Add cached gr.Examples (lazy) + generate defaults + fp8 link; remove stale tmp/ and __pycache__/

app.py

@@ -127,7 +127,16 @@ def remote_upsample(prompt, width, height):
 
 
 @spaces.GPU(duration=240, size="xlarge")
-def generate(prompt, mode, upsampler, width, height, seed, randomize_seed, progress=gr.Progress(track_tqdm=True)):
+def generate(
+    prompt,
+    mode="Default · 20 steps",
+    upsampler=UPSAMPLERS[0],
+    width=1024,
+    height=1024,
+    seed=0,
+    randomize_seed=False,
+    progress=gr.Progress(track_tqdm=True),
+):
     t_enter = time.perf_counter()
     if randomize_seed or seed < 0:
         seed = random.randint(0, MAX_SEED)
@@ -199,6 +208,7 @@ with gr.Blocks(theme=gr.themes.Citrus(), title="Ideogram 4") as demo:
         "Ideogram's first open-weights model — a 9.3B-parameter text-to-image foundation model at the "
         "forefront of design, with best-in-class text rendering.\n\n"
         "[Model](https://huggingface.co/ideogram-ai/ideogram-4-nf4) · "
+        "[Model (fp8)](https://huggingface.co/ideogram-ai/ideogram-4-fp8) · "
         "[Blog](https://ideogram.ai/blog/ideogram-4.0/)"
     )
 
@@ -224,6 +234,19 @@ with gr.Blocks(theme=gr.themes.Citrus(), title="Ideogram 4") as demo:
             out_image = gr.Image(label="Output", type="pil")
             out_caption = gr.JSON(label="Caption fed to the model (upsampled when enabled)")
 
+    gr.Examples(
+        examples=[
+            ["a ginger cat wearing a tiny wizard hat reading a spellbook"],
+            ["an isometric illustration of a tiny city floating in the clouds"],
+            ["a golden retriever on a skateboard"],
+        ],
+        inputs=[prompt],
+        outputs=[out_image, seed, out_caption],
+        fn=generate,
+        cache_examples=True,
+        cache_mode="lazy",
+    )
+
     run.click(
         generate,
         inputs=[prompt, mode, upsampler, width, height, seed, randomize],

tmp/ideogram4_space/app.py

@@ -1,98 +0,0 @@
-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()