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app.py

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+#!/usr/bin/env python
+#patch 0.01
+import os
+import random
+import uuid
+import gradio as gr
+import numpy as np
+from PIL import Image
+import spaces
+import torch
+from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
+
+huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+
+#DESCRIPTIONx = """## STABLE INSTRUCT 📦
+
+#"""
+
+examples = [
+    ["assets/4.png", "Change the color of the jacket to white."],
+    ["assets/1.png", "Change the picture to black and white."],
+    ["assets/2.png", "Add the chocolate topping to the ice cream."],
+    ["assets/3.png", "Make the burger look spicy."],
+]
+
+model_id = "timbrooks/instruct-pix2pix"
+pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None)
+pipe.to("cuda")
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+
+DESCRIPTION = """
+"""
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>⚠️Running on CPU, This may not work on CPU.</p>"
+
+MAX_SEED = np.iinfo(np.int32).max
+CACHE_EXAMPLES = False
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+def save_image(img):
+    unique_name = str(uuid.uuid4()) + ".png"
+    img.save(unique_name)
+    return unique_name
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+@spaces.GPU
+def img2img_generate(
+    prompt: str,
+    init_image: gr.Image,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 0,
+    guidance_scale: float = 7,
+    randomize_seed: bool = False,
+    num_inference_steps=30,
+    strength: float = 0.8,
+    NUM_IMAGES_PER_PROMPT=1,
+    use_resolution_binning: bool = True,
+    progress=gr.Progress(track_tqdm=True),
+):
+    pipe.to(device)
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+    generator = torch.Generator().manual_seed(seed)
+    
+    if not use_negative_prompt:
+        negative_prompt = None  # type: ignore
+    
+    init_image = init_image.resize((768, 768))
+
+    
+    output = pipe(
+        prompt=prompt,
+        image=init_image,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        strength=strength,
+        num_images_per_prompt=NUM_IMAGES_PER_PROMPT,
+        output_type="pil",
+    ).images
+
+    return output
+
+css = '''
+.gradio-container{max-width: 800px !important}
+h1{text-align:center}
+'''
+with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
+  #  gr.Markdown(DESCRIPTIONx)
+    with gr.Group():
+        with gr.Row(equal_height=True):
+            with gr.Column(scale=1):
+                img2img_prompt = gr.Text(
+                    label="Instruct",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your instruction",
+                    container=False,
+                )
+                init_image = gr.Image(label="Image", type="pil")
+                with gr.Row():
+                    img2img_run_button = gr.Button("Generate", variant="primary")
+            with gr.Column(scale=1):
+                img2img_output = gr.Gallery(label="Result", elem_id="gallery")
+        with gr.Accordion("Advanced options", open=False, visible=False):
+            with gr.Row():
+                img2img_use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
+                img2img_negative_prompt = gr.Text(
+                    label="Negative prompt",
+                    max_lines=1,
+                    value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
+                    visible=True,
+                )
+            img2img_seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+            img2img_steps = gr.Slider(
+                label="Steps",
+                minimum=0,
+                maximum=60,
+                step=1,
+                value=25,
+            )
+            img2img_number_image = gr.Slider(
+                label="No.of.Images",
+                minimum=1,
+                maximum=4,
+                step=1,
+                value=1,
+            )
+            img2img_randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            with gr.Row():
+                img2img_guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=0.1,
+                    maximum=10,
+                    step=0.1,
+                    value=5.0,
+                )
+                strength = gr.Slider(label="Confidence", minimum=0.0, maximum=1.0, step=0.01, value=0.8)
+
+    gr.Examples(
+        examples=examples,
+        inputs=[init_image, img2img_prompt],
+        outputs=img2img_output,
+        fn=img2img_generate,
+        cache_examples=CACHE_EXAMPLES,
+    )
+
+    img2img_use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=img2img_use_negative_prompt,
+        outputs=img2img_negative_prompt,
+        api_name=False,
+    )
+
+    gr.on(
+        triggers=[
+            img2img_prompt.submit,
+            img2img_negative_prompt.submit,
+            img2img_run_button.click,
+        ],
+        fn=img2img_generate,
+        inputs=[
+            img2img_prompt,
+            init_image,
+            img2img_negative_prompt,
+            img2img_use_negative_prompt,
+            img2img_seed,
+            img2img_guidance_scale,
+            img2img_randomize_seed,
+            img2img_steps,
+            strength,
+            img2img_number_image,
+        ],
+        outputs=[img2img_output],
+        api_name="image-to-image",
+    )
+
+    #gr.Markdown("⚠️ users are accountable for the content they generate and are responsible for ensuring it meets appropriate ethical standards")
+
+if __name__ == "__main__":
+    demo.queue().launch(show_api=False, debug=False)

