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--- |
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pipeline_tag: text-to-image |
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widget: |
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- text: >- |
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black fluffy gorgeous dangerous cat animal creature, large orange eyes, big |
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fluffy ears, piercing gaze, full moon, dark ambiance, best quality, |
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extremely detailed |
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output: |
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url: assets/final_output_00975_.png |
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- text: >- |
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(impressionistic realism by csybgh), a 50 something male, working in |
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banking, very short dyed dark curly balding hair, Afro-Asiatic ancestry, |
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talks a lot but listens poorly, stuck in the past, wearing a suit, he has a |
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certain charm, bronze skintone, sitting in a bar at night, he is smoking and |
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feeling cool, drunk on plum wine, masterpiece, 8k, hyper detailed, smokey |
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ambiance, perfect hands AND fingers |
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output: |
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url: assets/final_output_00780_.png |
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- text: >- |
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high quality pixel art, a pixel art silhouette of an anime space-themed girl |
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in a space-punk steampunk style, lying in her bed by the window of a |
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spaceship, smoking, with a rustic feel. The image should embody epic |
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portraiture and double exposure, featuring an isolated landscape visible |
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through the window. The colors should primarily be dynamic and |
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action-packed, with a strong use of negative space. The entire artwork |
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should be in pixel art style, emphasizing the characters shape and set |
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against a white background. Silhouette |
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output: |
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url: assets/final_output_00817_.png |
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- text: >- |
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The image features an older man, a long white beard and mustache, He has a |
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stern expression, giving the impression of a wise and experienced |
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individual. The mans beard and mustache are prominent, adding to his |
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distinguished appearance. The close-up shot of the mans face emphasizes his |
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facial features and the intensity of his gaze. |
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output: |
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url: assets/final_output_00987_.png |
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- text: >- |
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Super Closeup Portrait, action shot, Profoundly dark whitish meadow, glass |
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flowers, Stains, space grunge style, Jeanne d'Arc wearing White Olive green |
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used styled Cotton frock, Wielding thin silver sword, Sci-fi vibe, dirty, |
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noisy, Vintage monk style, very detailed, hd |
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output: |
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url: assets/final_output_00813_.png |
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- text: >- |
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cinematic film still of Kodak Motion Picture Film: (Sharp Detailed Image) An |
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Oscar winning movie for Best Cinematography a woman in a kimono standing on |
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a subway train in Japan Kodak Motion Picture Film Style, shallow depth of |
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field, vignette, highly detailed, high budget, bokeh, cinemascope, moody, |
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epic, gorgeous, film grain, grainy |
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output: |
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url: assets/final_output_00991_.png |
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- text: >- |
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1980s anime portrait of a character |
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output: |
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url: assets/final_output_00994_.png |
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license: apache-2.0 |
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--- |
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<Gallery /> |
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# Prometheus |
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Prometheus is presumed to be the first full rank finetune of Playground v2.5, developed by the creator of the Proteus model. This text-to-image generation model has been specifically adapted to enhance accessibility for the open-source community. |
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# Key Features and Considerations |
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Presumed First Full Rank Finetune of Playground v2.5: |
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Complete parameter update of Playground v2.5 architecture |
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Unique approach to fine-tuning this particular base model |
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# Enhanced Accessibility: |
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Custom sampling methods have been removed through brute force techniques |
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Designed to be more compatible with standard open-source tools and workflows |
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# Output Characteristics: |
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Aims to provide a balance between consistency and diversity in outputs |
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May exhibit some stylistic tendencies inherited from the training process |
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# Training Approach: |
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Utilizes the extensive Proteus datasets |
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Focused on maintaining model capabilities while increasing compatibility |
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# Custom CLIP Integration: |
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Incorporation of a specially trained CLIP model |
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Requires a clip skip setting of 2 for optimal performance |
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# About Prometheus |
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Prometheus represents an effort to make advanced text-to-image generation more accessible to the open-source community. Built upon the Playground v2.5 architecture, it has undergone a full rank finetune using the Proteus datasets. A key aspect of its development was the removal of custom sampling methods through brute force techniques, allowing the model to work more seamlessly with standard open-source tools and pipelines. |
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This approach aims to balance the model's performance capabilities with wider compatibility and ease of use. Users can expect outputs that reflect the model's training on Proteus datasets |
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# Recommended Settings |
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Clip Skip: 2 |
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CFG Scale: 7 |
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Steps: 25 - 50 |
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Sampler: DPM++ 2M SDE |
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Scheduler: Karras |
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Resolution: 1024x1024 |
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# Use it with 🧨 diffusers |
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```python |
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import torch |
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from diffusers import ( |
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StableDiffusionXLPipeline, |
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KDPM2AncestralDiscreteScheduler, |
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AutoencoderKL |
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) |
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# Load VAE component |
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vae = AutoencoderKL.from_pretrained( |
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"madebyollin/sdxl-vae-fp16-fix", |
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torch_dtype=torch.float16 |
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) |
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# Configure the pipeline |
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pipe = StableDiffusionXLPipeline.from_pretrained( |
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"dataautogpt3/PrometheusV1", |
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vae=vae, |
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torch_dtype=torch.float16 |
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) |
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pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config) |
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pipe.to('cuda') |
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# Define prompts and generate image |
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prompt = "a cat wearing sunglasses on the beach" |
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negative_prompt = "" |
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image = pipe( |
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prompt, |
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negative_prompt=negative_prompt, |
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width=1024, |
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height=1024, |
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guidance_scale=7, |
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num_inference_steps=50, |
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clip_skip=2 |
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).images[0] |
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image.save("generated_image.png") |
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``` |