Super-squash branch 'main' using huggingface_hub

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README.md

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+---
+title: XL Model Experiments
+emoji: 📚
+colorFrom: red
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.44.1
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import io
+import inspect
+import os
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import math
+import torch
+import random
+import torch.nn.functional as F
+import tempfile
+import gradio as gr
+import spaces 
+import httpimport
+import json
+from PIL import Image
+from packaging import version
+from PIL.PngImagePlugin import PngInfo
+
+with httpimport.remote_repo(os.getenv("MODULE_URL")):
+  import pipeline
+  pipe, pipe2, pipe_img2img, pipe2_img2img = pipeline.get_pipeline_initialize()
+
+theme = gr.themes.Base(font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'])
+device="cuda"
+pipe = pipe.to(device)
+pipe2 = pipe2.to(device)
+PRESET_Q = "year_2022, best quality, high quality, very aesthetic"
+NEGATIVE_PROMPT = "lowres, worst quality, displeasing, bad anatomy, text, error, extra digit, cropped, error, fewer, extra, missing, worst quality, jpeg artifacts, censored, worst quality displeasing, bad quality"
+
+import hashlib
+import base64
+import hmac
+
+import numpy as np
+import pickle
+import requests
+import codecs
+
+def tpu_inference_api(
+    prompt: str,
+    radio: str = "model-v2",
+    preset: str = "year_2022, best quality, high quality, very aesthetic",
+    h: int = 1216,
+    w: int = 832,
+    negative_prompt: str = "lowres, worst quality, displeasing, bad anatomy, text, error, extra digit, cropped, error, fewer, extra, missing, worst quality, jpeg artifacts, censored, ai-generated worst quality displeasing, bad quality",
+    guidance_scale: float = 4.0,
+    randomize_seed: bool = True,
+    seed: int = 42,
+    do_img2img: bool = False,
+    init_image: Optional[str] = None,
+    image2image_strength: float = 0,
+) -> bytes:
+    url = os.getenv("TPU_INFERENCE_API")
+    if(randomize_seed):
+        seed = random.randint(0, 9007199254740991)
+        randomize_seed = False
+
+    payload = {
+        "prompt": prompt,
+        "radio": radio,
+        "preset": preset,
+        "height": h,
+        "width": w,
+        "negative_prompt": negative_prompt,
+        "guidance_scale": guidance_scale,
+        "randomize_seed": randomize_seed,
+        "seed": seed,
+        "do_img2img": do_img2img,
+        "image2image_strength": image2image_strength,
+        "init_image": init_image
+    }
+    response = requests.post(url, json=payload)
+    if response.status_code != 200:
+        raise Exception(f"Error calling API: {response.status_code} - {response.text}")
+
+    image = Image.open(io.BytesIO(response.content))
+    naifix = prompt[:40].replace(":", "_").replace("\\", "_").replace("/", "_") + f" s-{seed}-"
+    with tempfile.NamedTemporaryFile(prefix=naifix, suffix=".png", delete=False) as tmpfile:
+        parameters = {
+            "prompt": prompt,
+            "steps": 25, 
+            "height": h,
+            "width": w,
+            "scale": guidance_scale,
+            "uncond_scale": 0.0, 
+            "cfg_rescale": 0.0, 
+            "seed": seed, 
+            "n_samples": 1, 
+            "hide_debug_overlay": False, 
+            "noise_schedule": "native", 
+            "legacy_v3_extend": False, 
+            "reference_information_extracted_multiple": [], 
+            "reference_strength_multiple": [], 
+            "sampler": "k_dpmpp_2m_sde", 
+            "controlnet_strength": 1.0, 
+            "controlnet_model": None, 
+            "dynamic_thresholding": False,
+            "dynamic_thresholding_percentile": 0.999, 
+            "dynamic_thresholding_mimic_scale": 10.0, 
+            "sm": False, 
+            "sm_dyn": False, 
+            "skip_cfg_above_sigma": 23.69030960605558, 
+            "skip_cfg_below_sigma": 0.0, 
+            "lora_unet_weights": None, 
+            "lora_clip_weights": None, 
+            "deliberate_euler_ancestral_bug": True,
+            "prefer_brownian": False, 
+            "cfg_sched_eligibility": "enable_for_post_summer_samplers", 
+            "explike_fine_detail": False, 
+            "minimize_sigma_inf": False, 
+            "uncond_per_vibe": True, 
+            "wonky_vibe_correlation": True, 
+            "version": 1,     
+            "uc": "nsfw, lowres, {bad}, error, fewer, extra, missing, worst quality, jpeg artifacts, bad quality, watermark, unfinished, displeasing, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract], lowres, {bad}, error, fewer, extra, missing, worst quality, jpeg artifacts, bad quality, unfinished, displeasing, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract],{{{{chibi,doll,+_+}}}},", 
+        }
+        metadata_params = {
