Update app.py

app.py

@@ -1,154 +1,35 @@
-from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
+import tensorflow as tf
+import transformers
 
-model_id = 'Souleater_Diffusion/souleater-diffusion.ckpt'
-prefix = ''
-     
-scheduler = DPMSolverMultistepScheduler(
-    beta_start=0.00085,
-    beta_end=0.012,
-    beta_schedule="scaled_linear",
-    num_train_timesteps=1000,
-    trained_betas=None,
-    predict_epsilon=True,
-    thresholding=True,
-    algorithm_type="dpmsolver++",
-    solver_type="midpoint",
-    lower_order_final=True,
-)
+# Load the model
+model = transformers.TFGPT2LMHeadModel.from_pretrained("souleater-diffusion.ckpt")
 
-pipe = StableDiffusionPipeline.from_pretrained(
-  model_id,
-  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-  scheduler=scheduler)
+# Generate pictures using the model
+def generate_picture(prompt):
+    input_ids = transformers.preprocessing.text.text_to_sequence(prompt, model.tokenizer.tokenize)
+    input_ids = tf.expand_dims(input_ids, 0)
+    output = model.generate(input_ids)
+    generated_text = model.tokenizer.decode(output[0], skip_special_tokens=True)
+    return generated_text
 
-pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
-  model_id,
-  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-  scheduler=scheduler)
+# GUI to enter the prompts
+from tkinter import *
 
-if torch.cuda.is_available():
-  pipe = pipe.to("cuda")
-  pipe_i2i = pipe_i2i.to("cuda")
+root = Tk()
+root.title("souleater-diffusion.ckpt Model")
 
-def error_str(error, title="Error"):
-    return f"""#### {title}
-            {error}"""  if error else ""
+prompt_entry = Entry(root)
+prompt_entry.pack()
 
-def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=True):
+def generate_callback():
+    prompt = prompt_entry.get()
+    result = generate_picture(prompt)
+    result_label.config(text=result)
 
-  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
-  prompt = f"{prefix} {prompt}" if auto_prefix else prompt
+generate_button = Button(root, text="Generate", command=generate_callback)
+generate_button.pack()
 
-  try:
-    if img is not None:
-      return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
-    else:
-      return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
-  except Exception as e:
-    return None, error_str(e)
+result_label = Label(root, text="")
+result_label.pack()
 
-def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
-
-    result = pipe(
-      prompt,
-      negative_prompt = neg_prompt,
-      num_inference_steps = int(steps),
-      guidance_scale = guidance,
-      width = width,
-      height = height,
-      generator = generator)
-    
-    return replace_nsfw_images(result)
-
-def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
-
-    ratio = min(height / img.height, width / img.width)
-    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
-    result = pipe_i2i(
-        prompt,
-        negative_prompt = neg_prompt,
-        init_image = img,
-        num_inference_steps = int(steps),
-        strength = strength,
-        guidance_scale = guidance,
-        width = width,
-        height = height,
-        generator = generator)
-        
-    return replace_nsfw_images(result)
-
-def replace_nsfw_images(results):
-
-    for i in range(len(results.images)):
-      if results.nsfw_content_detected[i]:
-        results.images[i] = Image.open("nsfw.png")
-    return results.images[0]
-
-css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
-"""
-with gr.Blocks(css=css) as demo:
-    gr.HTML(
-        f"""
-            <div class="main-div">
-              <div>
-                <h1>Stable Diffusion 2</h1>
-              </div>
-              <p>
-               Demo for <a href="https://huggingface.co/stabilityai/stable-diffusion-2">Stable Diffusion 2</a> Stable Diffusion model.<br>
-        Add the following tokens to your prompts for the model to work properly: <b></b>.
-              </p>
-              Running on <b>{"GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"}</b>
-            </div>
-        """
-    )
-    with gr.Row():
-        
-        with gr.Column(scale=55):
-          with gr.Group():
-              with gr.Row():
-                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
-                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
-              image_out = gr.Image(height=512)
-          error_output = gr.Markdown()
-
-        with gr.Column(scale=45):
-          with gr.Tab("Options"):
-            with gr.Group():
-              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
-              auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically ()", value=True)
-
-              with gr.Row():
-                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
-                steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
-
-              with gr.Row():
-                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
-                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
-              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
-          with gr.Tab("Image to image"):
-              with gr.Group():
-                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
-                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
-    auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
-
-    inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
-    outputs = [image_out, error_output]
-    prompt.submit(inference, inputs=inputs, outputs=outputs)
-    generate.click(inference, inputs=inputs, outputs=outputs)
-
-    gr.HTML("""
-    <div style="border-top: 1px solid #303030;">
-      <br>
-      <p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p>
-    </div>
-    """)
-
-demo.queue(concurrency_count=1)
-demo.launch()
+root.mainloop()