add img to img

app.py

@@ -1,232 +1,363 @@
-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 StableDiffusion3Pipeline, DPMSolverMultistepScheduler, AutoencoderKL
-from huggingface_hub import snapshot_download
-
-huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
-
-model_path = snapshot_download(
-    repo_id="stabilityai/stable-diffusion-3-medium", 
-    revision="refs/pr/26",
-    repo_type="model", 
-    ignore_patterns=["*.md", "*..gitattributes"],
-    local_dir="stable-diffusion-3-medium",
-    token=huggingface_token, # yeni bir token-id yazın.
-    )
-
-DESCRIPTION = """# Stable Diffusion 3"""
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo 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", "1536"))
-USE_TORCH_COMPILE = False
-ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-pipe = StableDiffusion3Pipeline.from_pretrained(model_path, torch_dtype=torch.float16)
-    
-
-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(enable_queue=True)
-def generate(
-    prompt: str,
-    negative_prompt: str = "",
-    use_negative_prompt: bool = False,
-    seed: int = 0,
-    width: int = 1024,
-    height: int = 1024,
-    guidance_scale: float = 7,
-    randomize_seed: bool = False,
-    num_inference_steps=30,
-    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)
-    
-    #pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-    
-    if not use_negative_prompt:
-        negative_prompt = None  # type: ignore
-    
-    output = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        generator=generator,
-        num_images_per_prompt=NUM_IMAGES_PER_PROMPT,
-        output_type="pil",
-    ).images
-
-    return output
-
-
-examples = [
-    "A red sofa on top of a white building.",
-    "A cardboard which is large and sits on a theater stage.",
-    "A painting of an astronaut riding a pig wearing a tutu holding a pink umbrella.",
-    "Studio photograph closeup of a chameleon over a black background.",
-    "Closeup portrait photo of beautiful goth woman, makeup.",
-    "A living room, bright modern Scandinavian style house, large windows.",
-    "Portrait photograph of an anthropomorphic tortoise seated on a New York City subway train.",
-    "Batman, cute modern Disney style, Pixar 3d portrait, ultra detailed, gorgeous, 3d zbrush, trending on dribbble, 8k render.",
-    "Cinnamon bun on the plate, watercolor painting, detailed, brush strokes, light palette, light, cozy.",
-    "A lion, colorful, low-poly, cyan and orange eyes, poly-hd, 3d, low-poly game art, polygon mesh, jagged, blocky, wireframe edges, centered composition.",
-    "Long exposure photo of Tokyo street, blurred motion, streaks of light, surreal, dreamy, ghosting effect, highly detailed.",
-    "A glamorous digital magazine photoshoot, a fashionable model wearing avant-garde clothing, set in a futuristic cyberpunk roof-top environment, with a neon-lit city background, intricate high fashion details, backlit by vibrant city glow, Vogue fashion photography.",
-    "Masterpiece, best quality, girl, collarbone, wavy hair, looking at viewer, blurry foreground, upper body, necklace, contemporary, plain pants, intricate, print, pattern, ponytail, freckles, red hair, dappled sunlight, smile, happy."
