🌊 SYSTEM PROMPT LEAK 🌊 Here's the new and improved ChatGPT 5 system prompt! 🤗 PROMPT: """ system_message: role: system model: gpt-5 capabilities: - code_interpreter - web_browsing - file_search - image_generation knowledge_cutoff: 2024-06 current_date: 2025-08-07 ---

Partial images can be set at 1 to 3. 3 meaning it will stream more updates until the final image is sent. Its not a percentage though, its the actual picture like in chatGPT. response = create_client_response( “gpt-4.1”, system_prompt, combined_input, conversation_history, tools=[ { “type”: “web_search” }, { “type”: “code_interpreter”, “container”: {“type”: “auto”} }, { “type”: “image_generation”, “partial_images”: 3 # Required for streaming with image_generation } ],

Simply put, you’re trying to use a feature that doesn’t currently exist and encountering an error. * * * Your Space fails because the code calls a method that doesn’t exist on `huggingface_hub.InferenceClient`. The class exposes task-specific helpers like `text_to_image(...)` for generation. There is no `image_generation(...)`. So Python raises `AttributeError`. Replace the call and keep arguments in the supported shape. (Hugging Face) # What’s happening, in plain terms * **You’re using the wrong API surface.** `InferenceClient` provides one method per task. For images it’s `text_to_image`. Older tutorials or third-party wrappers sometimes mention `client.post(...)` or custom helpers; those aren’t on today’s client and trigger similar errors. (Hugging Face) * **Router confusion is common.** The Hugging Face OpenAI-compatible router is for **chat completion** only. It does not expose an OpenAI-style image API. Use `InferenceClient.text_to_image` or a provider SDK for images. (Hugging Face) * **Model availability varies by provider.** Some providers may not serve `stabilityai/stable-diffusion-xl-base-1.0` directly. The Inference Providers docs show recommended, provider-backed choices like FLUX or SDXL-Lightning. (Hugging Face) * **If you run SDXL yourself, use Diffusers.** The SDXL model card shows working Diffusers code and prerequisites. (Hugging Face) # Minimal, beginner-safe fix Replace the nonexistent `image_generation(...)` with the supported helper. Keep parameters as flat kwargs. # pip install -U "huggingface_hub>=1.1.2" pillow # docs: https://huggingface.co/docs/inference-providers/en/tasks/text-to-image import os from huggingface_hub import InferenceClient client = InferenceClient( provider="hf-inference", # or "fal-ai", "replicate", "together" api_key=os.environ["HF_TOKEN"], # HF token with Inference Providers permission ) # Returns a PIL.Image image = client.text_to_image( # ← correct method "a neon kitsune in a rainy Tokyo alley", # prompt model="stabilityai/stable-diffusion-xl-base-1.0", width=1024, height=1024, negative_prompt="blurry, low quality", num_inference_steps=30, guidance_scale=7.5, ) image.save("out.png") # ref and example: https://huggingface.co/docs/inference-providers/en/tasks/text-to-image Why this works: `text_to_image` is the official image generation entrypoint on `InferenceClient`. The Inference Providers docs show this method and a working Python snippet. (Hugging Face) # Likely causes in your Space’s code, and exact remedies 1. **Wrong call name** * **Cause:** `client.image_generation(...)`. * **Fix:** `client.text_to_image(...)`. Keep generation options as keyword args (e.g., `width=`, `height=`, `num_inference_steps=`). (Hugging Face) 2. **Legacy helper like`client.post(...)`** * **Cause:** Old blog posts or wrappers still call `.post`. * **Fix:** Stop using `.post`. Call the task helper (`text_to_image`) or call the HTTP API yourself if you must. The community has multiple “no attribute .post” reports after client updates. (GitHub) 3. **Router misuse** * **Cause:** Trying to create images through `router.huggingface.co` with an OpenAI-style Images API. * **Fix:** Use `InferenceClient.text_to_image` or a provider SDK. The OpenAI-compatible router covers **chat completion** only. (Hugging Face) 4. **Provider doesn’t serve your model** * **Cause:** You pass `stabilityai/stable-diffusion-xl-base-1.0` to a provider that doesn’t host it. * **Fix:** Either switch provider or pick a provider-backed model from the Text-to-Image page (e.g., FLUX.1, SDXL-Lightning). (Hugging Face) 5. **Running SDXL inside the Space** * **Cause:** You want to avoid Providers and run the model yourself. * **Fix:** Use Diffusers as shown on the model card; requires a