import gradio as gr
import torch
from transformers import AutoConfig, AutoModelForCausalLM
from janus.models import MultiModalityCausalLM, VLChatProcessor
from PIL import Image
import numpy as np
import spaces
import logging
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Constants
DEFAULT_WIDTH = 384
DEFAULT_HEIGHT = 384
PARALLEL_SIZE = 5
PATCH_SIZE = 16
# Load model and processor with error handling
def load_model():
try:
model_path = "deepseek-ai/Janus-Pro-7B"
config = AutoConfig.from_pretrained(model_path)
language_config = config.language_config
language_config._attn_implementation = 'eager'
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logger.info(f"Loading model on device: {device}")
vl_gpt = AutoModelForCausalLM.from_pretrained(
model_path,
language_config=language_config,
trust_remote_code=True,
torch_dtype=torch.bfloat16 if device.type == "cuda" else torch.float32
).to(device)
vl_chat_processor = VLChatProcessor.from_pretrained(model_path)
return vl_gpt, vl_chat_processor, device
except Exception as e:
logger.error(f"Model loading failed: {str(e)}")
raise RuntimeError("Failed to load model. Please check the model path and dependencies.")
try:
vl_gpt, vl_chat_processor, device = load_model()
tokenizer = vl_chat_processor.tokenizer
except RuntimeError as e:
raise e
# Helper functions with improved memory management
def generate(input_ids, width, height, cfg_weight=5, temperature=1.0, parallel_size=5, progress=None):
try:
torch.cuda.empty_cache()
tokens = torch.zeros((parallel_size * 2, len(input_ids)), dtype=torch.int, device=device)
for i in range(parallel_size * 2):
tokens[i, :] = input_ids
if i % 2 != 0:
tokens[i, 1:-1] = vl_chat_processor.pad_id
with torch.no_grad():
inputs_embeds = vl_gpt.language_model.get_input_embeddings()(tokens)
generated_tokens = torch.zeros((parallel_size, 576), dtype=torch.int, device=device)
pkv = None
total_steps = 576
for i in range(total_steps):
if progress is not None:
progress((i + 1) / total_steps, desc="Generating image tokens")
outputs = vl_gpt.language_model.model(
inputs_embeds=inputs_embeds,
use_cache=True,
past_key_values=pkv
)
pkv = outputs.past_key_values
hidden_states = outputs.last_hidden_state
logits = vl_gpt.gen_head(hidden_states[:, -1, :])
logit_cond = logits[0::2, :]
logit_uncond = logits[1::2, :]
logits = logit_uncond + cfg_weight * (logit_cond - logit_uncond)
probs = torch.softmax(logits / temperature, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
generated_tokens[:, i] = next_token.squeeze(dim=-1)
next_token = torch.cat([next_token.unsqueeze(dim=1)] * 2, dim=1).view(-1)
img_embeds = vl_gpt.prepare_gen_img_embeds(next_token)
inputs_embeds = img_embeds.unsqueeze(dim=1)
return generated_tokens
except RuntimeError as e:
logger.error(f"Generation error: {str(e)}")
raise RuntimeError("Generation failed due to memory constraints. Try reducing the parallel size.")
finally:
torch.cuda.empty_cache()
def unpack(patches, width, height, parallel_size=5):
try:
patches = patches.detach().to(device='cpu', dtype=torch.float32).numpy()
patches = patches.transpose(0, 2, 3, 1)
patches = np.clip((patches + 1) / 2 * 255, 0, 255)
return [Image.fromarray(patch.astype(np.uint8)) for patch in patches]
except Exception as e:
logger.error(f"Unpacking error: {str(e)}")
raise RuntimeError("Failed to process generated image data.")
@torch.inference_mode()
@spaces.GPU(duration=120)
def generate_image(prompt, seed=None, guidance=5, t2i_temperature=1.0, progress=gr.Progress()):
try:
if not prompt.strip():
raise gr.Error("Please enter a valid prompt.")
if progress is not None:
progress(0, desc="Initializing...")
torch.cuda.empty_cache()
# Seed management
if seed is None:
seed = torch.seed()
else:
seed = int(seed)
torch.manual_seed(seed)
if device.type == "cuda":
torch.cuda.manual_seed(seed)
messages = [{'role': '<|User|>', 'content': prompt}, {'role': '<|Assistant|>', 'content': ''}]
text = vl_chat_processor.apply_sft_template_for_multi_turn_prompts(
conversations=messages,
sft_format=vl_chat_processor.sft_format,
system_prompt=''
) + vl_chat_processor.image_start_tag
input_ids = torch.tensor(tokenizer.encode(text), dtype=torch.long, device=device)
if progress is not None:
progress(0.1, desc="Generating image tokens...")
