basic app

app.py

@@ -1,154 +1,104 @@
+import torch
+import spaces
+from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL
+from transformers import AutoFeatureExtractor
+from demo_use_stylecode import process_single_image
+
+from ip_adapter.ip_adapter_instruct import IPAdapterInstruct
+from huggingface_hub import hf_hub_download
 import gradio as gr
-import numpy as np
-import random
+import cv2
 
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
+base_model_path = "SG161222/Realistic_Vision_V4.0_noVAE"
+vae_model_path = "stabilityai/sd-vae-ft-mse"
+image_encoder_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
+ip_ckpt = hf_hub_download(repo_id="CiaraRowles/stylecodes", filename="stylecodes_sd15_v1.bin", repo_type="model")
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
+safety_model_id = "CompVis/stable-diffusion-safety-checker"
+safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
+safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
+device = "cuda"
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
+noise_scheduler = DDIMScheduler(
+    num_train_timesteps=1000,
+    beta_start=0.00085,
+    beta_end=0.012,
+    beta_schedule="scaled_linear",
+    clip_sample=False,
+    set_alpha_to_one=False,
+    steps_offset=1,
+)
+vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch.float16)
+pipe = StableDiffusionPipelineCFG.from_pretrained(
+    base_model_path,
+    scheduler=noise_scheduler,
+    vae=vae,
+    torch_dtype=torch.float16,
+    feature_extractor=safety_feature_extractor,
+    safety_checker=safety_checker
+).to(device)
 
-        result = gr.Image(label="Result", show_label=False)
 
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
+cv2.setNumThreads(1)
 
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
+@spaces.GPU(enable_queue=True)
+def generate_image(images, prompt, negative_prompt,instruct_query, scale, nfaa_negative_prompt, progress=gr.Progress(track_tqdm=True)):
+
+    
+    
+
+    image = images
+    yield None
+    
+    base_size = 512
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+    # Calculate new width and height
+   
+
+    image = process_single_image(image_path="",prompt=prompt,num_inference_steps=20,image=image)
+    
+    yield image
+
+
+
+def swap_to_gallery(images):
+    return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False)
+
+def remove_back_to_files():
+    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
+css = '''
+h1{margin-bottom: 0 !important}
+'''
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("# IP-Adapter-Instruct demo")
+    gr.Markdown("Demo for the [CiaraRowles/IP-Adapter-Instruct model](https://huggingface.co/CiaraRowles/IP-Adapter-Instruct)")
+    with gr.Row():
+        with gr.Column():
+            files = gr.Image(
+                label="Input image",
+                type="pil"
+            )
+            uploaded_files = gr.Gallery(label="Your image", visible=False, columns=5, rows=1, height=125)
+            with gr.Column(visible=False) as clear_button:
+                remove_and_reupload = gr.ClearButton(value="Remove and upload new ones", components=files, size="sm")
+            prompt = gr.Textbox(label="Prompt",
+                       info="Try something like 'a photo of a man/woman/person'",
+                       placeholder="A photo of a [man/woman/person]...")
+            )
+            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="low quality")
+            submit = gr.Button("Submit")
+            with gr.Accordion(open=False, label="Advanced Options"):
+                nfaa_negative_prompts = gr.Textbox(label="Appended Negative Prompts", info="Negative prompts to steer generations towards safe for all audiences outputs", value="naked, bikini, skimpy, scanty, bare skin, lingerie, swimsuit, exposed, see-through")    
+                scale = gr.Slider(label="Scale", value=0.8, step=0.1, minimum=0, maximum=2)
+        with gr.Column():
+            gallery = gr.Gallery(label="Generated Images")
+        
+        submit.click(fn=generate_image,
+                    inputs=[files, prompt, negative_prompt,instruct_query, scale, nfaa_negative_prompts],
+                    outputs=gallery)
+    
+    gr.Markdown("This demo includes extra features to mitigate the implicit bias of the model and prevent explicit usage of it to generate content with faces of people, including third parties, that is not safe for all audiences, including naked or semi-naked people.")
+    gr.Markdown("based on: https://huggingface.co/spaces/multimodalart/Ip-Adapter-FaceID")
+
+demo.launch()