GPU and the SDXL license terms. (Hugging Face) # Gradio / Spaces patterns that don’t break **Provider call inside a Space** # pip install -U gradio "huggingface_hub>=1.1.2" pillow import gradio as gr from huggingface_hub import InferenceClient import os client = InferenceClient(provider="hf-inference", api_key=os.environ.get("HF_TOKEN")) def generate(prompt, w, h, steps, guidance, neg): return client.text_to_image( prompt, model="stabilityai/stable-diffusion-xl-base-1.0", width=w, height=h, num_inference_steps=steps, guidance_scale=guidance, negative_prompt=neg ) demo = gr.Interface( fn=generate, inputs=[gr.Text(label="Prompt"), gr.Slider(512, 1344, 1024, step=64, label="Width"), gr.Slider(512, 1344, 1024, step=64, label="Height"), gr.Slider(5, 50, 30, step=1, label="Steps"), gr.Slider(1.0, 12.0, 7.5, step=0.5, label="Guidance"), gr.Text(label="Negative prompt", value="blurry, low quality")], outputs=gr.Image(type="pil"), ) demo.launch() # method shape: https://huggingface.co/docs/inference-providers/en/tasks/text-to-image **ZeroGPU on Spaces** If you rely on ZeroGPU, keep generation inside the function that runs under a short GPU slot. Use the decorator and avoid long warmups. (Hugging Face) # If you prefer to self-host SDXL in the Space # pip install -U "diffusers>=0.30.0" transformers accelerate torch pillow # model card diffusers snippet: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 import torch from diffusers import StableDiffusionXLPipeline pipe = StableDiffusionXLPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, ).to("cuda") def generate_local(prompt, w=1024, h=1024, steps=30, guidance=7.5, neg="blurry, low quality"): out = pipe(prompt=prompt, height=h, width=w, num_inference_steps=steps, guidance_scale=guidance, negative_prompt=neg) return out.images[0] # full usage and refiner flow shown on the model card The SDXL card documents base-only and base+refiner pipelines, install hints, and optimization tips. (Hugging Face) # Diagnostic checklist (run top-to-bottom) 1. **Confirm method availability** from huggingface_hub import InferenceClient, __version__ print(__version__) # expect ≥ 1.1.x print(hasattr(InferenceClient(), "text_to_image")) # True means your client exposes the helper Use the helper if present; don’t invent `image_generation`. Docs show `text_to_image`. (Hugging Face) 2. **Use a provider-backed model first** Try FLUX or SDXL-Lightning to confirm routing works. Then point back to SDXL base if your provider supports it. (Hugging Face) 3. **Passing options** With `InferenceClient`, pass generation settings as keyword args, not nested under a `parameters` dict. The HTTP spec uses `parameters`, but the Python helper takes kwargs; the official snippet demonstrates the helper call. (Hugging Face) 4. **Do not send image calls to the OpenAI-compatible router** The router is chat-only; images won’t work there. (Hugging Face) 5. **If you self-host** Follow the SDXL model card Diffusers recipe and ensure a GPU. (Hugging Face) # Common symptoms → concrete fixes Symptom | Root cause | Fix ---|---|--- `AttributeError: 'InferenceClient' object has no attribute 'image_generation'` | No such method on the client. | Call `text_to_image(...)`. (Hugging Face) `AttributeError: 'InferenceClient' object has no attribute 'post'` | Legacy wrapper examples. Method removed/never existed. | Use task helpers. Don’t call `.post`. (GitHub) Calls to `router.huggingface.co` for images fail | Router exposes chat completion only. | Use `InferenceClient.text_to_image` or a provider SDK. (Hugging Face) Provider returns “model not supported” | Provider doesn’t host that ID. | Choose a recommended model or change provider. (Hugging Face) Diffusers errors in Space | Missing GPU or packages. | Follow the SDXL card diffusers section and GPU notes. (Hugging Face) # Short, curated extras **Official, stable** * **Text-to-Image with Inference Providers**. Shows `InferenceClient.text_to_image` Python usage and what arguments are supported. Good for verifying method names. (Hugging Face) * **SDXL model card (Diffusers recipes).** Covers base vs refiner, install, and performance tips. Useful if you self-host. (Hugging Face) * **ZeroGPU docs.** If your Space uses on-demand GPU allocation. (Hugging Face) **Community signals on breaking calls** * **Missing`.post` on `InferenceClient`.** Confirms legacy examples cause `AttributeError`. Useful sanity check if you still see attribute errors after renaming the method. (GitHub)

