Update app.py

app.py

@@ -16,7 +16,7 @@ import threading
 import asyncio
 from queue import Queue as BlockingQueue
 from functools import partial
-from multiprocessing import Process, , Queue
+from multiprocessing import Process, Queue
 
 # external
 
@@ -27,7 +27,7 @@ from lxml.html import fromstring
 from diffusers.utils import export_to_gif, load_image
 from huggingface_hub import hf_hub_download
 from safetensors.torch import load_file, save_file
-from diffusers import DiffusionPipeline, AnimateDiffPipeline, MotionAdapter, EulerAncestralDiscreteScheduler, DDIMScheduler, StableDiffusionXLPipeline, UNet2DConditionModel, AutoencoderKL, UNet3DConditionModel
+from diffusers import FluxPipeline, DiffusionPipeline, AnimateDiffPipeline, MotionAdapter, EulerAncestralDiscreteScheduler, DDIMScheduler, StableDiffusionXLPipeline, UNet2DConditionModel, AutoencoderKL, UNet3DConditionModel
 
 # logging
 
@@ -64,12 +64,13 @@ result = []
 
 # precision data
 
-fast=True
+seq=512
+fast=False
 fps=10
 time=1
 width=896
 height=896
-step=25
+step=50
 accu=7.5
 
 # ui data
@@ -115,7 +116,7 @@ function custom(){
 }
 """
 
-# torch pipe
+# torch pipes
 
 pipe = AnimateDiffPipeline.from_pretrained(base, motion_adapter=adapter).to(device)
 pipe.scheduler = DDIMScheduler(
@@ -130,6 +131,8 @@ pipe.scheduler = DDIMScheduler(
 pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter-plus_sd15.bin")
 pipe.enable_free_init(method="butterworth", use_fast_sampling=fast)
 
+pipe_flux = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device,dtype=dtype)
+
 # Parallelism
 
 def parallel(*pairs):
@@ -215,7 +218,18 @@ def handle_generate(*inp):
             last_motion = motion
     
         pipe.to(device,dtype=dtype)
-    
+
+        if not img:
+            img = pipe(
+                prompt=p1,
+                height=height,
+                width=width,
+                guidance_scale=accu,
+                num_inference_steps=step,
+                max_sequence_length=seq,
+                generator=torch.Generator("cuda").manual_seed(0)
+            ).images[0]
+
         calc_out.append(
             pipe(
                 prompt=p1,