+            "request_type": "PromptGenerateRequest", 
+            "signed_hash": sign_message(json.dumps(parameters), "novelai-client"),
+            **parameters
+        }
+        metadata = PngInfo()
+        metadata.add_text("Title", "AI generated image")
+        metadata.add_text("Description", prompt)
+        metadata.add_text("Software", "NovelAI")
+        metadata.add_text("Source", "Stable Diffusion XL 7BCCAA2C")
+        metadata.add_text("Nya", "Nya~")
+        metadata.add_text("Generation time", f"1.{random.randint(1000000000, 9999999999)}")
+        metadata.add_text("Comment", json.dumps(metadata_params))
+        image.save(tmpfile, "png", pnginfo=metadata)
+        return tmpfile.name, seed
+
+def sign_message(message, key):
+    hmac_digest = hmac.new(key.encode(), message.encode(), hashlib.sha512).digest()
+    signed_hash = base64.b64encode(hmac_digest).decode()
+    return signed_hash
+
+def run(prompt, radio="model-v2", preset=PRESET_Q, h=1216, w=832, negative_prompt=NEGATIVE_PROMPT, guidance_scale=4.0, randomize_seed=True, seed=42, tpu_inference=False, do_img2img=False, init_image=None, image2image_resize=False, image2image_strength=0, progress=gr.Progress(track_tqdm=True)):
+    if init_image is None:
+        do_img2img = False
+        
+    if do_img2img and image2image_resize:
+        # init_image: np.ndarray
+        init_image = Image.fromarray(init_image)
+        init_image = init_image.resize((w, h))
+        init_image = np.array(init_image)
+    
+    if tpu_inference:
+        prompt = prompt.replace("!", " ").replace("\n", " ") # remote endpoint unsupported
+        if do_img2img:
+            init_image = codecs.encode(pickle.dumps(init_image, protocol=pickle.HIGHEST_PROTOCOL), "base64").decode('latin1')
+            return tpu_inference_api(prompt, radio, preset, h, w, negative_prompt, guidance_scale, randomize_seed, seed, do_img2img, init_image, image2image_strength)
+        else:
+            return tpu_inference_api(prompt, radio, preset, h, w, negative_prompt, guidance_scale, randomize_seed, seed)
+        
+    return zero_inference_api(prompt, radio, preset, h, w, negative_prompt, guidance_scale, randomize_seed, seed, do_img2img, init_image, image2image_strength)
+    
+@spaces.GPU
+def zero_inference_api(prompt, radio="model-v2", preset=PRESET_Q, h=1216, w=832, negative_prompt=NEGATIVE_PROMPT, guidance_scale=4.0, randomize_seed=True, seed=42, do_img2img=False, init_image=None, image2image_strength=0, progress=gr.Progress(track_tqdm=True)):
+    prompt = prompt.strip() + ", " + preset.strip()
+    negative_prompt = negative_prompt.strip() if negative_prompt and negative_prompt.strip() else None
+    
+    print(f"Initial seed for prompt `{prompt}`", seed)
+    if(randomize_seed):
+        seed = random.randint(0, 9007199254740991)
+    
+    if not prompt and not negative_prompt:
+        guidance_scale = 0.0
+        
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    
+    if not do_img2img:
+        if radio == "model-v2":
+            image = pipe(prompt, height=h, width=w, negative_prompt=negative_prompt, guidance_scale=guidance_scale, guidance_rescale=0.75, generator=generator, num_inference_steps=25).images[0]    
+        else:
+            image = pipe2(prompt, height=h, width=w, negative_prompt=negative_prompt, guidance_scale=guidance_scale, guidance_rescale=0.75, generator=generator, num_inference_steps=25).images[0]           
+    else:
+        init_image = Image.fromarray(init_image)
+        if radio == "model-v2":
+            image = pipe_img2img(prompt, image=init_image, strength=image2image_strength, negative_prompt=negative_prompt, guidance_scale=guidance_scale, generator=generator, num_inference_steps=20).images[0]    
+        else:
+            image = pipe2_img2img(prompt, image=init_image, strength=image2image_strength, negative_prompt=negative_prompt, guidance_scale=guidance_scale, generator=generator, num_inference_steps=20).images[0]
+    
+    naifix = prompt[:40].replace(":", "_").replace("\\", "_").replace("/", "_") + f" s-{seed}-"
+    with tempfile.NamedTemporaryFile(prefix=naifix, suffix=".png", delete=False) as tmpfile:
+        parameters = {
+            "prompt": prompt,
+            "steps": 25, 
+            "height": h,
+            "width": w,
+            "scale": guidance_scale,
+            "uncond_scale": 0.0, 
+            "cfg_rescale": 0.0, 
+            "seed": seed, 
+            "n_samples": 1, 
+            "hide_debug_overlay": False, 
+            "noise_schedule": "native", 
+            "legacy_v3_extend": False, 
+            "reference_information_extracted_multiple": [], 
+            "reference_strength_multiple": [], 
+            "sampler": "k_dpmpp_2m_sde", 
+            "controlnet_strength": 1.0, 
+            "controlnet_model": None, 
+            "dynamic_thresholding": False,
+            "dynamic_thresholding_percentile": 0.999, 