Simply put, you’re trying to use a feature that doesn’t currently exist and encountering an error. * * * Your Space fails because the code calls a method that doesn’t exist on `huggingface_hub.InferenceClient`. The class exposes task-specific helpers like `text_to_image(...)` for generation. There is no `image_generation(...)`. So Python raises `AttributeError`. Replace the call and keep arguments in the supported shape. (Hugging Face) # What’s happening, in plain terms * **You’re using the wrong API surface.** `InferenceClient` provides one method per task. For images it’s `text_to_image`. Older tutorials or third-party wrappers sometimes mention `client.post(...)` or custom helpers; those aren’t on today’s client and trigger similar errors. (Hugging Face) * **Router confusion is common.** The Hugging Face OpenAI-compatible router is for **chat completion** only. It does not expose an OpenAI-style image API. Use `InferenceClient.text_to_image` or a provider SDK for images. (Hugging Face) * **Model availability varies by provider.** Some providers may not serve `stabilityai/stable-diffusion-xl-base-1.0` directly. The Inference Providers docs show recommended, provider-backed choices like FLUX or SDXL-Lightning. (Hugging Face) * **If you run SDXL yourself, use Diffusers.** The SDXL model card shows working Diffusers code and prerequisites. (Hugging Face) # Minimal, beginner-safe fix Replace the nonexistent `image_generation(...)` with the supported helper. Keep parameters as flat kwargs. # pip install -U "huggingface_hub>=1.1.2" pillow # docs: https://huggingface.co/docs/inference-providers/en/tasks/text-to-image import os from huggingface_hub import InferenceClient client = InferenceClient( provider="hf-inference", # or "fal-ai", "replicate", "together" api_key=os.environ["HF_TOKEN"], # HF token with Inference Providers permission ) # Returns a PIL.Image image = client.text_to_image( # ← correct method "a neon kitsune in a rainy Tokyo alley", # prompt model="stabilityai/stable-diffusion-xl-base-1.0", width=1024, height=1024, negative_prompt="blurry, low quality", num_inference_steps=30, guidance_scale=7.5, ) image.save("out.png") # ref and example: https://huggingface.co/docs/inference-providers/en/tasks/text-to-image Why this works: `text_to_image` is the official image generation entrypoint on `InferenceClient`. The Inference Providers docs show this method and a working Python snippet. (Hugging Face) # Likely causes in your Space’s code, and exact remedies 1. **Wrong call name** * **Cause:** `client.image_generation(...)`. * **Fix:** `client.text_to_image(...)`. Keep generation options as keyword args (e.g., `width=`, `height=`, `num_inference_steps=`). (Hugging Face) 2. **Legacy helper like`client.post(...)`** * **Cause:** Old blog posts or wrappers still call `.post`. * **Fix:** Stop using `.post`. Call the task helper (`text_to_image`) or call the HTTP API yourself if you must. The community has multiple “no attribute .post” reports after client updates. (GitHub) 3. **Router misuse** * **Cause:** Trying to create images through `router.huggingface.co` with an OpenAI-style Images API. * **Fix:** Use `InferenceClient.text_to_image` or a provider SDK. The OpenAI-compatible router covers **chat completion** only. (Hugging Face) 4. **Provider doesn’t serve your model** * **Cause:** You pass `stabilityai/stable-diffusion-xl-base-1.0` to a provider that doesn’t host it. * **Fix:** Either switch provider or pick a provider-backed model from the Text-to-Image page (e.g., FLUX.1, SDXL-Lightning). (Hugging Face) 5. **Running SDXL inside the Space** * **Cause:** You want to avoid Providers and run the model yourself. * **Fix:** Use Diffusers as shown on the model card; requires a GPU and the SDXL license terms. (Hugging Face) # Gradio / Spaces patterns that don’t break **Provider call inside a Space** # pip install -U gradio "huggingface_hub>=1.1.2" pillow import gradio as gr from huggingface_hub import InferenceClient import os client = InferenceClient(provider="hf-inference", api_key=os.environ.get("HF_TOKEN")) def generate(prompt, w, h, steps, guidance, neg): return