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from typing import Any
import gradio as gr
import PIL
import spaces
import torch
from hi_diffusers import HiDreamImagePipeline, HiDreamImageTransformer2DModel
from hi_diffusers.schedulers.flash_flow_match import (
FlashFlowMatchEulerDiscreteScheduler,
)
from transformers import LlamaForCausalLM, PreTrainedTokenizerFast, AutoTokenizer
# Constants
MODEL_PREFIX: str = "HiDream-ai"
LLAMA_MODEL_NAME: str = "meta-llama/Meta-Llama-3.1-8B-Instruct"
MODEL_PATH = "HiDream-ai/HiDream-I1-Dev"
MODEL_CONFIGS: dict[str, Any] = {
"guidance_scale": 0.0,
"num_inference_steps": 28,
"shift": 6.0,
"scheduler": FlashFlowMatchEulerDiscreteScheduler,
}
# Model configurations
# MODEL_CONFIGS: dict[str, dict] = {
# "full": {
# "path": f"{MODEL_PREFIX}/HiDream-I1-Full",
# "guidance_scale": 5.0,
# "num_inference_steps": 50,
# "shift": 3.0,
# "scheduler": FlowUniPCMultistepScheduler,
# },
# "fast": {
# "path": f"{MODEL_PREFIX}/HiDream-I1-Fast",
# "guidance_scale": 0.0,
# "num_inference_steps": 16,
# "shift": 3.0,
# "scheduler": FlashFlowMatchEulerDiscreteScheduler,
# },
# }
# Supported image sizes
RESOLUTION_OPTIONS: list[str] = [
"1024 x 1024 (Square)",
"768 x 1360 (Portrait)",
"1360 x 768 (Landscape)",
"880 x 1168 (Portrait)",
"1168 x 880 (Landscape)",
"1248 x 832 (Landscape)",
"832 x 1248 (Portrait)",
]
tokenizer = AutoTokenizer.from_pretrained(LLAMA_MODEL_NAME, use_fast=False)
text_encoder = LlamaForCausalLM.from_pretrained(
LLAMA_MODEL_NAME,
output_hidden_states=True,
output_attentions=True,
torch_dtype=torch.bfloat16,
).to("cuda")
transformer = HiDreamImageTransformer2DModel.from_pretrained(
MODEL_PATH,
subfolder="transformer",
torch_dtype=torch.bfloat16,
).to("cuda")
scheduler = MODEL_CONFIGS["scheduler"](
num_train_timesteps=1000,
shift=MODEL_CONFIGS["shift"],
use_dynamic_shifting=False,
)
pipe = HiDreamImagePipeline.from_pretrained(
MODEL_PATH,
scheduler=scheduler,
tokenizer_4=tokenizer,
text_encoder_4=text_encoder,
torch_dtype=torch.bfloat16,
).to("cuda", torch.bfloat16)
pipe.transformer = transformer
@spaces.GPU(duration=90)
def generate_image(
prompt: str,
resolution: str,
seed: int,
) -> tuple[PIL.Image.Image, int]:
if seed == -1:
seed = torch.randint(0, 1_000_000, (1,)).item()
height, width = tuple(map(int, resolution.replace(" ", "").split("x")))
generator = torch.Generator("cuda").manual_seed(seed)
image = pipe(
prompt=prompt,
height=height,
width=width,
guidance_scale=MODEL_CONFIGS["guidance_scale"],
num_inference_steps=MODEL_CONFIGS["num_inference_steps"],
generator=generator,
).images[0]
torch.cuda.empty_cache()
return image, seed
# Gradio UI
with gr.Blocks(title="HiDream Image Generator") as demo:
gr.Markdown("## 🌈 HiDream Image Generator")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(
label="Prompt",
placeholder="e.g. A futuristic city with floating cars at sunset",
lines=3,
)
resolution = gr.Radio(
choices=RESOLUTION_OPTIONS,
value=RESOLUTION_OPTIONS[0],
label="Resolution",
)
seed = gr.Number(label="Seed (-1 for random)", value=-1, precision=0)
generate_btn = gr.Button("Generate Image", variant="primary")
seed_used = gr.Number(label="Seed Used", interactive=False)
with gr.Column():
output_image = gr.Image(label="Generated Image", type="pil")
generate_btn.click(
fn=generate_image,
inputs=[prompt, resolution, seed],
outputs=[output_image, seed_used],
)
if __name__ == "__main__":
demo.launch()
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