generated_tokens = generate(
input_ids,
DEFAULT_WIDTH,
DEFAULT_HEIGHT,
cfg_weight=guidance,
temperature=t2i_temperature,
parallel_size=PARALLEL_SIZE,
progress=progress
)
if progress is not None:
progress(0.9, desc="Processing images...")
patches = vl_gpt.gen_vision_model.decode_code(
generated_tokens.to(dtype=torch.int),
shape=[PARALLEL_SIZE, 8, DEFAULT_WIDTH // PATCH_SIZE, DEFAULT_HEIGHT // PATCH_SIZE]
)
images = unpack(patches, DEFAULT_WIDTH, DEFAULT_HEIGHT, PARALLEL_SIZE)
return images
except Exception as e:
logger.error(f"Generation failed: {str(e)}", exc_info=True)
if "index out of range" in str(e).lower():
raise gr.Error("Image generation failed due to internal error. Please try again with different parameters.")
else:
raise gr.Error(f"Image generation failed: {str(e)}")
def create_interface():
with gr.Blocks(title="Janus-Pro-7B Image Generator", theme=gr.themes.Soft()) as demo:
gr.Markdown("""
# Text-to-Image Generation with Janus-Pro-7B
**Generate high-quality images from text prompts using DeepSeek's advanced multimodal AI model.**
""")
with gr.Row():
with gr.Column(scale=3):
prompt_input = gr.Textbox(
label="Prompt",
placeholder="Describe the image you want to generate...",
lines=3
)
generate_btn = gr.Button("Generate Images", variant="primary")
with gr.Accordion("Advanced Settings", open=False):
with gr.Group():
seed_input = gr.Number(
label="Seed",
value=None,
precision=0,
info="Leave empty for random seed"
)
guidance_slider = gr.Slider(
label="CFG Guidance Weight",
minimum=3,
maximum=10,
value=5,
step=0.5,
info="Higher values = more prompt adherence, lower values = more creativity"
)
temp_slider = gr.Slider(
label="Temperature",
minimum=0.1,
maximum=1.0,
value=1.0,
step=0.1,
info="Higher values = more randomness, lower values = more deterministic"
)
with gr.Column(scale=2):
output_gallery = gr.Gallery(
label="Generated Images",
columns=2,
height=600,
preview=True
)
status = gr.Textbox(
label="Status",
interactive=False
)
gr.Examples(
examples=[
["A futuristic cityscape at sunset with flying cars and holographic advertisements"],
["An astronaut riding a horse in photorealistic style"],
["A cute robotic cat sitting on a stack of ancient books, digital art"]
],
inputs=prompt_input
)
gr.Markdown("""
## Model Information
- **Model:** [Janus-Pro-7B](https://huggingface.co/deepseek-ai/Janus-Pro-7B)
- **Output Resolution:** 384x384 pixels
- **Parallel Generation:** 5 images per request
""")
# Footer Section
gr.Markdown("""
<hr style="margin-top: 2em; margin-bottom: 1em;">
<div style="text-align: center; color: #666; font-size: 0.9em;">
Created with ❤️ by <a href="https://bilsimaging.com" target="_blank" style="color: #2563eb; text-decoration: none;">bilsimaging.com</a>
</div>
""")
# Visitor Badge
gr.HTML("""
<div style="text-align: center; margin-top: 1em;">
<a href="https://visitorbadge.io/status?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FBils%2FDeepseekJanusPro%2F">
<img src="https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FBils%2FDeepseekJanusPro%2F&countColor=%23263759"
alt="Visitor Badge"
style="display: inline-block; margin: 0 auto;">
</a>
</div>
""")
generate_btn.click(
generate_image,
inputs=[prompt_input, seed_input, guidance_slider, temp_slider],
outputs=output_gallery,
api_name="generate"
)
demo.load(
fn=lambda: f"Device Status: {'GPU ✅' if device.type == 'cuda' else 'CPU ⚠️'}",
outputs=status,
queue=False
)
return demo
if __name__ == "__main__":
demo = create_interface()
demo.launch(share=True)