client.text_to_image( prompt, model="stabilityai/stable-diffusion-xl-base-1.0", width=w, height=h, num_inference_steps=steps, guidance_scale=guidance, negative_prompt=neg ) demo = gr.Interface( fn=generate, inputs=[gr.Text(label="Prompt"), gr.Slider(512, 1344, 1024, step=64, label="Width"), gr.Slider(512, 1344, 1024, step=64, label="Height"), gr.Slider(5, 50, 30, step=1, label="Steps"), gr.Slider(1.0, 12.0, 7.5, step=0.5, label="Guidance"), gr.Text(label="Negative prompt", value="blurry, low quality")], outputs=gr.Image(type="pil"), ) demo.launch() # method shape: https://huggingface.co/docs/inference-providers/en/tasks/text-to-image **ZeroGPU on Spaces** If you rely on ZeroGPU, keep generation inside the function that runs under a short GPU slot. Use the decorator and avoid long warmups. (Hugging Face) # If you prefer to self-host SDXL in the Space # pip install -U "diffusers>=0.30.0" transformers accelerate torch pillow # model card diffusers snippet: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 import torch from diffusers import StableDiffusionXLPipeline pipe = StableDiffusionXLPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, ).to("cuda") def generate_local(prompt, w=1024, h=1024, steps=30, guidance=7.5, neg="blurry, low quality"): out = pipe(prompt=prompt, height=h, width=w, num_inference_steps=steps, guidance_scale=guidance, negative_prompt=neg) return out.images[0] # full usage and refiner flow shown on the model card The SDXL card documents base-only and base+refiner pipelines, install hints, and optimization tips. (Hugging Face) # Diagnostic checklist (run top-to-bottom) 1. **Confirm method availability** from huggingface_hub import InferenceClient, __version__ print(__version__) # expect ≥ 1.1.x print(hasattr(InferenceClient(), "text_to_image")) # True means your client exposes the helper Use the helper if present; don’t invent `image_generation`. Docs show `text_to_image`. (Hugging Face) 2. **Use a provider-backed model first** Try FLUX or SDXL-Lightning to confirm routing works. Then point back to SDXL base if your provider supports it. (Hugging Face) 3. **Passing options** With `InferenceClient`, pass generation settings as keyword args, not nested under a `parameters` dict. The HTTP spec uses `parameters`, but the Python helper takes kwargs; the official snippet demonstrates the helper call. (Hugging Face) 4. **Do not send image calls to the OpenAI-compatible router** The router is chat-only; images won’t work there. (Hugging Face) 5. **If you self-host** Follow the SDXL model card Diffusers recipe and ensure a GPU. (Hugging Face) # Common symptoms → concrete fixes Symptom | Root cause | Fix ---|---|--- `AttributeError: 'InferenceClient' object has no attribute 'image_generation'` | No such method on the client. | Call `text_to_image(...)`. (Hugging Face) `AttributeError: 'InferenceClient' object has no attribute 'post'` | Legacy wrapper examples. Method removed/never existed. | Use task helpers. Don’t call `.post`. (GitHub) Calls to `router.huggingface.co` for images fail | Router exposes chat completion only. | Use `InferenceClient.text_to_image` or a provider SDK. (Hugging Face) Provider returns “model not supported” | Provider doesn’t host that ID. | Choose a recommended model or change provider. (Hugging Face) Diffusers errors in Space | Missing GPU or packages. | Follow the SDXL card diffusers section and GPU notes. (Hugging Face) # Short, curated extras **Official, stable** * **Text-to-Image with Inference Providers**. Shows `InferenceClient.text_to_image` Python usage and what arguments are supported. Good for verifying method names. (Hugging Face) * **SDXL model card (Diffusers recipes).** Covers base vs refiner, install, and performance tips. Useful if you self-host. (Hugging Face) * **ZeroGPU docs.** If your Space uses on-demand GPU allocation. (Hugging Face) **Community signals on breaking calls** * **Missing`.post` on `InferenceClient`.** Confirms legacy examples cause `AttributeError`. Useful sanity check if you still see attribute errors after renaming the method. (GitHub)