requirements.txt

@@ -0,0 +1,8 @@
+torchvision==0.18.1
+diffusers==0.28.2
+transformers==4.41.2
+sentencepiece
+peft
+accelerate
+spaces
+pillow

std.txt

@@ -0,0 +1,379 @@
+#!/usr/bin/env python
+#patch 0.01
+import os
+import random
+import uuid
+import gradio as gr
+import numpy as np
+from PIL import Image
+import spaces
+import torch
+from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
+
+huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+
+examples = [
+    ["assets/1.png", "Change the picture to black and white."],
+    ["assets/2.png", "Add the chocolate topping to the ice cream."],
+    ["assets/3.png", "Make the burger look spicy."],
+    ["assets/4.png", "Change the color of the jacket to white."],
+]
+
+model_id = "timbrooks/instruct-pix2pix"
+pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None)
+pipe.to("cuda")
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+
+DESCRIPTION = """
+"""
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>⚠️Running on CPU, This may not work on CPU.</p>"
+
+MAX_SEED = np.iinfo(np.int32).max
+CACHE_EXAMPLES = False
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+def save_image(img):
+    unique_name = str(uuid.uuid4()) + ".png"
+    img.save(unique_name)
+    return unique_name
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+@spaces.GPU
+def img2img_generate(
+    prompt: str,
+    init_image: gr.Image,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 0,
+    guidance_scale: float = 7,
+    randomize_seed: bool = False,
+    num_inference_steps=30,
+    strength: float = 0.8,
+    NUM_IMAGES_PER_PROMPT=1,
+    use_resolution_binning: bool = True,
+    progress=gr.Progress(track_tqdm=True),
+):
+    pipe.to(device)
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+    generator = torch.Generator().manual_seed(seed)
+    
+    if not use_negative_prompt:
+        negative_prompt = None  # type: ignore
+    
+    init_image = init_image.resize((768, 768))
+
+    
+    output = pipe(
+        prompt=prompt,
+        image=init_image,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        strength=strength,
+        num_images_per_prompt=NUM_IMAGES_PER_PROMPT,
+        output_type="pil",
+    ).images
+
+    return output
+
+css = '''
+.gradio-container{max-width: 800px !important}
+h1{text-align:center}
+'''
+with gr.Blocks(css=css, theme="xiaobaiyuan/theme_brief") as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Group():
+        with gr.Row(equal_height=True):
+            with gr.Column(scale=1):
+                img2img_prompt = gr.Text(
+                    label="Instruct",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                    container=False,
+                )
+                init_image = gr.Image(label="Image", type="pil")
+                with gr.Row():
+                    img2img_run_button = gr.Button("Generate", variant="primary")
+            with gr.Column(scale=1):
+                img2img_output = gr.Gallery(label="Result", elem_id="gallery")
+        with gr.Accordion("Advanced options", open=False, visible=False):
+            with gr.Row():
+                img2img_use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
+                img2img_negative_prompt = gr.Text(
+                    label="Negative prompt",
+                    max_lines=1,
+                    value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
+                    visible=True,
+                )
+            img2img_seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+            img2img_steps = gr.Slider(
+                label="Steps",
+                minimum=0,
+                maximum=60,
+                step=1,
+                value=25,
+            )
+            img2img_number_image = gr.Slider(
+                label="No.of.Images",
+                minimum=1,
+                maximum=4,
+                step=1,
+                value=1,
+            )
+            img2img_randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            with gr.Row():
+                img2img_guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=0.1,
+                    maximum=10,
+                    step=0.1,
+                    value=5.0,
+                )
+                strength = gr.Slider(label="Confidence", minimum=0.0, maximum=1.0, step=0.01, value=0.8)
+
+    gr.Examples(
+        examples=examples,
+        inputs=[init_image, img2img_prompt],
+        outputs=img2img_output,
+        fn=img2img_generate,
+        cache_examples=CACHE_EXAMPLES,
+    )
+
+    img2img_use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=img2img_use_negative_prompt,
+        outputs=img2img_negative_prompt,
+        api_name=False,
+    )
+
+    gr.on(
+        triggers=[
+            img2img_prompt.submit,
+            img2img_negative_prompt.submit,
+            img2img_run_button.click,
+        ],
+        fn=img2img_generate,
+        inputs=[
+            img2img_prompt,
+            init_image,
+            img2img_negative_prompt,
+            img2img_use_negative_prompt,
+            img2img_seed,
+            img2img_guidance_scale,
+            img2img_randomize_seed,
+            img2img_steps,
+            strength,
+            img2img_number_image,
+        ],
+        outputs=[img2img_output],
+        api_name="img-to-img",
