Upload /tmp/ideogram4_space/app.py with huggingface_hub

tmp/ideogram4_space/app.py

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+import os
+import sys
+
+os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")
+
+# Use the bundled diffusers source (PR #2: huggingface/diffusers-new-model-addition-ideogram).
+_HERE = os.path.dirname(os.path.abspath(__file__))
+sys.path.insert(0, os.path.join(_HERE, "diffusers_src", "src"))
+
+import random
+
+import spaces
+import torch
+import gradio as gr
+from diffusers import Ideogram4Pipeline
+
+MODEL_ID = "diffusers-internal-dev/ideogram-4-nf4"
+
+pipe = Ideogram4Pipeline.from_pretrained(MODEL_ID, torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+MAX_SEED = 2**31 - 1
+
+
+@spaces.GPU(duration=180)
+def generate(
+    prompt: str,
+    width: int,
+    height: int,
+    num_inference_steps: int,
+    guidance_scale: float,
+    seed: int,
+    randomize_seed: bool,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if randomize_seed or seed < 0:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator(device="cuda").manual_seed(int(seed))
+
+    steps = int(num_inference_steps)
+    kwargs = dict(
+        prompt=prompt,
+        width=int(width),
+        height=int(height),
+        num_inference_steps=steps,
+        generator=generator,
+    )
+    if guidance_scale > 0:
+        kwargs["guidance_scale"] = float(guidance_scale)
+        kwargs["guidance_schedule"] = None
+    else:
+        # PR default is len 48 (7.0 x45 + 3.0 x3); rebuild it for any step count.
+        tail = min(3, max(0, steps - 1))
+        kwargs["guidance_schedule"] = (7.0,) * (steps - tail) + (3.0,) * tail
+
+    image = pipe(**kwargs).images[0]
+    return image, seed
+
+
+with gr.Blocks(title="Ideogram 4 (NF4) — diffusers preview") as demo:
+    gr.Markdown(
+        "## Ideogram 4 (NF4) — diffusers preview\n"
+        f"Private demo of [`{MODEL_ID}`](https://huggingface.co/{MODEL_ID}) using the "
+        "[diffusers PR #2](https://github.com/huggingface/diffusers-new-model-addition-ideogram/pull/2) "
+        "branch, running on ZeroGPU."
+    )
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="Prompt",
+                value="A photo of a cat holding a sign that says hello world",
+                lines=3,
+            )
+            run = gr.Button("Generate", variant="primary")
+            with gr.Accordion("Advanced", open=False):
+                with gr.Row():
+                    width = gr.Slider(512, 2048, value=1024, step=64, label="Width")
+                    height = gr.Slider(512, 2048, value=1024, step=64, label="Height")
+                steps = gr.Slider(8, 64, value=48, step=1, label="Inference steps")
+                guidance = gr.Slider(
+                    0.0, 15.0, value=0.0, step=0.1,
+                    label="Guidance scale (0 = recommended schedule: 7.0 → 3.0)",
+                )
+                with gr.Row():
+                    seed = gr.Number(label="Seed", value=0, precision=0)
+                    randomize = gr.Checkbox(label="Randomize seed", value=True)
+        with gr.Column():
+            out_image = gr.Image(label="Output", type="pil")
+            out_seed = gr.Number(label="Seed used", interactive=False, precision=0)
+
+    run.click(
+        generate,
+        inputs=[prompt, width, height, steps, guidance, seed, randomize],
+        outputs=[out_image, out_seed],
+    )
+
+demo.queue().launch()