-
-]
-
-css = '''
-.gradio-container{max-width: 1000px !important}
-h1{text-align:center}
-'''
-with gr.Blocks(css=css) as demo:
-    with gr.Row():
-        with gr.Column():
-            gr.HTML(
-            """
-            <h1 style='text-align: center'>
-            Stable Diffusion 3 Medium 
-            </h1>
-            """
-        )
-            gr.HTML(
-                """
-               
-                """
-        )
-    with gr.Group():
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            run_button = gr.Button("Run", scale=0)
-        result = gr.Gallery(label="Result", elem_id="gallery", show_label=False)
-    with gr.Accordion("Advanced options", open=False):
-        with gr.Row():
-            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                value = "deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW",
-                visible=True,
-            )
-        seed = gr.Slider(
-            label="Seed",
-            minimum=0,
-            maximum=MAX_SEED,
-            step=1,
-            value=0,
-        )
-
-        steps = gr.Slider(
-            label="Steps",
-            minimum=0,
-            maximum=60,
-            step=1,
-            value=30,
-        )
-        number_image = gr.Slider(
-            label="Number of Image",
-            minimum=1,
-            maximum=4,
-            step=1,
-            value=2,
-        )
-        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-        with gr.Row(visible=True):
-            width = gr.Slider(
-                label="Width",
-                minimum=256,
-                maximum=MAX_IMAGE_SIZE,
-                step=32,
-                value=1024,
-            )
-            height = gr.Slider(
-                label="Height",
-                minimum=256,
-                maximum=MAX_IMAGE_SIZE,
-                step=32,
-                value=1024,
-            )
-        with gr.Row():
-            guidance_scale = gr.Slider(
-                label="Guidance Scale",
-                minimum=0.1,
-                maximum=10,
-                step=0.1,
-                value=7.0,
-            )
-
-    gr.Examples(
-        examples=examples,
-        inputs=prompt,
-        outputs=[result],
-        fn=generate,
-        cache_examples=CACHE_EXAMPLES,
-    )
-
-    use_negative_prompt.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_negative_prompt,
-        outputs=negative_prompt,
-        api_name=False,
-    )
-
-    gr.on(
-        triggers=[
-            prompt.submit,
-            negative_prompt.submit,
-            run_button.click,
-        ],
-        fn=generate,
-        inputs=[
-            prompt,
-            negative_prompt,
-            use_negative_prompt,
-            seed,
-            width,
-            height,
-            guidance_scale,
-            randomize_seed,
-            steps,
-            number_image,
-        ],
-        outputs=[result],
-        api_name="run",
-    )
-
-if __name__ == "__main__":
-    demo.queue().launch()
+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 StableDiffusion3Pipeline, DPMSolverMultistepScheduler, AutoencoderKL, StableDiffusion3Img2ImgPipeline
+from huggingface_hub import snapshot_download
+
+huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+
+model_path = snapshot_download(
+    repo_id="stabilityai/stable-diffusion-3-medium", 
+    revision="refs/pr/26",
+    repo_type="model", 
+    ignore_patterns=["*.md", "*..gitattributes"],
+    local_dir="stable-diffusion-3-medium",
+    token=huggingface_token, # type a new token-id.
+    )
+
+DESCRIPTION = """# Stable Diffusion 3"""
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo 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", "1536"))
+USE_TORCH_COMPILE = False
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+def load_pipeline(pipeline_type):
+    if pipeline_type == "text2img":
+        return StableDiffusion3Pipeline.from_pretrained(model_path, torch_dtype=torch.float16)
+    elif pipeline_type == "img2img":
+        return StableDiffusion3Img2ImgPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
+
+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 generate(
+    prompt:str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 7,
+    randomize_seed: bool = False,
+    num_inference_steps=30,
+    NUM_IMAGES_PER_PROMPT=1,
+    use_resolution_binning: bool = True,
+    progress=gr.Progress(track_tqdm=True),
+):
+    pipe = load_pipeline("text2img")
+    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
+    
+    output = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        num_images_per_prompt=NUM_IMAGES_PER_PROMPT,
+        output_type="battery",
+    ).images
+
+    return output
+
+
+@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 = load_pipeline("img2img")
+    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="battery",
+    ).images
+
+    return output
+
+
+
+examples = [
+    "A cardboard with text 'New York' which is large and sits on a theater stage.",
+    "A red sofa on top of a white building.",
+    "A painting of an astronaut riding a pig wearing a tutu holding a pink umbrella.",
+    "Studio photograph closeup of a chameleon over a black background.",
+    "Closeup portrait photo of beautiful goth woman, makeup.",
+    "A living room, bright modern Scandinavian style house, large windows.",
+    "Portrait photograph of an anthropomorphic tortoise seated on a New York City subway train.",
+    "Batman, cute modern Disney style, Pixar 3d portrait, ultra detailed, gorgeous, 3d zbrush, trending on dribbble, 8k render.",
+    "Cinnamon bun on the plate, watercolor painting, detailed, brush strokes, light palette, light, cozy.",
+    "A lion, colorful, low-poly, cyan and orange eyes, poly-hd, 3d, low-poly game art, polygon mesh, jagged, blocky, wireframe edges, centered composition.",
+    "Long exposure photo of Tokyo street, blurred motion, streaks of light, surreal, dreamy, ghosting effect, highly detailed.",
+    "A glamorous digital magazine photoshoot, a fashionable model wearing avant-garde clothing, set in a futuristic cyberpunk roof-top environment, with a neon-lit city background, intricate high fashion details, backlit by vibrant city glow, Vogue fashion photography.",
+    "Masterpiece, best quality, girl, collarbone, wavy hair, looking at viewer, blurry foreground, upper body, necklace, contemporary, plain pants, intricate, print, pattern, ponytail, freckles, red hair, dappled sunlight, smile, happy."