+    )
+
+if __name__ == "__main__":
+    demo.queue().launch(show_api=False, debug=False#!/usr/bin/env python
+#patch 0.01
+import os
+import random
+import uuid
+import gradio as gr
+import numpy as np
+from PIL import Image
+import spaces
+import torch
+from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
+
+huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+
+examples = [
+    ["assets/1.png", "Change the picture to black and white."],
+    ["assets/2.png", "Add the chocolate topping to the ice cream."],
+    ["assets/3.png", "Make the burger look spicy."],
+    ["assets/4.png", "Change the color of the jacket to white."],
+]
+
+model_id = "timbrooks/instruct-pix2pix"
+pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None)
+pipe.to("cuda")
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+
+DESCRIPTION = """
+"""
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>⚠️Running on CPU, This may not work on CPU.</p>"
+
+MAX_SEED = np.iinfo(np.int32).max
+CACHE_EXAMPLES = False
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+def save_image(img):
+    unique_name = str(uuid.uuid4()) + ".png"
+    img.save(unique_name)
+    return unique_name
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+@spaces.GPU
+def img2img_generate(
+    prompt: str,
+    init_image: gr.Image,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 0,
+    guidance_scale: float = 7,
+    randomize_seed: bool = False,
+    num_inference_steps=30,
+    strength: float = 0.8,
+    NUM_IMAGES_PER_PROMPT=1,
+    use_resolution_binning: bool = True,
+    progress=gr.Progress(track_tqdm=True),
+):
+    pipe.to(device)
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+    generator = torch.Generator().manual_seed(seed)
+    
+    if not use_negative_prompt:
+        negative_prompt = None  # type: ignore
+    
+    init_image = init_image.resize((768, 768))
+
+    
+    output = pipe(
+        prompt=prompt,
+        image=init_image,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        strength=strength,
+        num_images_per_prompt=NUM_IMAGES_PER_PROMPT,
+        output_type="pil",
+    ).images
+
+    return output
+
+css = '''
+.gradio-container{max-width: 800px !important}
+h1{text-align:center}
+'''
+with gr.Blocks(css=css, theme="xiaobaiyuan/theme_brief") as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Group():
+        with gr.Row(equal_height=True):
+            with gr.Column(scale=1):
+                img2img_prompt = gr.Text(
+                    label="Instruct",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                    container=False,
+                )
+                init_image = gr.Image(label="Image", type="pil")
+                with gr.Row():
+                    img2img_run_button = gr.Button("Generate", variant="primary")
+            with gr.Column(scale=1):
+                img2img_output = gr.Gallery(label="Result", elem_id="gallery")
+        with gr.Accordion("Advanced options", open=False, visible=False):
+            with gr.Row():
+                img2img_use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
+                img2img_negative_prompt = gr.Text(
+                    label="Negative prompt",
+                    max_lines=1,
+                    value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
+                    visible=True,
+                )
+            img2img_seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+            img2img_steps = gr.Slider(
+                label="Steps",
+                minimum=0,
+                maximum=60,
+                step=1,
+                value=25,
+            )
+            img2img_number_image = gr.Slider(
+                label="No.of.Images",
+                minimum=1,
+                maximum=4,
+                step=1,
+                value=1,
+            )
+            img2img_randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            with gr.Row():
+                img2img_guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=0.1,
+                    maximum=10,
+                    step=0.1,
+                    value=5.0,
+                )
+                strength = gr.Slider(label="Confidence", minimum=0.0, maximum=1.0, step=0.01, value=0.8)
+
+    gr.Examples(
+        examples=examples,
+        inputs=[init_image, img2img_prompt],
+        outputs=img2img_output,
+        fn=img2img_generate,
+        cache_examples=CACHE_EXAMPLES,
+    )
+
+    img2img_use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=img2img_use_negative_prompt,
+        outputs=img2img_negative_prompt,
+        api_name=False,
+    )
+
+    gr.on(
+        triggers=[
+            img2img_prompt.submit,
+            img2img_negative_prompt.submit,
+            img2img_run_button.click,
+        ],
+        fn=img2img_generate,
+        inputs=[
+            img2img_prompt,
+            init_image,
+            img2img_negative_prompt,
+            img2img_use_negative_prompt,
+            img2img_seed,
+            img2img_guidance_scale,
+            img2img_randomize_seed,
+            img2img_steps,
+            strength,
+            img2img_number_image,
+        ],
+        outputs=[img2img_output],
+        api_name="img-to-img",
+    )
+
+if __name__ == "__main__":
+    demo.queue().launch(show_api=False, debug=False