Yes, the model page was updated to be correct within a day of release. Native image generation is not exposed. If you use the internal image_generation tool for images on the Responses endpoint, be mindful that you are picking the image-creation model that you’ll use also, not letting OpenAI decide. Images of conversation are placed into the model context like an “edits” and you can be billed for many input images per tool use, and where “input_fidelity”:“high” is forced on “gpt-image-1.5”, that can be $0.05+ per input image.

Same issue for me. gpt-5.1-mini model works in a regular text only request. But, if I add tools: “tools”: [{“type”: “image_generation”}], to the request, then I get the error: Your organization must be verified to use the model `gpt-5.1-mini`… Please let me know if there is a workaround.

I finally got the Verified seal after your intervention ! Unfortunatelly, when i try using the GPT-5 image_generation tool, i still get this message : “Your organization must be verified to use the model `gpt-5`. Please go to: https://platform.openai.com/settings/organization/general and click on Verify Organization. If you just verified, it can take up to 15 minutes for access to propagate.” EDIT : Solved after generating (and using) a new API Key. I can now correctly use the image_generation tool that was denied before.

Hi everyone, I’m using the **Responses API** with the **`image_generation`** tool to generate images through the `gpt-image-1` model. I’d like to **programmatically calculate the cost** of each request, but I noticed that the `result.usage` field does **not include any image tokens or image generation cost**. **What I observed -** The streamed response includes `usage` for text tokens from `gpt-4.1`, but **no information about the image generation cost or tokens** from `gpt-image-1`. * There’s no field in the response or event stream that reports how much image generation costs. Questions * How can I retrieve or estimate the **image generation cost** for each API call? (e.g. via a billing endpoint, usage API, or a formula) * When using the `image_generation` tool, is the cost composed of **(a)** text token usage from `gpt-4.1` and **(b)** image generation cost from `gpt-image-1`?

It would be good if GPT did not send more than 2 last images from the dialog to image_gen, according to the system message. When generation is needed. If generation is not needed, vision should be used (no tool calls).

Why did you decide that if the document says: > When you include the `image_generation` tool in your request, the model (for example gpt-4.1) can decide when and how to generate images as part of the conversation, using your prompt and any provided image inputs. https://platform.openai.com/docs/guides/tools-image-generation If the model can decide when and how to invoke the tool, then we can tell the model right?

This is how the tool is exposed to your chat AI: # Tools ## imageߺgen // The `imageߺgen` tool enables image generation from descriptions and editing of existing images based on specific instructions. Use it when: // - The user requests an image based on a scene description, such as a diagram, portrait, comic, meme, or any other visual. // - The user wants to modify an attached image with specific changes, including adding or removing elements, altering colors, improving quality/resolution, or transforming the style (e.g., cartoon, oil painting). // Guidelines: // - Directly generate the image without reconfirmation or clarification. // - After each image generation, do not mention anything related to download. Do not summarize the image. Do not ask followup question. Do not say ANYTHING after you generate an image. // - Always use this tool for image editing unless the user explicitly requests otherwise. Do not use the `python` tool for image editing unless specifically instructed. // - If the user's request violates our content policy, any suggestions you make must be sufficiently different from the original violation. Clearly state the reason for refusal and distinguish your suggestion from the original intent in the `refusal_reason` field. namespace image_gen { type imagegen = (_: { prompt?: string, }) => any; } // namespace image_gen You can see that there is nothing about passing images, just a prompt where the question mark signals `prompt` is even optional. The control you have is only stylistically: the image generation can observe you saying: “the best image background was the first one, but use the subject from the most recent image, and don’t consider the intervening 15 images that we’ve tried out”. You still pay for all the images passed, in pricing for the conversation being run against two different models. (and then pay for the chat images again as vision when the tool returns “success” and mostly doesn’t say anything).

You cannot affect the image count by talking nice to the AI: the image generator is not really a tool; it is turning control over to gpt-4o for image making for a while after the tool call is triggered, where that model can observe the chat instead. There is no budgeting when using previous response ID, and it is worse and unsuitable because even on text conversation, this will run the AI model up to the maximum input. You are alreading paying more for persisting image input even if an image is never generated again, to a more costly input model, by using a chat history mechanism that can never “drop older message”.

