From diffusers import StableDiffusionPipeline import torch

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from diffusers import StableDiffusionPipeline import torch # Load the Stable Diffusion model pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16) pipeline = pipeline.to("cuda") # Define the prompt for the image generation prompt = ( "A digital painting of a Latina-Gypsy Venus, around 4, with a large nose and narrow lips, dressed in erotic red lingerie made of roses in various shades of red. The roses cover her intimate areas as if they are silk instead of clothing. Her long hair is braided and adorned with a hairpin shaped like the Bitcoin logo. She has a naive yet wise expression, standing barefoot with her hands posed as if dancing flamenco. Nearby, a flamingo is present on a Venus-like planet. The background has a warm, surreal atmosphere with shades of red. She is holding a banana in her mouth colored in Yellow #FFFF00. The color palette includes IndianRed #CD5C5C, LightCoral #F08080, Salmon #FA8072, DarkSalmon #E9967A, LightSalmon #FFA07A, Crimson #DC143C, Red #FF0000, FireBrick #B22222, DarkRed #8B0000. Also use shades of Yellow #FFFF00, Olive #808000, Lime #00FF00, Green #008000." ) # Settings for image generation guidance_scale = 8.5 # Guidance scale for prompt adherence num_inference_steps = 70 # Number of diffusion steps height = 768 # Height of the generated image width = 768 # Width of the generated image seed = 12345 # Seed for reproducibility # Generate the image with torch.no_grad(): generator = torch.manual_seed(seed) image = pipeline(prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, height=height, width=width, generator=generator).images[0] # Save the generated image image.save("latina_gypsy_venus_bitcoin.png") print("Digital painting of Latina-Gypsy Venus with Bitcoin elements generated and saved as 'latina_gypsy_venus_

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HelgaBlue

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Generation Data

Protokolle

Prompts

Prompts kopieren

from diffusers import StableDiffusionPipeline import torch # Load the Stable Diffusion model pipeline = StableDiffusionPipeline . from_pretrained("CompVis/stable-diffusion-v1-4" , torch_dtype=torch . float16) pipeline = pipeline . to("cuda") # Define the prompt for the image generation prompt = ( "A digital painting of a Latina-Gypsy Venus , around 4 , with a large nose and narrow lips , dressed in erotic red lingerie made of roses in various shades of red . The roses cover her intimate areas as if they are silk instead of clothing . Her long hair is braided and adorned with a hairpin shaped like the Bitcoin logo . She has a naive yet wise expression , standing barefoot with her hands posed as if dancing flamenco . Nearby , a flamingo is present on a Venus-like planet . The background has a warm , surreal atmosphere with shades of red . She is holding a banana in her mouth colored in Yellow #FFFF00 . The color palette includes IndianRed #CD5C5C , LightCoral #F08080 , Salmon #FA8072 , DarkSalmon #E9967A , LightSalmon #FFA07A , Crimson #DC143C , Red #FF0000 , FireBrick #B22222 , DarkRed #8B0000 . Also use shades of Yellow #FFFF00 , Olive #808000 , Lime #00FF00 , Green #008000 . " ) # Settings for image generation guidance_scale = 8 . 5 # Guidance scale for prompt adherence num_inference_steps = 70 # Number of diffusion steps height = 768 # Height of the generated image width = 768 # Width of the generated image seed = 12345 # Seed for reproducibility # Generate the image with torch . no_grad(): generator = torch . manual_seed(seed) image = pipeline(prompt , guidance_scale=guidance_scale , num_inference_steps=num_inference_steps , height=height , width=width , generator=generator) . images[0] # Save the generated image image . save("latina_gypsy_venus_bitcoin . png") print("Digital painting of Latina-Gypsy Venus with Bitcoin elements generated and saved as 'latina_gypsy_venus_

Info

Prompts

from diffusers import StableDiffusionPipeline import torch # Load the Stable Diffusion model pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16) pipeline = pipeline.to("cuda") # Define the prompt for the image generation prompt = ( "A digital painting of a Latina-Gypsy Venus, around 4, with a large nose and narrow lips, dressed in erotic red lingerie made of roses in various shades of red. The roses cover her intimate areas as if they are silk instead of clothing. Her long hair is braided and adorned with a hairpin shaped like the Bitcoin logo. She has a naive yet wise expression, standing barefoot with her hands posed as if dancing flamenco. Nearby, a flamingo is present on a Venus-like planet. The background has a warm, surreal atmosphere with shades of red. She is holding a banana in her mouth colored in Yellow #FFFF00. The color palette includes IndianRed #CD5C5C, LightCoral #F08080, Salmon #FA8072, DarkSalmon #E9967A, LightSalmon #FFA07A, Crimson #DC143C, Red #FF0000, FireBrick #B22222, DarkRed #8B0000. Also use shades of Yellow #FFFF00, Olive #808000, Lime #00FF00, Green #008000." ) # Settings for image generation guidance_scale = 8.5 # Guidance scale for prompt adherence num_inference_steps = 70 # Number of diffusion steps height = 768 # Height of the generated image width = 768 # Width of the generated image seed = 12345 # Seed for reproducibility # Generate the image with torch.no_grad(): generator = torch.manual_seed(seed) image = pipeline(prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, height=height, width=width, generator=generator).images[0] # Save the generated image image.save("latina_gypsy_venus_bitcoin.png") print("Digital painting of Latina-Gypsy Venus with Bitcoin elements generated and saved as 'latina_gypsy_venus_

CFG-Scale

7

Schritte

25

Sammler

euler

Seed

87374702

Scheduler

karras

Bildgröße

688 X 1024

Modell

SeaArt Infinity

Erstellen

Größe

688X1024

Datum

Aug 25, 2024

Modell

Studio

Typ

cell

Checkpoint & LoRA

Checkpoint

SeaArt Infinity

#SeaArt Infinity

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