Update app_fast.py

app_fast.py

@@ -17,7 +17,7 @@ text_to_video_pipe = WanPipeline.from_pretrained(MODEL_ID, vae=vae, torch_dtype=
 image_to_video_pipe = WanImageToVideoPipeline.from_pretrained(MODEL_ID, vae=vae, torch_dtype=torch.bfloat16)
 
 for pipe in [text_to_video_pipe, image_to_video_pipe]:
-    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=8.0)
+    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=5.0)
     pipe.to("cuda")
 
 
@@ -42,22 +42,22 @@ for pipe in [text_to_video_pipe, image_to_video_pipe]:
 #LORA_FILENAME = "HSToric_color_Wan22_5b_LoRA.safetensors"
 
 LORA_REPO_ID = "AlekseyCalvin/Phenakistiscopes_Wan22_5B_T2V_LoRA"
-LORA_FILENAME = "PhenakistiscopesV4_000002300.safetensors"
+LORA_FILENAME = "Phenakistiscopes_MergedVers4_3_2_wan5b_wEMAsigmaRel020.safetensors"
 causvid_path = hf_hub_download(repo_id=LORA_REPO_ID, filename=LORA_FILENAME)
-pipe.load_lora_weights(causvid_path, adapter_name="causvid_lora")
-pipe.set_adapters(["causvid_lora"], adapter_weights=[0.95])
+pipe.load_lora_weights(causvid_path, adapter_name="wan_lora")
+pipe.set_adapters(["wan_lora"], adapter_weights=[1.05])
 pipe.fuse_lora()
 
 # Constants
 MOD_VALUE = 32
 DEFAULT_H_SLIDER_VALUE = 896
 DEFAULT_W_SLIDER_VALUE = 896
-NEW_FORMULA_MAX_AREA = 720 * 1024
-SLIDER_MIN_H, SLIDER_MAX_H = 256, 1024
-SLIDER_MIN_W, SLIDER_MAX_W = 256, 1024
+NEW_FORMULA_MAX_AREA = 1536 * 1536
+SLIDER_MIN_H, SLIDER_MAX_H = 256, 1536
+SLIDER_MIN_W, SLIDER_MAX_W = 256, 1536
 MAX_SEED = np.iinfo(np.int32).max
-FIXED_FPS = 24
-MIN_FRAMES_MODEL = 25
+FIXED_FPS = 16
+MIN_FRAMES_MODEL = 17
 MAX_FRAMES_MODEL = 193
 
 default_prompt_i2v = "make this image come alive, cinematic motion, smooth animation"
@@ -98,12 +98,12 @@ def get_duration(input_image, prompt, height, width,
                    guidance_scale, steps,
                    seed, randomize_seed, 
                    progress):
-    if steps > 4 and duration_seconds > 4:
-        return 90
-    elif steps > 4 or duration_seconds > 4:
-        return 75
+    if steps > 5 and duration_seconds > 5:
+        return 30
+    elif steps > 5 or duration_seconds > 5:
+        return 25
     else:
-        return 60
+        return 20
 
 @spaces.GPU(duration=get_duration)
 def generate_video(input_image, prompt, height, width, negative_prompt=default_negative_prompt, duration_seconds=2, guidance_scale=0, steps=4, seed=44, randomize_seed=False, progress=gr.Progress(track_tqdm=True)):
@@ -147,7 +147,7 @@ with gr.Blocks() as demo:
             prompt_input = gr.Textbox(label="Prompt", value=default_prompt_i2v)
             duration_seconds_input = gr.Slider(minimum=round(MIN_FRAMES_MODEL/FIXED_FPS,1), maximum=round(MAX_FRAMES_MODEL/FIXED_FPS,1), step=0.1, value=2, label="Duration (seconds)", info=f"Clamped to model's {MIN_FRAMES_MODEL}-{MAX_FRAMES_MODEL} frames at {FIXED_FPS}fps.")
 
-            with gr.Accordion("Advanced Settings", open=False):
+            with gr.Accordion("Advanced Settings", open=True):
                 negative_prompt_input = gr.Textbox(label="Negative Prompt", value=default_negative_prompt, lines=3)
                 seed_input = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42, interactive=True)
                 randomize_seed_checkbox = gr.Checkbox(label="Randomize seed", value=True, interactive=True)