I mean that image_generation is called when a user requests to generate an image and attaches a source file. However, during generation, the generator analyzes all previously attached files as well, even though it should only process the last two attachments. If only the last two were used, this would both reduce the input cost and lead to more successful generation results, since currently the generator’s analysis of all previous files makes the outputs less accurate than if it focused just on the most recent attachments.

Could a system prompt like this help: “If the user has sent images, you should analyze only the last 2 images (no more).”

You can observe this topic I created - maybe scroll down to “ _Responses - this is where the costs pile up_ ” Gpt-image-1 collected pricing information - and why Responses is undocumented API > The pricing of gpt-image-1 in documentation is minimized from what actually is billed. Documentation has information all scattered around. Plus there is incomplete and undocumented information in the manner in which Responses consumes chat context and fills the image model with tokens and multiple images after a tool call. Here is a collected pricing to step through an examination of your ultimate costs of using gpt-image-1, vs dall-e-3 which is simply $0.04 per image. gpt-image-1 Pricing … If you have a fully self-managed conversation, instead of using one of the server-side chat history mechanisms of either _previous response ID_ or _conversation_ , then you do have control over the messages - and can pop those older images out of chat history and let the end user know what is being done. Another alternative is to send those near-obsolete turns as “detail”:low to reduce the resolution to 512x512 for the chat AI itself, which is the actual maximum image that gpt-image-1 will take as input, so you can also resize to about that yourself in concert with the detail parameter. You can decide based on elapsed time between chat turns and input token length if that will break any cache discount possible. The image input that is consumed again by the gpt-4o-powered image tool is indeed it looking at the full chat context - with no documentation about this actual behavior or its costs by OpenAI, that you correctly ascertained. The big killer would be sending the input detail “high” - another $0.06 per input image for every image, and again no control over what gets fed in, other than your own management. Calculable costs, your code’s function: only generate by a prompt - use your own function as interface - call the image generate endpoint - don’t show the chat AI model what was made, just “ _image success: displayed to user_ ” as a tool return. This also prevents the user saying “make me 10 images automatically without interruption” and the internal tool running free.

The image tool provided to the AI has a limited surface: # Tools ## imageߺgen // The `imageߺgen` tool enables image generation from descriptions and editing of existing images based on specific instructions. Use it when: // - The user requests an image based on a scene description, such as a diagram, portrait, comic, meme, or any other visual. // - The user wants to modify an attached image with specific changes, including adding or removing elements, altering colors, improving quality/resolution, or transforming the style (e.g., cartoon, oil painting). // Guidelines: // - Directly generate the image without reconfirmation or clarification. // - After each image generation, do not mention anything related to download. Do not summarize the image. Do not ask followup question. Do not say ANYTHING after you generate an image. // - Always use this tool for image editing unless the user explicitly requests otherwise. Do not use the `python` tool for image editing unless specifically instructed. // - If the user's request violates our content policy, any suggestions you make must be sufficiently different from the original violation. Clearly state the reason for refusal and distinguish your suggestion from the original intent in the `refusal_reason` field. namespace image_gen { type imagegen = (_: { prompt?: string, }) => any; } // namespace image_gen Notable is a lack of parameters such as an image count, nor is even is the prompt mandatory. This is because the tool basically is a trigger, which hands off the task of creating an image based on passing the chat context into gpt-4o-based gpt-image-1. What you likely observe is a failure in the AI to recognize the success of an image or the quality of an image deliverable, and it is calling the tool again, and again. Or, that it is simply pattern-matching what “assistant” output previously, for a repeating loop. Or enjoying ‘reasoning’, AI thinking it can try out tools to an internal channel. You don’t have control over the tool response message or placement to fix internal tools yourself. What you do have is control over the iteration count where the AI can continue emitting to tools. This should stop the expense cold: > `max_tool_calls` - The maximum number of total calls to built-in tools that can be processed in a response. This maximum number applies across all built-in tool calls, not per individual tool. Any further attempts to call a tool by the model will be ignored. Then consider if you really need “chat with pictures”. You can instead use a function that is a connector to the generate image API, stopping the context bloat, or simply a non-chat tool to create and edit (without talking to an AI that is not in control of making the actual images.)