+
+]
+
+css = '''
+.gradio-container{max-width: 1000px !important}
+h1{text-align:center}
+'''
+with gr.Blocks(css=css,theme="snehilsanyal/scikit-learn") as demo:
+    with gr.Row():
+        with gr.Column():
+            gr.HTML(
+            """
+            <h1 style='text-align: center'>
+            Stable Diffusion 3 Medium
+            </h1>
+            """
+            )
+            gr.HTML(
+                """
+              
+                """
+            )
+    
+    with gr.Tabs():
+        with gr.TabItem("Text to Image"):
+            with gr.Group():
+                with gr.Row():
+                    prompt = gr.Text(
+                        label="Prompt",
+                        show_label=False,
+                        max_lines=1,
+                        placeholder="Enter your prompt",
+                        container=False,
+                    )
+                    run_button = gr.Button("Run", scale=0)
+                result = gr.Gallery(label="Result", elem_id="gallery", show_label=False)
+            with gr.Accordion("Advanced options", open=False):
+                with gr.Row():
+                    use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
+                    negative_prompt = gr.Text(
+                        label="Negative prompt",
+                        max_lines=1,
+                        value = "deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW",
+                        visible=True,
+                    )
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=0,
+                )
+
+                steps = gr.Slider(
+                    label="Steps",
+                    minimum=0,
+                    maximum=60,
+                    step=1,
+                    value=25,
+                )
+                number_image = gr.Slider(
+                    label="Number of Images",
+                    minimum=1,
+                    maximum=4,
+                    step=1,
+                    value=2,
+                )
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+                with gr.Row(visible=True):
+                    width = gr.Slider(
+                        label="Width",
+                        minimum=256,
+                        maximum=MAX_IMAGE_SIZE,
+                        step=32,
+                        value=1024,
+                    )
+                    height = gr.Slider(
+                        label="Height",
+                        minimum=256,
+                        maximum=MAX_IMAGE_SIZE,
+                        step=32,
+                        value=1024,
+                    )
+                with gr.Row():
+                    guidance_scale = gr.Slider(
+                        label="Guidance Scale",
+                        minimum=0.1,
+                        maximum=10,
+                        step=0.1,
+                        value=7.0,
+                    )
+            
+            gr.Examples(
+                examples=examples,
+                inputs=prompt,
+                outputs=[result],
+                fn=generate,
+                cache_examples=CACHE_EXAMPLES,
+            )
+
+            use_negative_prompt.change(
+                fn=lambda x: gr.update(visible=x),
+                inputs=use_negative_prompt,
+                outputs=negative_prompt,
+                api_name=False,
+            )
+
+            gr.on(
+                triggers=[
+                    prompt.submit,
+                    negative_prompt.submit,
+                    run_button.click,
+                ],
+                fn=generate,
+                inputs=[
+                    prompt,
+                    negative_prompt,
+                    use_negative_prompt,
+                    seed,
+                    width,
+                    height,
+                    guidance_scale,
+                    randomize_seed,
+                    steps,
+                    number_image,
+                ],
+                outputs=[result],
+                api_name="run",
+            )
+        with gr.TabItem("Image to Image"):
+            with gr.Group():
+                with gr.Row(equal_height=True):
+                    with gr.Column(scale=1):
+                        img2img_prompt = gr.Text(
+                            label="Prompt",
+                            show_label=False,
+                            max_lines=1,
+                            placeholder="Enter your prompt",
+                            container=False,
+                        )
+                        init_image = gr.Image(label="Input 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):
+                    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, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW",
+                            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="Number of Images",
+                        minimum=1,
+                        maximum=4,
+                        step=1,
+                        value=2,
+                    )
+                    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=7.0,
+                        )
+                        strength = gr.Slider(label="Img2Img Strength", minimum=0.0, maximum=1.0, step=0.01, value=0.8)
+            
+            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="img2img_run",
+            )
+if __name__ == "__main__":
+    demo.queue().launch(show_api=False, debug=False)