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| """ | |
| Owner spec, 2026-09-13 ("ابتكار نظام مجاني لتوليد الوسائط صور صوت فديو | |
| بطريقة احترافية بالامكانيات المتوفرة"): the SECOND free ZeroGPU Space | |
| this account is entitled to. Hugging Face's own documentation is | |
| explicit that a free account may run TWO ZeroGPU Gradio Spaces (PRO | |
| gets 10, organisations 50) — one was already spent on video | |
| (ai-system/huggingface-video/app.py) and the other was simply sitting | |
| unused, which is a real free GPU going to waste. | |
| Why this is a genuine quality jump and not a second copy of what we | |
| already have: Cloudflare Workers AI serves flux-1-schnell, a model | |
| DISTILLED to produce an image in about four diffusion steps. That | |
| distillation is precisely what the owner complained about with a real | |
| generated image — cartoon-like rendering and a deformed hand. Running | |
| the same model ourselves would change nothing. So this Space runs a | |
| different, undistilled model at a real step count on a real H200. | |
| Model choice is a licensing decision as much as a quality one, because | |
| Nova is sold as a product: | |
| - FLUX.1-dev produces excellent images and is NON-COMMERCIAL. Unusable | |
| here, no matter how good it looks. | |
| - FLUX.1-schnell is Apache-2.0 and fine to sell — but it IS the | |
| distilled model Cloudflare already gives us for free, so it buys | |
| nothing. | |
| - Stable Diffusion 3.5 Medium (used here) is covered by Stability's | |
| Community License: free for commercial use while annual revenue is | |
| under $1M, which this project is comfortably within. It is | |
| materially better than SDXL at anatomy — hands specifically — which | |
| is the actual complaint being answered. | |
| ONE-TIME MANUAL STEP, and this Space will fail loudly without it: the | |
| SD 3.5 repo is GATED on Hugging Face. Visit | |
| huggingface.co/stabilityai/stable-diffusion-3.5-medium while logged in | |
| as the account owning HF_TOKEN and accept the licence once. Until that | |
| happens the pipeline load below raises, the Space returns nothing, and | |
| ai-system/app/hf_image.py falls back to Cloudflare exactly as it does | |
| when the daily GPU quota runs out — degraded, never broken. | |
| Honest and unchanged from the video Space's own docstring: whether | |
| server-to-server Gradio API calls from Render reliably receive real | |
| ZeroGPU time is still NOT confirmed by live testing. Hugging Face's | |
| docs do state that authenticated calls draw on the account's own quota | |
| while unauthenticated ones land in a stricter shared pool, and | |
| hf_image.py passes the token accordingly — so the design follows the | |
| documented path, but the real-world result still needs one live test. | |
| """ | |
| import os | |
| import tempfile | |
| import gradio as gr | |
| import spaces | |
| import torch | |
| from diffusers import StableDiffusion3Pipeline | |
| _MODEL_ID = "stabilityai/stable-diffusion-3.5-medium" | |
| # Directly answering the owner's real complaint ("وجه مشوه", a deformed | |
| # hand): an undistilled model actually responds to a negative prompt, | |
| # which flux-1-schnell does not support at all. Kept to genuine defects | |
| # only — deliberately NOT "cartoon", since a cartoon is sometimes | |
| # exactly what was asked for. | |
| _NEGATIVE_PROMPT = ( | |
| "deformed hands, extra fingers, missing fingers, fused fingers, malformed limbs, " | |
| "disfigured face, asymmetric eyes, low quality, blurry, jpeg artifacts, watermark, text" | |
| ) | |
| # 28 steps is Stability's own documented default for this model, and | |
| # roughly seven times what the distilled model we are replacing can use. | |
| # ZeroGPU caps a single call at about 120 seconds, so raise this only | |
| # after reading this Space's own real timing logs — not by guessing. | |
| _STEPS = 28 | |
| _GUIDANCE = 4.5 | |
| _SIZE = 1024 | |
| # Gated model access is authenticated with the Space runtime secret. Set HF_TOKEN | |
| # in the Space's Settings -> Variables and secrets (never in repository files). | |
| _HF_TOKEN = os.environ.get("HF_TOKEN", "").strip() | |
| pipe = StableDiffusion3Pipeline.from_pretrained( | |
| _MODEL_ID, | |
| torch_dtype=torch.bfloat16, | |
| token=_HF_TOKEN or None, | |
| ) | |
| def generate(prompt: str) -> str: | |
| """The only GPU-touching function here — ZeroGPU grants real CUDA | |
| access solely for this call's duration, which is why .to("cuda") | |
| lives inside it rather than at module import time (same structure as | |
| the video Space, for the same reason).""" | |
| pipe.to("cuda") | |
| image = pipe( | |
| prompt=prompt, | |
| negative_prompt=_NEGATIVE_PROMPT, | |
| num_inference_steps=_STEPS, | |
| guidance_scale=_GUIDANCE, | |
| width=_SIZE, | |
| height=_SIZE, | |
| ).images[0] | |
| out_path = os.path.join(tempfile.mkdtemp(), "output.png") | |
| image.save(out_path) | |
| return out_path | |
| # api_name defaults to the function name ("generate") for gr.Interface — | |
| # matches hf_image.py's client.predict(..., api_name="/generate"). | |
| demo = gr.Interface( | |
| fn=generate, | |
| inputs=[gr.Textbox(label="prompt")], | |
| outputs=gr.Image(label="image", type="filepath"), | |
| title="Nova AI — Real Image (SD 3.5 Medium, ZeroGPU)", | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |