File size: 6,380 Bytes
2af0f78 98102a9 2af0f78 5289176 2af0f78 5289176 2af0f78 2c5ff7b 2af0f78 0c93bf5 2af0f78 f437d5e 98102a9 2af0f78 5289176 2af0f78 98102a9 5289176 2af0f78 5289176 2af0f78 5289176 2af0f78 3d1f292 2af0f78 0c93bf5 2af0f78 3d1f292 2af0f78 3d1f292 2af0f78 3d1f292 2af0f78 f2231b1 2af0f78 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
from __future__ import annotations
import os
import uuid
import random
from typing import Annotated
import gradio as gr
from PIL import Image
from huggingface_hub import InferenceClient
from ._core import ROOT_DIR, get_hf_token, DEFAULT_PROVIDERS, handle_hf_error
from app import _log_call_end, _log_call_start, _truncate_for_log
from ._docstrings import autodoc
HF_API_TOKEN = get_hf_token()
# Single source of truth for the LLM-facing tool description
TOOL_SUMMARY = (
"Generate an image from a text prompt via Hugging Face serverless inference; "
"tunable model/steps/guidance/size, supports negative prompt and seed; returns a PIL.Image. "
"Return the generated media to the user in this format ``."
)
@autodoc(
summary=TOOL_SUMMARY,
)
def Generate_Image(
prompt: Annotated[str, "Text description of the image to generate."],
model_id: Annotated[str, "Hugging Face model id in the form 'creator/model-name' (e.g., Tongyi-MAI/Z-Image-Turbo)."] = "Tongyi-MAI/Z-Image-Turbo",
negative_prompt: Annotated[str, "What should NOT appear in the image."] = (
"(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, "
"missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, "
"mutated, ugly, disgusting, blurry, amputation, misspellings, typos"
),
steps: Annotated[int, "Number of denoising steps (1–100). Higher = slower, potentially higher quality."] = 35,
cfg_scale: Annotated[float, "Classifier-free guidance scale (1–20). Higher = follow the prompt more closely."] = 7.0,
seed: Annotated[int, "Random seed for reproducibility. Use -1 for a random seed per call."] = -1,
width: Annotated[int, "Output width in pixels (64–1216, multiple of 32 recommended)."] = 1024,
height: Annotated[int, "Output height in pixels (64–1216, multiple of 32 recommended)."] = 1024,
sampler: Annotated[str, "Sampling method label (UI only). Common options: 'DPM++ 2M Karras', 'DPM++ SDE Karras', 'Euler', 'Euler a', 'Heun', 'DDIM'."] = "DPM++ 2M Karras",
) -> str:
_log_call_start(
"Generate_Image",
prompt=_truncate_for_log(prompt, 200),
model_id=model_id,
steps=steps,
cfg_scale=cfg_scale,
seed=seed,
size=f"{width}x{height}",
)
if not prompt or not prompt.strip():
_log_call_end("Generate_Image", "error=empty prompt")
raise gr.Error("Please provide a non-empty prompt.")
enhanced_prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
last_error: Exception | None = None
for provider in DEFAULT_PROVIDERS:
try:
client = InferenceClient(api_key=HF_API_TOKEN, provider=provider)
image = client.text_to_image(
prompt=enhanced_prompt,
negative_prompt=negative_prompt,
model=model_id,
width=width,
height=height,
num_inference_steps=steps,
guidance_scale=cfg_scale,
seed=seed if seed != -1 else random.randint(1, 1_000_000_000),
)
filename = f"image_{uuid.uuid4().hex[:8]}.png"
output_path = os.path.join(ROOT_DIR, filename)
image.save(output_path)
_log_call_end("Generate_Image", f"provider={provider} size={image.size} saved_to={filename}")
return output_path
except Exception as exc:
last_error = exc
continue
msg = str(last_error) if last_error else "Unknown error"
_log_call_end("Generate_Image", f"error={_truncate_for_log(msg, 200)}")
handle_hf_error(msg, model_id, context="Image generation")
def build_interface() -> gr.Interface:
return gr.Interface(
fn=Generate_Image,
inputs=[
gr.Textbox(label="Prompt", placeholder="Enter a prompt", lines=2, info="Text description of the image to generate"),
gr.Textbox(
label="Model",
value="Tongyi-MAI/Z-Image-Turbo",
placeholder="creator/model-name",
max_lines=1,
info="<a href=\"https://huggingface.co/models?pipeline_tag=text-to-image&inference_provider=nebius,cerebras,novita,fireworks-ai,together,fal-ai,groq,featherless-ai,nscale,hyperbolic,sambanova,cohere,replicate,scaleway,publicai,hf-inference&sort=trending\" target=\"_blank\" rel=\"noopener noreferrer\">Browse models</a>",
),
gr.Textbox(
label="Negative Prompt",
value=(
"(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, "
"missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, "
"mutated, ugly, disgusting, blurry, amputation, misspellings, typos"
),
lines=2,
info="What should NOT appear in the image",
),
gr.Slider(minimum=1, maximum=100, value=35, step=1, label="Steps", info="Number of denoising steps (1–100)"),
gr.Slider(minimum=1.0, maximum=20.0, value=7.0, step=0.1, label="CFG Scale", info="Classifier-free guidance scale (1–20)"),
gr.Slider(minimum=-1, maximum=1_000_000_000, value=-1, step=1, label="Seed (-1 = random)", info="Random seed for reproducibility"),
gr.Slider(minimum=64, maximum=1216, value=1024, step=32, label="Width", info="Output width in pixels"),
gr.Slider(minimum=64, maximum=1216, value=1024, step=32, label="Height", info="Output height in pixels"),
gr.Radio(
label="Sampler",
value="DPM++ 2M Karras",
choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"],
info="Sampling method",
),
],
outputs=gr.Image(label="Generated Image"),
title="Generate Image",
description=(
"<div style=\"text-align:center\">Generate images via Hugging Face serverless inference. "
"Default model is Z-Image-Turbo.</div>"
),
api_description=TOOL_SUMMARY,
flagging_mode="never",
)
__all__ = ["Generate_Image", "build_interface"]
|