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import gradio as gr | |
import requests | |
import time | |
import json | |
import base64 | |
import os | |
from io import BytesIO | |
import io | |
import html | |
import PIL | |
from PIL import Image | |
import re | |
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): | |
try: | |
images=[] | |
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) | |
image = Image.open(io.BytesIO(image_bytes)) | |
images.append(image) | |
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): | |
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 | |
}) | |
job = prodia_client.wait(result) | |
return job["imageUrl"] | |
def img2img(input_image, denoising, prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, seed): | |
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 | |
}) | |
job = prodia_client.wait(result) | |
return 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("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", | |
placeholder="Prompt", show_label=False, lines=3) | |
negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, | |
value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly") | |
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.Image(show_label=False, type="filepath", interactive=False) | |
send_to_img2img = gr.Button(value="Send to img2img") | |
text_button.click(txt2img, inputs=[prompt, negative_prompt, model, steps, sampler, cfg_scale, width, height, | |
seed], outputs=image_output, concurrency_limit=64) | |
with gr.Tab("img2img", id='i2i'): | |
with gr.Row(): | |
with gr.Column(scale=6, min_width=600): | |
i2i_prompt = gr.Textbox("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", | |
placeholder="Prompt", show_label=False, lines=3) | |
i2i_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, | |
value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly") | |
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") | |
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.Image(show_label=False, type="filepath", 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, concurrency_limit=64) | |
send_to_img2img.click(send_to_img2img_def, inputs=image_output, 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(image): | |
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="pil") | |
with gr.Column(): | |
exif_output = gr.HTML(label="EXIF Data") | |
send_to_txt2img_btn = gr.Button("Send to txt2img") | |
image_input.upload(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) | |
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("space warrior, beautiful, female, ultrarealistic, soft lighting, 8k", | |
placeholder="Prompt", show_label=False, lines=3) | |
hf_negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=False, lines=3, | |
value="3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly") | |
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("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") | |
demo.queue(max_size=80, api_open=False).launch(max_threads=256, show_api=False) | |