Artiprocher commited on
Commit
812f04c
1 Parent(s): 2927c6c

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +7 -14
app.py CHANGED
@@ -6,17 +6,6 @@ login(token=os.getenv('HF_AK'))
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  from diffsynth import download_models
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  download_models(["Kolors", "FLUX.1-dev"], downloading_priority=["HuggingFace", "ModelScope"])
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- def get_file_list(path):
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- file_list = []
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- for filename in os.listdir(path):
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- file_path = os.path.join(path, filename)
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- if os.path.isdir(file_path):
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- file_list.extend(get_file_list(file_path))
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- else:
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- file_list.append(file_path)
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- return file_list
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- print([i for i in get_file_list("models") if "cache" not in i])
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-
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  import gradio as gr
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  from diffsynth import ModelManager, SDImagePipeline, SDXLImagePipeline, SD3ImagePipeline, HunyuanDiTImagePipeline, FluxImagePipeline
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  import os, torch
@@ -141,6 +130,12 @@ def load_model(model_type, model_path):
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  return model_manager, pipe
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  model_dict = {}
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  with gr.Blocks() as app:
@@ -222,7 +217,6 @@ with gr.Blocks() as app:
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  outputs=[output_image],
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  triggers=run_button.click
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  )
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- @spaces.GPU(duration=60)
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  def generate_image(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, seed, *args, progress=gr.Progress()):
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  _, pipe = load_model(model_type, model_path)
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  input_params = {
@@ -255,8 +249,7 @@ with gr.Blocks() as app:
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  "masks": masks,
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  "mask_scales": mask_scales,
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  })
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- torch.manual_seed(seed)
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- image = pipe(**input_params)
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  return image
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  @gr.on(inputs=[output_image] + canvas_list, outputs=canvas_list, triggers=output_to_painter_button.click)
 
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  from diffsynth import download_models
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  download_models(["Kolors", "FLUX.1-dev"], downloading_priority=["HuggingFace", "ModelScope"])
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  import gradio as gr
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  from diffsynth import ModelManager, SDImagePipeline, SDXLImagePipeline, SD3ImagePipeline, HunyuanDiTImagePipeline, FluxImagePipeline
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  import os, torch
 
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  return model_manager, pipe
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+ @spaces.GPU(duration=60)
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+ def infer(pipe, input_params, seed):
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+ torch.manual_seed(seed)
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+ return pipe(**input_params)
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+
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+
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  model_dict = {}
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  with gr.Blocks() as app:
 
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  outputs=[output_image],
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  triggers=run_button.click
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  )
 
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  def generate_image(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, seed, *args, progress=gr.Progress()):
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  _, pipe = load_model(model_type, model_path)
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  input_params = {
 
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  "masks": masks,
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  "mask_scales": mask_scales,
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  })
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+ image = infer(pipe, input_params, seed)
 
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  return image
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  @gr.on(inputs=[output_image] + canvas_list, outputs=canvas_list, triggers=output_to_painter_button.click)