Add 10Eros v1 as selectable model (FP8 full checkpoint, not LoRA)

app.py

@@ -30,11 +30,30 @@ SULPHUR_ASSETS = [
 LTX_ASSETS = [
     ("SulphurAI/Sulphur-2-base", "experimental/sulphur_experimental_lora_v1.safetensors", LORAS_DIR),
     ("DeepBeepMeep/LTX-2", "ltx-2.3-22b-distilled-lora-384.safetensors",     LORAS_DIR),
-    ("TenStrip/LTX2.3-10Eros", "10Eros_v1-fp8mixed_learned.safetensors",     LORAS_DIR),
     ("DeepBeepMeep/LTX-2", "ltx-2.3-22b_vae.safetensors",                    CKPTS_DIR),
     ("DeepBeepMeep/LTX-2", "ltx-2.3-22b_text_embedding_projection.safetensors", CKPTS_DIR),
     ("DeepBeepMeep/LTX-2", "ltx-2.3-22b_embeddings_connector.safetensors",   CKPTS_DIR),
 ]
+EROS_ASSETS = [
+    ("TenStrip/LTX2.3-10Eros", "10Eros_v1-fp8mixed_learned.safetensors",     CKPTS_DIR),
+]
+
+EROS_FINETUNE = {
+    "model": {
+        "name": "10Eros v1",
+        "visible": True,
+        "architecture": "ltx2_22B",
+        "parent_model_type": "ltx2_22B",
+        "description": "LTX-2.3 fine-tune by TenStrip. FP8 mixed precision.",
+        "URLs": [str(CKPTS_DIR / "10Eros_v1-fp8mixed_learned.safetensors")],
+        "preload_URLs": [],
+    },
+    "num_inference_steps": 25,
+    "video_length": 81,
+    "resolution": "832x480",
+    "guidance_scale": 3.5,
+    "alt_guidance_scale": 3.5,
+}
 
 SULPHUR_FINETUNE = {
     "model": {
@@ -90,7 +109,7 @@ def setup():
             check=True,
         )
 
-    for repo, fname, dest in SULPHUR_ASSETS + LTX_ASSETS:
+    for repo, fname, dest in SULPHUR_ASSETS + LTX_ASSETS + EROS_ASSETS:
         _download(repo, fname, dest)
 
     # Gemma text encoder — must stay in its subfolder (Wan2GP looks there by name)
@@ -111,6 +130,7 @@ def setup():
 
     FINETUNES_DIR.mkdir(parents=True, exist_ok=True)
     (FINETUNES_DIR / "sulphur_2_base.json").write_text(json.dumps(SULPHUR_FINETUNE, indent=2))
+    (FINETUNES_DIR / "eros_10_v1.json").write_text(json.dumps(EROS_FINETUNE, indent=2))
     print("[setup] Done.")
 
 
@@ -119,8 +139,10 @@ setup()
 RESOLUTIONS = ["832x480", "480x832", "640x640", "1024x576", "576x1024"]
 
 
+MODEL_MAP = {"Sulphur 2 Base": "sulphur-2", "10Eros v1": "eros-10"}
+
 @spaces.GPU(duration=120)
-def generate_video(image, prompt, resolution, steps, guidance_scale, frames, seed):
+def generate_video(image, prompt, model_choice, resolution, steps, guidance_scale, frames, seed):
     if image is None:
         raise gr.Error("Please upload an image.")
     if not prompt.strip():
@@ -134,7 +156,7 @@ def generate_video(image, prompt, resolution, steps, guidance_scale, frames, see
         "--image",  image,
         "--prompt", prompt,
         "--output", str(out_file),
-        "--model",  "sulphur-2",
+        "--model",  MODEL_MAP.get(model_choice, "sulphur-2"),
         "--seed",   str(int(seed)),
         "--resolution", resolution,
         "--steps",  str(int(steps)),
@@ -180,11 +202,12 @@ def generate_video(image, prompt, resolution, steps, guidance_scale, frames, see
 
 
 with gr.Blocks(title="Sulphur — Image to Video") as demo:
-    gr.Markdown("# Sulphur — Image to Video\nUsing New Experimental Lora v1")
+    gr.Markdown("# Sulphur — Image to Video")
     with gr.Row():
         with gr.Column(scale=1):
-            image_in  = gr.Image(type="filepath", label="Input Image")
-            prompt_in = gr.Textbox(label="Prompt", placeholder="Describe the motion…", lines=3)
+            image_in   = gr.Image(type="filepath", label="Input Image")
+            prompt_in  = gr.Textbox(label="Prompt", placeholder="Describe the motion…", lines=3)
+            model_radio = gr.Radio(list(MODEL_MAP.keys()), value="Sulphur 2 Base", label="Model")
             with gr.Accordion("Advanced", open=False):
                 resolution_dd = gr.Dropdown(RESOLUTIONS, value="832x480", label="Resolution")
                 steps_sl      = gr.Slider(1, 50, value=8,   step=1,   label="Steps")
@@ -198,7 +221,7 @@ with gr.Blocks(title="Sulphur — Image to Video") as demo:
 
     run_btn.click(
         fn=generate_video,
-        inputs=[image_in, prompt_in, resolution_dd, steps_sl, guidance_sl, frames_sl, seed_num],
+        inputs=[image_in, prompt_in, model_radio, resolution_dd, steps_sl, guidance_sl, frames_sl, seed_num],
         outputs=[video_out, log_out],
     )
 

generate.py

@@ -13,6 +13,7 @@ WAN2GP_ROOT = Path(os.environ.get("WAN2GP_ROOT", "/tmp/Wan2GP"))
 
 MODEL_SHORTHANDS = {
     "sulphur-2": "sulphur_2_base",
+    "eros-10":   "eros_10_v1",
 }
 
 DEFAULTS = {
@@ -22,6 +23,20 @@ DEFAULTS = {
         "resolution":          "832x480",
         "video_length":        81,
     },
+    "eros_10_v1": {
+        "num_inference_steps": 25,
+        "guidance_scale":      3.5,
+        "resolution":          "832x480",
+        "video_length":        81,
+    },
+}
+
+LORAS_BY_MODEL = {
+    "sulphur_2_base": (
+        ["sulphur_experimental_lora_v1.safetensors"],
+        ["0.5"],
+    ),
+    "eros_10_v1": ([], []),
 }
 
 
@@ -48,6 +63,7 @@ def main():
 
     model_type = MODEL_SHORTHANDS.get(args.model, args.model)
     defaults   = DEFAULTS.get(model_type, DEFAULTS["sulphur_2_base"])
+    loras, lora_multipliers = LORAS_BY_MODEL.get(model_type, ([], []))
 
     image_path = str(Path(args.image.strip()).resolve())
     if not Path(image_path).exists():
@@ -83,11 +99,8 @@ def main():
         "seed":                 args.seed,
         "image_prompt_type":    "S",
         "input_video_strength": 1.0,
-        "activated_loras": [
-            "sulphur_experimental_lora_v1.safetensors",
-            "10Eros_v1-fp8mixed_learned.safetensors",
-        ],
-        "loras_multipliers": ["0.5", "0.8"],
+        "activated_loras":   loras,
+        "loras_multipliers": lora_multipliers,
     }
 
     p(f"Model:      {model_type}")