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import os
import io
import requests
from PIL import Image
import gradio as gr
# ------------------------------------------------------------------------------
# Make sure you export your API token before running this script:
# export CHUTES_API_TOKEN="your_real_token_here"
# ------------------------------------------------------------------------------
API_TOKEN = os.getenv("CHUTES_API_TOKEN")
if API_TOKEN is None:
raise ValueError("Please set the environment variable CHUTES_API_TOKEN before running.")
def generate_image(prompt: str,
resolution: str,
guidance_scale: float,
num_inference_steps: int,
shift: int) -> Image.Image:
"""
Calls the HiDream (Chutes) API to generate an image from the given prompt
and parameters. Assumes the API responds with raw image bytes (e.g. PNG or JPEG)
rather than JSON. Returns a PIL.Image on success. Raises a RuntimeError on failure.
"""
headers = {
"Authorization": f"Bearer {API_TOKEN}",
"Content-Type": "application/json"
}
payload = {
"seed": None,
"shift": shift,
"prompt": prompt,
"resolution": resolution,
"guidance_scale": guidance_scale,
"num_inference_steps": num_inference_steps
}
response = requests.post(
"https://chutes-hidream.chutes.ai/generate",
headers=headers,
json=payload
)
# If the status code isn’t 200, raise immediately
if response.status_code != 200:
# Try to show any text body if present
raw_text = response.text.strip()
raise RuntimeError(f"API returned HTTP {response.status_code}:\n{raw_text}")
# At this point we assume response.content is raw image bytes.
try:
img = Image.open(io.BytesIO(response.content)).convert("RGB")
return img
except Exception as e:
# If decoding as an image fails, include a hex preview of the first few bytes
prefix = response.content[:16].hex()
raise RuntimeError(
f"Failed to decode image from response content (first 16 bytes: {prefix}...):\n{str(e)}"
)
# Build the Gradio interface
with gr.Blocks(title="HiDream Unlimited") as demo:
gr.Markdown(
"## HiDream Unlimited\n\n"
"Generate unlimited AI-driven images powered by HiDream.\n\n"
"Enter your prompt and tweak the parameters, then click **Generate**."
)
with gr.Row():
with gr.Column(scale=1):
prompt_in = gr.Textbox(
label="Prompt",
placeholder="e.g. a serene sunset over a coral reef, digital painting",
lines=2
)
resolution_in = gr.Dropdown(
choices=["512x512", "768x768", "1024x1024"],
value="1024x1024",
label="Resolution"
)
guidance_in = gr.Slider(
minimum=0.0, maximum=20.0, step=0.5,
value=5.0, label="Guidance Scale"
)
steps_in = gr.Slider(
minimum=1, maximum=100, step=1,
value=50, label="Num Inference Steps"
)
shift_in = gr.Slider(
minimum=0, maximum=10, step=1,
value=3, label="Shift"
)
generate_btn = gr.Button("Generate Image", variant="primary")
with gr.Column(scale=1):
output_img = gr.Image(label="Generated Image", interactive=False)
generate_btn.click(
fn=generate_image,
inputs=[prompt_in, resolution_in, guidance_in, steps_in, shift_in],
outputs=output_img
)
if __name__ == "__main__":
demo.launch() |