Thafx
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Duplicate from Thafx/sdrv202
Browse files- .gitattributes +34 -0
- README.md +13 -0
- app.py +189 -0
- requirements.txt +10 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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title: SD:// Realistic Vision v2.0
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emoji: 📷
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colorFrom: yellow
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colorTo: red
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sdk: gradio
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sdk_version: 3.18.0
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app_file: app.py
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pinned: true
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duplicated_from: Thafx/sdrv202
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
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import gradio as gr
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import torch
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from PIL import Image
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model_id = 'SG161222/Realistic_Vision_V2.0'
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prefix = 'RAW photo,'
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scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
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pipe = StableDiffusionPipeline.from_pretrained(
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model_id,
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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scheduler=scheduler)
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pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
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model_id,
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
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scheduler=scheduler)
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if torch.cuda.is_available():
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pipe = pipe.to("cuda")
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pipe_i2i = pipe_i2i.to("cuda")
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def error_str(error, title="Error"):
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return f"""#### {title}
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{error}""" if error else ""
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def _parse_args(prompt, generator):
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parser = argparse.ArgumentParser(
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description="making it work."
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)
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parser.add_argument(
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"--no-half-vae", help="no half vae"
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)
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cmdline_args = parser.parse_args()
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command = cmdline_args.command
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conf_file = cmdline_args.conf_file
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conf_args = Arguments(conf_file)
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opt = conf_args.readArguments()
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if cmdline_args.config_overrides:
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for config_override in cmdline_args.config_overrides.split(";"):
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config_override = config_override.strip()
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if config_override:
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var_val = config_override.split("=")
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assert (
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len(var_val) == 2
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), f"Config override '{var_val}' does not have the form 'VAR=val'"
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conf_args.add_opt(opt, var_val[0], var_val[1], force_override=True)
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def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
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generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
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prompt = f"{prefix} {prompt}" if auto_prefix else prompt
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try:
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if img is not None:
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return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
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else:
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return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
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except Exception as e:
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return None, error_str(e)
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def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
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result = pipe(
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prompt,
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negative_prompt = neg_prompt,
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num_inference_steps = int(steps),
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guidance_scale = guidance,
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width = width,
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height = height,
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generator = generator)
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return result.images[0]
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def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
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ratio = min(height / img.height, width / img.width)
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img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
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result = pipe_i2i(
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prompt,
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negative_prompt = neg_prompt,
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init_image = img,
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num_inference_steps = int(steps),
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strength = strength,
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guidance_scale = guidance,
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width = width,
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height = height,
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generator = generator)
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return result.images[0]
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def fake_safety_checker(images, **kwargs):
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return result.images[0], [False] * len(images)
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pipe.safety_checker = fake_safety_checker
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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}
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"""
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with gr.Blocks(css=css) as demo:
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gr.HTML(
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f"""
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<div class="main-div">
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<div>
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<h1 style="color:orange;">📷 Realistic Vision V2.0 📸</h1>
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</div>
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<p>
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Demo for <a href="https://huggingface.co/SG161222/Realistic_Vision_V2.0">Realistic Vision V2.0</a>
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Stable Diffusion model by <a href="https://huggingface.co/SG161222/"><abbr title="SG1611222">Eugene</abbr></a>. {"" if prefix else ""}
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Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU ⚡</b>"}.
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</p>
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<p>Please use the prompt template below to get an example of the desired generation results:
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</p>
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<b>Prompt</b>:
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<details><code>
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RAW photo, * subject *, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
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<br>
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<br>
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<q><i>
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Example: RAW photo, a close up portrait photo of 26 y.o woman in wastelander clothes, long haircut, pale skin, slim body, background is city ruins, <br>
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(high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
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</i></q>
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</code></details>
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<br>
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<b>Negative Prompt</b>:
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<details><code>
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(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, <br>
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low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, <br>
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dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, <br>
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extra legs, fused fingers, too many fingers, long neck
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</code></details>
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<br>
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Have Fun & Enjoy ⚡ <a href="https://www.thafx.com"><abbr title="Website">//THAFX</abbr></a>
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<br>
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</div>
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"""
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)
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with gr.Row():
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with gr.Column(scale=55):
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with gr.Group():
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with gr.Row():
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prompt = gr.Textbox(label="Prompt", show_label=False,max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
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generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
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image_out = gr.Image(height=512)
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error_output = gr.Markdown()
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with gr.Column(scale=45):
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with gr.Tab("Options"):
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with gr.Group():
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neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
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auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (RAW photo,)", value=prefix, visible=prefix)
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with gr.Row():
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guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
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steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
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with gr.Row():
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width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
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height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
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seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
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with gr.Tab("Image to image"):
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with gr.Group():
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image = gr.Image(label="Image", height=256, tool="editor", type="pil")
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strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
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auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
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inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
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outputs = [image_out, error_output]
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prompt.submit(inference, inputs=inputs, outputs=outputs)
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generate.click(inference, inputs=inputs, outputs=outputs)
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demo.queue(concurrency_count=1)
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demo.launch()
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requirements.txt
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--extra-index-url https://download.pytorch.org/whl/cu113
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invisible-watermark
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fonts
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font-roboto
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torch
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diffusers
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transformers
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git+https://github.com/huggingface/transformers
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accelerate
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ftfy
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