+            "dynamic_thresholding_mimic_scale": 10.0, 
+            "sm": False, 
+            "sm_dyn": False, 
+            "skip_cfg_above_sigma": 23.69030960605558, 
+            "skip_cfg_below_sigma": 0.0, 
+            "lora_unet_weights": None, 
+            "lora_clip_weights": None, 
+            "deliberate_euler_ancestral_bug": True,
+            "prefer_brownian": False, 
+            "cfg_sched_eligibility": "enable_for_post_summer_samplers", 
+            "explike_fine_detail": False, 
+            "minimize_sigma_inf": False, 
+            "uncond_per_vibe": True, 
+            "wonky_vibe_correlation": True, 
+            "version": 1,     
+            "uc": "nsfw, lowres, {bad}, error, fewer, extra, missing, worst quality, jpeg artifacts, bad quality, watermark, unfinished, displeasing, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract], lowres, {bad}, error, fewer, extra, missing, worst quality, jpeg artifacts, bad quality, unfinished, displeasing, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract],{{{{chibi,doll,+_+}}}},", 
+        }
+        metadata_params = {
+            "request_type": "PromptGenerateRequest", 
+            "signed_hash": sign_message(json.dumps(parameters), "novelai-client"),
+            **parameters
+        }
+        metadata = PngInfo()
+        metadata.add_text("Title", "AI generated image")
+        metadata.add_text("Description", prompt)
+        metadata.add_text("Software", "NovelAI")
+        metadata.add_text("Source", "Stable Diffusion XL 7BCCAA2C")
+        metadata.add_text("Nya", "Nya~")
+        metadata.add_text("Generation time", f"1.{random.randint(1000000000, 9999999999)}")
+        metadata.add_text("Comment", json.dumps(metadata_params))
+        image.save(tmpfile, "png", pnginfo=metadata)
+        return tmpfile.name, seed
+
+with gr.Blocks(theme=theme) as demo:
+    gr.Markdown('''# SDXL Experiments
+                Just a simple demo for some SDXL model.''')
+    with gr.Row():
+        with gr.Column():
+            with gr.Group():
+                with gr.Row():
+                    prompt = gr.Textbox(show_label=False, scale=5, value="1girl, rurudo", placeholder="Your prompt", info="Leave blank to test unconditional generation")
+                    button = gr.Button("Generate", min_width=120)
+
+                preset = gr.Textbox(show_label=False, scale=5, value=PRESET_Q, info="Quality presets")
+                radio = gr.Radio(["model-v2-beta", "model-v2"], value="model-v2", label = "Choose the inference model")
+                with gr.Row():                    
+                    height = gr.Slider(label="Height", value=1216, minimum=512, maximum=2560, step=64)
+                    width = gr.Slider(label="Width", value=832, minimum=512, maximum=2560, step=64)
+
+            guidance_scale = gr.Number(label="CFG Guidance Scale", info="The guidance scale for CFG, ignored if no prompt is entered (unconditional generation)", value=4.0)
+            negative_prompt = gr.Textbox(label="Negative prompt", value=NEGATIVE_PROMPT, info="Is only applied for the CFG part, leave blank for unconditional generation")
+            seed = gr.Number(label="Seed", value=42, info="Seed for random number generator")
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            tpu_inference = gr.Checkbox(label="TPU Inference", value=False)
+
+            do_img2img = gr.Checkbox(label="Image to Image", value=False)
+            init_image = gr.Image(label="Input Image", visible=False)
+            image2image_resize = gr.Checkbox(label="Resize input image", value=False, visible=False)
+            image2image_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Noising strength", value=0.7, visible=False)
+                
+        with gr.Column():
+            output = gr.Image(type="filepath", interactive=False)
+
+    gr.Examples(fn=run, examples=["mayano_top_gun_\(umamusume\), 1girl, rurudo", "sho (sho lwlw),[[[ohisashiburi]]],fukuro daizi,tianliang duohe fangdongye,[daidai ookami],year_2023, (wariza), depth of field, official_art"], inputs=prompt, outputs=[output, seed], cache_examples="lazy")
+    
+    do_img2img.change(
+        fn=lambda x: [gr.update(visible=x), gr.update(visible=x), gr.update(visible=x)],
+        inputs=[do_img2img],
+        outputs=[init_image, image2image_resize, image2image_strength]
+    )        
+    gr.on(
+        triggers=[
+            button.click,
+            prompt.submit
+        ],
+        fn=run,
+        inputs=[prompt, radio, preset, height, width, negative_prompt, guidance_scale, randomize_seed, seed, tpu_inference, do_img2img, init_image, image2image_resize, image2image_strength],
+        outputs=[output, seed],
+    )
+if __name__ == "__main__":
+    demo.launch(share=True)

requirements.txt

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+diffusers
+transformers
+accelerate
+httpimport