Upload folder using huggingface_hub

app.py

@@ -108,6 +108,11 @@ NEG_EMB = _fetch("neg_emb.pt", Path("./neg_emb.pt"))
 # restoring at 2MP rather than 3.7MP gives up some of its headroom on very
 # large outputs. It still upscales ~4.9x from a 360x240 input.
 WORK_AREA = int(os.environ.get("SEEDVR2_WORK_AREA", 1920 * 1080))
+DEFAULT_SEED = int(os.environ.get("SEEDVR2_SEED", 666))
+# Upstream runs under bf16 autocast; fp16 is an override for artifact probes.
+AUTOCAST_DTYPE = {"bf16": torch.bfloat16, "fp16": torch.float16}[
+    os.environ.get("SEEDVR2_AUTOCAST", "bf16")
+]
 
 # Single-process "distributed" context. The model code routes every device
 # placement through common.distributed, which reads these.
@@ -160,7 +165,7 @@ def _transform():
     )
 
 
-def _duration(image, steps=1, progress=None) -> int:
+def _duration(image, steps=1, seed=0, progress=None) -> int:
     """Every request restores at the same working resolution (WORK_AREA), so cost
     tracks the step count and little else. Measured on a cold worker, then
     given headroom; kept as small as honesty allows, because the request is
@@ -195,7 +200,7 @@ def save_result(image):
 
 @spaces.GPU(duration=_duration)
 @torch.no_grad()
-def upscale_image(image, steps=1, progress=gr.Progress(track_tqdm=True)):
+def upscale_image(image, steps=1, seed=0, progress=gr.Progress(track_tqdm=True)):
     if image is None:
         raise gr.Error("Upload an image first.")
 
@@ -218,8 +223,10 @@ def upscale_image(image, steps=1, progress=gr.Progress(track_tqdm=True)):
     runner.config.diffusion.timesteps.sampling.steps = int(steps)
     runner.configure_diffusion()
     # Fixed seed: a restorer should be deterministic for a given input, and the
-    # knob was noise in a tool whose job is 'make this photo better'.
-    set_seed(666, same_across_ranks=True)
+    # knob was noise in a tool whose job is 'make this photo better'. The API
+    # takes an override (hidden in the UI) so artifacts can be checked across
+    # seeds without a redeploy.
+    set_seed(int(seed) if seed else DEFAULT_SEED, same_across_ranks=True)
 
     img = Image.open(image).convert("RGB") if isinstance(image, str) else image.convert("RGB")
     tensor = T.ToTensor()(img).unsqueeze(0)  # (t=1, c, h, w)
@@ -246,7 +253,7 @@ def upscale_image(image, steps=1, progress=gr.Progress(track_tqdm=True)):
         for n, a, latent in zip(noise, aug_noise, latents)
     ]
 
-    with torch.autocast("cuda", torch.bfloat16, enabled=True):
+    with torch.autocast("cuda", AUTOCAST_DTYPE, enabled=True):
         videos = runner.inference(
             noises=noise, conditions=conditions, dit_offload=False, **text_embeds
         )
@@ -290,6 +297,8 @@ with gr.Blocks(css=UPSAMPLER_CSS, theme=UPSAMPLER_THEME) as demo:
         with gr.Column():
             image_in = gr.Image(label="Image", type="filepath")
             steps = gr.Slider(1, 4, value=1, step=1, label="Steps")
+            # API-only: 0 keeps the fixed default seed.
+            seed = gr.Number(value=0, precision=0, visible=False)
             run = gr.Button("Upscale Image", variant="primary")
         with gr.Column():
             image_out = gr.Image(label="Result", type="filepath")
@@ -298,7 +307,7 @@ with gr.Blocks(css=UPSAMPLER_CSS, theme=UPSAMPLER_THEME) as demo:
     # publish the endpoint as "//upscale_image".
     run.click(
         upscale_image,
-        inputs=[image_in, steps],
+        inputs=[image_in, steps, seed],
         outputs=[image_out],
         api_name="upscale_image",
     )

upsampler_theme.py

@@ -1,54 +0,0 @@
-"""Shared Upsampler look-and-feel for every Upsampler/* HF Space.
-
-`create_and_push.py` uploads this file alongside each Space's app.py, so every
-Space imports the exact same theme, CSS, header, and footer. Keep this the
-single source of truth (v3 recipe): Soft indigo/purple theme, gradient primary
-button, Gradio's own footer hidden, 1000px max width, minimal header/footer.
-"""
-
-import gradio as gr
-
-UPSAMPLER_THEME = gr.themes.Soft(
-    primary_hue=gr.themes.colors.indigo,
-    secondary_hue=gr.themes.colors.purple,
-    neutral_hue=gr.themes.colors.slate,
-    font=[gr.themes.GoogleFont("Inter"), "system-ui", "sans-serif"],
-).set(
-    button_primary_background_fill="linear-gradient(90deg, #6366f1 0%, #a855f7 100%)",
-    button_primary_background_fill_hover="linear-gradient(90deg, #4f46e5 0%, #9333ea 100%)",
-    button_primary_text_color="#ffffff",
-    button_primary_border_color="*primary_500",
-)
-
-# Hide Gradio's built-in footer and keep the app narrow and centered.
-UPSAMPLER_CSS = """
-footer { display: none !important; }
-.gradio-container { max-width: 1000px !important; margin: 0 auto !important; }
-#usp-header h1 { font-size: 1.7rem; font-weight: 700; margin: 0 0 .25rem; }
-#usp-header p { opacity: .6; margin: 0; }
-#usp-footer { opacity: .5; font-size: .85rem; margin-top: 1.25rem; }
-#usp-footer a { text-decoration: none; }
-"""
-
-
-def header_html(title: str, subtitle: str) -> str:
-    return f"""<div id="usp-header">
-  <h1>{title}</h1>
-  <p>{subtitle}</p>
-</div>"""
-
-
-_LINK_STYLE = "color:#8b7cf6;font-weight:600;text-decoration:none"
-
-
-def footer_html(description: str, tool_url: str, tool_anchor: str) -> str:
-    """SEO footer (v4 recipe): a short paragraph describing what the model
-    does (unique per Space, keyword-bearing) plus the Upsampler attribution
-    with a deep link to the matching /free-* tool on upsampler.com. No model
-    credit/license line (the README frontmatter carries the license). Spaces
-    target model-name queries; the site pages keep the intent queries
-    ("free X no signup"), so the two never compete."""
-    return f"""<div id="usp-footer">
-  <p style="margin:0 0 10px">{description}</p>
-  <p style="margin:0">Maintained by <a href="https://upsampler.com" target="_blank" rel="noopener" style="{_LINK_STYLE}">Upsampler</a>. Check out the <a href="{tool_url}" target="_blank" rel="noopener" style="{_LINK_STYLE}">{tool_anchor}</a>, no sign-up required.</p>
-</div>"""