from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler import gradio as gr import torch from PIL import Image model_id = 'Cosmo-Hug/FeverDream' prefix = '' scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained( model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, scheduler=scheduler) pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained( model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, scheduler=scheduler) if torch.cuda.is_available(): pipe = pipe.to("cuda") pipe_i2i = pipe_i2i.to("cuda") def error_str(error, title="Error"): return f"""#### {title} {error}""" if error else "" def _parse_args(prompt, generator): parser = argparse.ArgumentParser( description="making it work." ) parser.add_argument( "--no-half-vae", help="no half vae" ) cmdline_args = parser.parse_args() command = cmdline_args.command conf_file = cmdline_args.conf_file conf_args = Arguments(conf_file) opt = conf_args.readArguments() if cmdline_args.config_overrides: for config_override in cmdline_args.config_overrides.split(";"): config_override = config_override.strip() if config_override: var_val = config_override.split("=") assert ( len(var_val) == 2 ), f"Config override '{var_val}' does not have the form 'VAR=val'" conf_args.add_opt(opt, var_val[0], var_val[1], force_override=True) def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False): generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None prompt = f"{prefix} {prompt}" if auto_prefix else prompt try: if img is not None: return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None else: return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None except Exception as e: return None, error_str(e) def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator): result = pipe( prompt, negative_prompt = neg_prompt, num_inference_steps = int(steps), guidance_scale = guidance, width = width, height = height, generator = generator) return result.images[0] def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator): ratio = min(height / img.height, width / img.width) img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS) result = pipe_i2i( prompt, negative_prompt = neg_prompt, init_image = img, num_inference_steps = int(steps), strength = strength, guidance_scale = guidance, width = width, height = height, generator = generator) return result.images[0] def fake_safety_checker(images, **kwargs): return result.images[0], [False] * len(images) pipe.safety_checker = fake_safety_checker 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} """ with gr.Blocks(css=css) as demo: gr.HTML( f"""

📷 Fever Dream 📸

Demo for Fever Dream Stable Diffusion model by Cosmo-Hug. {"" if prefix else ""} Running on {"GPU 🔥" if torch.cuda.is_available() else f"CPU ⚡"}.

Please use the prompt template below to get an example of the desired generation results:

Prompt:
Example: close up of a young woman wearing a black and gold liquid splash dress, pretty face, detailed eyes, soft lips, floating in outer space and planets in the background, fluid, wet, dripping, waxy, smooth, realistic, octane render

Negative Prompt:
two, few, couple, group, lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, dehydrated, extra limbs, clone, disfigured, gross, malformed, extra arms, extra legs, fingers, long neck, username, watermark, signature

Have Fun & Enjoy âš¡ //THAFX
""" ) with gr.Row(): with gr.Column(scale=55): with gr.Group(): with gr.Row(): prompt = gr.Textbox(label="Prompt", show_label=False,max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False) generate = gr.Button(value="Generate").style(rounded=(False, True, True, False)) image_out = gr.Image(height=512) error_output = gr.Markdown() with gr.Column(scale=45): with gr.Tab("Options"): with gr.Group(): neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image") auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (RAW photo,)", value=prefix, visible=prefix) with gr.Row(): guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15) steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1) with gr.Row(): width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8) height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8) seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1) with gr.Tab("Image to image"): with gr.Group(): image = gr.Image(label="Image", height=256, tool="editor", type="pil") strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5) auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False) inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix] outputs = [image_out, error_output] prompt.submit(inference, inputs=inputs, outputs=outputs) generate.click(inference, inputs=inputs, outputs=outputs) demo.queue(concurrency_count=1) demo.launch()