–.!;?_]", '', starting_text)
response = gpt2_pipe(starting_text, max_length=(len(starting_text) + random.randint(60, 90)), num_return_sequences=1)
response_list = []
for x in response:
resp = x['generated_text'].strip()
if resp != starting_text and len(resp) > (len(starting_text) + 4) and resp.endswith((":", "-", "—")) is False:
response_list.append(resp+'\n')
response_end = "\n".join(response_list)
response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
response_end = response_end.replace("<", "").replace(">", "")
if response_end != "":
return response_end
idea = gr.Textbox(show_label=False, placeholder="Enter your idea", max_lines=3, lines=1, interactive=True)
prompt_button = gr.Button(value="Generate Prompt", variant="primary")
prompt_full = gr.Textbox(label="Full Prompt", max_lines=8, lines=3, interactive=False, show_copy_button=True)
send_to_txt2img_prompt = gr.Button(value="Send FULL PROMPT to txt2img")
send_to_img2img_prompt = gr.Button(value="Send FULL PROMPT to img2img")
send_to_txt2img_prompt.click(send_prompt, inputs=prompt_full, outputs=prompt)
send_to_img2img_prompt.click(send_prompt, inputs=prompt_full, outputs=i2i_prompt)
idea.submit(prompt_gen, inputs=idea, outputs=prompt_full)
prompt_button.click(prompt_gen, inputs=idea, outputs=prompt_full)
with gr.Tab("BLIP"):
with gr.Tab("Base"):
gr.load("models/Salesforce/blip-image-captioning-base", title="BLIP-base")
with gr.Tab("Large"):
gr.load("models/Salesforce/blip-image-captioning-large", title="BLIP-large")
with gr.Tab("Classification"):
gr.load("models/google/vit-base-patch16-224", title="ViT Classification")
#with gr.Tab("Segmentation"):
# gr.load("models/mattmdjaga/segformer_b2_clothes", title="SegFormer Segmentation")
with gr.Tab("Visual Question Answering"):
gr.load("models/dandelin/vilt-b32-finetuned-vqa", title="ViLT VQA")
demo.queue(max_size=80, api_open=False).launch(max_threads=256, show_api=False)
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