Upload app.py

app.py

@@ -4,8 +4,7 @@ import json
 import logging
 import torch
 from PIL import Image
-from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
-from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
+from diffusers import DiffusionPipeline
 from diffusers import FluxControlNetPipeline, FluxControlNetModel, FluxMultiControlNetModel
 from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
 import copy
@@ -22,29 +21,16 @@ from flux import (search_civitai_lora, select_civitai_lora, search_civitai_lora_
 from tagger.tagger import predict_tags_wd, compose_prompt_to_copy
 from tagger.fl2flux import predict_tags_fl2_flux
 
-# Load LoRAs from JSON file
-with open('loras.json', 'r') as f:
-    loras = json.load(f)
-
-dtype = torch.bfloat16
-#dtype = torch.float8_e4m3fn
-device = "cuda" if torch.cuda.is_available() else "cpu"
 # Initialize the base model
 base_model = models[0]
 controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union'
 #controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union-alpha'
-taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
-good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
-pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
+pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
 controlnet_union = None
 controlnet = None
 last_model = models[0]
 last_cn_on = False
 
-MAX_SEED = 2**32-1
-
-pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
-
 # https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union
 # https://huggingface.co/spaces/jiuface/FLUX.1-dev-Controlnet-Union
 def change_base_model(repo_id: str, cn_on: bool):
@@ -53,6 +39,8 @@ def change_base_model(repo_id: str, cn_on: bool):
     global controlnet
     global last_model
     global last_cn_on
+    dtype = torch.bfloat16
+    #dtype = torch.float8_e4m3fn
     try:
         if (repo_id == last_model and cn_on is last_cn_on) or not is_repo_name(repo_id) or not is_repo_exists(repo_id): return gr.update(visible=True)
         if cn_on:
@@ -62,7 +50,6 @@ def change_base_model(repo_id: str, cn_on: bool):
             controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype)
             controlnet = FluxMultiControlNetModel([controlnet_union])
             pipe = FluxControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet, torch_dtype=dtype)
-            #pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
             last_model = repo_id
             last_cn_on = cn_on
             #progress(1, desc=f"Model loaded: {repo_id} / ControlNet Loaded: {controlnet_model_union_repo}")
@@ -71,8 +58,7 @@ def change_base_model(repo_id: str, cn_on: bool):
             #progress(0, desc=f"Loading model: {repo_id}")
             print(f"Loading model: {repo_id}")
             clear_cache()
-            pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=dtype, vae=taef1)
-            pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
+            pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=dtype)
             last_model = repo_id
             last_cn_on = cn_on
             #progress(1, desc=f"Model loaded: {repo_id}")
@@ -84,6 +70,12 @@ def change_base_model(repo_id: str, cn_on: bool):
 
 change_base_model.zerogpu = True
 
+# Load LoRAs from JSON file
+with open('loras.json', 'r') as f:
+    loras = json.load(f)
+
+MAX_SEED = 2**32-1
+
 class calculateDuration:
     def __init__(self, activity_name=""):
         self.activity_name = activity_name
@@ -123,13 +115,9 @@ def update_selection(evt: gr.SelectData, width, height):
 @spaces.GPU(duration=70)
 def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress=gr.Progress(track_tqdm=True)):
     global pipe
-    global taef1
-    global good_vae
     global controlnet
     global controlnet_union
     try:
-        good_vae.to("cuda")
-        taef1.to("cuda")
         pipe.to("cuda")
         generator = torch.Generator(device="cuda").manual_seed(seed)
         
@@ -138,7 +126,7 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scal
             modes, images, scales = get_control_params()
             if not cn_on or len(modes) == 0:
                 progress(0, desc="Start Inference.")
-                for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
+                image = pipe(
                     prompt=prompt_mash,
                     num_inference_steps=steps,
                     guidance_scale=cfg_scale,
@@ -146,15 +134,12 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scal
                     height=height,
                     generator=generator,
                     joint_attention_kwargs={"scale": lora_scale},
-                    output_type="pil",
-                    good_vae=good_vae,
-                ):
-                    yield img
+                ).images[0]
             else:
                 progress(0, desc="Start Inference with ControlNet.")
                 if controlnet is not None: controlnet.to("cuda")
                 if controlnet_union is not None: controlnet_union.to("cuda")
-                for img in pipe(
+                image = pipe(
                     prompt=prompt_mash,
                     control_image=images,
                     control_mode=modes,
@@ -165,19 +150,15 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scal
                     controlnet_conditioning_scale=scales,
                     generator=generator,
                     joint_attention_kwargs={"scale": lora_scale},
-                ).images:
-                    yield img
+                ).images[0]
     except Exception as e:
         print(e)
         raise gr.Error(f"Inference Error: {e}")
+    return image
 
 def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height,
               lora_scale, lora_json, cn_on, progress=gr.Progress(track_tqdm=True)):
     global pipe
-    global taef1
-    global good_vae
-    global controlnet
-    global controlnet_union
     if selected_index is None and not is_valid_lora(lora_json):
         gr.Info("LoRA isn't selected.")
     #    raise gr.Error("You must select a LoRA before proceeding.")
@@ -216,23 +197,17 @@ def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, wid
             seed = random.randint(0, MAX_SEED)
     
     progress(0, desc="Running Inference.")
-    image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress)
-    # Consume the generator to get the final image
-    final_image = None
-    for image in image_generator:
-        final_image = image
-        yield image, seed  # Yield intermediate images and seed
+
+    image = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, cn_on, progress)
     if is_valid_lora(lora_json):
         pipe.unfuse_lora()
         pipe.unload_lora_weights()
     if selected_index is not None: pipe.unload_lora_weights()
     pipe.to("cpu")
-    good_vae.to("cpu")
-    taef1.to("cpu")
     if controlnet is not None: controlnet.to("cpu")
     if controlnet_union is not None: controlnet_union.to("cpu")
     clear_cache()
-    return final_image, seed  # Return the final image and seed
+    return image, seed  
 
 def get_huggingface_safetensors(link):
   split_link = link.split("/")