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Configuration error
Configuration error
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# gradio_blip3o_next_min.py
import time
from dataclasses import dataclass
import torch
from PIL import Image
from transformers import AutoTokenizer
from blip3o.model import *
import gradio as gr
from huggingface_hub import snapshot_download
# -----------------------------
# Minimal config and runner
# -----------------------------
@dataclass
class T2IConfig:
device: str = "cuda:0"
dtype: torch.dtype = torch.bfloat16
# fixed generation config (no UI controls)
scale: int = 0
seq_len: int = 729
top_p: float = 0.95
top_k: int = 1200
class TextToImageInference:
def __init__(self, config: T2IConfig):
self.config = config
self.device = torch.device(config.device)
self._load_models()
def _load_models(self):
model_path = snapshot_download(repo_id='BLIP3o/BLIP3o-NEXT-GRPO-Geneval-3B')
self.model = blip3oQwenForInferenceLM.from_pretrained(
model_path, torch_dtype=self.config.dtype
).to(self.device)
self.tokenizer = AutoTokenizer.from_pretrained(model_path)
if hasattr(self.tokenizer, "padding_side"):
self.tokenizer.padding_side = "left"
@torch.inference_mode()
def generate_image(self, prompt: str) -> Image.Image:
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": f"Please generate image based on the following caption: {prompt}",
},
]
input_text = self.tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
input_text += f"<im_start><S{self.config.scale}>"
inputs = self.tokenizer(
[input_text], return_tensors="pt", padding=True, truncation=True
)
_, images = self.model.generate_images(
inputs.input_ids.to(self.device),
inputs.attention_mask.to(self.device),
max_new_tokens=self.config.seq_len,
do_sample=True,
top_p=self.config.top_p,
top_k=self.config.top_k,
)
return images[0]
# Try loading once at startup for simplicity
LOAD_ERROR = None
inference = None
try:
inference = TextToImageInference(T2IConfig())
except Exception as e:
LOAD_ERROR = f"❌ Failed to load model: {e}"
def run_generate(prompt, progress=gr.Progress(track_tqdm=True)):
t0 = time.time()
if LOAD_ERROR:
return None, LOAD_ERROR
if not prompt or not prompt.strip():
return None, "⚠️ Please enter a prompt."
try:
img = inference.generate_image(prompt.strip())
return img, f"✅ Done in {time.time() - t0:.2f}s."
except torch.cuda.OutOfMemoryError:
if torch.cuda.is_available():
torch.cuda.empty_cache()
return None, "❌ CUDA OOM. Try reducing other GPU workloads."
except Exception as e:
return None, f"❌ Error: {e}"
with gr.Blocks(title="BLIP3o-NEXT-GRPO-Geneval — Text ➜ Image") as demo:
gr.Markdown("# BLIP3o-NEXT-GRPO-Geneval — Text ➜ Image")
with gr.Row():
with gr.Column(scale=3):
prompt = gr.Textbox(
label="Prompt",
placeholder="Describe the image you want to generate...",
lines=4,
)
run_btn = gr.Button("Generate", variant="primary")
with gr.Column(scale=4):
out_img = gr.Image(label="Generated Image", format="png")
status = gr.Markdown("")
run_btn.click(
fn=run_generate,
inputs=[prompt],
outputs=[out_img, status],
queue=True,
api_name="generate",
)
if __name__ == "__main__":
demo.queue().launch(share=True)
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