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"""
@spaces.GPU()
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, lora_id=None, lora_scale=0.95, progress=gr.Progress(track_tqdm=True)):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
# for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
# prompt=prompt,
# guidance_scale=guidance_scale,
# num_inference_steps=num_inference_steps,
# width=width,
# height=height,
# generator=generator,
# output_type="pil",
# good_vae=good_vae,
# ):
# yield img, seed
# Handle LoRA loading
# Load LoRA weights and prepare joint_attention_kwargs
if lora_id:
pipe.unload_lora_weights()
pipe.load_lora_weights(lora_id)
joint_attention_kwargs = {"scale": lora_scale}
else:
joint_attention_kwargs = None
try:
# Call the custom pipeline function with the correct keyword argument
for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
prompt=prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
output_type="pil",
good_vae=good_vae, # Assuming good_vae is defined elsewhere
joint_attention_kwargs=joint_attention_kwargs, # Fixed parameter name
):
yield img, seed
finally:
# Unload LoRA weights if they were loaded
if lora_id:
pipe.unload_lora_weights()
# def query(lora_id, prompt, steps=28, cfg_scale=3.5, randomize_seed=True, seed=-1, width=1024, height=1024):
# if prompt == "" or prompt == None:
# return None
# if lora_id.strip() == "" or lora_id == None:
# lora_id = "black-forest-labs/FLUX.1-dev"
# key = random.randint(0, 999)
# API_URL = "https://api-inference.huggingface.co/models/"+ lora_id.strip()
# API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
# headers = {"Authorization": f"Bearer {API_TOKEN}"}
# # prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
# # print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')
# prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
# # print(f'\033[1mGeneration {key}:\033[0m {prompt}')
# # If seed is -1, generate a random seed and use it
# if randomize_seed:
# seed = random.randint(1, 4294967296)
# payload = {
# "inputs": prompt,
# "steps": steps,
# "cfg_scale": cfg_scale,
# "seed": seed,
# "parameters": {
# "width": width, # Pass the width to the API
# "height": height # Pass the height to the API
# }
# }
# response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
# if response.status_code != 200:
# print(f"Error: Failed to get image. Response status: {response.status_code}")
# print(f"Response content: {response.text}")
# if response.status_code == 503:
# raise gr.Error(f"{response.status_code} : The model is being loaded")
# raise gr.Error(f"{response.status_code}")
# try:
# image_bytes = response.content
# image = Image.open(io.BytesIO(image_bytes))
# print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
# return image, seed, seed
# except Exception as e:
# print(f"Error when trying to open the image: {e}")
# return None
examples = [
"a tiny astronaut hatching from an egg on the moon",
"a cat holding a sign that says hello world",
"an anime illustration of a wiener schnitzel",
]
css = """
#col-container {
margin: 0 auto;
max-width: 960px;
}
.generate-btn {
background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%) !important;
border: none !important;
color: white !important;
}
.generate-btn:hover {
transform: translateY(-2px);
box-shadow: 0 5px 15px rgba(0,0,0,0.2);
}
"""
with gr.Blocks(css=css) as app:
gr.HTML("